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ICT for financial development: Shaping the new landscape
CHAPTER SIX
ICT for financial development: Shaping the new landscape Contents 6.1 Introductory 6.2 ICT and financial development 6.3 Financial market evolution trajectories 6.4 How ICT impacts on financial markets 6.5 ICT and financial innovations References Further reading
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6.1 Introductory This chapter presents the results of our analysis of the role of information and communication technology (ICT) in various aspects of financial development in the European countries belonging to the Organisation for Economic Co-operation and Development (OECD). We begin with a few introductory remarks, including an overview of the chapter’s structure, the dataset, and our research methods. Some supplementary technical comments are also offered. In the next section we focus on the impact of ICT on the financial system at large and on selected segments of it. We present both graphical evidence and estimates of the panel models with regard to financial development, the banking sector, the insurance industry, and, from a broader perspective, the gross savings rate. The third section of the chapter considers the evolution of financial markets in the countries examined—we discuss the graphical evidence on the changes that have occurred, above all, in stock markets and briefly analyse selected indicators for bond markets. The fourth section analyses the influence of ICT on the financial markets, drawing conclusions based both on local polynomial regressions and on panel models. The fifth and final section shows the results relating to the linkages between ICT and financial innovations, in the particular case of exchange-traded funds (ETFs). As a supplement to the ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00006-3
© 2019 Elsevier Inc. All rights reserved.
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study of ETFs we discuss the significance of ICT for mutual funds, a type of investment fund that may be regarded as the main, and much better established, alternative to ETFs. The variables used in our analysis are presented in Table 6.1, in the approximate order in which they are referenced in the discussion. They can be divided broadly into two groups—first, our two ICT indicators (FBS and IU); and second, all the remaining variables that can be labelled as ‘financial’, i.e. associated in some way with financial development. The latter group comprises three partly overlapping sub-categories (for the explanation of the abbreviations, see Table 6.1): general indicators of financial development (FD, FI, FM, BD_GDP, Ins_GDP, and SAV_GDP), discussed in Section 6.2; financial market indicators (FM, FMD, FMA, FME, SMC_GDP, SMTV_GDP, SPV, ODP_GDP, and ODPub_GDP), discussed in Sections 6.3 and 6.4; and two measures linked to the financial innovations analysed (ETF_GDP and MFA_GDP), discussed in Section 6.5. The base time period covered is 1990–2016, and the data are annual (both the choice of period and the frequency of data depended on the availability of data, which was insufficient for a longer period or for quarterly or monthly frequency); we do not extend our study to 2017, as not all of the key indicators for that year were available at the time of analysis. Further, for some variables the period is severely shortened in relation to the base period, as the required data were not available. This is the case, for instance, of the assets of ETFs, in that the first such funds were launched in Europe in the early 2000s, and of data on electronic payments (consistent data are available only in some years since 2011). Moreover, ETFs are traded only in some countries—the others are omitted not for lack of data but simply because the ETF market is non-existent (or because of problems in correct attribution of the funds, as discussed below). For the full list of countries, see Table 4.1 in Section 4.2. Luxembourg was excluded as an outlier among European members of the OECD given its unique position as a regional financial hub—a small country with an oversized financial system whose development depended mainly on international factors rather than local adoption of ICT. There are three main data sources for this chapter: World Telecommunication/ICT Indicators (for the two indicators of ICT adoption), the IMF Financial Development Index Database (for the indexes of financial development), and the Global Financial Development Database (from which a number of variables were extracted). The remaining databases used are OECD Insurance Statistics, World Development Indicators, and Thomson Reuter’s Lipper. Note that for the purposes of data consistency we used only data from the international sources with broad geographical and time coverage and with common
Abbreviations
Name of variable
Units
Source(s) of data
Supplementary comments
Category
FBS
Fixed broadband subscriptions
Per 100 inhabitants
–
ICT
IU
Internet users
Per 100 inhabitants
–
ICT
FD
Financial development
Index
World Telecommunication/ICT Indicators World Telecommunication/ ICT Indicators IMF Financial Development Index Database
General
FI
Development of Index financial institutions
IMF Financial Development Index Database
FM
Development of financial markets
Index
IMF Financial Development Index Database
BD_GDP
Bank deposits
% of GDP
Ins_GDP
Total insurance spending Gross savings
% of GDP
Global Financial Development Database OECD Insurance Statistics
Index normalised with values ranging between 0 and 1 (higher values, higher level of financial development); based on the values of sub-indexes FI and FM Index normalised with values ranging between 0 and 1 (higher values, higher level of development); based on the values of three sub-indexes Index normalised with values ranging between 0 and 1 (higher values, higher level of development); based on the values of three sub-indexes: FMD, FMA, and FME – Value of direct gross premiums
General
Sav_GDP
% of GDP
World Development Indicators
General
General/financial markets
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Table 6.1 List of variables in the analysis of implications of ICT adoption for financial development
General
Difference between disposable income and General consumption (indicator known as ‘gross domestic savings’ in the pre-2006 editions of the World Development Indicators)
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Abbreviations
Name of variable
Units
Source(s) of data
Supplementary comments
Category
Electr_paym
Electronic payments
% of respondents aged 15+
Global Financial Development Database
General
FMD
Financial market depth Index
IMF Financial Development Index Database
FMA
Financial markets access Index
IMF Financial Development Index Database
FME
Financial market efficiency
IMF Financial Development Index Database
SMC_GDP
Stock market % of GDP capitalisation Stock market total value % of GDP traded Stock price volatility Index
Global Financial Development Database Global Financial Development Database Global Financial Development Database
Electronic payments (made automatically, including online payments) in the past 12 months Index normalised with values ranging between 0 and 1 (higher values, higher level of development) Index normalised with values ranging between 0 and 1 (higher values, higher level of development) Index normalised with values ranging between 0 and 1 (higher values, higher level of development) –
Financial markets
–
Financial markets
Outstanding domestic % of GDP private debt securities Outstanding domestic % of GDP public debt securities
Global Financial Development Database
SPV
ODP_GDP
ODPub_GDP
Global Financial Development Database
Financial markets
Financial markets
Financial markets
Average of 360-day volatility of the Financial markets national stock market index (calculated by Bloomberg) – Financial markets –
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SMTV_GDP
Index
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Table 6.1 List of variables in the analysis of implications of ICT adoption for financial development—cont’d
Assets of primary-listed % of GDP ETFs
Thomson Reuter’s Lipper
MFA_GDP
Assets of mutual funds
Global Financial Development Database
% of GDP
Sum of the total net assets of ETFs primary- Financial listed in the country. Country of ETF’s innovations primary listing is defined, as in Deutsche Bank (2017), as the location of fund’s primary exchange. This is usually that of first listing and also of the highest turnover in the funds’ shares. Lipper’s classification is used For some countries the values were revised Financial (assets of ETFs were removed in order to innovations ensure no overlapping with ETF_GDP)
Note: For information on methodology and data sources of the databases, see their documentation. The ‘categories’ in the last column are mostly technical and serve to ensure clarity of the analysis.
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methodology; we avoided national sources, as they could lead to errors owing to varying methodology or currency conversion. Another problematic issue was missing observations. Nonetheless, for the variables examined most thoroughly, data were available for the full time period—the breaks cited are for particular countries in some specific periods and do not undermine robust analysis (we explain these issues within either our country-specific or panel data analysis by indicating the data breaks). The most complete datasets were for the ICT variables and the indexes of financial development; the least complete for Electr_paym (only data for 2011 and 2014 are available, and in 2014 for some countries only; there are no data for Iceland), ETF_GDP, and the debt securities indicators (ODP_GDP and ODPub_GDP); for the remaining variables, the problem of missing data is not severe. Another observation bears on the impact of the sizes of economies—our dataset covers such enormously different countries and economies as Estonia or Lithuania, on the one hand, and France or Germany, on the other. To mitigate the effect of size differences (and facilitate between-country comparisons), our variables are expressed as percent of GDP (in all cases the conversions were already made in the original databases, so no further calculations were necessary); obviously, this does not apply to indexes or other similar variables for which such conversions were not essential. One more very specific stipulation concerns the variable ETF_GDP. Given the structure of the European stock exchanges, which in some cases operate in multiple countries or consist of more than one country segment, accurate attribution of certain ETFs to a particular country is sometimes very difficult or impossible. We accordingly decided to consider only the countries for which data on ETF_GDP can be estimated with sufficient reliability. Consequently, even though there are some ETFs classified as primary-listed in Finland, Iceland, Latvia, and Lithuania, these countries were excluded in the analysis of ETF_GDP; in any event, given the extremely low ratio of local ETF assets to GDP in these countries, this decision may be regarded as practically negligible as regards potential distortion of our results. The same applies to Belgium, the Netherlands, and Portugal (which together with France form the Euronext exchange). In order not to omit France, with one of the largest European ETF markets, we assigned all the ETFs listed on Euronext to the French market (which accounts for over 95% of the assets of ETFs primary-listed on Euronext). Finally, due to the organisational structure of the Italian and UK stock exchanges, which are both part of the London Stock Exchange Group, the exact classification of ETFs between Italy and the United Kingdom is also somewhat problematic. However, we decided to leave data on the two countries unchanged, as no sufficiently reliable method of division between the two markets could be devised.
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In our study of the links between ICT and the financial system, we utilise a few well-established research methods. First, we examine the graphical evidence on the relevant variables, in a two-part analysis. The first part focuses on the trajectories of changes at country level (i.e. their timelines) in order to identify the main trends and national differences. The second part examines the density functions in order to assess the distribution of variables, including comparisons between variables. Second, we consider the estimates of local polynomial regressions for the pairs of variables, prepared using panel data—in each case we juxtapose one of the ICT indicators to another selected variable (non-ICT, i.e. financial); this part of the analysis is preceded by a brief introductory examination of correlation coefficients. The analysis bears exclusively on the graphical evidence obtained within the local polynomial regressions, as this can be considered sufficient confirmation of the implicit direction of the causal relationship between ICT and the selected variables; it also allows for some basic comparisons of the strength of the relationships (in other words, formulating basic statements concerning the strength of the impact of ICT). Finally, we use panel models with a single explanatory variable—always one of the ICT indicators, either FBS or IU; as dependent variables we include the indicators of financial development.
6.2 ICT and financial development This section focuses on the links between ICT deployment and various aspects of financial development (apart from financial market development, which is discussed separately in the two subsequent sections). We track variables representing financial development at large, the development of the banking sector and the insurance industry, savings, and electronic payments (for details on the variables, see Section 6.1). This is the set of variables that, for clarity, we labelled as ‘general’ variables, as distinct from two other groups of variables bearing, respectively, on ‘financial markets’ and ‘financial innovations’. First we conduct an introductory graphical analysis based on timelines and density functions, examining changes over time, differences between countries, and the distribution of the variables. Next we analyse further graphical evidence, examining the graphs representing the local polynomial regressions in order to gain basic insights into the direction and strength of the effects of the diffusion of ICT on the financial development variables. Finally, we interpret the estimates of the panel models to extend and conclude the preceding stages of the analysis. The first variable in Fig. 6.1—FD—is the IMF’s most general and comprehensive indicator of financial development; FD (like FI and FM) has a
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range of 0–1, higher values indicating higher levels of development. In most European countries the level of financial development was increasing or at least broadly stable between 1990 and 2016. In some economies, however, the peak was reached around the time of the 2008 global financial crisis and the euro-area sovereign debt crisis. Declines are registered in subsequent years in such countries as Greece, Iceland, the Netherlands, Portugal, and the United Kingdom. And even in countries apparently less affected by the turmoil in the global and European financial systems, some variability is discernible—see, for instance, the brief dip in FD in Switzerland (which overall has been the most financially developed of all the countries analysed and also a global leader, for some time actually ranked first worldwide in FD). The density function (see first part of Fig. 6.2) shows that the vast majority of FD values in 1990–2016 are in the range between 0.3 and 0.8, and mostly above the midpoint of 0.5. Extremely low values were very rare, found exclusively in the post-communist countries in the early 1990s, i.e. at the beginning of their economic and political transformation. Despite
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the substantial gains in financial development in the 1990s and early 2000s (the exception being Slovakia, where FD has remained almost flat at around 0.3), these countries have remained significantly less developed financially than those of Western Europe. This is clearly represented by the two peaks in the density function curve—one for the less advanced countries, and the second, a higher peak, for the more advanced, which are relatively more frequent in our sample. However, even in the group of the ‘old’ countries of the EU there is considerable heterogeneity, with such Southern countries as Greece and Portugal lagging behind at levels of FD closer to the postcommunist countries. Interestingly, this does not apply to Spain, which since the turn of the century has been among the most finally developed in the world, at FD values close to the United Kingdom. Now let us examine the two indicators that make up FD in order to determine some of the reasons behind these disparities. We start our analysis of the components of FD with FI, the index of financial institutions (see the second part of Fig. 6.1 and the first part of Fig. 6.2). Before examining our empirical data it is necessary to refer to the index methodology (for a detailed discussion, see Sahay et al., 2015; Svirydzenka, 2016). The variables used to construct the index refer, above all, to the banking sector (only one of the three sub-indexes takes account also of pension funds, mutual funds, and insurance), so FI can be regarded as a de facto index of banking development. Accordingly we analyse it jointly with another banking variable, namely BD_GDP (which is not used in calculating FI, so these two variables are not strictly overlapping). The variability of FI over time has been rather limited in most countries—in most of the advanced European economies it has been substantially above 0.5 for the entire period. The highest levels of FI are observed in Denmark, France, Italy, Switzerland, and the United Kingdom. Clearly, some post-communist countries entered the 1990s with substantially underdeveloped financial institutions (see, e.g. the very low levels of FI in Latvia or Lithuania). But this group is by no means homogenous, given that in some other post-communist countries, such as the Czech Republic and Hungary, levels of FI were relatively high already in 1990 and have stayed broadly constant. The FI density function shows that values of FI have been slightly less scattered than those of FM and mostly higher than 0.5, with a marginal share of values below 0.3. The ratio of bank deposits to GDP, BD_GDP, has remained very stable in most countries, with insignificant year-to-year changes (see the fourth part of Fig. 6.1). The few exceptions are for countries where significant variations resulted from particular events,
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such as financial crisis (see the fall in BD_GDP in Iceland after 2008) or entry into the euro area (see the increase in Greece in the early 2000s). The highest ratios of bank deposits are scored in Switzerland, which strengthened its position as European leader of the banking industry as uncertainty in the European financial and political system fuelled deposits in the traditional ‘safe harbour’ of Swiss banks. Regionally, on average BD_GDP has been markedly lower in the less developed countries. The difference in BD_GDP between the more and the less advanced European economies is greater than that in FI. This suggests that even though the post-communist countries have gained some ground on the more advanced in terms of efficiency and access to financial institutions, the disparities in the ‘depth’ of financial institutions (measured, say, by credit to the private sector or bank deposits) continue to be sizeable, as the data for countries such as the Czech Republic or Poland demonstrate. The distribution of BD_GDP (see the third part of Fig. 6.2) is relatively symmetrical at an average of about 50% of GDP; the long right tail of the distribution depends, above all, on Switzerland. The second component of FD, and the next variable we analyse, is one of the core indicators of our analysis—FM. The development of the financial markets is discussed in depth in the next section; here we only formulate some basic preliminary conclusions. The first and most striking observation suggested by the third part of Fig. 6.1 is the great variability of FM over time, much greater than that of the other core component of FD (i.e. FI). International differences are also incomparably greater than in the case of FI—we have countries with FM close to 0 over the entire period (Latvia, Lithuania, and Slovakia) and others where it averaged more than 0.7 (e.g. Switzerland). These disparities are clearly evidenced by the distribution of the variable (see the first part of Fig. 6.2), as the values of FM are spread over practically the entire spectrum, from 0.1 to 0.8 (although values greater than 0.9 were extremely rare), with no dominant value. The next variable analysed, Ins_GDP, is the ratio of total expenditure on insurance services to GDP in our sample countries. Like banking, the insurance industry is to some extent comprised within one of the sub-indexes of FI (through the somewhat similar indicators of the value of insurance premiums), but it is worth evaluating separately in order to assess the basic changes over time and international differences. For some countries analysis is hindered by lack of data—see, for example, the short timelines for Latvia, Lithuania, and Slovenia. For insurance, unlike most of the variables mentioned above, international differences are not exclusively between the post-communist countries and the other European OECD members;
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instead there is considerable heterogeneity within each group. However, the difference between the two groups is clear, as the less developed countries lag behind (see the fifth part of Fig. 6.1). For example, the values of Ins_GDP in France average 2 percentage points higher than in Germany, but in both countries they are much higher than in the post-communist countries, even those with the highest levels of insurance spending such as the Czech Republic or Poland. The graph also shows the broad stability of Ins_GDP over time, albeit with some exceptions; nonetheless in most of the sample insurance spending has been slowly increasing. Ireland and the United Kingdom are the only countries where Ins_GDP has exceeded 10% for sustained periods, owing to the strong position of their local insurance companies on a regional scale. This conclusion is confirmed by the probability function of the variable, which indicates that in the vast majority of cases values were just a few percentage points of GDP, with a mean of about 5% (see the third part of Fig. 6.2). Analysis of the graphical evidence for the savings rate, Sav_GDP, leads to some interesting conclusions (see the sixth part of Fig. 6.1). The differences between countries cannot be simply attributed to disparities in economic development or even to the state of their financial systems (see the low levels in the United Kingdom). Rather, the timelines of Sav_GDP are highly country-specific. Significantly, there are only a few countries in which gross savings clearly increased between 1990 and 2016, above all such postcommunist countries as Latvia and Lithuania (but also Sweden). The increase in Ireland was preceded by a significant decline in the aftermath of the 2008 global financial crisis. The probability function of Sav_GDP (see the third part of Fig. 6.2) shows that its distribution was rather symmetrical and most values were at approximately 20%. The analysis of Sav_GDP serves to broaden the study—it would be a definite oversimplification to assume that this variable is shaped exclusively or even predominantly by the adoption of ICT or by the factors in financial development; for more on this topic see the extensive literature on savings, including Leff (1969), Singh (1972), Venieris and Gupta (1986), Gupta (1987), Mason (1988), Collins (1991), Bailliu and Reisen (1998), Ozcan, Gunay, and Ertac (2003), us Swaleheen (2008), Yang, Zhang, and Zhou (2012), Beckmann (2013). In particular, Kolasa and Liberda (2015) identified the key factors that affect the savings rate in OECD countries. The final variable examined at this stage represents one of the potential channels of intermediation between the adoption of ICT and financial development (in particular in the banking sector)—namely, Electr_paym.
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This may be considered as supplementary here, in that while it does not directly represent any of the fundamental aspects of financial development it is nevertheless plausible that an increase in electronic payments is at once an important prerequisite for and a sign of financial development. It also applies to financial inclusion; here we focus on financial development, but the effects on financial access and inclusion should not be disregarded. For more on these topics see Section 3.3 and such publications as Donner and Tellez (2008), Maurer (2012), Klapper and Singer (2014), Slozko and Pelo (2014), Arun and Kamath (2015), and Demirguc-Kunt, Klapper, Singer, Ansar, and Hess (2018). The most serious problem here is sharply limited data availability (exclusively for 2011 and 2014). For these reasons we do not show the timelines for Electr_paym, as they are only marginally informative (at most two observations for each country). The lowest values of Electr_paym both in 2011 and 2014 were in Italy, Hungary, Poland, and Lithuania (all but Italy among the less developed economies in our sample). The highest values are in the highly developed Nordic countries of Finland, Denmark, and Sweden (in 2014 almost 100% of the respondents there reported having utilised electronic payments). Nevertheless, as the probability function shows (see second part of Fig. 6.2), electronic payments are common in Europe—in 2014 in all examined countries the value of Electr_paym exceeded 50%, and in most it was near or above 80%. After this examination of the graphical evidence, we proceed to the core matter of this section, i.e. analysis of the relationships between the diffusion of ICT and the variables representing financial development (based exclusively on the panel data rather than country-specific evidence). We start with the correlation coefficients (not presented in a separate table owing to the large number of variables) and graphical representations of the local polynomial regressions (see Figs. 6.3 and 6.4). Focusing on the estimates of the panel models, we consider the relationship of each general variable with both ICT indicators, i.e. IU and FBS. IU, which is more wide-ranging and covers various types of Internet access, is presented first. FBS, presented afterward, refers to high-speed connections via fixed broadband. The first, fundamental financial development variable we examine in relation to ICT is FD. The graphs for IU and FBS show that the direction and strength of this relationship are difficult to establish. Nevertheless, in both cases the impact of ICT appears to be mainly positive or at least neutral (the only exception is the negative correlation for the highest values of IU, while for the highest values of FBS the correlation is strongly positive). This is confirmed by the correlation coefficients, which are positive both for FD vs IU and for FD vs FBS. Interestingly, for IU the coefficient is much higher
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(0.39 against 0.25). This could be seen as implying that mere access to Internet, regardless of speed or quality of the connection, is more important for financial development in these countries. However, the graphical evidence in Figs. 6.3 and 6.4 suggests a contradictory conclusion. We shall return to
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Fig. 6.3 IU vs general variables. Local polynomial regressions. 1990–2016, annual data. Note: On x-axis—IU; raw data used; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. (continued)
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Fig. 6.4 FBS vs general variables. Local polynomial regressions. 1990–2016, annual data. Note: On x-axis—FBS; raw data used; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1.
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Fig. 6.4, cont’d
this issue in interpreting the estimates of the panel models to explain the reasons for this inconsistency. As observed above, Figs. 6.3 and 6.4 show that the relationship between the adoption of ICT and overall financial development is generally positive. To determine which aspects of financial development are most affected by the new technologies, we consider the two sub-indexes, FI and FM. For FI the picture is quite clear—the diffusion of ICT and the development of financial institutions are positively related, in particular in the case of fixed broadband connections. For FM (financial market development), the relationship with the ICT indicators is harder to pinpoint. For Internet broadly defined, i.e. measured with IU, for values of 0 to 40 the correlation is
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positive (and stronger for lower levels of both variables), similar to FD. This might mean that the positive effect of ICT diffusion for financial market development materialises, above all, in the less highly developed European economies (where both IU and FM are mostly lower). In the interval of IU from 40 to 70 the relationship is weakly negative, and for values higher than 70 it can be described as inverted U-shaped: first strongly positive and then substantially negative, precluding clear-cut conclusions. However, as in case of FD, for the highest values of IU the relationship with FM (as well as FI) is negative. This is explained by reference to the financial development index values (either at large or measured by the two sub-indexes) in certain countries, such as Iceland and Norway, which are among the leaders in ICT adoption but have been surpassed by others in financial development. As Fig. 6.4 shows, the relationship between FBS and FM is clearer—for the lower values of FBS it is mixed but for values above about 20 it is strictly positive. The correlation coefficients for all four pairs of variables (FI vs IU, etc.) are positive, between 0.2 and 0.4, which further confirms the main implications of the estimates of the local polynomial regressions. In short, the graphical representations of the local polynomial regressions show that when FD is decomposed into FI and FM, the positive relationship between ICT and overall financial development depends more on the development of financial institutions than of financial markets. Moreover, the graphical evidence again suggests a more definite positive impact of FBS than IU. After these indications from the general indexes of financial development, let us focus on some specific variables linked to this process. First we examine one of the indicators of banking development, i.e. the ratio of bank deposits to GDP, BD_GDP. The correlation coefficients of BD_GDP with both ICT variables are positive and greater than 0.3, implying a positive relationship between the spread of the new technologies and the growth of the banking sector. This is almost unequivocally confirmed by both local polynomial regressions, regardless of which dimension of ICT diffusion is considered (see Figs. 6.3 and 6.4). Referring again to our discussion of FD and its sub-indexes, we can add that ICT adoption may have contributed to financial development through its positive effect on bank deposits. Ins_GDP is our proxy for development of the insurance industry. The results for Ins_GDP are to some extent similar to those for BD_GDP— compare the relevant parts of Figs. 6.3 and 6.4. The correlation coefficient of BD_GDP with Ins_GDP is 0.42, which explains these results. Nevertheless, the relationship between ICT and insurance development is noticeably weaker, as is evinced by the practically almost flat lines in both figures and
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the correlation coefficients of just 0.06 (Ins_GDP vs FBS) and 0.17 (Ins_GDP vs IU). To conclude, banks would appear to be more strongly influenced by ICT diffusion than insurance companies; these relationships are we further verified and compared using panel models. In contrast with the other variables, the relationship between ICT and gross savings (Sav_GDP) is hard to determine. Despite the positive correlation coefficients (0.18 for both ICT indicators), the actual relationship is somewhat more complicated, as is indicated by Figs. 6.3 and 6.4. It can be characterised as U-shaped, especially for IU (for FBS it is mostly positive but with a negative coefficient for the lower values of FBS). So it is not easy to determine the direction and strength of the relationship between savings and increased ICT use in our sample countries. With some stipulations, we can say that the impact of ICT is neutral or weakly positive (taking into account fast Internet connections measured with FBS). The results obtained for our last general variable—Electr_paym—are consistent with expectations, given the basic attributes of this financial service. Both correlation coefficients and the results of the local polynomial regressions make it clear that the relationship between ICT diffusion and electronic payments is strongly positive; the correlation coefficients are 0.83 (Electr_paym vs IU) and 0.71 (Electr_paym vs FBS). Apparently, that is, adoption of ICT is a fundamental precondition for the increasing utilisation of electronic payments in a society. The reverse relationship is implausible, of course, but a positive feedback loop is obviously possible—growing recourse to electronic payments may encourage more people to obtain access to the Internet. This process may also be supported by government policy as a way of attenuating financial exclusion (Ouma, Odongo, & Were, 2017). In any event, the results for Electr_paym should be interpreted with caution, as they are based on a limited dataset (only 46 observations). Tables 6.2 and 6.3 convey the panel model estimates with IU and FBS, respectively, as explanatory variable. All values are in log, although for clarity we refer to base variables (i.e. we refer to ‘IU’ instead of ‘LnIU’). This also applies to Sections 6.4 and 6.5. There is one necessary general stipulation prior to this analysis. In discussing the results for the local polynomial regressions, we observed that they imply somewhat different conclusions from those implied by the correlation coefficients. These differences also obtain with respect to the estimates of the panel models. One of the most important of the various reasons for these discrepancies is that in most of the relationships the observations are clustered in a few intervals—see, for example,
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Table 6.2 IU vs general variables. Fixed effects regressions. 1990–2016, annual data LnFD LnFI LnFM LnBD_GDP
LnIU R2 (within) No. of obs. Rho F (prob F)
LnIU R2 (within) No. of obs. Rho F (prob > F)
0.07 [0.00] 0.64 644 0.91 1119.4 [0.00]
0.04 [0.00] 0.41 645 0.83 425.3 [0.00]
0.11 [0.00] 0.37 645 0.84 366.2 [0.00]
0.07 [0.00] 0.37 599 0.88 343.9 [0.00]
LnIns_GDP
LnSav_GDP
LnElectr_paym
0.08 [0.00] 0.42 567 0.91 385.1 [0.00]
0.005 [0.00] 0.00 590 0.61 1.46 [0.23]
4.99 [0.62] 0.75 46 0.88 64.8 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
Table 6.3 FBS vs general variables. Fixed effects regressions. 1990–2016, annual data LnFD LnFI LnFM LnBD_GDP
LnFBS R2 (within) No. of obs. Rho F (prob > F) LnFBS R2 (within) No. of obs. Rho F (prob > F)
0.02 [0.00] 0.14 422 0.94 66.8 [0.00]
0.02 [0.00] 0.26 422 0.90 144.9 [0.00]
0.00 [0.00] 0.00 422 0.94 0.07 [0.79]
0.06 [0.00] 0.36 399 0.94 218.8 [0.00]
LnIns_GDP
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LnElectr_paym
0.01 [0.00] 0.01 379 0.94 5.4 [0.02]
0.01 [0.00] 0.02 410 0.75 10.7 [0.00]
3.33 [0.39] 0.77 46 0.92 72.4 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
FD vs IU in Fig. 6.3—with most observations at either very low or very high levels of IU. This suggests that the inconsistencies in the relationships identified by local polynomial regressions may actually be overstated, as is demonstrated by the panel models discussed in the subsequent paragraphs. The positive impact of the diffusion of ICT on overall financial development as proxied by FD is confirmed regardless of the explanatory variable
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chosen. What is important is that the within R2 of the IU model is the third highest of all those estimated (only for the two models with Electr_paym as the dependent variable is it higher), indicating a relatively good fit. For the FBS model, however, it is much lower. Even so, both ICT indicators prove to be statistically significant. Comparing the coefficients of the two ICT variables indicates that the number of Internet users has a stronger impact on financial development than does the number of fixed broadband subscriptions (a conclusion already foreshadowed by the correlation coefficients). However, given the substantial difference in number of observations (much lower in the case of FBS, because this type of service was introduced more recently), this conclusion may be oversimplified. Considered jointly with the higher value of the within R2 in the IU model, it suggests that initial access to the Internet fosters financial development more than such further advancements as better quality of connections. This may depend partly on the still limited use of fixed broadband subscriptions in some European countries, whereas other Internet services (e.g. mobile broadband) have become widely available in all our sample economies. Our results are in line with the findings of previous studies (see Section 3.3). Estimating the models for the sub-indexes of financial development uncovers a somewhat more complicated picture than for financial development in general (FD), at least as far as financial market development is concerned. For both FI and FM the fit is generally worse than for the overall index, as is shown by the within R2. This applies in particular to the model with FBS as explanatory and FM as dependent variable—its within R2 is nearly 0 (see Table 6.3). The only exception is the FBS model for FI, where the within R2 is higher than for the FD model, suggesting that FBS may better explain the development of financial institutions in our sample countries. The coefficients of both IU and FBS are positive and statistically significant in the models with FI as the dependent variable, implying that ICT has a positive effect on the development of financial institutions. These results come as no surprise, as they are consistent with previous empirical studies (see Section 3.3). The channels whereby ICT may impact on banks are multiple, one of the foremost being the spread of mobile banking (Alalwan, Dwivedi, & Rana, 2017; Shaikh & Karjaluoto, 2015; Sharma, Govindaluri, Al-Muharrami, & Tarhini, 2017) or, more broadly, Internet banking (Szopi nski, 2016; Takieddine & Sun, 2015). Another area of growing importance is bound up with one of the categories of fintech, namely ‘regtech’—use of ICT in the context of financial regulation and oversight (Arner, Barberis, & Buckley, 2016). Regtech lowers the cost of compliance
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with regulatory requirements; it also facilitates entry of start-ups into the highly regulated financial industry (Larsen & Gilani, 2017). And this part of the fintech sector can contribute to the stability of the financial system overall (Arner, Barberis, & Buckley, 2018; Arner, Zetzsche, Buckley, & Barberis, 2017), thus strengthening people’s trust in financial institutions and attracting them to the formal financial system. However, given its relatively brief existence the impact of regtech is still difficult to assess. The results for FM are more complicated—only the coefficient of IU is statistically significant and positive, indicating that this dimension of ICT diffusion is more important for the development of the financial markets, given that no impact, either positive or negative, was found for FBS. We discuss this issue in more detail in Section 6.4, analysing selected aspects of the relationship of ICT with FM. The positive impact of ICT penetration on the development of financial institutions is confirmed by the models using BD_GDP as the indicator of banking development. As could be expected given the correlation coefficients and the estimates of the local polynomial regressions, both IU and FBS are positive and statistically significant, with comparable values of the coefficients and the within R2. Consequently we confirm, using bank deposits, the positive impact of ICT on the primary category of financial institutions. Once again, this result is consistent with the bulk of the previous literature. Furthermore, it shows that in our sample countries the growing deployment of ICT between 1990 and 2016 has not constituted a threat to the banking industry (as by undercutting banks’ position within the financial system in favour of various fintech companies offering similar services). Apart from the general channels of ICT impact on the banking sector, others relating more specifically to changes in deposits can be traced to the benefits for the financial performance and the competitiveness of the deposit banks, as suggested among others by Monyoncho (2015) and Nkiru, Sidi, and Abomeh (2018). The second major type of financial institution considered here is insurance companies (investment funds are discussed in Section 6.5). See the models with Ins_GDP in Tables 6.2 and 6.3. The estimates of the IU model are similar to those for banking but with slightly better fit (the models can be compared directly, since the datasets were almost identical). IU proves to be a statistically significant determinant of the development of the insurance industry. However, this does not go for the other ICT indicator—while it is statistically significant, the extremely low value of within R2 precludes all meaningful interpretation. The importance of ICT for the insurance
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industry can be explained as relating to such factors as the emergence of digital insurance (Nicoletti, 2016) and electronic payments (Nwankwo, Ajemunigbohun, & Iyun, 2015). In addition, the industry is affected by the rise of insurance services provided by fintech companies, or ‘insurtech,’ defined by Ricciardi (2018, p. 8) as ‘… innovation-based companies … that generate value … by disrupting or solving problems across the insurance value chain through the engagement of technology’. The heightened availability of regtech may result in increased insurance spending owing, say, to improvements in customer service processes such as claims management (Cappiello, 2018; Cortis, Debattista, Debono, & Farrell, 2019). Nevertheless, this is still a relatively new part of the financial system. Estimates of the panel models with Sav_GDP provide no information on the effect of ICT deployment on the gross savings rate. This was perhaps to be expected, given the markedly non-linear relationship between Sav_GDP and both IU and FBS (see Figs. 6.3 and 6.4), which impedes the application of our models. Finally, the results for Electr_paym obtained with the panel models strictly confirm the foregoing conclusions arising from both correlation coefficients and local polynomial regressions—namely, the positive influence of ICT on electronic payments (at least their reported use, insofar as the data on Electr_paym are drawn from surveys). The values of the within R2 of both models estimated for Electr_paym are the highest of all those in Tables 6.2 and 6.3, indicating the best fit. A comparison of the coefficients of IU and FBS shows that the popularity of the electronic payments depends more heavily on overall access to the Internet than on the availability of fixed broadband connections. This is readily explained—electronic payments are possible through devices using various types of connection, not necessarily only those with high speed and stability. For example, in many countries financial transactions are conducted to a large extent via mobile devices (Iman, 2018; Liebana-Cabanillas, Mun˜oz-Leiva, & Sa´nchez-Ferna´ndez, 2018; Shaikh & Karjaluoto, 2015), including transactions using near-field communication (NFC), i.e. communication between two devices brought into proximity (de Luna, Montoro-Rı´os, & Liebana-Cabanillas, 2018; Jeffus, Zeltmann, Griffin, & Chen, 2015). Yet stable, rapid connections are indispensable to construct and operate the infrastructure of electronic payment systems, which means that the role of FBS cannot be disregarded. Further, the more sophisticated technologies impact on the intentions of their users (Jun, Cho, & Park, 2018), as electronic payments can be provided at lower prices than other similar financial services.
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Table 6.4 Results of the analysis of general variables: summary Variable Impact of ICT adoption
FD FI FM BD_GDP Ins_GDP Sav_GDP Electr_paym
Positive Positive Positive (IU)/not identified (FBS) Positive Positive Not identified Positive
Note: For explanation of the variables, see Table 6.1 in Section 6.1.
Our key conclusions on the relationship between ICT diffusion and various aspects of financial development are summarised in Table 6.4. In most cases we found a positive effect, with the sole exception of savings, for which neither the strength nor the direction of the correlation could be established. For the financial market development the results for the FBS variable are inconclusive. To sum up, we determined positive effects of ICT adoption both on financial development overall and on selected aspects of it.
6.3 Financial market evolution trajectories This section begins our analysis of the relationships between the diffusion of ICT and the development of the financial markets. The main results will be presented in Section 6.4. The present section is given over to graphical analysis, with two elements, analogous to the first two parts of Section 6.2 analysing the general variables. In the first part we examine the timelines of the financial variables to trace changes over time (the time period is 1990–2016 or shorter, depending on data availability) and compare levels of financial market development in the European OECD members. The second part comments briefly on their distribution using density functions. The financial market variables are those described in Table 6.1, examined in the same order. We start with the most general indicator of financial market development, i.e. FM, discussed in Section 6.2, with some additional insights. We then analyse the three sub-indexes of FM, accounting respectively for the depth, accessibility and efficiency of the financial markets, followed by a discussion of three variables representing various dimensions of the stock market: namely, capitalisation, turnover, and volatility. Finally, in line with Sahay et al. (2015) and Svirydzenka (2016), we take total outstanding domestic private and public debt securities as proxies for the development of the bond markets. Other financial market segments are omitted
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for methodological reasons and problems of data availability. The discussion is continued in Section 6.4. The first variable (Figs. 6.1 and 6.2) is our core indicator for financial market development—FM, the index published by the IMF, which is in fact one of the two sub-indexes of the broadest index of financial development, FD. FM itself is the resultant of three sub-indexes, representing the depth, accessibility, and efficiency of the financial markets, designated respectively FMD, FMA, and FME. The values of FM are normalised to range from 0 to 1, higher values implying higher levels of development. Most of the component variables of FM are linked to the stock markets, but a few represent the bond markets; efficiency is gauged exclusively by the stock market turnover ratio. The other financial market categories are not included. This means that, de facto, we analyse mainly the development of stock markets and to a lesser extent bond markets. This approach is suitable given the main aim of our analysis, namely determining the impact of ICT diffusion on various aspects of financial development; as we showed in Section 3.3, stock markets are among the parts of the financial system most powerfully affected by the information and communication technologies. Some of the variables used to calculate FM are taken separately: for instance, SMC_GDP, to assess differences in levels of FM over time and between countries. The only financial variable that is entirely separate from FM is SPV—stock price volatility is not covered directly by the index. As we stated in Section 6.2, FM displays high temporal variability, substantially greater than that of FD or FI, which may well imply that this segment of the financial system has experienced much greater fluctuations in development than have financial institutions, whose trajectory has been relatively stable. In most of our sample countries FM increased until the 2008 global financial crisis, which initiated at the beginning of financial market decline or stabilisation. This is evident in the trend lines for countries such as Belgium, the Czech Republic, France, Iceland, Norway, Sweden and the United Kingdom; the decline was particularly severe in the case of Iceland, which was strongly affected by the financial crisis—a comparison of the changes in FI and FM in Iceland shows that the disruptions were much more severe for the financial markets than for financial institutions. In some countries, such as Greece, the decline was aggravated by the euro-area debt crisis. There are also a number of countries where the financial market development trajectories from 1990 to 2016 differed considerably—above all the postcommunist countries (we address this issue in the next paragraph within the comparison of the levels of FM in the analysed countries), but also some
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particular cases, including Germany, Ireland, and Switzerland. In Germany, high levels of FM had already been reached by the late 1990s, and the financial markets did not experience substantial variability over the ensuing years. However, the downtrend after 2008 is clear. In Ireland changes in financial market development have been rather minor—indeed, this is one of the most stable countries in our sample in this regard (even during the global financial crisis declines were not as sharp as in other economies, and were followed by rebounds). However, the country’s FM values have not been among the highest in our sample. In Switzerland, like Germany, levels of FM were very high by the end of the 1990s, with a perceptible downtrend after 2008. Here, however, the distinctive feature is the abrupt decline in 2004–2006 to the lowest levels in the entire period. This can be attributed to the diminution in stock market capitalisation and especially turnover. This issue is addressed as part of the analysis of the stock market variables. After this examination of changes over time, we now focus on international differences in financial market development. What is most striking is the significant disparities in FM, as our sample includes both economies with some of the world’s highest levels of FM (Norway, Switzerland, and the United Kingdom in some years) and countries where it has remained close to 0, indicating seriously underdeveloped financial markets. The latter group consists exclusively of post-communist countries: the least developed financial markets are those of Latvia, Lithuania, and Slovakia, all with FM just over 0.1 at best. Comparing this with the values for various groups of countries (see Svirydzenka, 2016, p. 22), we can see that these three countries had levels of FM not only below the global average but also below the mean for the emerging markets (albeit still much higher than in the low-income and developing countries). That is, these countries’ financial markets were underdeveloped not only by European standards. The main reason is low stock market capitalisation and turnover. In three other post-communist countries, the Czech Republic, Estonia, and Slovenia, there has been some development, but after rapid increases in FM in the early 1990s its levels have declined, indicating that the original development was not sustainable. Naturally, the low ranking of the post-communist countries in this regard is no surprise, given that at the turn of the 1990s their financial markets were tiny or non-existent. Despite this initial disadvantage, two of them—Hungary and Poland—attained levels of FM close to or even above 0.5 (the mean for the advanced markets according to Svirydzenka, 2016), comparable to countries such as Greece and Portugal or, among the more advanced economies, Belgium and Denmark.
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The remaining countries in our sample show average values of FM in the period 1990–2016 between 0.5 and 0.8. With the exception of outliers (Greece on the downside and Switzerland on the upside), the group of advanced economies is rather homogenous in terms of financial market development. To some extent the absence of substantial disparities may be due to integration within the European Union and also within the euro area. In fact, only three of our sample countries are outside the EU. One readily apparent relationship is that between the size of the economy and the level of FM (a larger economy seems to contribute to the development of financial markets). Recall that all our indicators are relative, i.e. expressed in proportion to GDP. The density function of FM confirms the conclusions drawn from analysis of national timelines, namely that the distribution of FM is rather even. Our sample includes countries at various levels of financial market development during the period, and the differences were more significant than for FD or FI. After this analysis of the broad indicator of financial market development, let us now briefly address its three component sub-indexes: FMD, FMA, and FME. As with FM, the values of all three range between 0 and 1, higher values corresponding to higher levels of development of the relevant dimension (for more details on the methodology see Svirydzenka, 2016). FMD stands for the depth of financial markets; it is based on indicators of various aspects of the size of stock and bond markets: capitalisation and turnover of the stock markets (we also analyse these two, SMC_GDP and SMTV_GDP, separately) as well as volume of debt securities issued by financial and non-financial corporations (considered separately, though in our analysis we use ODP_GDP which stands for the sum of these two values). All these variables are in percentage of GDP. For most countries, the FMD timelines resemble those of FM, as regards both changes over time and differences between countries (see first part of Fig. 6.5), so we do not discuss them. However, it is worth noticing that some countries have attained values of FMD close to the maximum limit of 1—Switzerland and the United Kingdom have been close to 1 for most of the 2000s, confirming these nations’ status as global leaders in depth of financial markets. The FMD density function shows, however, that most of the other countries had much lower levels of this variable, below 0.4, and the remainder between 0.4 and 0.9. Levels higher than 0.9 were rare (see first part of Fig. 6.6).
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The next variable, FMA, corresponds to financial market access—it too consists of two component variables: share of total stock market capitalisation accounted for by companies outside the top 10 and total number of corporations that are debt issuers. Unlike FMD, the FMA trajectories for some countries differ considerably from those of FM (see second part of Fig. 6.5). In about half the sample there was no substantial change in FMA between 1990 and 2016 (see, e.g. Belgium, Iceland, Poland, and Sweden); for the other countries there was fairly regular growth, save for Slovenia, which experienced rapid growth up to 2009 and dramatic decline thereafter. Comparing countries, the post-communist countries generally have much lower levels of FMA, except Hungary and Poland, which have attained levels broadly on a par with some of the less advanced ‘old’ EU members. The density function indicates that in most countries FMA values are spread rather evenly between 0.2 and 0.6. Higher levels are uncommon (see second part of Fig. 6.6), but are found in some countries that are global leaders in financial market access by this gauge. For the most part these are smaller economies—see in particular Austria, Ireland, Norway, and Switzerland in Fig. 6.5. In the larger countries FMA was usually much lower. The third and final sub-index of FM, labelled FME, is financial market efficiency. This is a single variable; it is defined as the ratio of stock market turnover to capitalisation and should thus be regarded as a somewhat approximate measure of financial market efficiency. Values of FME have been highly volatile in our sample (see third part of Fig. 6.5), and full interpretation would require country-by-country and year-by-year analysis. Here we limit ourselves to several general remarks. The lowest levels of FME, with no substantial improvement, are found in the smallest postcommunist countries such as Latvia or Lithuania, and the high levels of Estonia and Slovenia were not sustained. Hungary and Poland were comparable to the advanced EU economies. There are a number of countries in which FME was close to 1 for a shorter or longer period (most consistently in Germany, Italy, the Netherlands, and Spain), but it is difficult to discern a common explanation for this apparent efficiency of financial markets. Considerable variability is also evidenced in the distribution of FME—its values are significantly spread out (to some extent similar to the distribution of FM); only values ranging from 0.5 to 0.8 are relatively less common (see third part of Fig. 6.6). The analysis of the three sub-factors in FM thus shows that the results are relatively consistent (albeit with some exceptions), which means that European financial markets tend to develop in similar ways in all three dimensions.
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At this point, we turn specifically to our three stock market variables: SMC_GDP, SMTV_GDP, and SPV. The examination of overall development of the financial markets picked up significant international differences. In what follows we offer some insights by studying first our proxy for stock market size (i.e. capitalisation), then liquidity (turnover, which could also be seen as another size indicator), and finally price volatility. The timelines of SMC_GDP (see fourth part of Fig. 6.5) show that in most of our sample countries in the period considered (1990–2016), capitalisation it has been substantially below 100% of GDP; the density function (see fourth part of Fig. 6.6) suggests the same conclusion, as the majority of observations are well below that level (and most, in fact, below 50). The countries with the largest stock markets relative to the national economy were Finland and Iceland (although only temporarily, for a few years), the Netherlands, Sweden, Switzerland, and the United Kingdom (these four for a sustained period). As for overall financial development, Switzerland was also the European leader in SMC_GDP; in some years it exceeded 250, one of the highest values anywhere in the world. To some extent the decline in SMC_GDP between 2004 and 2006 explains the decrease in FM (although SMTV_GDP was a more important factor). The difference in the capitalisation of the stock markets between the post-communist and the more advanced European economies is clear; one factor is certainly the much shorter history of the equity markets in the former group; these countries had to build stock markets from scratch starting in the 1990s, whereas in the latter group they were already quite advanced. Analysis of SMC_GDP over time shows that national equity markets generally expanded up to 2008, which marked the onset of a downtrend, even in countries such as Switzerland. Generally, SMC_GDP is the most stable stock market indicator, given the measurement methodology based on the market value of all listed companies. Even though equity prices can undergo considerable variations, the stabilising element is their number, which depends, above all, on the activities of issuers (such as initial public offerings). The changes in SMTV_GDP (see fifth part of Fig. 6.5) are relatively similar to those in SMC_GDP—the size and the liquidity of European stock markets are closely related (the correlation coefficient between these two variables is about 0.8). This is not surprising, as both indicators, albeit from different perspectives, are based on the number of equities and their market value. Fig. 6.5 shows, however, that turnover tends to change more from year to year than capitalisation. Compare, for instance, the graphs of SMC_GDP and SMTV_GDP for Spain—the trajectory of capitalisation
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has been rather stable while that of turnover has fluctuated (witness the short-lived record-high levels of turnover on the verge of the 21st century). One of the striking features of Fig. 6.5 is SMTV_GDP for Switzerland, with the very deep decline of trading in 2004–2006 from 144% of GDP in 2003 to 12% in 2005—one of the steepest decreases in our sample and a key factor in the significant decrease of FM in that period. However, this finding should not be read as really indicating the sudden demise of the Swiss equity market—its highly transitory nature and the fact that it was not accompanied by any comparable decline in capitalisation indicate that these results may actually stem from some methodological or measurement issues in the source database. Examination of the turnover data in the World Federation of Exchanges (WFE) database confirms this intuition, as it offers no evidence for the sudden decline—on the contrary, it shows a continuation of the growth that began in the early 1990s. As with capitalisation (SMC_GDP), there is a group of regional leaders in stock market turnover, namely the Netherlands, Spain, Sweden, Switzerland, and the United Kingdom (Finland and Iceland registered short-lived periods of high activity, which then gave way to very low values of SMTV_GDP). Denmark, France, Germany, and Italy constitute the next group, characterised by a notable increase in SMTV_GDP although still lagging behind the leaders. SMTV_GDP was lowest in the post-communist countries, mostly below 10, and not substantially higher in Greece or Portugal. The density function (see fourth part of Fig. 6.6) shows that the heterogeneity of SMTV_GDP in our sample is limited, also in comparison with SMC_GDP—most values are considerably below 50, with rare exceptions—countries where equity turnover exceeded 100% or even 200% of GDP (Spain and Switzerland). The next variable, stock price volatility (SPV), is not a direct indicator of market development but does offer additional insights into the attributes of the European equity markets. For technical reasons SPV is not measured in relation to GDP: unlike capitalisation or turnover, it is an index variable. Timelines of SPV for the European countries (see the sixth part of Fig. 6.5) show that changes in volatility followed a similar trajectory. In most countries SPV was relatively low in the 1990s and increased at the beginning of the new century, in connection with global financial uncertainty due, among other things to the dot.com bubble (and then crash) on some major stock markets or events such as terrorist attack of 11 September 2001. In the years that followed, European stock markets became more tranquil, on average. However, the global financial crisis of 2008 produced a sharp
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increase in SPV in all our sample countries (see the spike in 2009, though some effects were discernible already in 2008). In most countries the period of heightened volatility was relatively brief and within a few years it had subsided to pre-crisis levels. The effects of the euro-area debt crisis on SPV were much more moderate, with another much less marked spike in 2011 in most countries. Generally speaking, the trend since 2009 has been downward, though specific countries were affected more severely by either the global or the euro-area crisis, most notably Iceland and Greece respectively. In Iceland SPV in 2009 was the highest in our entire dataset at close to 100; by comparison, there are only a few cases in which it reached even 50. That is, the Icelandic stock market was affected much more strongly than any other European market by the global financial turmoil; this conclusion is also supported by the data on the decline in capitalisation and turnover. In Greece, stock price volatility continued to grow after 2009, SPV hitting a record high of 45 in 2016. The cause was the euro-area sovereign debt crisis and its severe consequences for the Greek economic and financial system, including the stock markets (capitalisation and turnover also declined). Examining national stock market volatility according to levels of economic development reveals rather minor differences. In the final years of our period stock markets in some of the post-communist countries were actually less volatile than in the more economically advanced countries. In some of the countries that launched stock exchanges in the 1990s, such as Estonia, Latvia and Slovakia, volatility was initially quite high, reflecting the small size and limited liquidity of these new equity markets. Their growth over the years and then gradual integration into the EU (all three countries adopted the single currency) may be regarded as the stabilising factor, as the values of SPV have declined. The SPV density function in the sixth part of Fig. 6.6 shows that the differences both over time and between countries were small, SPV rarely exceeding 40 and in the vast majority of cases holding below 20. Two final financial variables are proxies for bond market development, namely the value of outstanding domestic private debt securities (ODP_GDP) and domestic public debt securities (ODPub_GDP) in relation to GDP. Owing to lack of data, for most countries the timelines for ODP_GDP and ODPub_GDP are much shorter than in the case of stock market size variables. Consequently, the conclusions should be regarded with caution. The charts of ODP_GDP and ODPub_GDP (the seventh and eighth parts of Fig. 6.5, respectively) show that European countries are rather
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heterogeneous both in trends and in average values of outstanding debt securities. Denmark, Iceland, and Ireland have the highest values of private bonds, while Belgium and Italy are the leaders in public debt securities. A look at these two lists shows that high values of ODP_GDP are not necessarily accompanied by comparably high values of ODPub_GDP, indicating that the two segments of the debt market tend to develop independently; this is confirmed by the correlation coefficient between them, which while positive is very low at 0.13. The smallest bond markets, both private and public, are found in the post-communist countries, which suggests the relative underdevelopment of their debt market. This is particularly true as regards the indicator of private bonds, in that a higher volume of public debt securities should not necessarily be seen as unambiguously positive. It could reflect rising public debt and accompanying economic and financial problems. However, ODP_GDP has also been low in Switzerland and the United Kingdom, i.e. the advanced economies with very highly developed financial markets as gauged by the other indicators. Moreover, it is difficult to establish strong trends in the level of either ODP_GDP or ODPub_GDP. For instance, the relative volume of private debt securities has generally declined in such countries as Belgium, Finland and Germany, whereas in Ireland, the Netherlands, Portugal, and Spain ODP_GDP has increased. The value of outstanding public debt securities has declined in Belgium, Denmark, Ireland, Sweden, and Switzerland while increasing in the Czech Republic, France, Germany, and the United Kingdom. Generally, the local debt market has grown in some of the countries that experienced the most severe problems during the global financial crisis and the euro-area debt crisis. One of the reasons for this is government anti-crisis policies, financed in part by public debt issuance. However, notwithstanding the serious problems of Greece, ODPub_GDP has actually been declining there in most recent years, owing essentially to difficulty in accessing the debt markets due to the high risk of default. The density functions of the two bond market variables are similar (see the fifth part of Fig. 6.6). Most observations were substantially lower than 50% of GDP, for private and public securities alike. The functions also show that the public segment of the European bond markets has been larger, on average, than the private segment. Another interesting issue is the linkages between stock and bond market development. No matter which pair of indicators is considered, the correlation coefficients are close to 0—that is, there is no direct relationship between these two segments of the financial system.
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6.4 How ICT impacts on financial markets We now address one of the pivotal issues for this study—the impact of ICT on the financial markets, with a special focus on the development of this part of the financial system. Preliminary graphical evidence consisting in the timelines and density functions of the financial market variables was discussed in Section 6.3. We identified the main trends and international differences for our sample of European members of the OECD. Here we discuss the graphical representations of the local polynomial regressions (supplemented by correlation coefficients) and the estimates of the panel models to identify the main attributes of the relationship between ICT and financial markets. First we take the IMF’s most comprehensive index of financial market development, namely FM (and its three sub-indexes); next we focus on stock market variables; and finally we examine the proxies for bond market development. In each case we consider the results for both of our ICT indicators, namely Internet users (IU) and fixed broadband subscriptions (FBS), in order to pinpoint possible differences between the role of access as such and availability of fast and stable connections. The dataset is identical to that used in the rest of this chapter (for details on the variables see Table 6.1). The relationship between the general index of financial market development, FM, and ICT penetration was studied in Section 6.2. The current section reproduces the relevant graphs and repeats the main conclusions reached in that section. But we also extend the analysis with some additional insights that will serve as the starting point for further analysis of the specific stock and bond market variables. The first part of Fig. 6.7 graphs the local polynomial regression for IU vs FM. Using this evidence alone, it is hard to establish either the direction or the strength of the impact of ICT diffusion (in terms of usership) and the development of financial markets. For the lowest values of IU, from 0 to 40, the relationship appears to be positive, in particular for the lowest levels of both IU and FM. The number of observations clustered in this initial interval is large compared with the higher levels of IU. This can be seen as confirmation of the positive impact of the increasing number of Internet users on the development of the financial markets in the early years of our sample period (in the vast majority of cases these observations refer to the 1990s, given that in subsequent years ICT diffusion increased substantially). For the next interval, namely IU ranging from 40 to 70, the relationship is
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Fig. 6.7 IU vs financial markets variables. Local polynomial regressions. 1990–2016, annual data. Note: On x-axis—IU; raw data used; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. (continued)
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weakly negative, suggesting that at later stages of ICT deployment its role in supporting the development of the financial markets weakens or even turns slightly negative. This surprising result would appear to reflect trends in FM—in most European countries, after very rapid initial increases, the growth of FM has slowed (and in some cases, in connection with the global financial crisis, stagnated or even declined). Considering levels of IU, these results refer to the mid-developed European economies, and mostly in the period around 2008 rather than the final years of our period. At the same time, the values of IU have continued to increase. The third interval, IU ranging from 70 to 90, again implies a significantly positive relationship. This is followed by the fourth interval, values of 90 and up, in which the relationship becomes strongly negative. These latter two intervals refer essentially to the most advanced economies and the last years of the time interval. The seemingly contradictory results here (as for the IU interval of 40–70) reflect the turmoil in their financial markets post-2008. Clearly, for the 2000s, especially in the pre- and post-crisis period, the impact of rising IU on financial market development is harder to determine than for the 1990s. The correlation coefficient between FM and IU is 0.38. That is, taking the entire set of observations we have a positive relationship between the
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Table 6.5 IU vs financial market variables. Fixed effects regressions. 1990–2016, annual data LnFM LnFMD LnFMA LnFME LnSMC_GDP
LnIU R2 (within) No. of obs. Rho F (prob > F) LnIU R2 (within) No. of obs. Rho F (prob > F)
0.11 [0.00] 0.37 645 0.84 366.2 [0.00]
0.23 [0.00] 0.69 646 0.87 1444.9 [0.00]
0.11 [0.00] 0.28 646 0.88 248.4 [0.00]
0.05 [0.01] 0.02 646 0.63 16.9 [0.00]
0.18 [0.01] 0.37 584 0.76 333.3 [0.00]
LnSMTV_GDP
LnSPV
LnODP_GDP
LnODPub_GDP
0.28 [0.01] 0.31 586 0.73 242.5 [0.00]
0.03 [0.00] 0.03 582 0.45 19.4 [0.00]
0.08 [0.01] 0.06 457 0.78 32.2 [0.00]
0.04 [0.00] 0.06 487 0.72 30.9 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
two variables. The periods where the correlation is positive seem to outweigh those with no or negative correlations. The panel model estimate for this pair of variables (see the first part of Table 6.5) confirms this conclusion—the coefficient of the single explanatory variable, IU, is positive and statistically significant, with an estimated value of 0.11. Comparing this with the values of the corresponding parameters estimated for FD and FI (see Table 6.2), we find that development of the financial markets is much more strongly affected by growing access to the Internet than is overall financial development or the development of financial institutions. However, the fit of the FM model, as indicated by the within R2, is not as good, especially compared with the FD model, which means that it is less useful for analysing the dependent variable. Still, when compared with the models for the other financial market variables (see Table 6.5) it is the second highest, together with the model for SMC_GDP. After this discussion of the IU indicator, we now turn to our second measure of ICT diffusion, namely fixed broadband subscriptions (FBS). The implications of the first part of Fig. 6.8 largely overlap with those concerning IU on the basis of its local polynomial regression, here too with several different intervals. Again, for the lowest values of FBS (up to 10) the impact of the new technology on FM appears to be positive; for levels of FM in the range of 10–25 it is moderately negative; and in the 25–40 interval it is positive. Unlike the results for IU, however, for FBS the fourth and final
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interval supports the thesis of a strong positive influence on the development of the financial markets. The caveat here is that the number of observations with FBS close to 45 is extremely low, which means that the last stage of the relationship hypothesised refers to just a few countries in the last few years.
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This datum may be regarded as an outlier rather than meaningful evidence. Excluding this fourth interval, the overall impact of FBS on FM is much weaker than that of IU as evidenced by the lower slope of the line in Fig. 6.8. The correlation coefficient between FM and FBS is much lower than for IU, the more general ICT indicator. It comes to 0.22, confirming the conclusion that the effect of FBS on financial market development is weaker than that of IU. Estimates of the panel model with FBS as the explanatory and FM as the dependent variable (see first part of Fig. 6.6) show that this tool should not be used to assess the relationship between the two variables—the model’s within R2 of the model is close to 0, and the coefficient of FBS is also 0 and is statistically insignificant. To conclude, the empirical evidence presented here strongly indicates that the diffusion of ICT has contributed to the development of the financial markets, above all, through growing Internet access and that the increase in the number of fixed broadband subscriptions has been less important. Obviously IU and FBS, important as they may be, are only two dimensions of ICT; and the European financial markets could well have been affected by other elements of ICT not considered in our analysis (although most other, similar technologies are in fact heavily conditioned by those
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considered here, in particular access to the Internet broadly defined). Again we find that the simple possibility of using Internet services is more important to financial market development than the speed and quality of the web connections. In the rest of this section, we seek to identify the channels whereby ICT affects FM, studying its role in the stock and bond markets. Our analysis of the linkages between ICT adoption and development of the financial market can be compared with previous works on this issue. However, these are very few in number, especially compared with studies of the ICT-financial development nexus or those on the banking sector. Because most empirical research concentrates on stock markets (and, albeit to a much lesser extent, on bond markets), we discuss the relevant studies as part of the analysis of the stock and bond market variables. In any event, the positive impact of various types of ICT on the development of other financial market segments has been confirmed: for money markets by Imakubo and Soejima (2010) and van Lelyveld (2014) and for derivatives markets by Daniel (2010), Wilkins and Woodman (2010) Benito (2012), Lannoo and Valiante (2013), and Heath, Kelly, Manning, Markose, and Shaghaghi (2016). We can now briefly discuss the graphical representations of the local polynomial regressions obtained for the three sub-indexes of FM: FMD, FMA, and FME (see the seventh, eighth, and ninth parts of Figs. 6.7 and 6.8). We begin with their relationship with IU. Clearly, the curves for all three sub-indexes somewhat resemble that of the aggregate. The parallel is closest for FMD, while for the other two there are some visible disparities. The relationship between IU and FMA is different, as the part of the curve corresponding to the third and fourth intervals is flatter, as in the case of the other variables. But the rest of the IU-FMA curve is rather similar, if on the whole flatter than the general FM curve (still with a slight upward slope). The great variability of FME makes determination of the relationship between this variable and IU rather difficult—see, for example, the many observations of FME close to 1 (the maximum) over practically the entire range of IU levels, implying that no meaningful conclusion at European level is possible (country-specific analysis seems more robust). Nevertheless, despite some caveats, the estimates of the local polynomial regressions show a mostly positive effect of IU on financial market development in the case of FMD and FMA, while in the case of FME the relationship is hard to determine. The results concerning the influence of the second ICT variable, FBS, on the three aspects of FM are more heterogeneous. For the depth of financial
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markets (FD), the apparent effect of the increasing use of fixed broadband connections is similar to the relationship between IU and FM (at least in terms of its direction), with a clear positive effect of ICT adoption for all levels of FBS above 20. For access to the financial markets (FMA), however, the results are slightly different. The impact of FBS on FMA is difficult to determine on the basis of the local polynomial regressions—the relevant curve is practically flat; the strong uptick for levels of FBS above 40 refers to a very small number of observations and can be considered as an outlier. The results for the third component, financial market efficiency (FME), are even more variable than those for FM generally. As in the case of IU vs FME, the great variability of efficiency results in an inconclusive relationship between FBS and FME, both in direction and in strength: the curve is essentially flat, with some minor ups and downs. In short, the local polynomial estimates offer evidence exclusively of the positive impact of FBS on the depth dimension of FM; for the other two sub-indexes the results are inconclusive. We also estimated panel models for each sub-index, using the ICT indicators as explanatory variables. The results for FMD (see second parts of Tables 6.5 and 6.6) indicate the positive impact of our ICT proxies, both IU and FBS. Moreover, the within R2 of the IU model for depth is the highest of all those estimated, indicating that this model performs best in terms of goodness of fit and robustness. In the case of the FBS vs FMD, Table 6.6 FBS vs financial market variables. Fixed effects regressions. 1990–2016, annual data LnFM LnFMD LnFMA LnFME LnSMC_GDP
LnFBS R2 (within) No. of obs. Rho F (prob > F) LnFBS R2 (within) No. of obs. Rho F (prob > F)
0.00 [0.00] 0.05 [0.00] 0.00 0.15 422 423 0.94 0.95 0.07 [0.79] 71.9 [0.00]
0.02 [0.00] 0.01 423 0.92 7.45 [0.00]
0.03 [0.05] 0.00 423 0.81 3.89 [0.05]
0.01 [0.01] 0.00 380 0.85 0.77 [0.38]
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0.05 [0.02] 0.02 378 0.87 7.62 [0.00]
0.008 [0.01] 0.00 419 0.43 0.57 [0.44]
0.13 [0.02] 0.16 287 0.85 50.6 [0.00]
0.01 [0.00] 0.01 294 0.78 4.0 [0.04]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
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the relevant value is considerably lower but still among the highest. In general the coefficients for FMD are much higher than for FM, FMA, and FME, meaning that this is the dimension of the financial markets most powerfully affected by the diffusion of ICT. In the case of FMA (see third parts of Tables 6.5 and 6.6) the very poor fit hinders the interpretation of the FBS model, but the IU model confirms the positive impact of Internet access on this dimension of financial market development; interestingly, the results are very similar to those for FM (except for the within R2). Finally, no meaningful conclusion is possible for FME (see fourth parts of Tables 6.5 and 6.6). To conclude, the panel models constitute further evidence for the positive impact of ICT on the depth of European financial markets. As to financial market access, the positive role of IU stands confirmed, and the results for FBS again prove to be inconclusive. No relationship was established between ICT diffusion and financial market efficiency. To refine the analysis of the indicators of overall financial market development, we focus on one key component, namely stock markets. We begin with the most basic gauge of stock market size—the capitalisation of the listed companies in proportion to GDP (SMC_GDP). The estimates of the local polynomial regressions for this variable (see the second parts of Figs. 6.7 and 6.8) closely resemble the results for FM, as regards IU and FBS alike. For low values of IU (and for extremely low values of FBS) the impact of ICT on stock market capitalisation is weakly positive. Next, for somewhat higher values (IU of 40–70, FBS of 5–20) it is negative, but again weak. In the third interval of IU values, ranging from 70 to 90, the relationship is clearly positive, but in the fourth (values exceeding 90 and in some cases near the maximum of 100, which indicates full saturation in terms of users) it turns strongly negative. For FBS the third interval is the final one, in that above the level of 20 the influence of FBS is almost unambiguously positive, rather moderate initially but very strongly for the highest values. However, as with the results obtained for FBS vs FM, the final ‘uptick’ in FBS vs SMC_GDP can be taken as an outlier, given the extremely low number of observations in that range. Generally, the relationship of SMC_GDP to our two ICT variables is similar. The graphical representation of the local polynomial regressions makes it clear that both the direction and strength of the impact of ICT on the capitalisation of European stock markets have evolved over time and differed between countries. The impact was positive, above all, at the beginning of the sample period (especially in the less advanced economies, which were catching up both in ICT and in stock market size) and in the early 2000s up until the global
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financial crisis of 2008, corresponding to the third interval. For the remaining years the links were generally quite insignificant, as is indicated by the nearly ‘flat’ parts of the relevant curves in Figs. 6.7 and 6.8. Generally, the results for FBS are less clear-cut, and the evidence of positive impact is rather inconclusive, as for FM. However, given the underlying reasons for the declines in SMC_GDP, which depended on various national and global macroeconomic factors, it seems quite improbable that even in these periods the diffusion of ICT could have had a negative impact on the size of European stock markets. Indeed, it is more likely that a positive effect of the new technologies was offset, or more than offset, by the strong negative influence of, say, the withdrawal of investors during the global financial turmoil or the euro-area sovereign debt crisis. Before proceeding to the interpretation of the panel model estimates, let us mention the correlation coefficients between SMC_GDP and the two ICT indicators. For both pairs of variables they are positive—SMC_GDP vs IU is 0.32 and SMC_GDP vs FBS is 0.19—providing some support for positive effects, especially that of Internet use on stock market capitalisation. The panel estimates (see fifth parts of Tables 6.5 and 6.6) closely resemble those for FM, even in terms of goodness of fit (see the almost identical within R2). The coefficient of IU is statistically significant and positive, although that of FBS is near 0 (as is the within R2). The most substantial difference is the considerably higher coefficient of IU in this case than in that of FM (0.18 vs 0.11). That is, the panel estimates confirm the main findings drawn from the local polynomial regressions, namely that increasing access to the Internet has had a positive effect on the size of European stock markets, while the results as regards fixed broadband subscriptions are inconclusive. The positive impact of IU on size appears to be more substantial than on the development of the financial markets overall (obviously, this is a somewhat simplistic conclusion, insofar as the two models are not perfectly comparable; for instance, they differ in number of observations, although they are mostly overlapping). Section 3.3 discussed possible channels whereby ICT may affect the development of the stock markets. Here let us add that from the standpoint of stock market capitalisation there are at least two other potentially important if less straightforward explanations for the positive linkage between ICT diffusion and SMC_GDP. First, ICT companies (a synonym for ‘high-tech’ companies) are relatively riskier and more information-intensive than other companies (Bruinshoofd & De Haan, 2005) which may result in their preference for equity financing (see, inter alia, the seminal study by Carpenter
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and Petersen (2002), who argue that this form of financing, via IPOs, is crucial and leads to substantial growth in size). Further, ICT company investment decisions tend to be more seriously affected by financial crises. As Giebel and Kraft (2018) show, they limit their investment more sharply than other types of enterprise; this tendency may help explain the weakening of the linkage between ICT adoption and increasing stock market capitalisation in Europe in the post-crisis period. Given the increasing size of the ICT sector in most European economies, this can be seen as one of the mechanisms of transmission of ICT adoption to stock market size. A second possible channel, which should be seen in broader perspective, is suggested by Hobijn and Jovanovic (2001). They demonstrate that over the last few decades revolutions in information technology have been a key determinant of stock market capitalisation in proportion to GDP. At first the technological shift leads to a decline in SMC_GDP but this initial decrease is followed by a sharp increase. And this explanation is largely consistent with the previous one, as the authors contend that technological revolutions favour new companies, which have a comparative advantage in adoption of the new technologies. As a technical note, given that SMC_GDP is one of the components of the IMF’s financial market development index, the clear similarity of its relationship with ICT adoption (measured either in terms of IU or in terms of FBS, and demonstrated both by local polynomial regressions and by panel models) and that of FM overall is not highly surprising. Nonetheless, the single element of FM and its importance in shaping the index values should not be overemphasised (for more details on FM and other financial development indexes, see Sahay et al., 2015, and Svirydzenka, 2016). SMC_GDP and FM in our dataset are positively correlated, with a coefficient of 0.69. Most of these conclusions relative to SMC_GDP also hold for SMTV_GDP, i.e. the ratio of stock market trading volume to GDP, which is the other fundamental dimension we analyse here. The relevant graphs for SMTV_GDP (see the third parts of Figs. 6.7 and 6.8) yield observations almost identical to those for SMC_GDP. To begin with, the relationship with ICT adoption as measured by IU again displays a series of stages, with changing sign and strength of the impact of ICT. For levels of IU lower than 70, the relationship is rather weak (see the nearly flat curve in Fig. 6.7). For values ranging from 70 to 90 it is substantially positive, but for the highest levels of ICT adoption, above 90, it is definitely negative. The explanation for the changing direction of the effect is similar to that provided for SMC_GDP. That is, it reflects the changes in the sample countries between
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1990 and 2016, in particular the rise in equity market trading in the years prior to the global financial crisis of 2008 and the subsequent fall. As for our second ICT variable, FBS, there are slight differences between the estimates of the local polynomial regressions. For FBS in the interval of 30–40, its relation with SMTV_GDP is substantially negative; for the two financial market variables analysed earlier, FM and SMC_GDP, the negatively sloped segments of the relevant curves were much less evident (for FM, almost imperceptible). This could be interpreted as evidence of a negative impact of fixed broadband subscriptions on stock market turnover where this technology has achieved a high degree of penetration. Such a reading would be oversimplified, however, because for most countries these data refer to the final years of our sample period, which was a time of rapid FBS diffusion but also, owing to economic and financial turmoil, a decline in activity on the majority of European stock markets. The two processes were largely independent, and it would be misleading to contend that the diffusion of FBS was the main factor (or a factor at all) in the decline of SMTV_GDP. We further address this issue by referring to the estimates of the panel models. The indicator of stock market turnover is positively correlated with both measures of ICT adoption, with correlation coefficients 0.33 for SMTV_GDP vs IU and 0.20 for SMTV_GDP vs FBS. That is, they are marginally higher than those for capitalisation, indicating a slightly stronger relationship with the diffusion of ICT. This conclusion is confirmed by panel estimates (see the sixth parts of Tables 6.5 and 6.6). As with FM and SMC_GDP, with FBS as sole explanatory variable the model yields no meaningful conclusion owing to an extremely low within R2 and the statistically insignificant coefficient for FBS. With IU as independent variable, instead, the model shows the positive influence of greater Internet access on trading volume—the correlation coefficient with IU is 0.28 and statistically significant. This result can be compared with the coefficient of 0.18 for SMC_GDP, using an almost identical dataset (all available data on the stock markets of the European members of the OECD). The implication is that the impact of ICT diffusion has been relatively stronger on turnover than on total capitalisation. To sum up this part of our analysis, once again we find a positive influence of IU on SMTV_GDP, whereas the results for the effect of FBS are inconclusive. Again, this can be read as meaning that mere access to the Internet as such has a more substantial effect on equity turnover than does the availability of high-quality connections. Perhaps this is because it gives
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more consumers and investors access to information on the stock market and online brokerage accounts, whereas the fixed broadband is generally important only for the establishment and management of market infrastructures. This was suggested by Bogan (2008), who demonstrated that a rise in the share of households using Internet led to a significant increase in stock market participation. Section 3.3 gives a more in-depth explanation of the impact of ICT on stock exchange turnover. It is worth adding that there are some other possible channels of transmission between ICT and equity trading. Bank, Larch, and Peter (2011) showed that trading activity (as well as liquidity) is positively affected by search engines, thanks to the attenuation of information asymmetries. Qualitatively similar results were obtained by Zhang, Song, Shen, and Zhang (2016) in a study of market reactions to Internet news that correlates news with turnover. According to Aouadi, Arouri, and Teulon (2013), increases in online searches concerning the entire stock market cause a decrease in liquidity, but searches for specific stocks have the opposite effect. As Shen, Zhang, Xiong, Li, and Zhang (2016) show, online trading period information is a better proxy for the Internet information flow than its non-period counterpart. Tantaopas, Padungsaksawasdi, and Treepongkaruna (2016) confirmed the positive effect on market efficiency of the investors’ attention, as gauged by the volume of online search (but the effects on trading volume were found to be mixed). Zhang, Shen, Zhang, and Xiong (2013) prove that open source information can contribute significantly to market efficiency and the speed of information dissemination. Additionally, stock markets can be affected by developments in the fintech industry, such as the application of blockchain to post-trading activities (Geranio, 2017) or the provision of other exchange services (Haddad & Hornuf, 2019). Our discussion of ICT’s impact on stock market development (as measured by capitalisation and turnover) can be compared with previous empirical studies. In Section 3.3 we briefly recounted the main empirical analyses in this field—most of which found evidence of the positive role of ICT, and in particular of Internet access. Our results are therefore consistent with the conclusions of Ngassam and Gani (2003), Zagorchev, Vasconcellos, and Bae (2011), Hossein, Fatemeh, and Seyed (2013), Lee, Alford, Cresson, and Gardner (2017), and Pradhan, Mallik, Bagchi, and Sharma (2018). Our findings are also in line with some other, indirectly related studies. According to Liang and Guo (2015), the effect of Internet access on stock market participation by Chinese households depends on their social interactions— increased access may actually reduce participation if it undercuts the positive
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effects of social interactions. Zhang and Zhang (2015) showed that the increasing availability of online trading accounts attracted many new investors to the equity markets; but as these investors are to a large extent uninformed, their trades do not affect the price informativeness of market activity. Narayan (2018) checked the impact of technology investments on the stock markets in a sample of Islamic and non-Islamic countries, demonstrating that their profitability is positively linked to investment in technology. It is worth remarking that the reverse relation between stock markets and ICT has also been demonstrated empirically—by Brown, Martinsson, and Petersen (2017), for example, who documented the significance of stock markets for the growth of the high-tech sector and consequently for the technology-led growth of the economy. Analysis of the next variable, price volatility (SPV), supplements the foregoing, in that its movements cannot be interpreted as evidence of changes in the level of stock market development. Volatility in fact represents a separate dimension of the markets. Unlike the analysis of capitalisation and turnover, the results on volatility are quite unambiguous: they tell very strongly against any significant linkage between ICT diffusion and the volatility of the stock market. The graphs of the local polynomial regression estimates are almost flat (see fourth parts of Figs. 6.7 and 6.8), aside from the apparently negative relationship at the highest levels of both IU (over 90) and FBS (over 35). This negative correlation is readily explained by the fact that it reflects the moderation of volatility in the last few years of our sample period, which correspond to the maximum ICT penetration. Further evidence against any significant impact of ICT adoption on SPV comes from the correlation coefficients and panel model estimates. The correlation between either IU or FBS and SPV has always been nearly nil—in both cases the relevant coefficients are about 0.05. The goodness of fit of both panel models with each ICT indicator as single explanatory variable is extremely low, with within R2 values close to 0 (see seventh parts of Tables 6.5 and 6.6). Only in the case of IU was the relevant coefficient statistically significant, and at 0.03 it might imply some weak positive impact of ICT. But given the very poor fit, our analysis offers no significant evidence either for or against the influence of ICT on SPV in Europe. Our findings may be compared with those of previous studies. According to Shen et al. (2016), the Internet information flow can decrease the persistence of volatility. In an interesting study, Jin, Shen, and Zhang (2016) verified the impact of microblogging (which would be impossible without ICT) on several attributes of the Chinese stock market. They found that it is now an
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alternative information source and has increased the speed of information diffusion, thus diminishing price volatility. On the other hand, Dimpfl and Jank (2016) found some evidence that the increasing attention of investors, evidenced by a greater number of online search queries relating to the stock market index, magnifies realised volatility, which means that this Internet service may actually increase SPV. Similar conclusions were reached by Zhang et al. (2016), who studied reactions to online news Sekmen and Hatipoglu (2019) verified the impact of high-frequency trading and algorithmic trading (both impossible without ICT) on the volatility of the Turkish stock market and concluded that these activities had a disruptive impact. The remainder of this section deals with bond markets. We consider two proxies for their level of development: ODP_GDP (outstanding private debt securities over GDP) and ODPub_GDP (public bonds over GDP). In both cases, problems of data availability limit the examination to domestic securities alone. As both variables display the value of securities in being, they may be taken as indicators of the bond market size. Analysis of such other factors as turnover is impossible owing to insufficiently consistent data. We begin this portion of our study with what in most countries is the smaller bond market segment, i.e. private domestic debt securities. The graph of the local polynomial regression for IU vs ODP_GDP (see the fifth part of Fig. 6.7) displays some resemblance to both our indicators of stock market development, SMC_GDP and SMTV_GDP. For levels of IU lower than 70 the curve in Fig. 6.7 is almost flat, indicating no correlation between IU and ODP_GDP. For higher values, however, ranging from 70 to 95, the impact of increased Internet access is perceptibly positive; for the few observations at IU above 95, again the graph shows no identifiable influence. Given the distributions of these two variables, with most of the observations clustered at relatively low levels of both (see Fig. 6.7), the local polynomial regression shows that in the observation period 1990–2016 the impact of IU on the value of outstanding private debt securities in Europe was moderately positive. It is worth pointing out that unlike the financial market variables examined above, ODP_GDP is never negatively affected by IU for any interval of values of the latter. That is, the effects of Internet access on this segment of the bond market are less ambiguous than for the stock market. Interpretation of the results for the second ICT variable, FBS, is a bit more complicated (see the fifth part of Fig. 6.8). As with IU, the relationship between FBS and ODP_GDP appears to be mostly neutral for levels of FBS lower than 30 (for values below 20 it is slightly positive and for those from 20 to 30 it is very weakly negative). For higher levels, between 30 and 40,
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fixed broadband connections appear to exert a strongly positive effect. The substantial downturn at levels of FBS above 40 may be regarded as an outlier, given the extremely small number of observations in this range. Consequently, as in the relationship of IU with ODP_GDP, when the entire range of observations is considered the impact of FBS on the value of outstanding private debt securities has been positive, and more so than for IU. This might be read as implying that, in contrast with stock markets, for European private bond markets fast, high-quality Internet connection is more important than simple access to Internet per se. This presumably reflects the structure of the bond market, whose participants are rarely individuals but mostly institutional investors, which account for the preponderance of transactions. Institutional investors have greater needs and higher expectations for their communication facilities, which may help explain why IU is relatively less significant here than FBS. And even though financial institutions are also major participants in many European stock markets, the importance of retail investors cannot be disregarded. As the analysis set out in the preceding paragraphs shows, for stock exchanges IU reflects the impact of ICT on the development of the stock markets more accurately than FBS. Graphical representations of the linkages between the public bond market variable, ODPub_GDP, and the two indicators of ICT diffusion are quite similar (see sixth parts of Figs. 6.7 and 6.8). For most levels of both IU and FBS the relationship between ICT adoption and outstanding public debt securities is neutral (or weakly negative, as for IU of 40–90 and FBS of 20–40). For the highest levels of the two ICT variables, however, the results are radically different. The relationship between IU and ODPub_GDP suddenly becomes strongly positive as IU approaches 100 (i.e. full saturation), whereas for FBS exceeding 40 the impact on ODPub_GDP appears to turn strongly negative. Here again, however, we are dealing with a strictly limited number of observations. Generally, the local polynomial regressions suggest that the influence of ICT on the size of the public bond market is fairly insignificant. We shall check this conclusion using the panel models, but we can already affirm that the impact of ICT diffusion on the volume of outstanding public as opposed to private debt securities has been negligible. In other words, the changes in ODPub_GDP appear to have been shaped more by other factors, such as decisions by the authorities. Before analysing the correlations and panel models, one additional observation is in order. As noted above, the local polynomial regressions indicate substantial differences between the impact of ICT on ODP_GDP and on ODPub_GDP—compare the relevant parts of Figs. 6.7 and 6.8. This is
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interesting, as it shows that the private and public segments of the bond market have been affected differently by the new technologies. The impact on ODP_GDP has been to some extent similar to those on both stock market indicators (capitalisation and turnover), whereas in the case of ODPub_GDP there are substantial differences. This dissimilarity may be due to the fact that the private bond market, like the stock market, is factored into corporate financing decisions. One may thus suppose that changes in the economic and financial system, such as the effects engendered by the increasing penetration of ICT, will affect corporations’ decisions to undertake various investment projects that can be financed through either bond or stock issuance. Generally, this tendency is found in countries experiencing economic development. Importantly, such countries usually rely less on public debt issuance, thanks to sounder public finances. This means that the effects on the size of the two segments may tend to be opposite. The correlation coefficients with the two ICT variables differ dramatically between the two segments of the bond market. For private bonds the correlation is positive, with coefficients of around 0.30 (ODP_GDP vs IU) and 0.35 (ODP_GDP vs FBS). For public bonds, by contrast, the correlation is negative, with similar coefficients of 0.13 (ODPub_GDP vs IU) and 0.12 (ODPub_GDP vs FBS). Interestingly, ODPub_GDP is the only financial market variable considered in this section that is negatively (if extremely weakly) correlated with the indicators of ICT adoption. The estimates of the panel models for ODP_GDP (see eighth parts of Tables 6.5 and 6.6) confirm the moderately positive impact of ICT: the coefficients for both IU and FBS are positive and statically significant in the relevant models. In keeping with the indications of the local polynomial regressions, the impact of FBS seems perceptibly stronger, as indicated by its much higher coefficient. However, straightforward comparison with the IU model is impossible owing to different datasets; furthermore, the fit of the IU model is extremely poor, which means that it should not be deemed indicative. And while the two models’ coefficients for the public bond market are also positive and statistically significant (see ninth parts of Tables 6.5 and 6.6), the within R2 of both models is close to 0 (especially for FBS), which means that they do not provide meaningful evidence for positive linkages between ICT and ODPub_GDP. Accordingly, we can reiterate the finding of insignificant impact of ICT diffusion on ODPub_GDP. Nevertheless, the divergent values of the correlation coefficients and estimates of the panel models show that for this variable too the results are inconclusive.
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Table 6.7 Results of the analysis of financial market variables: summary Variable Impact of ICT adoption
FM FMD FMA FME SMC_GDP SMTV_GDP SPV ODP_GDP ODPub_GDP
Positive (IU)/inconclusive Positive Positive (IU)/inconclusive Inconclusive/none Positive (IU)/inconclusive Positive (IU)/inconclusive None Positive Inconclusive/none
(FBS) (FBS) (FBS) (FBS)
Note: For explanation of the variables, see Table 6.1 in Section 6.1.
Given the rarity of previous works on the link between ICT diffusion and bond market development, it is difficult to juxtapose our results with earlier findings. However, the conclusions of what studies we have found are generally consistent with our own. Styles and Tennyson (2007) concentrated on one specific segment of the bond market and in a way addressed the issue of the impact of ICT on its development. Studying access to financial information on municipalities through the Internet and its relationship with a number of variables, including the level of debt, they confirmed the positive relationship. Astrauskait_e (2014) verified the impact of ICT adoption on the development of the bond market in Lithuania, but with highly inconclusive results—positive effects associated, above all, with the broadband indicator, suggesting that the diffusion of ICT influences the development of the bond market through its effect on investors rather than issuers. The findings of the current section are summarised in Table 6.7. For most of the financial market variables analysed we found a positive impact of ICT adoption as gauged by at least one of our ICT indicators. But there are exceptions: for stock price volatility we concluded that the role of ICT has been broadly neutral, and for outstanding domestic public debt securities the results were inconclusive (from a different perspective, this implies a neutral relationship). Thus we can say that the two processes—ICT diffusion and public bond market growth—proceeded for the most part independently. The results for the efficiency sub-index of FM, FME, were also inconclusive.
6.5 ICT and financial innovations In this final section we focus on how ICT has affected our chosen category of financial innovation, namely exchange-traded funds (ETFs). We
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gauge the relative size, or level of development, of the national ETFs market as assets over GDP (ETF_GDP); for additional data and methodological comments see Section 6.1. First, we examine the graphical evidence on ETF_GDP: national development trajectories and density function. The first part of the analysis serves to discover the main international differences and trends over time. Second, we analyse the graphs of the local polynomial regressions to detect the main features of the relationships between our indicators of ICT diffusion (IU and FBS) and ETF_GDP. Finally, we use the correlation coefficients and panel model estimates for additional verification of the impact of ICT on the size of the ETF markets. ETFs are not the only type of investment fund we study—we also examine the parallel results on the assets of mutual funds over GDP (MFA_GDP). This serves to supplement the analysis of ETFs, so as to compare the changes in the market for conventional investment funds with the parallel changes in the market for innovative funds and examine the role of ICT as regards both. The core subject of this section, however, is ETFs. ETFs, of course, are not the sole type of financial innovation presumably influenced by the adoption of ICT, but we decided to concentrate on this instrument owing to problems with other possible choices, such as insufficient data (on highfrequency trading, for instance) or difficulties of quantification (in particular with regard to various types of fintech). The period covered is 2000–2016 for ETFs and 1990–2016 for mutual funds. We examine the assets of ETFs in 11 European OECD members (see Fig. 6.9); due to different periods of ETF presence on different national financial markets, for most countries data are not available for the entire period 2000–2016 (indeed only France has data available for the full period of our analysis). It is apparent that European countries are highly heterogeneous in the size of ETF markets (i.e. in ETF_GDP). The leader here is the United Kingdom, with ETF assets exceeding 9% of GDP in 2016; the British ETFs market has experienced substantial growth since the early 2000s. Next comes Switzerland, where ETF assets came to about 8% of GDP in 2016. But the growth of the Swiss ETFs market has been more volatile (see Fig. 6.9). France and Germany also have somewhat well-developed ETF markets—in both countries ETF_GDP has been significantly lower than in the United Kingdom or Switzerland, but still much higher than in any other country analysed here. Like the British, the French and German ETF markets have grown almost constantly, with just a few dips. ETF markets in the remaining countries have been small, although two groups can be distinguished—those with slightly more developed ETF markets and those with extremely low levels of ETF_GDP. The first group consists of
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Fig. 6.9 Development trajectories of the assets of ETFs as % of GDP. 2000–2016, annual country-level data. Note: Raw data used. (continued)
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Fig. 6.9, cont’d
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countries where ETF_GDP has approached or exceeded 0.1%: Italy, Spain, and Sweden. The Italian ETFs market is the smallest of the three but has experienced the most significant growth. Spain and Sweden display considerable variations in ETF_GDP during the period (Fig. 6.9), but the overarching trend for these years has been positive. The second group comprises Greece, Hungary, Norway (the leader in this group, almost on a par with the previous set of countries in some years), and Poland. In all these countries the development trajectories of ETF_GDP have been very unstable, growth repeatedly interrupted by usually sharper declines. Except for Norway the trend in this group has been strictly negative. In short, the timelines for ETF_GDP indicates significant diversity among European ETF markets in terms both of levels of development and of trends. However, taking the full dataset and the density function of ETF_GDP (see first part of Fig. 6.10), it is clear that in the large majority .6
.4
.2
0 0
2
4 6 ETF_GDP
8
10
.025 .02 .015 .01 .005 0 0
50
100
150
MFA_GDP
Fig. 6.10 Density functions of the financial innovation variables, annual data. Note: Raw data used. Ireland excluded. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
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of cases ETF assets are very low in relation to GDP, below 1.5%. Higher levels are quite rare, attained in just a few countries and mostly in years nearing 2016. Generally, that is, European ETF markets have remained at low levels of development (with significant growth only in the four largest). The possible determinants are multiple, but in our analysis we focus on one factor only, i.e. ICT diffusion. The development trajectories of MFA_GDP, like ETF_GDP, display considerable international differences (see Fig. 6.11). Mutual fund assets have generally been highest in relation to GDP in the large European economies, i.e. France, Germany, and the United Kingdom (exceeding 50% in some years, usually the most recent), and in some smaller but comparatively highly developed economies, including but not limited to Austria, Iceland, and the Netherlands (the leader in our sample, with a value of over 100% in 2016), Sweden, and Switzerland. In most of these countries the size of the national mutual fund market grew over our time period. Note that we have excluded one country, Ireland, which can be regarded as an outlier with mutual fund assets incomparably greater than in other countries (in some years almost 800% of GDP). In fact, Ireland is the domicile of choice of many European investment funds thanks to legal and tax advantages for financial companies (Clarke & White, 2018; Weiler, 2015); as a consequence the values of MFA_GDP simply cannot be taken as an accurate measure of the development of the local market (for similar reasons Ireland is also excluded from our analysis of ETF_GDP). There are also two other groups of countries as indicated by the MFA_GDP timelines. First are such Southern European countries as Greece, Italy, Portugal, and Spain, where MFA_GDP has been lower on average than in the other advanced economies and has actually declined between 1990 and 2016. Both of these features can be explained by their economic situation, in particular in the wake of the 2008 financial crisis and the sovereign debt crisis, which would appear to have undermined investor interest in this category of investment fund. Interestingly, in the case of Italy ETF_GDP and MFA_GDP moved in exactly opposite directions— that is, ETFs were not affected by the general downtrend in investment fund assets. The second group is the post-communist countries, where mutual fund assets have been very low (in some cases, such as Lithuania, practically nil), with no substantial or sustainable growth (even in the leader of this group, Hungary, mutual fund assets are below 20% of GDP; in Poland they have averaged 3.6%).
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Fig. 6.11 Timelines of the assets of mutual funds (as % of GDP). 1990–2016, annual data. Note: Raw data used. Ireland excluded.
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The density function of MFA_GDP (second part of Fig. 6.10) confirms these conclusions—for all countries and years, levels of MFA_GDP higher than 50% were rare; most observations were between 10% and 30%. Comparison of the density functions for ETF_GDP and MFA_GDP shows similar distributions—in the vast majority of cases the values are barely above 0. For comparison, in the United States, the country with the most advanced investment fund industry, ETF_GDP was higher than 13% at the end of 2016 and MFA_GDP has been over 50% since at least 1997. Significantly, an examination of developments in MFA_GDP and ETF_GDP reveals that the market for innovative funds has not developed in all the countries with high levels of mutual fund assets but only, for the most part, in the largest economies. However, the markedly positive correlation coefficient of 0.57 shows that changes in ETF_GDP and MFA_GDP have generally followed similar trajectories. This certainly suggests that a substantial national market for mutual funds (in most cases thanks to the large size of the economy) and a high level of economic development are preconditions for the substantial growth of ETFs. The rest of this section focuses on our fundamental issue—the linkages between ICT and various aspects of financial development, in this case the market for ETFs and for their conventional counterpart, traditional mutual funds. We start with the interpretations of local polynomial regressions. The relationship between IU and ETF_GDP can be described as twostage (see first part of Fig. 6.12). For low and moderate degrees of ICT (measured by IU) the impact of this technology on the assets of ETFs appears to be neutral (see the relatively flat curve for values of IU below 70). This portion of the curve is explained by the insignificant growth of the ETF markets in some countries and the lower rate of increase in ETF_GDP (despite the rapid growth of IU) in countries such as Switzerland initially, compared with the faster pace of change in later years. For higher levels of IU the relationship with ETF_GDP is radically different—it is strongly positive, suggesting that increased access to the Internet may contribute to the development of the ETFs market in Europe. This conclusion applies, above all, to countries such as Germany and the United Kingdom in the final years of our period. There is also a third stage, for the highest values of IU in our sample (95 and over), where the correlation is significantly negative. Nonetheless, this stage may be considered country-specific: in fact it refers to some of the most highly developed European economies but with severely underdeveloped ETF markets (such as Norway). These results distort the generally strong positive relationship found at the highest levels of both ETF_GDP and
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10
150
8
100 ETF_GDP
MFA_GDP
6
4
50
2
0
0 20
40
60 IU
Kernel = epanechnikov, degree = 4, bandwidth = 13.53
80
100
0
20
40
60
80
100
IU Kernel = epanechnikov, degree = 3, bandwidth = 10.27
Fig. 6.12 IU vs financial innovation variables. Local polynomial regressions, annual data. Note: On x-axis—IU; raw data used; Ireland excluded; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
IU—see the clearly upward segment on the right-hand side of the relevant graph. The results for MFA_GDP vs IU (see second part of Fig. 6.12) are highly similar to those for ETF_GDP. The relationship between MFA_GDP and IU is also two-stage (with a minor third stage for the highest levels of IU). For IU ranging from 0 to 70, despite some variability, the relationship can be characterised as neutral. In the second stage, with IU between 70 and 95, the impact of Internet access is unquestionably positive. To conclude, the estimates of the local polynomial regressions indicate a positive influence of high levels of IU penetration on the assets of both categories of investment fund; for low and medium levels, however, no impact is detected. The local polynomial regressions of ETF_GDP vs FBS are more clearcut than those vs IU (see first part of Fig. 6.13). There are again two stages: first, neutral (FBS ranging from 0 to 20) and second, considerably positive (FBS above 20). However, unlike IU, FBS displays no third stage. These results would appear to imply that for most countries FBS has played at most a minor role in supporting the development of the national ETFs market. Still, in the countries with the greatest ICT penetration in terms of fixed broadband subscriptions, accompanied by the highest values of ETF assets,
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MFA_GDP
6
4 50
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0 0
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30 FBS
Kernel = epanechnikov, degree = 0, bandwidth = 2.82
40
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0
10
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FBS Kernel = epanechnikov, degree = 4, bandwidth = 8.18
Fig. 6.13 FBS vs financial innovation variables. Local polynomial regressions, annual data. Note: On x-axis—FBS; raw data used; Ireland excluded; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
the effects of FBS are definitely positive. Estimates for MFA_GDP are very similar (see second part of Fig. 6.13)—the cut-off point between the first and second stage is again FBS of 20, as in the case of ETF_GDP. However, again the positive relationship refers to relatively few observations, as most are clustered in the interval corresponding to neutral impact. In the final part of our analysis we concentrate on the estimates of the panel models. As before, we begin our examination with the correlation coefficients. ETF_GDP is positively correlated with both measures of ICT diffusion: the coefficient for IU is 0.42 and for FBS, 0.56. Among all the variables considered in this chapter, ETF_GDP is thus one of the most closely correlated with our indicators of ICT adoption (only for Electr_paym are the coefficients higher). For MFA_GDP too, both correlation coefficients are positive, but considerably lower at 0.14 and 0.10 respectively. Estimates of the panel models with each of the ICT indicators as the single explanatory variable confirm the polynomial regressions and correlation coefficients. When the dependent variable is ETF_GDP, the coefficient of IU is positive and statistically significant (see Table 6.8); the same applies to the coefficient of FBS (see Table 6.9). Importantly, both of these models have values of within R2 among the highest of all those tested, which
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Table 6.8 IU vs financial innovation variables. Fixed effects regressions, annual data LnETF_GDP LnMFA_GDP
LnIU R2 (within) No. of obs. Rho F (prob > F)
5.15 [0.46] 0.49 134 0.75 121.8 [0.00]
0.37 [0.00] 0.29 452 0.85 179.1 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
Table 6.9 FBS vs financial innovation variables. Fixed effects regressions, annual data LnETF_GDP LnMFA_GDP
LnFBS R2 (within) No. of obs. Rho F (prob > F)
1.88 [0.11] 0.69 134 0.84 271.3 [0.00]
0.09 [0.01] 0.09 408 0.91 42.1 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
indicates their relatively good fit. As the two models were estimated on an identical dataset, the coefficients can be compared (with the usual caveats). The coefficient of IU is 5.15 and that of FBS is much lower (1.88), which indicates that IU may be making a substantially greater contribution to the expansion of ETF markets in European countries. Similar results are found for MFA_GDP, but the goodness of fit of these two models is far inferior (especially where the independent variable is FBS). Since the dataset for estimating MFA_GDP is much larger than that for ETF_GDP, the two sets of models cannot be reliably compared. Overall, it can be said that our study offers evidence that ICT does have an impact on the size of the markets for both ETFs and traditional mutual funds in our sample of European OECD members. The influence of ICT on investment fund markets should be considered in the framework of the broader effect of the new technologies on financial markets in general (see Section 3.3 and our discussion in Section 6.4). Identification of the exact channels of impact is complicated by lack of data, but we can say that the main mechanism whereby new technologies could have boosted the growth of both ETFs and mutual funds is the positive influence
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of ICT adoption on the development of the stock and bond markets, at least in some dimensions (see Section 6.4). As regards the impact of ICT on ETF markets, however, it appears necessary to take into account also some categories of fintech that could affect the innovative funds: robo advisors and social trading platforms (in particular the former; for more on fintech see Section 3.3). Even though the volume of assets managed by robo advisory platforms in Europe is still small by comparison with the more mature US market (Kaya, 2017; Phoon & Koh, 2017; Vives, 2017), this is a fast-growing segment of the financial industry, facilitated by the new technologies. Significantly, the fundamental investment instrument used by robo advisors is ETF shares. Consequently, the emergence of robo advisors, already discernible to some extent in the last years covered by our analysis (2015 and 2016), is bound to contribute to the development of the ETFs market—and in a broader perspective to financial inclusion—by attracting retail investors (Jung, Glaser, & K€ opplin, 2019; Schwinn & Teo, 2018). Social trading platforms are less important, indeed of negligible significance in most European countries. An important aspect of social trading is that users can copy the investment strategies of trusted traders and apply automated tools to execute transactions (Berger, Wenzel, & Wohlgemuth, 2018; Dorfleitner et al., 2018; Kromidha & Li, 2019), including trades in ETF shares. That is, they constitute yet another instance of an ICT-enabled service that employs ETFs. To finally conclude our analysis of the linkages between ICT deployment and the emergence of financial innovations, it is clear that both general access to the Internet and an increase in fixed broadband subscriptions can contribute to the development of the ETFs market. But the presence and strength of such links appear to be highly country-specific. Other determinants also need to be taken into account. Our results are consistent with the findings of earlier empirical work on the linkages between ICT and investment funds, and in particular studies of its impact on the development of ETF markets (see Lechman & Marszk, 2015; for more details see Section 3.3). Our findings are also analogous to the implications of the analysis by Marszk and Lechman (2019), in a study of a partially overlapping sample of European countries from 2004 to 2016 that confirmed the positive impact of both FBS and IU on the development of ETF markets. Finally, the positive impact of FBS on the assets of mutual funds is in keeping with Khodayari and Sanoubar (2016). In the case of IU, however, our results here are contradictory, finding a negative impact. As a conclusive observation, let us add that throughout the current section we have assumed that ICT diffusion can affect the emergence of
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innovative financial instruments; the possible inverse relationship was not considered. This approach may be motivated by the plausibly low likelihood that ETFs may have a causal impact on the launch and diffusion of ICT— above all, participation in and the scope of ETF markets is incomparably lower than ICT penetration, which is pervasive in all the countries analysed. Nevertheless, the possible impact of ETFs on ICT should not be utterly disregarded, in view of potential indirect channels of transmission, such as easier access to financing for the technology companies thanks to ETFs (e.g. increased stock market liquidity; Madhavan & Sobczyk, 2016). However, such reverse causal links have yet to be studied.
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Further reading Marszk, A., & Lechman, E. (2018). New technologies and diffusion of innovative financial products: Evidence on exchange-traded funds in selected emerging and developed economies. Journal of Macroeconomics, [in press].
ICT for financial development: Shaping the new landscape Contents 6.1 Introductory 6.2 ICT and financial development 6.3 Financial market evolution trajectories 6.4 How ICT impacts on financial markets 6.5 ICT and financial innovations References Further reading
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6.1 Introductory This chapter presents the results of our analysis of the role of information and communication technology (ICT) in various aspects of financial development in the European countries belonging to the Organisation for Economic Co-operation and Development (OECD). We begin with a few introductory remarks, including an overview of the chapter’s structure, the dataset, and our research methods. Some supplementary technical comments are also offered. In the next section we focus on the impact of ICT on the financial system at large and on selected segments of it. We present both graphical evidence and estimates of the panel models with regard to financial development, the banking sector, the insurance industry, and, from a broader perspective, the gross savings rate. The third section of the chapter considers the evolution of financial markets in the countries examined—we discuss the graphical evidence on the changes that have occurred, above all, in stock markets and briefly analyse selected indicators for bond markets. The fourth section analyses the influence of ICT on the financial markets, drawing conclusions based both on local polynomial regressions and on panel models. The fifth and final section shows the results relating to the linkages between ICT and financial innovations, in the particular case of exchange-traded funds (ETFs). As a supplement to the ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00006-3
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study of ETFs we discuss the significance of ICT for mutual funds, a type of investment fund that may be regarded as the main, and much better established, alternative to ETFs. The variables used in our analysis are presented in Table 6.1, in the approximate order in which they are referenced in the discussion. They can be divided broadly into two groups—first, our two ICT indicators (FBS and IU); and second, all the remaining variables that can be labelled as ‘financial’, i.e. associated in some way with financial development. The latter group comprises three partly overlapping sub-categories (for the explanation of the abbreviations, see Table 6.1): general indicators of financial development (FD, FI, FM, BD_GDP, Ins_GDP, and SAV_GDP), discussed in Section 6.2; financial market indicators (FM, FMD, FMA, FME, SMC_GDP, SMTV_GDP, SPV, ODP_GDP, and ODPub_GDP), discussed in Sections 6.3 and 6.4; and two measures linked to the financial innovations analysed (ETF_GDP and MFA_GDP), discussed in Section 6.5. The base time period covered is 1990–2016, and the data are annual (both the choice of period and the frequency of data depended on the availability of data, which was insufficient for a longer period or for quarterly or monthly frequency); we do not extend our study to 2017, as not all of the key indicators for that year were available at the time of analysis. Further, for some variables the period is severely shortened in relation to the base period, as the required data were not available. This is the case, for instance, of the assets of ETFs, in that the first such funds were launched in Europe in the early 2000s, and of data on electronic payments (consistent data are available only in some years since 2011). Moreover, ETFs are traded only in some countries—the others are omitted not for lack of data but simply because the ETF market is non-existent (or because of problems in correct attribution of the funds, as discussed below). For the full list of countries, see Table 4.1 in Section 4.2. Luxembourg was excluded as an outlier among European members of the OECD given its unique position as a regional financial hub—a small country with an oversized financial system whose development depended mainly on international factors rather than local adoption of ICT. There are three main data sources for this chapter: World Telecommunication/ICT Indicators (for the two indicators of ICT adoption), the IMF Financial Development Index Database (for the indexes of financial development), and the Global Financial Development Database (from which a number of variables were extracted). The remaining databases used are OECD Insurance Statistics, World Development Indicators, and Thomson Reuter’s Lipper. Note that for the purposes of data consistency we used only data from the international sources with broad geographical and time coverage and with common
Abbreviations
Name of variable
Units
Source(s) of data
Supplementary comments
Category
FBS
Fixed broadband subscriptions
Per 100 inhabitants
–
ICT
IU
Internet users
Per 100 inhabitants
–
ICT
FD
Financial development
Index
World Telecommunication/ICT Indicators World Telecommunication/ ICT Indicators IMF Financial Development Index Database
General
FI
Development of Index financial institutions
IMF Financial Development Index Database
FM
Development of financial markets
Index
IMF Financial Development Index Database
BD_GDP
Bank deposits
% of GDP
Ins_GDP
Total insurance spending Gross savings
% of GDP
Global Financial Development Database OECD Insurance Statistics
Index normalised with values ranging between 0 and 1 (higher values, higher level of financial development); based on the values of sub-indexes FI and FM Index normalised with values ranging between 0 and 1 (higher values, higher level of development); based on the values of three sub-indexes Index normalised with values ranging between 0 and 1 (higher values, higher level of development); based on the values of three sub-indexes: FMD, FMA, and FME – Value of direct gross premiums
General
Sav_GDP
% of GDP
World Development Indicators
General
General/financial markets
ICT for financial development: Shaping the new landscape
Table 6.1 List of variables in the analysis of implications of ICT adoption for financial development
General
Difference between disposable income and General consumption (indicator known as ‘gross domestic savings’ in the pre-2006 editions of the World Development Indicators)
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Continued
Abbreviations
Name of variable
Units
Source(s) of data
Supplementary comments
Category
Electr_paym
Electronic payments
% of respondents aged 15+
Global Financial Development Database
General
FMD
Financial market depth Index
IMF Financial Development Index Database
FMA
Financial markets access Index
IMF Financial Development Index Database
FME
Financial market efficiency
IMF Financial Development Index Database
SMC_GDP
Stock market % of GDP capitalisation Stock market total value % of GDP traded Stock price volatility Index
Global Financial Development Database Global Financial Development Database Global Financial Development Database
Electronic payments (made automatically, including online payments) in the past 12 months Index normalised with values ranging between 0 and 1 (higher values, higher level of development) Index normalised with values ranging between 0 and 1 (higher values, higher level of development) Index normalised with values ranging between 0 and 1 (higher values, higher level of development) –
Financial markets
–
Financial markets
Outstanding domestic % of GDP private debt securities Outstanding domestic % of GDP public debt securities
Global Financial Development Database
SPV
ODP_GDP
ODPub_GDP
Global Financial Development Database
Financial markets
Financial markets
Financial markets
Average of 360-day volatility of the Financial markets national stock market index (calculated by Bloomberg) – Financial markets –
Financial markets
ICT-Driven Economic and Financial Development
SMTV_GDP
Index
196
Table 6.1 List of variables in the analysis of implications of ICT adoption for financial development—cont’d
Assets of primary-listed % of GDP ETFs
Thomson Reuter’s Lipper
MFA_GDP
Assets of mutual funds
Global Financial Development Database
% of GDP
Sum of the total net assets of ETFs primary- Financial listed in the country. Country of ETF’s innovations primary listing is defined, as in Deutsche Bank (2017), as the location of fund’s primary exchange. This is usually that of first listing and also of the highest turnover in the funds’ shares. Lipper’s classification is used For some countries the values were revised Financial (assets of ETFs were removed in order to innovations ensure no overlapping with ETF_GDP)
Note: For information on methodology and data sources of the databases, see their documentation. The ‘categories’ in the last column are mostly technical and serve to ensure clarity of the analysis.
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ETF_GDP
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methodology; we avoided national sources, as they could lead to errors owing to varying methodology or currency conversion. Another problematic issue was missing observations. Nonetheless, for the variables examined most thoroughly, data were available for the full time period—the breaks cited are for particular countries in some specific periods and do not undermine robust analysis (we explain these issues within either our country-specific or panel data analysis by indicating the data breaks). The most complete datasets were for the ICT variables and the indexes of financial development; the least complete for Electr_paym (only data for 2011 and 2014 are available, and in 2014 for some countries only; there are no data for Iceland), ETF_GDP, and the debt securities indicators (ODP_GDP and ODPub_GDP); for the remaining variables, the problem of missing data is not severe. Another observation bears on the impact of the sizes of economies—our dataset covers such enormously different countries and economies as Estonia or Lithuania, on the one hand, and France or Germany, on the other. To mitigate the effect of size differences (and facilitate between-country comparisons), our variables are expressed as percent of GDP (in all cases the conversions were already made in the original databases, so no further calculations were necessary); obviously, this does not apply to indexes or other similar variables for which such conversions were not essential. One more very specific stipulation concerns the variable ETF_GDP. Given the structure of the European stock exchanges, which in some cases operate in multiple countries or consist of more than one country segment, accurate attribution of certain ETFs to a particular country is sometimes very difficult or impossible. We accordingly decided to consider only the countries for which data on ETF_GDP can be estimated with sufficient reliability. Consequently, even though there are some ETFs classified as primary-listed in Finland, Iceland, Latvia, and Lithuania, these countries were excluded in the analysis of ETF_GDP; in any event, given the extremely low ratio of local ETF assets to GDP in these countries, this decision may be regarded as practically negligible as regards potential distortion of our results. The same applies to Belgium, the Netherlands, and Portugal (which together with France form the Euronext exchange). In order not to omit France, with one of the largest European ETF markets, we assigned all the ETFs listed on Euronext to the French market (which accounts for over 95% of the assets of ETFs primary-listed on Euronext). Finally, due to the organisational structure of the Italian and UK stock exchanges, which are both part of the London Stock Exchange Group, the exact classification of ETFs between Italy and the United Kingdom is also somewhat problematic. However, we decided to leave data on the two countries unchanged, as no sufficiently reliable method of division between the two markets could be devised.
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In our study of the links between ICT and the financial system, we utilise a few well-established research methods. First, we examine the graphical evidence on the relevant variables, in a two-part analysis. The first part focuses on the trajectories of changes at country level (i.e. their timelines) in order to identify the main trends and national differences. The second part examines the density functions in order to assess the distribution of variables, including comparisons between variables. Second, we consider the estimates of local polynomial regressions for the pairs of variables, prepared using panel data—in each case we juxtapose one of the ICT indicators to another selected variable (non-ICT, i.e. financial); this part of the analysis is preceded by a brief introductory examination of correlation coefficients. The analysis bears exclusively on the graphical evidence obtained within the local polynomial regressions, as this can be considered sufficient confirmation of the implicit direction of the causal relationship between ICT and the selected variables; it also allows for some basic comparisons of the strength of the relationships (in other words, formulating basic statements concerning the strength of the impact of ICT). Finally, we use panel models with a single explanatory variable—always one of the ICT indicators, either FBS or IU; as dependent variables we include the indicators of financial development.
6.2 ICT and financial development This section focuses on the links between ICT deployment and various aspects of financial development (apart from financial market development, which is discussed separately in the two subsequent sections). We track variables representing financial development at large, the development of the banking sector and the insurance industry, savings, and electronic payments (for details on the variables, see Section 6.1). This is the set of variables that, for clarity, we labelled as ‘general’ variables, as distinct from two other groups of variables bearing, respectively, on ‘financial markets’ and ‘financial innovations’. First we conduct an introductory graphical analysis based on timelines and density functions, examining changes over time, differences between countries, and the distribution of the variables. Next we analyse further graphical evidence, examining the graphs representing the local polynomial regressions in order to gain basic insights into the direction and strength of the effects of the diffusion of ICT on the financial development variables. Finally, we interpret the estimates of the panel models to extend and conclude the preceding stages of the analysis. The first variable in Fig. 6.1—FD—is the IMF’s most general and comprehensive indicator of financial development; FD (like FI and FM) has a
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range of 0–1, higher values indicating higher levels of development. In most European countries the level of financial development was increasing or at least broadly stable between 1990 and 2016. In some economies, however, the peak was reached around the time of the 2008 global financial crisis and the euro-area sovereign debt crisis. Declines are registered in subsequent years in such countries as Greece, Iceland, the Netherlands, Portugal, and the United Kingdom. And even in countries apparently less affected by the turmoil in the global and European financial systems, some variability is discernible—see, for instance, the brief dip in FD in Switzerland (which overall has been the most financially developed of all the countries analysed and also a global leader, for some time actually ranked first worldwide in FD). The density function (see first part of Fig. 6.2) shows that the vast majority of FD values in 1990–2016 are in the range between 0.3 and 0.8, and mostly above the midpoint of 0.5. Extremely low values were very rare, found exclusively in the post-communist countries in the early 1990s, i.e. at the beginning of their economic and political transformation. Despite
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the substantial gains in financial development in the 1990s and early 2000s (the exception being Slovakia, where FD has remained almost flat at around 0.3), these countries have remained significantly less developed financially than those of Western Europe. This is clearly represented by the two peaks in the density function curve—one for the less advanced countries, and the second, a higher peak, for the more advanced, which are relatively more frequent in our sample. However, even in the group of the ‘old’ countries of the EU there is considerable heterogeneity, with such Southern countries as Greece and Portugal lagging behind at levels of FD closer to the postcommunist countries. Interestingly, this does not apply to Spain, which since the turn of the century has been among the most finally developed in the world, at FD values close to the United Kingdom. Now let us examine the two indicators that make up FD in order to determine some of the reasons behind these disparities. We start our analysis of the components of FD with FI, the index of financial institutions (see the second part of Fig. 6.1 and the first part of Fig. 6.2). Before examining our empirical data it is necessary to refer to the index methodology (for a detailed discussion, see Sahay et al., 2015; Svirydzenka, 2016). The variables used to construct the index refer, above all, to the banking sector (only one of the three sub-indexes takes account also of pension funds, mutual funds, and insurance), so FI can be regarded as a de facto index of banking development. Accordingly we analyse it jointly with another banking variable, namely BD_GDP (which is not used in calculating FI, so these two variables are not strictly overlapping). The variability of FI over time has been rather limited in most countries—in most of the advanced European economies it has been substantially above 0.5 for the entire period. The highest levels of FI are observed in Denmark, France, Italy, Switzerland, and the United Kingdom. Clearly, some post-communist countries entered the 1990s with substantially underdeveloped financial institutions (see, e.g. the very low levels of FI in Latvia or Lithuania). But this group is by no means homogenous, given that in some other post-communist countries, such as the Czech Republic and Hungary, levels of FI were relatively high already in 1990 and have stayed broadly constant. The FI density function shows that values of FI have been slightly less scattered than those of FM and mostly higher than 0.5, with a marginal share of values below 0.3. The ratio of bank deposits to GDP, BD_GDP, has remained very stable in most countries, with insignificant year-to-year changes (see the fourth part of Fig. 6.1). The few exceptions are for countries where significant variations resulted from particular events,
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such as financial crisis (see the fall in BD_GDP in Iceland after 2008) or entry into the euro area (see the increase in Greece in the early 2000s). The highest ratios of bank deposits are scored in Switzerland, which strengthened its position as European leader of the banking industry as uncertainty in the European financial and political system fuelled deposits in the traditional ‘safe harbour’ of Swiss banks. Regionally, on average BD_GDP has been markedly lower in the less developed countries. The difference in BD_GDP between the more and the less advanced European economies is greater than that in FI. This suggests that even though the post-communist countries have gained some ground on the more advanced in terms of efficiency and access to financial institutions, the disparities in the ‘depth’ of financial institutions (measured, say, by credit to the private sector or bank deposits) continue to be sizeable, as the data for countries such as the Czech Republic or Poland demonstrate. The distribution of BD_GDP (see the third part of Fig. 6.2) is relatively symmetrical at an average of about 50% of GDP; the long right tail of the distribution depends, above all, on Switzerland. The second component of FD, and the next variable we analyse, is one of the core indicators of our analysis—FM. The development of the financial markets is discussed in depth in the next section; here we only formulate some basic preliminary conclusions. The first and most striking observation suggested by the third part of Fig. 6.1 is the great variability of FM over time, much greater than that of the other core component of FD (i.e. FI). International differences are also incomparably greater than in the case of FI—we have countries with FM close to 0 over the entire period (Latvia, Lithuania, and Slovakia) and others where it averaged more than 0.7 (e.g. Switzerland). These disparities are clearly evidenced by the distribution of the variable (see the first part of Fig. 6.2), as the values of FM are spread over practically the entire spectrum, from 0.1 to 0.8 (although values greater than 0.9 were extremely rare), with no dominant value. The next variable analysed, Ins_GDP, is the ratio of total expenditure on insurance services to GDP in our sample countries. Like banking, the insurance industry is to some extent comprised within one of the sub-indexes of FI (through the somewhat similar indicators of the value of insurance premiums), but it is worth evaluating separately in order to assess the basic changes over time and international differences. For some countries analysis is hindered by lack of data—see, for example, the short timelines for Latvia, Lithuania, and Slovenia. For insurance, unlike most of the variables mentioned above, international differences are not exclusively between the post-communist countries and the other European OECD members;
ICT for financial development: Shaping the new landscape
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instead there is considerable heterogeneity within each group. However, the difference between the two groups is clear, as the less developed countries lag behind (see the fifth part of Fig. 6.1). For example, the values of Ins_GDP in France average 2 percentage points higher than in Germany, but in both countries they are much higher than in the post-communist countries, even those with the highest levels of insurance spending such as the Czech Republic or Poland. The graph also shows the broad stability of Ins_GDP over time, albeit with some exceptions; nonetheless in most of the sample insurance spending has been slowly increasing. Ireland and the United Kingdom are the only countries where Ins_GDP has exceeded 10% for sustained periods, owing to the strong position of their local insurance companies on a regional scale. This conclusion is confirmed by the probability function of the variable, which indicates that in the vast majority of cases values were just a few percentage points of GDP, with a mean of about 5% (see the third part of Fig. 6.2). Analysis of the graphical evidence for the savings rate, Sav_GDP, leads to some interesting conclusions (see the sixth part of Fig. 6.1). The differences between countries cannot be simply attributed to disparities in economic development or even to the state of their financial systems (see the low levels in the United Kingdom). Rather, the timelines of Sav_GDP are highly country-specific. Significantly, there are only a few countries in which gross savings clearly increased between 1990 and 2016, above all such postcommunist countries as Latvia and Lithuania (but also Sweden). The increase in Ireland was preceded by a significant decline in the aftermath of the 2008 global financial crisis. The probability function of Sav_GDP (see the third part of Fig. 6.2) shows that its distribution was rather symmetrical and most values were at approximately 20%. The analysis of Sav_GDP serves to broaden the study—it would be a definite oversimplification to assume that this variable is shaped exclusively or even predominantly by the adoption of ICT or by the factors in financial development; for more on this topic see the extensive literature on savings, including Leff (1969), Singh (1972), Venieris and Gupta (1986), Gupta (1987), Mason (1988), Collins (1991), Bailliu and Reisen (1998), Ozcan, Gunay, and Ertac (2003), us Swaleheen (2008), Yang, Zhang, and Zhou (2012), Beckmann (2013). In particular, Kolasa and Liberda (2015) identified the key factors that affect the savings rate in OECD countries. The final variable examined at this stage represents one of the potential channels of intermediation between the adoption of ICT and financial development (in particular in the banking sector)—namely, Electr_paym.
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This may be considered as supplementary here, in that while it does not directly represent any of the fundamental aspects of financial development it is nevertheless plausible that an increase in electronic payments is at once an important prerequisite for and a sign of financial development. It also applies to financial inclusion; here we focus on financial development, but the effects on financial access and inclusion should not be disregarded. For more on these topics see Section 3.3 and such publications as Donner and Tellez (2008), Maurer (2012), Klapper and Singer (2014), Slozko and Pelo (2014), Arun and Kamath (2015), and Demirguc-Kunt, Klapper, Singer, Ansar, and Hess (2018). The most serious problem here is sharply limited data availability (exclusively for 2011 and 2014). For these reasons we do not show the timelines for Electr_paym, as they are only marginally informative (at most two observations for each country). The lowest values of Electr_paym both in 2011 and 2014 were in Italy, Hungary, Poland, and Lithuania (all but Italy among the less developed economies in our sample). The highest values are in the highly developed Nordic countries of Finland, Denmark, and Sweden (in 2014 almost 100% of the respondents there reported having utilised electronic payments). Nevertheless, as the probability function shows (see second part of Fig. 6.2), electronic payments are common in Europe—in 2014 in all examined countries the value of Electr_paym exceeded 50%, and in most it was near or above 80%. After this examination of the graphical evidence, we proceed to the core matter of this section, i.e. analysis of the relationships between the diffusion of ICT and the variables representing financial development (based exclusively on the panel data rather than country-specific evidence). We start with the correlation coefficients (not presented in a separate table owing to the large number of variables) and graphical representations of the local polynomial regressions (see Figs. 6.3 and 6.4). Focusing on the estimates of the panel models, we consider the relationship of each general variable with both ICT indicators, i.e. IU and FBS. IU, which is more wide-ranging and covers various types of Internet access, is presented first. FBS, presented afterward, refers to high-speed connections via fixed broadband. The first, fundamental financial development variable we examine in relation to ICT is FD. The graphs for IU and FBS show that the direction and strength of this relationship are difficult to establish. Nevertheless, in both cases the impact of ICT appears to be mainly positive or at least neutral (the only exception is the negative correlation for the highest values of IU, while for the highest values of FBS the correlation is strongly positive). This is confirmed by the correlation coefficients, which are positive both for FD vs IU and for FD vs FBS. Interestingly, for IU the coefficient is much higher
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(0.39 against 0.25). This could be seen as implying that mere access to Internet, regardless of speed or quality of the connection, is more important for financial development in these countries. However, the graphical evidence in Figs. 6.3 and 6.4 suggests a contradictory conclusion. We shall return to
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Fig. 6.3 IU vs general variables. Local polynomial regressions. 1990–2016, annual data. Note: On x-axis—IU; raw data used; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. (continued)
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Fig. 6.4 FBS vs general variables. Local polynomial regressions. 1990–2016, annual data. Note: On x-axis—FBS; raw data used; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1.
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Fig. 6.4, cont’d
this issue in interpreting the estimates of the panel models to explain the reasons for this inconsistency. As observed above, Figs. 6.3 and 6.4 show that the relationship between the adoption of ICT and overall financial development is generally positive. To determine which aspects of financial development are most affected by the new technologies, we consider the two sub-indexes, FI and FM. For FI the picture is quite clear—the diffusion of ICT and the development of financial institutions are positively related, in particular in the case of fixed broadband connections. For FM (financial market development), the relationship with the ICT indicators is harder to pinpoint. For Internet broadly defined, i.e. measured with IU, for values of 0 to 40 the correlation is
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positive (and stronger for lower levels of both variables), similar to FD. This might mean that the positive effect of ICT diffusion for financial market development materialises, above all, in the less highly developed European economies (where both IU and FM are mostly lower). In the interval of IU from 40 to 70 the relationship is weakly negative, and for values higher than 70 it can be described as inverted U-shaped: first strongly positive and then substantially negative, precluding clear-cut conclusions. However, as in case of FD, for the highest values of IU the relationship with FM (as well as FI) is negative. This is explained by reference to the financial development index values (either at large or measured by the two sub-indexes) in certain countries, such as Iceland and Norway, which are among the leaders in ICT adoption but have been surpassed by others in financial development. As Fig. 6.4 shows, the relationship between FBS and FM is clearer—for the lower values of FBS it is mixed but for values above about 20 it is strictly positive. The correlation coefficients for all four pairs of variables (FI vs IU, etc.) are positive, between 0.2 and 0.4, which further confirms the main implications of the estimates of the local polynomial regressions. In short, the graphical representations of the local polynomial regressions show that when FD is decomposed into FI and FM, the positive relationship between ICT and overall financial development depends more on the development of financial institutions than of financial markets. Moreover, the graphical evidence again suggests a more definite positive impact of FBS than IU. After these indications from the general indexes of financial development, let us focus on some specific variables linked to this process. First we examine one of the indicators of banking development, i.e. the ratio of bank deposits to GDP, BD_GDP. The correlation coefficients of BD_GDP with both ICT variables are positive and greater than 0.3, implying a positive relationship between the spread of the new technologies and the growth of the banking sector. This is almost unequivocally confirmed by both local polynomial regressions, regardless of which dimension of ICT diffusion is considered (see Figs. 6.3 and 6.4). Referring again to our discussion of FD and its sub-indexes, we can add that ICT adoption may have contributed to financial development through its positive effect on bank deposits. Ins_GDP is our proxy for development of the insurance industry. The results for Ins_GDP are to some extent similar to those for BD_GDP— compare the relevant parts of Figs. 6.3 and 6.4. The correlation coefficient of BD_GDP with Ins_GDP is 0.42, which explains these results. Nevertheless, the relationship between ICT and insurance development is noticeably weaker, as is evinced by the practically almost flat lines in both figures and
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the correlation coefficients of just 0.06 (Ins_GDP vs FBS) and 0.17 (Ins_GDP vs IU). To conclude, banks would appear to be more strongly influenced by ICT diffusion than insurance companies; these relationships are we further verified and compared using panel models. In contrast with the other variables, the relationship between ICT and gross savings (Sav_GDP) is hard to determine. Despite the positive correlation coefficients (0.18 for both ICT indicators), the actual relationship is somewhat more complicated, as is indicated by Figs. 6.3 and 6.4. It can be characterised as U-shaped, especially for IU (for FBS it is mostly positive but with a negative coefficient for the lower values of FBS). So it is not easy to determine the direction and strength of the relationship between savings and increased ICT use in our sample countries. With some stipulations, we can say that the impact of ICT is neutral or weakly positive (taking into account fast Internet connections measured with FBS). The results obtained for our last general variable—Electr_paym—are consistent with expectations, given the basic attributes of this financial service. Both correlation coefficients and the results of the local polynomial regressions make it clear that the relationship between ICT diffusion and electronic payments is strongly positive; the correlation coefficients are 0.83 (Electr_paym vs IU) and 0.71 (Electr_paym vs FBS). Apparently, that is, adoption of ICT is a fundamental precondition for the increasing utilisation of electronic payments in a society. The reverse relationship is implausible, of course, but a positive feedback loop is obviously possible—growing recourse to electronic payments may encourage more people to obtain access to the Internet. This process may also be supported by government policy as a way of attenuating financial exclusion (Ouma, Odongo, & Were, 2017). In any event, the results for Electr_paym should be interpreted with caution, as they are based on a limited dataset (only 46 observations). Tables 6.2 and 6.3 convey the panel model estimates with IU and FBS, respectively, as explanatory variable. All values are in log, although for clarity we refer to base variables (i.e. we refer to ‘IU’ instead of ‘LnIU’). This also applies to Sections 6.4 and 6.5. There is one necessary general stipulation prior to this analysis. In discussing the results for the local polynomial regressions, we observed that they imply somewhat different conclusions from those implied by the correlation coefficients. These differences also obtain with respect to the estimates of the panel models. One of the most important of the various reasons for these discrepancies is that in most of the relationships the observations are clustered in a few intervals—see, for example,
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Table 6.2 IU vs general variables. Fixed effects regressions. 1990–2016, annual data LnFD LnFI LnFM LnBD_GDP
LnIU R2 (within) No. of obs. Rho F (prob F)
LnIU R2 (within) No. of obs. Rho F (prob > F)
0.07 [0.00] 0.64 644 0.91 1119.4 [0.00]
0.04 [0.00] 0.41 645 0.83 425.3 [0.00]
0.11 [0.00] 0.37 645 0.84 366.2 [0.00]
0.07 [0.00] 0.37 599 0.88 343.9 [0.00]
LnIns_GDP
LnSav_GDP
LnElectr_paym
0.08 [0.00] 0.42 567 0.91 385.1 [0.00]
0.005 [0.00] 0.00 590 0.61 1.46 [0.23]
4.99 [0.62] 0.75 46 0.88 64.8 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
Table 6.3 FBS vs general variables. Fixed effects regressions. 1990–2016, annual data LnFD LnFI LnFM LnBD_GDP
LnFBS R2 (within) No. of obs. Rho F (prob > F) LnFBS R2 (within) No. of obs. Rho F (prob > F)
0.02 [0.00] 0.14 422 0.94 66.8 [0.00]
0.02 [0.00] 0.26 422 0.90 144.9 [0.00]
0.00 [0.00] 0.00 422 0.94 0.07 [0.79]
0.06 [0.00] 0.36 399 0.94 218.8 [0.00]
LnIns_GDP
LnSav_GDP
LnElectr_paym
0.01 [0.00] 0.01 379 0.94 5.4 [0.02]
0.01 [0.00] 0.02 410 0.75 10.7 [0.00]
3.33 [0.39] 0.77 46 0.92 72.4 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
FD vs IU in Fig. 6.3—with most observations at either very low or very high levels of IU. This suggests that the inconsistencies in the relationships identified by local polynomial regressions may actually be overstated, as is demonstrated by the panel models discussed in the subsequent paragraphs. The positive impact of the diffusion of ICT on overall financial development as proxied by FD is confirmed regardless of the explanatory variable
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chosen. What is important is that the within R2 of the IU model is the third highest of all those estimated (only for the two models with Electr_paym as the dependent variable is it higher), indicating a relatively good fit. For the FBS model, however, it is much lower. Even so, both ICT indicators prove to be statistically significant. Comparing the coefficients of the two ICT variables indicates that the number of Internet users has a stronger impact on financial development than does the number of fixed broadband subscriptions (a conclusion already foreshadowed by the correlation coefficients). However, given the substantial difference in number of observations (much lower in the case of FBS, because this type of service was introduced more recently), this conclusion may be oversimplified. Considered jointly with the higher value of the within R2 in the IU model, it suggests that initial access to the Internet fosters financial development more than such further advancements as better quality of connections. This may depend partly on the still limited use of fixed broadband subscriptions in some European countries, whereas other Internet services (e.g. mobile broadband) have become widely available in all our sample economies. Our results are in line with the findings of previous studies (see Section 3.3). Estimating the models for the sub-indexes of financial development uncovers a somewhat more complicated picture than for financial development in general (FD), at least as far as financial market development is concerned. For both FI and FM the fit is generally worse than for the overall index, as is shown by the within R2. This applies in particular to the model with FBS as explanatory and FM as dependent variable—its within R2 is nearly 0 (see Table 6.3). The only exception is the FBS model for FI, where the within R2 is higher than for the FD model, suggesting that FBS may better explain the development of financial institutions in our sample countries. The coefficients of both IU and FBS are positive and statistically significant in the models with FI as the dependent variable, implying that ICT has a positive effect on the development of financial institutions. These results come as no surprise, as they are consistent with previous empirical studies (see Section 3.3). The channels whereby ICT may impact on banks are multiple, one of the foremost being the spread of mobile banking (Alalwan, Dwivedi, & Rana, 2017; Shaikh & Karjaluoto, 2015; Sharma, Govindaluri, Al-Muharrami, & Tarhini, 2017) or, more broadly, Internet banking (Szopi nski, 2016; Takieddine & Sun, 2015). Another area of growing importance is bound up with one of the categories of fintech, namely ‘regtech’—use of ICT in the context of financial regulation and oversight (Arner, Barberis, & Buckley, 2016). Regtech lowers the cost of compliance
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with regulatory requirements; it also facilitates entry of start-ups into the highly regulated financial industry (Larsen & Gilani, 2017). And this part of the fintech sector can contribute to the stability of the financial system overall (Arner, Barberis, & Buckley, 2018; Arner, Zetzsche, Buckley, & Barberis, 2017), thus strengthening people’s trust in financial institutions and attracting them to the formal financial system. However, given its relatively brief existence the impact of regtech is still difficult to assess. The results for FM are more complicated—only the coefficient of IU is statistically significant and positive, indicating that this dimension of ICT diffusion is more important for the development of the financial markets, given that no impact, either positive or negative, was found for FBS. We discuss this issue in more detail in Section 6.4, analysing selected aspects of the relationship of ICT with FM. The positive impact of ICT penetration on the development of financial institutions is confirmed by the models using BD_GDP as the indicator of banking development. As could be expected given the correlation coefficients and the estimates of the local polynomial regressions, both IU and FBS are positive and statistically significant, with comparable values of the coefficients and the within R2. Consequently we confirm, using bank deposits, the positive impact of ICT on the primary category of financial institutions. Once again, this result is consistent with the bulk of the previous literature. Furthermore, it shows that in our sample countries the growing deployment of ICT between 1990 and 2016 has not constituted a threat to the banking industry (as by undercutting banks’ position within the financial system in favour of various fintech companies offering similar services). Apart from the general channels of ICT impact on the banking sector, others relating more specifically to changes in deposits can be traced to the benefits for the financial performance and the competitiveness of the deposit banks, as suggested among others by Monyoncho (2015) and Nkiru, Sidi, and Abomeh (2018). The second major type of financial institution considered here is insurance companies (investment funds are discussed in Section 6.5). See the models with Ins_GDP in Tables 6.2 and 6.3. The estimates of the IU model are similar to those for banking but with slightly better fit (the models can be compared directly, since the datasets were almost identical). IU proves to be a statistically significant determinant of the development of the insurance industry. However, this does not go for the other ICT indicator—while it is statistically significant, the extremely low value of within R2 precludes all meaningful interpretation. The importance of ICT for the insurance
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industry can be explained as relating to such factors as the emergence of digital insurance (Nicoletti, 2016) and electronic payments (Nwankwo, Ajemunigbohun, & Iyun, 2015). In addition, the industry is affected by the rise of insurance services provided by fintech companies, or ‘insurtech,’ defined by Ricciardi (2018, p. 8) as ‘… innovation-based companies … that generate value … by disrupting or solving problems across the insurance value chain through the engagement of technology’. The heightened availability of regtech may result in increased insurance spending owing, say, to improvements in customer service processes such as claims management (Cappiello, 2018; Cortis, Debattista, Debono, & Farrell, 2019). Nevertheless, this is still a relatively new part of the financial system. Estimates of the panel models with Sav_GDP provide no information on the effect of ICT deployment on the gross savings rate. This was perhaps to be expected, given the markedly non-linear relationship between Sav_GDP and both IU and FBS (see Figs. 6.3 and 6.4), which impedes the application of our models. Finally, the results for Electr_paym obtained with the panel models strictly confirm the foregoing conclusions arising from both correlation coefficients and local polynomial regressions—namely, the positive influence of ICT on electronic payments (at least their reported use, insofar as the data on Electr_paym are drawn from surveys). The values of the within R2 of both models estimated for Electr_paym are the highest of all those in Tables 6.2 and 6.3, indicating the best fit. A comparison of the coefficients of IU and FBS shows that the popularity of the electronic payments depends more heavily on overall access to the Internet than on the availability of fixed broadband connections. This is readily explained—electronic payments are possible through devices using various types of connection, not necessarily only those with high speed and stability. For example, in many countries financial transactions are conducted to a large extent via mobile devices (Iman, 2018; Liebana-Cabanillas, Mun˜oz-Leiva, & Sa´nchez-Ferna´ndez, 2018; Shaikh & Karjaluoto, 2015), including transactions using near-field communication (NFC), i.e. communication between two devices brought into proximity (de Luna, Montoro-Rı´os, & Liebana-Cabanillas, 2018; Jeffus, Zeltmann, Griffin, & Chen, 2015). Yet stable, rapid connections are indispensable to construct and operate the infrastructure of electronic payment systems, which means that the role of FBS cannot be disregarded. Further, the more sophisticated technologies impact on the intentions of their users (Jun, Cho, & Park, 2018), as electronic payments can be provided at lower prices than other similar financial services.
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Table 6.4 Results of the analysis of general variables: summary Variable Impact of ICT adoption
FD FI FM BD_GDP Ins_GDP Sav_GDP Electr_paym
Positive Positive Positive (IU)/not identified (FBS) Positive Positive Not identified Positive
Note: For explanation of the variables, see Table 6.1 in Section 6.1.
Our key conclusions on the relationship between ICT diffusion and various aspects of financial development are summarised in Table 6.4. In most cases we found a positive effect, with the sole exception of savings, for which neither the strength nor the direction of the correlation could be established. For the financial market development the results for the FBS variable are inconclusive. To sum up, we determined positive effects of ICT adoption both on financial development overall and on selected aspects of it.
6.3 Financial market evolution trajectories This section begins our analysis of the relationships between the diffusion of ICT and the development of the financial markets. The main results will be presented in Section 6.4. The present section is given over to graphical analysis, with two elements, analogous to the first two parts of Section 6.2 analysing the general variables. In the first part we examine the timelines of the financial variables to trace changes over time (the time period is 1990–2016 or shorter, depending on data availability) and compare levels of financial market development in the European OECD members. The second part comments briefly on their distribution using density functions. The financial market variables are those described in Table 6.1, examined in the same order. We start with the most general indicator of financial market development, i.e. FM, discussed in Section 6.2, with some additional insights. We then analyse the three sub-indexes of FM, accounting respectively for the depth, accessibility and efficiency of the financial markets, followed by a discussion of three variables representing various dimensions of the stock market: namely, capitalisation, turnover, and volatility. Finally, in line with Sahay et al. (2015) and Svirydzenka (2016), we take total outstanding domestic private and public debt securities as proxies for the development of the bond markets. Other financial market segments are omitted
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for methodological reasons and problems of data availability. The discussion is continued in Section 6.4. The first variable (Figs. 6.1 and 6.2) is our core indicator for financial market development—FM, the index published by the IMF, which is in fact one of the two sub-indexes of the broadest index of financial development, FD. FM itself is the resultant of three sub-indexes, representing the depth, accessibility, and efficiency of the financial markets, designated respectively FMD, FMA, and FME. The values of FM are normalised to range from 0 to 1, higher values implying higher levels of development. Most of the component variables of FM are linked to the stock markets, but a few represent the bond markets; efficiency is gauged exclusively by the stock market turnover ratio. The other financial market categories are not included. This means that, de facto, we analyse mainly the development of stock markets and to a lesser extent bond markets. This approach is suitable given the main aim of our analysis, namely determining the impact of ICT diffusion on various aspects of financial development; as we showed in Section 3.3, stock markets are among the parts of the financial system most powerfully affected by the information and communication technologies. Some of the variables used to calculate FM are taken separately: for instance, SMC_GDP, to assess differences in levels of FM over time and between countries. The only financial variable that is entirely separate from FM is SPV—stock price volatility is not covered directly by the index. As we stated in Section 6.2, FM displays high temporal variability, substantially greater than that of FD or FI, which may well imply that this segment of the financial system has experienced much greater fluctuations in development than have financial institutions, whose trajectory has been relatively stable. In most of our sample countries FM increased until the 2008 global financial crisis, which initiated at the beginning of financial market decline or stabilisation. This is evident in the trend lines for countries such as Belgium, the Czech Republic, France, Iceland, Norway, Sweden and the United Kingdom; the decline was particularly severe in the case of Iceland, which was strongly affected by the financial crisis—a comparison of the changes in FI and FM in Iceland shows that the disruptions were much more severe for the financial markets than for financial institutions. In some countries, such as Greece, the decline was aggravated by the euro-area debt crisis. There are also a number of countries where the financial market development trajectories from 1990 to 2016 differed considerably—above all the postcommunist countries (we address this issue in the next paragraph within the comparison of the levels of FM in the analysed countries), but also some
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particular cases, including Germany, Ireland, and Switzerland. In Germany, high levels of FM had already been reached by the late 1990s, and the financial markets did not experience substantial variability over the ensuing years. However, the downtrend after 2008 is clear. In Ireland changes in financial market development have been rather minor—indeed, this is one of the most stable countries in our sample in this regard (even during the global financial crisis declines were not as sharp as in other economies, and were followed by rebounds). However, the country’s FM values have not been among the highest in our sample. In Switzerland, like Germany, levels of FM were very high by the end of the 1990s, with a perceptible downtrend after 2008. Here, however, the distinctive feature is the abrupt decline in 2004–2006 to the lowest levels in the entire period. This can be attributed to the diminution in stock market capitalisation and especially turnover. This issue is addressed as part of the analysis of the stock market variables. After this examination of changes over time, we now focus on international differences in financial market development. What is most striking is the significant disparities in FM, as our sample includes both economies with some of the world’s highest levels of FM (Norway, Switzerland, and the United Kingdom in some years) and countries where it has remained close to 0, indicating seriously underdeveloped financial markets. The latter group consists exclusively of post-communist countries: the least developed financial markets are those of Latvia, Lithuania, and Slovakia, all with FM just over 0.1 at best. Comparing this with the values for various groups of countries (see Svirydzenka, 2016, p. 22), we can see that these three countries had levels of FM not only below the global average but also below the mean for the emerging markets (albeit still much higher than in the low-income and developing countries). That is, these countries’ financial markets were underdeveloped not only by European standards. The main reason is low stock market capitalisation and turnover. In three other post-communist countries, the Czech Republic, Estonia, and Slovenia, there has been some development, but after rapid increases in FM in the early 1990s its levels have declined, indicating that the original development was not sustainable. Naturally, the low ranking of the post-communist countries in this regard is no surprise, given that at the turn of the 1990s their financial markets were tiny or non-existent. Despite this initial disadvantage, two of them—Hungary and Poland—attained levels of FM close to or even above 0.5 (the mean for the advanced markets according to Svirydzenka, 2016), comparable to countries such as Greece and Portugal or, among the more advanced economies, Belgium and Denmark.
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The remaining countries in our sample show average values of FM in the period 1990–2016 between 0.5 and 0.8. With the exception of outliers (Greece on the downside and Switzerland on the upside), the group of advanced economies is rather homogenous in terms of financial market development. To some extent the absence of substantial disparities may be due to integration within the European Union and also within the euro area. In fact, only three of our sample countries are outside the EU. One readily apparent relationship is that between the size of the economy and the level of FM (a larger economy seems to contribute to the development of financial markets). Recall that all our indicators are relative, i.e. expressed in proportion to GDP. The density function of FM confirms the conclusions drawn from analysis of national timelines, namely that the distribution of FM is rather even. Our sample includes countries at various levels of financial market development during the period, and the differences were more significant than for FD or FI. After this analysis of the broad indicator of financial market development, let us now briefly address its three component sub-indexes: FMD, FMA, and FME. As with FM, the values of all three range between 0 and 1, higher values corresponding to higher levels of development of the relevant dimension (for more details on the methodology see Svirydzenka, 2016). FMD stands for the depth of financial markets; it is based on indicators of various aspects of the size of stock and bond markets: capitalisation and turnover of the stock markets (we also analyse these two, SMC_GDP and SMTV_GDP, separately) as well as volume of debt securities issued by financial and non-financial corporations (considered separately, though in our analysis we use ODP_GDP which stands for the sum of these two values). All these variables are in percentage of GDP. For most countries, the FMD timelines resemble those of FM, as regards both changes over time and differences between countries (see first part of Fig. 6.5), so we do not discuss them. However, it is worth noticing that some countries have attained values of FMD close to the maximum limit of 1—Switzerland and the United Kingdom have been close to 1 for most of the 2000s, confirming these nations’ status as global leaders in depth of financial markets. The FMD density function shows, however, that most of the other countries had much lower levels of this variable, below 0.4, and the remainder between 0.4 and 0.9. Levels higher than 0.9 were rare (see first part of Fig. 6.6).
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The next variable, FMA, corresponds to financial market access—it too consists of two component variables: share of total stock market capitalisation accounted for by companies outside the top 10 and total number of corporations that are debt issuers. Unlike FMD, the FMA trajectories for some countries differ considerably from those of FM (see second part of Fig. 6.5). In about half the sample there was no substantial change in FMA between 1990 and 2016 (see, e.g. Belgium, Iceland, Poland, and Sweden); for the other countries there was fairly regular growth, save for Slovenia, which experienced rapid growth up to 2009 and dramatic decline thereafter. Comparing countries, the post-communist countries generally have much lower levels of FMA, except Hungary and Poland, which have attained levels broadly on a par with some of the less advanced ‘old’ EU members. The density function indicates that in most countries FMA values are spread rather evenly between 0.2 and 0.6. Higher levels are uncommon (see second part of Fig. 6.6), but are found in some countries that are global leaders in financial market access by this gauge. For the most part these are smaller economies—see in particular Austria, Ireland, Norway, and Switzerland in Fig. 6.5. In the larger countries FMA was usually much lower. The third and final sub-index of FM, labelled FME, is financial market efficiency. This is a single variable; it is defined as the ratio of stock market turnover to capitalisation and should thus be regarded as a somewhat approximate measure of financial market efficiency. Values of FME have been highly volatile in our sample (see third part of Fig. 6.5), and full interpretation would require country-by-country and year-by-year analysis. Here we limit ourselves to several general remarks. The lowest levels of FME, with no substantial improvement, are found in the smallest postcommunist countries such as Latvia or Lithuania, and the high levels of Estonia and Slovenia were not sustained. Hungary and Poland were comparable to the advanced EU economies. There are a number of countries in which FME was close to 1 for a shorter or longer period (most consistently in Germany, Italy, the Netherlands, and Spain), but it is difficult to discern a common explanation for this apparent efficiency of financial markets. Considerable variability is also evidenced in the distribution of FME—its values are significantly spread out (to some extent similar to the distribution of FM); only values ranging from 0.5 to 0.8 are relatively less common (see third part of Fig. 6.6). The analysis of the three sub-factors in FM thus shows that the results are relatively consistent (albeit with some exceptions), which means that European financial markets tend to develop in similar ways in all three dimensions.
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At this point, we turn specifically to our three stock market variables: SMC_GDP, SMTV_GDP, and SPV. The examination of overall development of the financial markets picked up significant international differences. In what follows we offer some insights by studying first our proxy for stock market size (i.e. capitalisation), then liquidity (turnover, which could also be seen as another size indicator), and finally price volatility. The timelines of SMC_GDP (see fourth part of Fig. 6.5) show that in most of our sample countries in the period considered (1990–2016), capitalisation it has been substantially below 100% of GDP; the density function (see fourth part of Fig. 6.6) suggests the same conclusion, as the majority of observations are well below that level (and most, in fact, below 50). The countries with the largest stock markets relative to the national economy were Finland and Iceland (although only temporarily, for a few years), the Netherlands, Sweden, Switzerland, and the United Kingdom (these four for a sustained period). As for overall financial development, Switzerland was also the European leader in SMC_GDP; in some years it exceeded 250, one of the highest values anywhere in the world. To some extent the decline in SMC_GDP between 2004 and 2006 explains the decrease in FM (although SMTV_GDP was a more important factor). The difference in the capitalisation of the stock markets between the post-communist and the more advanced European economies is clear; one factor is certainly the much shorter history of the equity markets in the former group; these countries had to build stock markets from scratch starting in the 1990s, whereas in the latter group they were already quite advanced. Analysis of SMC_GDP over time shows that national equity markets generally expanded up to 2008, which marked the onset of a downtrend, even in countries such as Switzerland. Generally, SMC_GDP is the most stable stock market indicator, given the measurement methodology based on the market value of all listed companies. Even though equity prices can undergo considerable variations, the stabilising element is their number, which depends, above all, on the activities of issuers (such as initial public offerings). The changes in SMTV_GDP (see fifth part of Fig. 6.5) are relatively similar to those in SMC_GDP—the size and the liquidity of European stock markets are closely related (the correlation coefficient between these two variables is about 0.8). This is not surprising, as both indicators, albeit from different perspectives, are based on the number of equities and their market value. Fig. 6.5 shows, however, that turnover tends to change more from year to year than capitalisation. Compare, for instance, the graphs of SMC_GDP and SMTV_GDP for Spain—the trajectory of capitalisation
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has been rather stable while that of turnover has fluctuated (witness the short-lived record-high levels of turnover on the verge of the 21st century). One of the striking features of Fig. 6.5 is SMTV_GDP for Switzerland, with the very deep decline of trading in 2004–2006 from 144% of GDP in 2003 to 12% in 2005—one of the steepest decreases in our sample and a key factor in the significant decrease of FM in that period. However, this finding should not be read as really indicating the sudden demise of the Swiss equity market—its highly transitory nature and the fact that it was not accompanied by any comparable decline in capitalisation indicate that these results may actually stem from some methodological or measurement issues in the source database. Examination of the turnover data in the World Federation of Exchanges (WFE) database confirms this intuition, as it offers no evidence for the sudden decline—on the contrary, it shows a continuation of the growth that began in the early 1990s. As with capitalisation (SMC_GDP), there is a group of regional leaders in stock market turnover, namely the Netherlands, Spain, Sweden, Switzerland, and the United Kingdom (Finland and Iceland registered short-lived periods of high activity, which then gave way to very low values of SMTV_GDP). Denmark, France, Germany, and Italy constitute the next group, characterised by a notable increase in SMTV_GDP although still lagging behind the leaders. SMTV_GDP was lowest in the post-communist countries, mostly below 10, and not substantially higher in Greece or Portugal. The density function (see fourth part of Fig. 6.6) shows that the heterogeneity of SMTV_GDP in our sample is limited, also in comparison with SMC_GDP—most values are considerably below 50, with rare exceptions—countries where equity turnover exceeded 100% or even 200% of GDP (Spain and Switzerland). The next variable, stock price volatility (SPV), is not a direct indicator of market development but does offer additional insights into the attributes of the European equity markets. For technical reasons SPV is not measured in relation to GDP: unlike capitalisation or turnover, it is an index variable. Timelines of SPV for the European countries (see the sixth part of Fig. 6.5) show that changes in volatility followed a similar trajectory. In most countries SPV was relatively low in the 1990s and increased at the beginning of the new century, in connection with global financial uncertainty due, among other things to the dot.com bubble (and then crash) on some major stock markets or events such as terrorist attack of 11 September 2001. In the years that followed, European stock markets became more tranquil, on average. However, the global financial crisis of 2008 produced a sharp
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increase in SPV in all our sample countries (see the spike in 2009, though some effects were discernible already in 2008). In most countries the period of heightened volatility was relatively brief and within a few years it had subsided to pre-crisis levels. The effects of the euro-area debt crisis on SPV were much more moderate, with another much less marked spike in 2011 in most countries. Generally speaking, the trend since 2009 has been downward, though specific countries were affected more severely by either the global or the euro-area crisis, most notably Iceland and Greece respectively. In Iceland SPV in 2009 was the highest in our entire dataset at close to 100; by comparison, there are only a few cases in which it reached even 50. That is, the Icelandic stock market was affected much more strongly than any other European market by the global financial turmoil; this conclusion is also supported by the data on the decline in capitalisation and turnover. In Greece, stock price volatility continued to grow after 2009, SPV hitting a record high of 45 in 2016. The cause was the euro-area sovereign debt crisis and its severe consequences for the Greek economic and financial system, including the stock markets (capitalisation and turnover also declined). Examining national stock market volatility according to levels of economic development reveals rather minor differences. In the final years of our period stock markets in some of the post-communist countries were actually less volatile than in the more economically advanced countries. In some of the countries that launched stock exchanges in the 1990s, such as Estonia, Latvia and Slovakia, volatility was initially quite high, reflecting the small size and limited liquidity of these new equity markets. Their growth over the years and then gradual integration into the EU (all three countries adopted the single currency) may be regarded as the stabilising factor, as the values of SPV have declined. The SPV density function in the sixth part of Fig. 6.6 shows that the differences both over time and between countries were small, SPV rarely exceeding 40 and in the vast majority of cases holding below 20. Two final financial variables are proxies for bond market development, namely the value of outstanding domestic private debt securities (ODP_GDP) and domestic public debt securities (ODPub_GDP) in relation to GDP. Owing to lack of data, for most countries the timelines for ODP_GDP and ODPub_GDP are much shorter than in the case of stock market size variables. Consequently, the conclusions should be regarded with caution. The charts of ODP_GDP and ODPub_GDP (the seventh and eighth parts of Fig. 6.5, respectively) show that European countries are rather
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heterogeneous both in trends and in average values of outstanding debt securities. Denmark, Iceland, and Ireland have the highest values of private bonds, while Belgium and Italy are the leaders in public debt securities. A look at these two lists shows that high values of ODP_GDP are not necessarily accompanied by comparably high values of ODPub_GDP, indicating that the two segments of the debt market tend to develop independently; this is confirmed by the correlation coefficient between them, which while positive is very low at 0.13. The smallest bond markets, both private and public, are found in the post-communist countries, which suggests the relative underdevelopment of their debt market. This is particularly true as regards the indicator of private bonds, in that a higher volume of public debt securities should not necessarily be seen as unambiguously positive. It could reflect rising public debt and accompanying economic and financial problems. However, ODP_GDP has also been low in Switzerland and the United Kingdom, i.e. the advanced economies with very highly developed financial markets as gauged by the other indicators. Moreover, it is difficult to establish strong trends in the level of either ODP_GDP or ODPub_GDP. For instance, the relative volume of private debt securities has generally declined in such countries as Belgium, Finland and Germany, whereas in Ireland, the Netherlands, Portugal, and Spain ODP_GDP has increased. The value of outstanding public debt securities has declined in Belgium, Denmark, Ireland, Sweden, and Switzerland while increasing in the Czech Republic, France, Germany, and the United Kingdom. Generally, the local debt market has grown in some of the countries that experienced the most severe problems during the global financial crisis and the euro-area debt crisis. One of the reasons for this is government anti-crisis policies, financed in part by public debt issuance. However, notwithstanding the serious problems of Greece, ODPub_GDP has actually been declining there in most recent years, owing essentially to difficulty in accessing the debt markets due to the high risk of default. The density functions of the two bond market variables are similar (see the fifth part of Fig. 6.6). Most observations were substantially lower than 50% of GDP, for private and public securities alike. The functions also show that the public segment of the European bond markets has been larger, on average, than the private segment. Another interesting issue is the linkages between stock and bond market development. No matter which pair of indicators is considered, the correlation coefficients are close to 0—that is, there is no direct relationship between these two segments of the financial system.
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6.4 How ICT impacts on financial markets We now address one of the pivotal issues for this study—the impact of ICT on the financial markets, with a special focus on the development of this part of the financial system. Preliminary graphical evidence consisting in the timelines and density functions of the financial market variables was discussed in Section 6.3. We identified the main trends and international differences for our sample of European members of the OECD. Here we discuss the graphical representations of the local polynomial regressions (supplemented by correlation coefficients) and the estimates of the panel models to identify the main attributes of the relationship between ICT and financial markets. First we take the IMF’s most comprehensive index of financial market development, namely FM (and its three sub-indexes); next we focus on stock market variables; and finally we examine the proxies for bond market development. In each case we consider the results for both of our ICT indicators, namely Internet users (IU) and fixed broadband subscriptions (FBS), in order to pinpoint possible differences between the role of access as such and availability of fast and stable connections. The dataset is identical to that used in the rest of this chapter (for details on the variables see Table 6.1). The relationship between the general index of financial market development, FM, and ICT penetration was studied in Section 6.2. The current section reproduces the relevant graphs and repeats the main conclusions reached in that section. But we also extend the analysis with some additional insights that will serve as the starting point for further analysis of the specific stock and bond market variables. The first part of Fig. 6.7 graphs the local polynomial regression for IU vs FM. Using this evidence alone, it is hard to establish either the direction or the strength of the impact of ICT diffusion (in terms of usership) and the development of financial markets. For the lowest values of IU, from 0 to 40, the relationship appears to be positive, in particular for the lowest levels of both IU and FM. The number of observations clustered in this initial interval is large compared with the higher levels of IU. This can be seen as confirmation of the positive impact of the increasing number of Internet users on the development of the financial markets in the early years of our sample period (in the vast majority of cases these observations refer to the 1990s, given that in subsequent years ICT diffusion increased substantially). For the next interval, namely IU ranging from 40 to 70, the relationship is
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weakly negative, suggesting that at later stages of ICT deployment its role in supporting the development of the financial markets weakens or even turns slightly negative. This surprising result would appear to reflect trends in FM—in most European countries, after very rapid initial increases, the growth of FM has slowed (and in some cases, in connection with the global financial crisis, stagnated or even declined). Considering levels of IU, these results refer to the mid-developed European economies, and mostly in the period around 2008 rather than the final years of our period. At the same time, the values of IU have continued to increase. The third interval, IU ranging from 70 to 90, again implies a significantly positive relationship. This is followed by the fourth interval, values of 90 and up, in which the relationship becomes strongly negative. These latter two intervals refer essentially to the most advanced economies and the last years of the time interval. The seemingly contradictory results here (as for the IU interval of 40–70) reflect the turmoil in their financial markets post-2008. Clearly, for the 2000s, especially in the pre- and post-crisis period, the impact of rising IU on financial market development is harder to determine than for the 1990s. The correlation coefficient between FM and IU is 0.38. That is, taking the entire set of observations we have a positive relationship between the
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Table 6.5 IU vs financial market variables. Fixed effects regressions. 1990–2016, annual data LnFM LnFMD LnFMA LnFME LnSMC_GDP
LnIU R2 (within) No. of obs. Rho F (prob > F) LnIU R2 (within) No. of obs. Rho F (prob > F)
0.11 [0.00] 0.37 645 0.84 366.2 [0.00]
0.23 [0.00] 0.69 646 0.87 1444.9 [0.00]
0.11 [0.00] 0.28 646 0.88 248.4 [0.00]
0.05 [0.01] 0.02 646 0.63 16.9 [0.00]
0.18 [0.01] 0.37 584 0.76 333.3 [0.00]
LnSMTV_GDP
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LnODPub_GDP
0.28 [0.01] 0.31 586 0.73 242.5 [0.00]
0.03 [0.00] 0.03 582 0.45 19.4 [0.00]
0.08 [0.01] 0.06 457 0.78 32.2 [0.00]
0.04 [0.00] 0.06 487 0.72 30.9 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
two variables. The periods where the correlation is positive seem to outweigh those with no or negative correlations. The panel model estimate for this pair of variables (see the first part of Table 6.5) confirms this conclusion—the coefficient of the single explanatory variable, IU, is positive and statistically significant, with an estimated value of 0.11. Comparing this with the values of the corresponding parameters estimated for FD and FI (see Table 6.2), we find that development of the financial markets is much more strongly affected by growing access to the Internet than is overall financial development or the development of financial institutions. However, the fit of the FM model, as indicated by the within R2, is not as good, especially compared with the FD model, which means that it is less useful for analysing the dependent variable. Still, when compared with the models for the other financial market variables (see Table 6.5) it is the second highest, together with the model for SMC_GDP. After this discussion of the IU indicator, we now turn to our second measure of ICT diffusion, namely fixed broadband subscriptions (FBS). The implications of the first part of Fig. 6.8 largely overlap with those concerning IU on the basis of its local polynomial regression, here too with several different intervals. Again, for the lowest values of FBS (up to 10) the impact of the new technology on FM appears to be positive; for levels of FM in the range of 10–25 it is moderately negative; and in the 25–40 interval it is positive. Unlike the results for IU, however, for FBS the fourth and final
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interval supports the thesis of a strong positive influence on the development of the financial markets. The caveat here is that the number of observations with FBS close to 45 is extremely low, which means that the last stage of the relationship hypothesised refers to just a few countries in the last few years.
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This datum may be regarded as an outlier rather than meaningful evidence. Excluding this fourth interval, the overall impact of FBS on FM is much weaker than that of IU as evidenced by the lower slope of the line in Fig. 6.8. The correlation coefficient between FM and FBS is much lower than for IU, the more general ICT indicator. It comes to 0.22, confirming the conclusion that the effect of FBS on financial market development is weaker than that of IU. Estimates of the panel model with FBS as the explanatory and FM as the dependent variable (see first part of Fig. 6.6) show that this tool should not be used to assess the relationship between the two variables—the model’s within R2 of the model is close to 0, and the coefficient of FBS is also 0 and is statistically insignificant. To conclude, the empirical evidence presented here strongly indicates that the diffusion of ICT has contributed to the development of the financial markets, above all, through growing Internet access and that the increase in the number of fixed broadband subscriptions has been less important. Obviously IU and FBS, important as they may be, are only two dimensions of ICT; and the European financial markets could well have been affected by other elements of ICT not considered in our analysis (although most other, similar technologies are in fact heavily conditioned by those
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considered here, in particular access to the Internet broadly defined). Again we find that the simple possibility of using Internet services is more important to financial market development than the speed and quality of the web connections. In the rest of this section, we seek to identify the channels whereby ICT affects FM, studying its role in the stock and bond markets. Our analysis of the linkages between ICT adoption and development of the financial market can be compared with previous works on this issue. However, these are very few in number, especially compared with studies of the ICT-financial development nexus or those on the banking sector. Because most empirical research concentrates on stock markets (and, albeit to a much lesser extent, on bond markets), we discuss the relevant studies as part of the analysis of the stock and bond market variables. In any event, the positive impact of various types of ICT on the development of other financial market segments has been confirmed: for money markets by Imakubo and Soejima (2010) and van Lelyveld (2014) and for derivatives markets by Daniel (2010), Wilkins and Woodman (2010) Benito (2012), Lannoo and Valiante (2013), and Heath, Kelly, Manning, Markose, and Shaghaghi (2016). We can now briefly discuss the graphical representations of the local polynomial regressions obtained for the three sub-indexes of FM: FMD, FMA, and FME (see the seventh, eighth, and ninth parts of Figs. 6.7 and 6.8). We begin with their relationship with IU. Clearly, the curves for all three sub-indexes somewhat resemble that of the aggregate. The parallel is closest for FMD, while for the other two there are some visible disparities. The relationship between IU and FMA is different, as the part of the curve corresponding to the third and fourth intervals is flatter, as in the case of the other variables. But the rest of the IU-FMA curve is rather similar, if on the whole flatter than the general FM curve (still with a slight upward slope). The great variability of FME makes determination of the relationship between this variable and IU rather difficult—see, for example, the many observations of FME close to 1 (the maximum) over practically the entire range of IU levels, implying that no meaningful conclusion at European level is possible (country-specific analysis seems more robust). Nevertheless, despite some caveats, the estimates of the local polynomial regressions show a mostly positive effect of IU on financial market development in the case of FMD and FMA, while in the case of FME the relationship is hard to determine. The results concerning the influence of the second ICT variable, FBS, on the three aspects of FM are more heterogeneous. For the depth of financial
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markets (FD), the apparent effect of the increasing use of fixed broadband connections is similar to the relationship between IU and FM (at least in terms of its direction), with a clear positive effect of ICT adoption for all levels of FBS above 20. For access to the financial markets (FMA), however, the results are slightly different. The impact of FBS on FMA is difficult to determine on the basis of the local polynomial regressions—the relevant curve is practically flat; the strong uptick for levels of FBS above 40 refers to a very small number of observations and can be considered as an outlier. The results for the third component, financial market efficiency (FME), are even more variable than those for FM generally. As in the case of IU vs FME, the great variability of efficiency results in an inconclusive relationship between FBS and FME, both in direction and in strength: the curve is essentially flat, with some minor ups and downs. In short, the local polynomial estimates offer evidence exclusively of the positive impact of FBS on the depth dimension of FM; for the other two sub-indexes the results are inconclusive. We also estimated panel models for each sub-index, using the ICT indicators as explanatory variables. The results for FMD (see second parts of Tables 6.5 and 6.6) indicate the positive impact of our ICT proxies, both IU and FBS. Moreover, the within R2 of the IU model for depth is the highest of all those estimated, indicating that this model performs best in terms of goodness of fit and robustness. In the case of the FBS vs FMD, Table 6.6 FBS vs financial market variables. Fixed effects regressions. 1990–2016, annual data LnFM LnFMD LnFMA LnFME LnSMC_GDP
LnFBS R2 (within) No. of obs. Rho F (prob > F) LnFBS R2 (within) No. of obs. Rho F (prob > F)
0.00 [0.00] 0.05 [0.00] 0.00 0.15 422 423 0.94 0.95 0.07 [0.79] 71.9 [0.00]
0.02 [0.00] 0.01 423 0.92 7.45 [0.00]
0.03 [0.05] 0.00 423 0.81 3.89 [0.05]
0.01 [0.01] 0.00 380 0.85 0.77 [0.38]
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0.05 [0.02] 0.02 378 0.87 7.62 [0.00]
0.008 [0.01] 0.00 419 0.43 0.57 [0.44]
0.13 [0.02] 0.16 287 0.85 50.6 [0.00]
0.01 [0.00] 0.01 294 0.78 4.0 [0.04]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.
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the relevant value is considerably lower but still among the highest. In general the coefficients for FMD are much higher than for FM, FMA, and FME, meaning that this is the dimension of the financial markets most powerfully affected by the diffusion of ICT. In the case of FMA (see third parts of Tables 6.5 and 6.6) the very poor fit hinders the interpretation of the FBS model, but the IU model confirms the positive impact of Internet access on this dimension of financial market development; interestingly, the results are very similar to those for FM (except for the within R2). Finally, no meaningful conclusion is possible for FME (see fourth parts of Tables 6.5 and 6.6). To conclude, the panel models constitute further evidence for the positive impact of ICT on the depth of European financial markets. As to financial market access, the positive role of IU stands confirmed, and the results for FBS again prove to be inconclusive. No relationship was established between ICT diffusion and financial market efficiency. To refine the analysis of the indicators of overall financial market development, we focus on one key component, namely stock markets. We begin with the most basic gauge of stock market size—the capitalisation of the listed companies in proportion to GDP (SMC_GDP). The estimates of the local polynomial regressions for this variable (see the second parts of Figs. 6.7 and 6.8) closely resemble the results for FM, as regards IU and FBS alike. For low values of IU (and for extremely low values of FBS) the impact of ICT on stock market capitalisation is weakly positive. Next, for somewhat higher values (IU of 40–70, FBS of 5–20) it is negative, but again weak. In the third interval of IU values, ranging from 70 to 90, the relationship is clearly positive, but in the fourth (values exceeding 90 and in some cases near the maximum of 100, which indicates full saturation in terms of users) it turns strongly negative. For FBS the third interval is the final one, in that above the level of 20 the influence of FBS is almost unambiguously positive, rather moderate initially but very strongly for the highest values. However, as with the results obtained for FBS vs FM, the final ‘uptick’ in FBS vs SMC_GDP can be taken as an outlier, given the extremely low number of observations in that range. Generally, the relationship of SMC_GDP to our two ICT variables is similar. The graphical representation of the local polynomial regressions makes it clear that both the direction and strength of the impact of ICT on the capitalisation of European stock markets have evolved over time and differed between countries. The impact was positive, above all, at the beginning of the sample period (especially in the less advanced economies, which were catching up both in ICT and in stock market size) and in the early 2000s up until the global
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financial crisis of 2008, corresponding to the third interval. For the remaining years the links were generally quite insignificant, as is indicated by the nearly ‘flat’ parts of the relevant curves in Figs. 6.7 and 6.8. Generally, the results for FBS are less clear-cut, and the evidence of positive impact is rather inconclusive, as for FM. However, given the underlying reasons for the declines in SMC_GDP, which depended on various national and global macroeconomic factors, it seems quite improbable that even in these periods the diffusion of ICT could have had a negative impact on the size of European stock markets. Indeed, it is more likely that a positive effect of the new technologies was offset, or more than offset, by the strong negative influence of, say, the withdrawal of investors during the global financial turmoil or the euro-area sovereign debt crisis. Before proceeding to the interpretation of the panel model estimates, let us mention the correlation coefficients between SMC_GDP and the two ICT indicators. For both pairs of variables they are positive—SMC_GDP vs IU is 0.32 and SMC_GDP vs FBS is 0.19—providing some support for positive effects, especially that of Internet use on stock market capitalisation. The panel estimates (see fifth parts of Tables 6.5 and 6.6) closely resemble those for FM, even in terms of goodness of fit (see the almost identical within R2). The coefficient of IU is statistically significant and positive, although that of FBS is near 0 (as is the within R2). The most substantial difference is the considerably higher coefficient of IU in this case than in that of FM (0.18 vs 0.11). That is, the panel estimates confirm the main findings drawn from the local polynomial regressions, namely that increasing access to the Internet has had a positive effect on the size of European stock markets, while the results as regards fixed broadband subscriptions are inconclusive. The positive impact of IU on size appears to be more substantial than on the development of the financial markets overall (obviously, this is a somewhat simplistic conclusion, insofar as the two models are not perfectly comparable; for instance, they differ in number of observations, although they are mostly overlapping). Section 3.3 discussed possible channels whereby ICT may affect the development of the stock markets. Here let us add that from the standpoint of stock market capitalisation there are at least two other potentially important if less straightforward explanations for the positive linkage between ICT diffusion and SMC_GDP. First, ICT companies (a synonym for ‘high-tech’ companies) are relatively riskier and more information-intensive than other companies (Bruinshoofd & De Haan, 2005) which may result in their preference for equity financing (see, inter alia, the seminal study by Carpenter
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and Petersen (2002), who argue that this form of financing, via IPOs, is crucial and leads to substantial growth in size). Further, ICT company investment decisions tend to be more seriously affected by financial crises. As Giebel and Kraft (2018) show, they limit their investment more sharply than other types of enterprise; this tendency may help explain the weakening of the linkage between ICT adoption and increasing stock market capitalisation in Europe in the post-crisis period. Given the increasing size of the ICT sector in most European economies, this can be seen as one of the mechanisms of transmission of ICT adoption to stock market size. A second possible channel, which should be seen in broader perspective, is suggested by Hobijn and Jovanovic (2001). They demonstrate that over the last few decades revolutions in information technology have been a key determinant of stock market capitalisation in proportion to GDP. At first the technological shift leads to a decline in SMC_GDP but this initial decrease is followed by a sharp increase. And this explanation is largely consistent with the previous one, as the authors contend that technological revolutions favour new companies, which have a comparative advantage in adoption of the new technologies. As a technical note, given that SMC_GDP is one of the components of the IMF’s financial market development index, the clear similarity of its relationship with ICT adoption (measured either in terms of IU or in terms of FBS, and demonstrated both by local polynomial regressions and by panel models) and that of FM overall is not highly surprising. Nonetheless, the single element of FM and its importance in shaping the index values should not be overemphasised (for more details on FM and other financial development indexes, see Sahay et al., 2015, and Svirydzenka, 2016). SMC_GDP and FM in our dataset are positively correlated, with a coefficient of 0.69. Most of these conclusions relative to SMC_GDP also hold for SMTV_GDP, i.e. the ratio of stock market trading volume to GDP, which is the other fundamental dimension we analyse here. The relevant graphs for SMTV_GDP (see the third parts of Figs. 6.7 and 6.8) yield observations almost identical to those for SMC_GDP. To begin with, the relationship with ICT adoption as measured by IU again displays a series of stages, with changing sign and strength of the impact of ICT. For levels of IU lower than 70, the relationship is rather weak (see the nearly flat curve in Fig. 6.7). For values ranging from 70 to 90 it is substantially positive, but for the highest levels of ICT adoption, above 90, it is definitely negative. The explanation for the changing direction of the effect is similar to that provided for SMC_GDP. That is, it reflects the changes in the sample countries between
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1990 and 2016, in particular the rise in equity market trading in the years prior to the global financial crisis of 2008 and the subsequent fall. As for our second ICT variable, FBS, there are slight differences between the estimates of the local polynomial regressions. For FBS in the interval of 30–40, its relation with SMTV_GDP is substantially negative; for the two financial market variables analysed earlier, FM and SMC_GDP, the negatively sloped segments of the relevant curves were much less evident (for FM, almost imperceptible). This could be interpreted as evidence of a negative impact of fixed broadband subscriptions on stock market turnover where this technology has achieved a high degree of penetration. Such a reading would be oversimplified, however, because for most countries these data refer to the final years of our sample period, which was a time of rapid FBS diffusion but also, owing to economic and financial turmoil, a decline in activity on the majority of European stock markets. The two processes were largely independent, and it would be misleading to contend that the diffusion of FBS was the main factor (or a factor at all) in the decline of SMTV_GDP. We further address this issue by referring to the estimates of the panel models. The indicator of stock market turnover is positively correlated with both measures of ICT adoption, with correlation coefficients 0.33 for SMTV_GDP vs IU and 0.20 for SMTV_GDP vs FBS. That is, they are marginally higher than those for capitalisation, indicating a slightly stronger relationship with the diffusion of ICT. This conclusion is confirmed by panel estimates (see the sixth parts of Tables 6.5 and 6.6). As with FM and SMC_GDP, with FBS as sole explanatory variable the model yields no meaningful conclusion owing to an extremely low within R2 and the statistically insignificant coefficient for FBS. With IU as independent variable, instead, the model shows the positive influence of greater Internet access on trading volume—the correlation coefficient with IU is 0.28 and statistically significant. This result can be compared with the coefficient of 0.18 for SMC_GDP, using an almost identical dataset (all available data on the stock markets of the European members of the OECD). The implication is that the impact of ICT diffusion has been relatively stronger on turnover than on total capitalisation. To sum up this part of our analysis, once again we find a positive influence of IU on SMTV_GDP, whereas the results for the effect of FBS are inconclusive. Again, this can be read as meaning that mere access to the Internet as such has a more substantial effect on equity turnover than does the availability of high-quality connections. Perhaps this is because it gives
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more consumers and investors access to information on the stock market and online brokerage accounts, whereas the fixed broadband is generally important only for the establishment and management of market infrastructures. This was suggested by Bogan (2008), who demonstrated that a rise in the share of households using Internet led to a significant increase in stock market participation. Section 3.3 gives a more in-depth explanation of the impact of ICT on stock exchange turnover. It is worth adding that there are some other possible channels of transmission between ICT and equity trading. Bank, Larch, and Peter (2011) showed that trading activity (as well as liquidity) is positively affected by search engines, thanks to the attenuation of information asymmetries. Qualitatively similar results were obtained by Zhang, Song, Shen, and Zhang (2016) in a study of market reactions to Internet news that correlates news with turnover. According to Aouadi, Arouri, and Teulon (2013), increases in online searches concerning the entire stock market cause a decrease in liquidity, but searches for specific stocks have the opposite effect. As Shen, Zhang, Xiong, Li, and Zhang (2016) show, online trading period information is a better proxy for the Internet information flow than its non-period counterpart. Tantaopas, Padungsaksawasdi, and Treepongkaruna (2016) confirmed the positive effect on market efficiency of the investors’ attention, as gauged by the volume of online search (but the effects on trading volume were found to be mixed). Zhang, Shen, Zhang, and Xiong (2013) prove that open source information can contribute significantly to market efficiency and the speed of information dissemination. Additionally, stock markets can be affected by developments in the fintech industry, such as the application of blockchain to post-trading activities (Geranio, 2017) or the provision of other exchange services (Haddad & Hornuf, 2019). Our discussion of ICT’s impact on stock market development (as measured by capitalisation and turnover) can be compared with previous empirical studies. In Section 3.3 we briefly recounted the main empirical analyses in this field—most of which found evidence of the positive role of ICT, and in particular of Internet access. Our results are therefore consistent with the conclusions of Ngassam and Gani (2003), Zagorchev, Vasconcellos, and Bae (2011), Hossein, Fatemeh, and Seyed (2013), Lee, Alford, Cresson, and Gardner (2017), and Pradhan, Mallik, Bagchi, and Sharma (2018). Our findings are also in line with some other, indirectly related studies. According to Liang and Guo (2015), the effect of Internet access on stock market participation by Chinese households depends on their social interactions— increased access may actually reduce participation if it undercuts the positive
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effects of social interactions. Zhang and Zhang (2015) showed that the increasing availability of online trading accounts attracted many new investors to the equity markets; but as these investors are to a large extent uninformed, their trades do not affect the price informativeness of market activity. Narayan (2018) checked the impact of technology investments on the stock markets in a sample of Islamic and non-Islamic countries, demonstrating that their profitability is positively linked to investment in technology. It is worth remarking that the reverse relation between stock markets and ICT has also been demonstrated empirically—by Brown, Martinsson, and Petersen (2017), for example, who documented the significance of stock markets for the growth of the high-tech sector and consequently for the technology-led growth of the economy. Analysis of the next variable, price volatility (SPV), supplements the foregoing, in that its movements cannot be interpreted as evidence of changes in the level of stock market development. Volatility in fact represents a separate dimension of the markets. Unlike the analysis of capitalisation and turnover, the results on volatility are quite unambiguous: they tell very strongly against any significant linkage between ICT diffusion and the volatility of the stock market. The graphs of the local polynomial regression estimates are almost flat (see fourth parts of Figs. 6.7 and 6.8), aside from the apparently negative relationship at the highest levels of both IU (over 90) and FBS (over 35). This negative correlation is readily explained by the fact that it reflects the moderation of volatility in the last few years of our sample period, which correspond to the maximum ICT penetration. Further evidence against any significant impact of ICT adoption on SPV comes from the correlation coefficients and panel model estimates. The correlation between either IU or FBS and SPV has always been nearly nil—in both cases the relevant coefficients are about 0.05. The goodness of fit of both panel models with each ICT indicator as single explanatory variable is extremely low, with within R2 values close to 0 (see seventh parts of Tables 6.5 and 6.6). Only in the case of IU was the relevant coefficient statistically significant, and at 0.03 it might imply some weak positive impact of ICT. But given the very poor fit, our analysis offers no significant evidence either for or against the influence of ICT on SPV in Europe. Our findings may be compared with those of previous studies. According to Shen et al. (2016), the Internet information flow can decrease the persistence of volatility. In an interesting study, Jin, Shen, and Zhang (2016) verified the impact of microblogging (which would be impossible without ICT) on several attributes of the Chinese stock market. They found that it is now an
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alternative information source and has increased the speed of information diffusion, thus diminishing price volatility. On the other hand, Dimpfl and Jank (2016) found some evidence that the increasing attention of investors, evidenced by a greater number of online search queries relating to the stock market index, magnifies realised volatility, which means that this Internet service may actually increase SPV. Similar conclusions were reached by Zhang et al. (2016), who studied reactions to online news Sekmen and Hatipoglu (2019) verified the impact of high-frequency trading and algorithmic trading (both impossible without ICT) on the volatility of the Turkish stock market and concluded that these activities had a disruptive impact. The remainder of this section deals with bond markets. We consider two proxies for their level of development: ODP_GDP (outstanding private debt securities over GDP) and ODPub_GDP (public bonds over GDP). In both cases, problems of data availability limit the examination to domestic securities alone. As both variables display the value of securities in being, they may be taken as indicators of the bond market size. Analysis of such other factors as turnover is impossible owing to insufficiently consistent data. We begin this portion of our study with what in most countries is the smaller bond market segment, i.e. private domestic debt securities. The graph of the local polynomial regression for IU vs ODP_GDP (see the fifth part of Fig. 6.7) displays some resemblance to both our indicators of stock market development, SMC_GDP and SMTV_GDP. For levels of IU lower than 70 the curve in Fig. 6.7 is almost flat, indicating no correlation between IU and ODP_GDP. For higher values, however, ranging from 70 to 95, the impact of increased Internet access is perceptibly positive; for the few observations at IU above 95, again the graph shows no identifiable influence. Given the distributions of these two variables, with most of the observations clustered at relatively low levels of both (see Fig. 6.7), the local polynomial regression shows that in the observation period 1990–2016 the impact of IU on the value of outstanding private debt securities in Europe was moderately positive. It is worth pointing out that unlike the financial market variables examined above, ODP_GDP is never negatively affected by IU for any interval of values of the latter. That is, the effects of Internet access on this segment of the bond market are less ambiguous than for the stock market. Interpretation of the results for the second ICT variable, FBS, is a bit more complicated (see the fifth part of Fig. 6.8). As with IU, the relationship between FBS and ODP_GDP appears to be mostly neutral for levels of FBS lower than 30 (for values below 20 it is slightly positive and for those from 20 to 30 it is very weakly negative). For higher levels, between 30 and 40,
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fixed broadband connections appear to exert a strongly positive effect. The substantial downturn at levels of FBS above 40 may be regarded as an outlier, given the extremely small number of observations in this range. Consequently, as in the relationship of IU with ODP_GDP, when the entire range of observations is considered the impact of FBS on the value of outstanding private debt securities has been positive, and more so than for IU. This might be read as implying that, in contrast with stock markets, for European private bond markets fast, high-quality Internet connection is more important than simple access to Internet per se. This presumably reflects the structure of the bond market, whose participants are rarely individuals but mostly institutional investors, which account for the preponderance of transactions. Institutional investors have greater needs and higher expectations for their communication facilities, which may help explain why IU is relatively less significant here than FBS. And even though financial institutions are also major participants in many European stock markets, the importance of retail investors cannot be disregarded. As the analysis set out in the preceding paragraphs shows, for stock exchanges IU reflects the impact of ICT on the development of the stock markets more accurately than FBS. Graphical representations of the linkages between the public bond market variable, ODPub_GDP, and the two indicators of ICT diffusion are quite similar (see sixth parts of Figs. 6.7 and 6.8). For most levels of both IU and FBS the relationship between ICT adoption and outstanding public debt securities is neutral (or weakly negative, as for IU of 40–90 and FBS of 20–40). For the highest levels of the two ICT variables, however, the results are radically different. The relationship between IU and ODPub_GDP suddenly becomes strongly positive as IU approaches 100 (i.e. full saturation), whereas for FBS exceeding 40 the impact on ODPub_GDP appears to turn strongly negative. Here again, however, we are dealing with a strictly limited number of observations. Generally, the local polynomial regressions suggest that the influence of ICT on the size of the public bond market is fairly insignificant. We shall check this conclusion using the panel models, but we can already affirm that the impact of ICT diffusion on the volume of outstanding public as opposed to private debt securities has been negligible. In other words, the changes in ODPub_GDP appear to have been shaped more by other factors, such as decisions by the authorities. Before analysing the correlations and panel models, one additional observation is in order. As noted above, the local polynomial regressions indicate substantial differences between the impact of ICT on ODP_GDP and on ODPub_GDP—compare the relevant parts of Figs. 6.7 and 6.8. This is
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interesting, as it shows that the private and public segments of the bond market have been affected differently by the new technologies. The impact on ODP_GDP has been to some extent similar to those on both stock market indicators (capitalisation and turnover), whereas in the case of ODPub_GDP there are substantial differences. This dissimilarity may be due to the fact that the private bond market, like the stock market, is factored into corporate financing decisions. One may thus suppose that changes in the economic and financial system, such as the effects engendered by the increasing penetration of ICT, will affect corporations’ decisions to undertake various investment projects that can be financed through either bond or stock issuance. Generally, this tendency is found in countries experiencing economic development. Importantly, such countries usually rely less on public debt issuance, thanks to sounder public finances. This means that the effects on the size of the two segments may tend to be opposite. The correlation coefficients with the two ICT variables differ dramatically between the two segments of the bond market. For private bonds the correlation is positive, with coefficients of around 0.30 (ODP_GDP vs IU) and 0.35 (ODP_GDP vs FBS). For public bonds, by contrast, the correlation is negative, with similar coefficients of 0.13 (ODPub_GDP vs IU) and 0.12 (ODPub_GDP vs FBS). Interestingly, ODPub_GDP is the only financial market variable considered in this section that is negatively (if extremely weakly) correlated with the indicators of ICT adoption. The estimates of the panel models for ODP_GDP (see eighth parts of Tables 6.5 and 6.6) confirm the moderately positive impact of ICT: the coefficients for both IU and FBS are positive and statically significant in the relevant models. In keeping with the indications of the local polynomial regressions, the impact of FBS seems perceptibly stronger, as indicated by its much higher coefficient. However, straightforward comparison with the IU model is impossible owing to different datasets; furthermore, the fit of the IU model is extremely poor, which means that it should not be deemed indicative. And while the two models’ coefficients for the public bond market are also positive and statistically significant (see ninth parts of Tables 6.5 and 6.6), the within R2 of both models is close to 0 (especially for FBS), which means that they do not provide meaningful evidence for positive linkages between ICT and ODPub_GDP. Accordingly, we can reiterate the finding of insignificant impact of ICT diffusion on ODPub_GDP. Nevertheless, the divergent values of the correlation coefficients and estimates of the panel models show that for this variable too the results are inconclusive.
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Table 6.7 Results of the analysis of financial market variables: summary Variable Impact of ICT adoption
FM FMD FMA FME SMC_GDP SMTV_GDP SPV ODP_GDP ODPub_GDP
Positive (IU)/inconclusive Positive Positive (IU)/inconclusive Inconclusive/none Positive (IU)/inconclusive Positive (IU)/inconclusive None Positive Inconclusive/none
(FBS) (FBS) (FBS) (FBS)
Note: For explanation of the variables, see Table 6.1 in Section 6.1.
Given the rarity of previous works on the link between ICT diffusion and bond market development, it is difficult to juxtapose our results with earlier findings. However, the conclusions of what studies we have found are generally consistent with our own. Styles and Tennyson (2007) concentrated on one specific segment of the bond market and in a way addressed the issue of the impact of ICT on its development. Studying access to financial information on municipalities through the Internet and its relationship with a number of variables, including the level of debt, they confirmed the positive relationship. Astrauskait_e (2014) verified the impact of ICT adoption on the development of the bond market in Lithuania, but with highly inconclusive results—positive effects associated, above all, with the broadband indicator, suggesting that the diffusion of ICT influences the development of the bond market through its effect on investors rather than issuers. The findings of the current section are summarised in Table 6.7. For most of the financial market variables analysed we found a positive impact of ICT adoption as gauged by at least one of our ICT indicators. But there are exceptions: for stock price volatility we concluded that the role of ICT has been broadly neutral, and for outstanding domestic public debt securities the results were inconclusive (from a different perspective, this implies a neutral relationship). Thus we can say that the two processes—ICT diffusion and public bond market growth—proceeded for the most part independently. The results for the efficiency sub-index of FM, FME, were also inconclusive.
6.5 ICT and financial innovations In this final section we focus on how ICT has affected our chosen category of financial innovation, namely exchange-traded funds (ETFs). We
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gauge the relative size, or level of development, of the national ETFs market as assets over GDP (ETF_GDP); for additional data and methodological comments see Section 6.1. First, we examine the graphical evidence on ETF_GDP: national development trajectories and density function. The first part of the analysis serves to discover the main international differences and trends over time. Second, we analyse the graphs of the local polynomial regressions to detect the main features of the relationships between our indicators of ICT diffusion (IU and FBS) and ETF_GDP. Finally, we use the correlation coefficients and panel model estimates for additional verification of the impact of ICT on the size of the ETF markets. ETFs are not the only type of investment fund we study—we also examine the parallel results on the assets of mutual funds over GDP (MFA_GDP). This serves to supplement the analysis of ETFs, so as to compare the changes in the market for conventional investment funds with the parallel changes in the market for innovative funds and examine the role of ICT as regards both. The core subject of this section, however, is ETFs. ETFs, of course, are not the sole type of financial innovation presumably influenced by the adoption of ICT, but we decided to concentrate on this instrument owing to problems with other possible choices, such as insufficient data (on highfrequency trading, for instance) or difficulties of quantification (in particular with regard to various types of fintech). The period covered is 2000–2016 for ETFs and 1990–2016 for mutual funds. We examine the assets of ETFs in 11 European OECD members (see Fig. 6.9); due to different periods of ETF presence on different national financial markets, for most countries data are not available for the entire period 2000–2016 (indeed only France has data available for the full period of our analysis). It is apparent that European countries are highly heterogeneous in the size of ETF markets (i.e. in ETF_GDP). The leader here is the United Kingdom, with ETF assets exceeding 9% of GDP in 2016; the British ETFs market has experienced substantial growth since the early 2000s. Next comes Switzerland, where ETF assets came to about 8% of GDP in 2016. But the growth of the Swiss ETFs market has been more volatile (see Fig. 6.9). France and Germany also have somewhat well-developed ETF markets—in both countries ETF_GDP has been significantly lower than in the United Kingdom or Switzerland, but still much higher than in any other country analysed here. Like the British, the French and German ETF markets have grown almost constantly, with just a few dips. ETF markets in the remaining countries have been small, although two groups can be distinguished—those with slightly more developed ETF markets and those with extremely low levels of ETF_GDP. The first group consists of
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Fig. 6.9 Development trajectories of the assets of ETFs as % of GDP. 2000–2016, annual country-level data. Note: Raw data used. (continued)
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Fig. 6.9, cont’d
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countries where ETF_GDP has approached or exceeded 0.1%: Italy, Spain, and Sweden. The Italian ETFs market is the smallest of the three but has experienced the most significant growth. Spain and Sweden display considerable variations in ETF_GDP during the period (Fig. 6.9), but the overarching trend for these years has been positive. The second group comprises Greece, Hungary, Norway (the leader in this group, almost on a par with the previous set of countries in some years), and Poland. In all these countries the development trajectories of ETF_GDP have been very unstable, growth repeatedly interrupted by usually sharper declines. Except for Norway the trend in this group has been strictly negative. In short, the timelines for ETF_GDP indicates significant diversity among European ETF markets in terms both of levels of development and of trends. However, taking the full dataset and the density function of ETF_GDP (see first part of Fig. 6.10), it is clear that in the large majority .6
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Fig. 6.10 Density functions of the financial innovation variables, annual data. Note: Raw data used. Ireland excluded. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
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of cases ETF assets are very low in relation to GDP, below 1.5%. Higher levels are quite rare, attained in just a few countries and mostly in years nearing 2016. Generally, that is, European ETF markets have remained at low levels of development (with significant growth only in the four largest). The possible determinants are multiple, but in our analysis we focus on one factor only, i.e. ICT diffusion. The development trajectories of MFA_GDP, like ETF_GDP, display considerable international differences (see Fig. 6.11). Mutual fund assets have generally been highest in relation to GDP in the large European economies, i.e. France, Germany, and the United Kingdom (exceeding 50% in some years, usually the most recent), and in some smaller but comparatively highly developed economies, including but not limited to Austria, Iceland, and the Netherlands (the leader in our sample, with a value of over 100% in 2016), Sweden, and Switzerland. In most of these countries the size of the national mutual fund market grew over our time period. Note that we have excluded one country, Ireland, which can be regarded as an outlier with mutual fund assets incomparably greater than in other countries (in some years almost 800% of GDP). In fact, Ireland is the domicile of choice of many European investment funds thanks to legal and tax advantages for financial companies (Clarke & White, 2018; Weiler, 2015); as a consequence the values of MFA_GDP simply cannot be taken as an accurate measure of the development of the local market (for similar reasons Ireland is also excluded from our analysis of ETF_GDP). There are also two other groups of countries as indicated by the MFA_GDP timelines. First are such Southern European countries as Greece, Italy, Portugal, and Spain, where MFA_GDP has been lower on average than in the other advanced economies and has actually declined between 1990 and 2016. Both of these features can be explained by their economic situation, in particular in the wake of the 2008 financial crisis and the sovereign debt crisis, which would appear to have undermined investor interest in this category of investment fund. Interestingly, in the case of Italy ETF_GDP and MFA_GDP moved in exactly opposite directions— that is, ETFs were not affected by the general downtrend in investment fund assets. The second group is the post-communist countries, where mutual fund assets have been very low (in some cases, such as Lithuania, practically nil), with no substantial or sustainable growth (even in the leader of this group, Hungary, mutual fund assets are below 20% of GDP; in Poland they have averaged 3.6%).
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Fig. 6.11 Timelines of the assets of mutual funds (as % of GDP). 1990–2016, annual data. Note: Raw data used. Ireland excluded.
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The density function of MFA_GDP (second part of Fig. 6.10) confirms these conclusions—for all countries and years, levels of MFA_GDP higher than 50% were rare; most observations were between 10% and 30%. Comparison of the density functions for ETF_GDP and MFA_GDP shows similar distributions—in the vast majority of cases the values are barely above 0. For comparison, in the United States, the country with the most advanced investment fund industry, ETF_GDP was higher than 13% at the end of 2016 and MFA_GDP has been over 50% since at least 1997. Significantly, an examination of developments in MFA_GDP and ETF_GDP reveals that the market for innovative funds has not developed in all the countries with high levels of mutual fund assets but only, for the most part, in the largest economies. However, the markedly positive correlation coefficient of 0.57 shows that changes in ETF_GDP and MFA_GDP have generally followed similar trajectories. This certainly suggests that a substantial national market for mutual funds (in most cases thanks to the large size of the economy) and a high level of economic development are preconditions for the substantial growth of ETFs. The rest of this section focuses on our fundamental issue—the linkages between ICT and various aspects of financial development, in this case the market for ETFs and for their conventional counterpart, traditional mutual funds. We start with the interpretations of local polynomial regressions. The relationship between IU and ETF_GDP can be described as twostage (see first part of Fig. 6.12). For low and moderate degrees of ICT (measured by IU) the impact of this technology on the assets of ETFs appears to be neutral (see the relatively flat curve for values of IU below 70). This portion of the curve is explained by the insignificant growth of the ETF markets in some countries and the lower rate of increase in ETF_GDP (despite the rapid growth of IU) in countries such as Switzerland initially, compared with the faster pace of change in later years. For higher levels of IU the relationship with ETF_GDP is radically different—it is strongly positive, suggesting that increased access to the Internet may contribute to the development of the ETFs market in Europe. This conclusion applies, above all, to countries such as Germany and the United Kingdom in the final years of our period. There is also a third stage, for the highest values of IU in our sample (95 and over), where the correlation is significantly negative. Nonetheless, this stage may be considered country-specific: in fact it refers to some of the most highly developed European economies but with severely underdeveloped ETF markets (such as Norway). These results distort the generally strong positive relationship found at the highest levels of both ETF_GDP and
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Kernel = epanechnikov, degree = 4, bandwidth = 13.53
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Fig. 6.12 IU vs financial innovation variables. Local polynomial regressions, annual data. Note: On x-axis—IU; raw data used; Ireland excluded; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
IU—see the clearly upward segment on the right-hand side of the relevant graph. The results for MFA_GDP vs IU (see second part of Fig. 6.12) are highly similar to those for ETF_GDP. The relationship between MFA_GDP and IU is also two-stage (with a minor third stage for the highest levels of IU). For IU ranging from 0 to 70, despite some variability, the relationship can be characterised as neutral. In the second stage, with IU between 70 and 95, the impact of Internet access is unquestionably positive. To conclude, the estimates of the local polynomial regressions indicate a positive influence of high levels of IU penetration on the assets of both categories of investment fund; for low and medium levels, however, no impact is detected. The local polynomial regressions of ETF_GDP vs FBS are more clearcut than those vs IU (see first part of Fig. 6.13). There are again two stages: first, neutral (FBS ranging from 0 to 20) and second, considerably positive (FBS above 20). However, unlike IU, FBS displays no third stage. These results would appear to imply that for most countries FBS has played at most a minor role in supporting the development of the national ETFs market. Still, in the countries with the greatest ICT penetration in terms of fixed broadband subscriptions, accompanied by the highest values of ETF assets,
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FBS Kernel = epanechnikov, degree = 4, bandwidth = 8.18
Fig. 6.13 FBS vs financial innovation variables. Local polynomial regressions, annual data. Note: On x-axis—FBS; raw data used; Ireland excluded; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
the effects of FBS are definitely positive. Estimates for MFA_GDP are very similar (see second part of Fig. 6.13)—the cut-off point between the first and second stage is again FBS of 20, as in the case of ETF_GDP. However, again the positive relationship refers to relatively few observations, as most are clustered in the interval corresponding to neutral impact. In the final part of our analysis we concentrate on the estimates of the panel models. As before, we begin our examination with the correlation coefficients. ETF_GDP is positively correlated with both measures of ICT diffusion: the coefficient for IU is 0.42 and for FBS, 0.56. Among all the variables considered in this chapter, ETF_GDP is thus one of the most closely correlated with our indicators of ICT adoption (only for Electr_paym are the coefficients higher). For MFA_GDP too, both correlation coefficients are positive, but considerably lower at 0.14 and 0.10 respectively. Estimates of the panel models with each of the ICT indicators as the single explanatory variable confirm the polynomial regressions and correlation coefficients. When the dependent variable is ETF_GDP, the coefficient of IU is positive and statistically significant (see Table 6.8); the same applies to the coefficient of FBS (see Table 6.9). Importantly, both of these models have values of within R2 among the highest of all those tested, which
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Table 6.8 IU vs financial innovation variables. Fixed effects regressions, annual data LnETF_GDP LnMFA_GDP
LnIU R2 (within) No. of obs. Rho F (prob > F)
5.15 [0.46] 0.49 134 0.75 121.8 [0.00]
0.37 [0.00] 0.29 452 0.85 179.1 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
Table 6.9 FBS vs financial innovation variables. Fixed effects regressions, annual data LnETF_GDP LnMFA_GDP
LnFBS R2 (within) No. of obs. Rho F (prob > F)
1.88 [0.11] 0.69 134 0.84 271.3 [0.00]
0.09 [0.01] 0.09 408 0.91 42.1 [0.00]
Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.
indicates their relatively good fit. As the two models were estimated on an identical dataset, the coefficients can be compared (with the usual caveats). The coefficient of IU is 5.15 and that of FBS is much lower (1.88), which indicates that IU may be making a substantially greater contribution to the expansion of ETF markets in European countries. Similar results are found for MFA_GDP, but the goodness of fit of these two models is far inferior (especially where the independent variable is FBS). Since the dataset for estimating MFA_GDP is much larger than that for ETF_GDP, the two sets of models cannot be reliably compared. Overall, it can be said that our study offers evidence that ICT does have an impact on the size of the markets for both ETFs and traditional mutual funds in our sample of European OECD members. The influence of ICT on investment fund markets should be considered in the framework of the broader effect of the new technologies on financial markets in general (see Section 3.3 and our discussion in Section 6.4). Identification of the exact channels of impact is complicated by lack of data, but we can say that the main mechanism whereby new technologies could have boosted the growth of both ETFs and mutual funds is the positive influence
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of ICT adoption on the development of the stock and bond markets, at least in some dimensions (see Section 6.4). As regards the impact of ICT on ETF markets, however, it appears necessary to take into account also some categories of fintech that could affect the innovative funds: robo advisors and social trading platforms (in particular the former; for more on fintech see Section 3.3). Even though the volume of assets managed by robo advisory platforms in Europe is still small by comparison with the more mature US market (Kaya, 2017; Phoon & Koh, 2017; Vives, 2017), this is a fast-growing segment of the financial industry, facilitated by the new technologies. Significantly, the fundamental investment instrument used by robo advisors is ETF shares. Consequently, the emergence of robo advisors, already discernible to some extent in the last years covered by our analysis (2015 and 2016), is bound to contribute to the development of the ETFs market—and in a broader perspective to financial inclusion—by attracting retail investors (Jung, Glaser, & K€ opplin, 2019; Schwinn & Teo, 2018). Social trading platforms are less important, indeed of negligible significance in most European countries. An important aspect of social trading is that users can copy the investment strategies of trusted traders and apply automated tools to execute transactions (Berger, Wenzel, & Wohlgemuth, 2018; Dorfleitner et al., 2018; Kromidha & Li, 2019), including trades in ETF shares. That is, they constitute yet another instance of an ICT-enabled service that employs ETFs. To finally conclude our analysis of the linkages between ICT deployment and the emergence of financial innovations, it is clear that both general access to the Internet and an increase in fixed broadband subscriptions can contribute to the development of the ETFs market. But the presence and strength of such links appear to be highly country-specific. Other determinants also need to be taken into account. Our results are consistent with the findings of earlier empirical work on the linkages between ICT and investment funds, and in particular studies of its impact on the development of ETF markets (see Lechman & Marszk, 2015; for more details see Section 3.3). Our findings are also analogous to the implications of the analysis by Marszk and Lechman (2019), in a study of a partially overlapping sample of European countries from 2004 to 2016 that confirmed the positive impact of both FBS and IU on the development of ETF markets. Finally, the positive impact of FBS on the assets of mutual funds is in keeping with Khodayari and Sanoubar (2016). In the case of IU, however, our results here are contradictory, finding a negative impact. As a conclusive observation, let us add that throughout the current section we have assumed that ICT diffusion can affect the emergence of
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innovative financial instruments; the possible inverse relationship was not considered. This approach may be motivated by the plausibly low likelihood that ETFs may have a causal impact on the launch and diffusion of ICT— above all, participation in and the scope of ETF markets is incomparably lower than ICT penetration, which is pervasive in all the countries analysed. Nevertheless, the possible impact of ETFs on ICT should not be utterly disregarded, in view of potential indirect channels of transmission, such as easier access to financing for the technology companies thanks to ETFs (e.g. increased stock market liquidity; Madhavan & Sobczyk, 2016). However, such reverse causal links have yet to be studied.
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Further reading Marszk, A., & Lechman, E. (2018). New technologies and diffusion of innovative financial products: Evidence on exchange-traded funds in selected emerging and developed economies. Journal of Macroeconomics, [in press].