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Google search and stock returns in emerging markets
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http://www.elsevier.com/journals/borsa-istanbul-review/2214-8450
Full Length Article
Google search and stock returns in emerging markets Canh Phuc Nguyen a, Christophe Schinckus b, Thai Vu Hong Nguyen c,* a b
School of Banking, University of Economics Ho Chi Minh City, 59C Nguyen Dinh Chieu, District 3, Ho Chi Minh City, Viet Nam School of Finance and Economics, Taylor's University, Lakeside Campus, 1 Jalan Taylors, 47500 Subang Jaya, Selangor, Malaysia c School of Business and Management, RMIT University Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Viet Nam Received 30 March 2019; revised 3 June 2019; accepted 11 July 2019 Available online ▪ ▪ ▪
Abstract The Fama-French model offers a framework explaining the stock return variability by capturing the size, value, profitability, and investment patterns of firms; but it fails in capturing the low average returns on small stocks. This article contributes to asset pricing models by investigating the role that the volume of Google search might play as augmented factor in explaining the stock returns. Through system-GMM estimations with the data in the period of 2009e2016 for 5-emerging markets (Indonesia, Malaysia, Philippines, Thailand, and Vietnam), we find that FamaFrench model is not always effective. The increases in Google search volume appear to have significant negative impacts on stock returns in the case of Philippines, Thailand, and Vietnam. This suggests that investors might be more sensitive to bad news than good news in their investment decisions. Furthermore, our Google indicator is found to have an influence on the Fama-French factors in explaining the stock returns. _ Copyright © 2019, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL classifications: D53; G12; O16 Keywords: Asset-pricing model; Google search; Stock returns
1. Introduction In the literature of finance, the three-factor Fama-French model is seen as the most important model for explaining stock returns in past three decades. Precisely, this model explains the differences in the evolution of stock returns by using the market risk and the differences in firm's characteristics (firm size and market-to-book ratio) (Fama & French, 1993). Many empirical studies showed that the three-factor FamaFrench model has a better explanatory power than other asset pricing models such as CAPM for instance (Gaunt, 2004). However, the three-factor Fama-French model also generates a lot of debates in the finance literature since its success is the
* Corresponding author. E-mail addresses: [email protected] (C.P. Nguyen), Christophe. [email protected] (C. Schinckus), [email protected] (T.V. Hong Nguyen). _ Peer review under responsibility of Borsa Istanbul Anonim S¸irketi.
one based on time-varying investment opportunities (Petkova, 2006). Petkova (2006) suggested that the evolution of stock returns can also be explained by other factors such as investment or firm profitability for example. Since then, new factors have been considered and tested in recent studies. Fama and French extended their model to a five-factor model (see Fama and French (2015), which includes firm profitability and firm investment. This new version of the model generates new room for debates and new empirical investigations. This article aims at contributing to these debates by integrating an indicator summarizing the volume of Google requests for a company in the Fama-French model context. Specifically, we argue that this indicator can actually contribute to the Fama-French model in our understanding of stocks returns evolution in emerging markets. Recently, one can observe an increasing numbers of empirical studies investigating the impacts of the Google requests on the dynamics of financial prices (e.g., see Bijl, Kringhaug, Molnar, and Sandvik (2016); Tang and Zhu
https://doi.org/10.1016/j.bir.2019.07.001 _ 2214-8450/Copyright © 2019, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). _ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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(2017)). However, the potential influence of these requests on asset pricing models is still under-investigated in the literature. This article study contributes to the literature by examining the effects of investors' attention on stock returns in the context of 5-emerging markets including Indonesia, Malaysia, Philippines, Thailand, and Vietnam. We selected here 5emerging markets which are new emerging markets with high dynamic and development in economies and stock markets in Asia Pacific area. The economies of Indonesia, Malaysia, Philippines, Thailand, and Vietnam are recovering from the impacts of the 2008 global financial crisis with the average growth rates at around 6% (see Fig. 1, IMF (2017)). The stock markets of these countries are mainly composed by domestic private investors while they gradually open to foreign institutional investors (see Richards (2009); Majid, Meera, Omar, and Aziz (2009)). Furthermore, it is worth mentioning that Indonesia, Philippines, Thailand, and Vietnam are 4 in the top 20 highest internet using country in the world,1 which is used widely by people through desktop, laptop, mobile, smart phone, etc. Therefore, these five markets are a good sample for investigating the contribution of Google search on financial prices. Precisely, we use the volume of Google search to proxy the investor attention and examine their effects on stock returns. The article is structured as follows. Section 2 reviews the literature dealing with asset pricing model and the Google search. Section 3 presents the methodology and data an emphasize on Google search volume as an indicator. The results are discussed in Section 4 and some implications are suggested in the final section. 2. Literature review Asset pricing models define a popular area of research in the financial literature. Among the most common models used in finance, one can mention the CAPM, the APT, or the FamaFrench models. Fama and French (1992), as the pioneers, examined the joint roles of market beta, firm size, leverage, book-to-market equity and earning to price ratio in the crosssection of average stock returns. They found that market beta has little ability to explain the average stock returns even used alone or in combination with other variables. However, the firm size and the book-to-market equity seem to absorb the apparent roles of leverage and the earning to price in average return when they use them in a combination. This observation suggests that an extended version of the CAPM integrating the firm size and the book-to-market equity offered a higher explanatory power than the classical CAPM. As a result, the model suggested by Fama-French has been labeled threefactor model (or the three-factor Fama-French model). Improving their research, Fama and French (1993) confirmed that portfolios constructed to mimic risk factors related to market, size, and value all help to explain the returns to well-diversified stock portfolios. In the same vein, Fama and
1
See http://www.internetworldstats.com/top20.htm until 3/May/2018.
French (1995) provide a deeper economic foundation for their three-factor pricing model by relating the return factors to earnings shocks. More recently studies, Gaunt (2004) confirmed that the Fama-French model provides significantly improved explanatory power over the CAPM on Australian stock market. Working on the Indian stock markets, Connor and Sehgal (2001, p. 379) showed that the returns and crosssectional mean returns can be explained through three factors Fama-French model (rather than the classical CAPM). However, there also exist some empirical evidences suggesting that the three-factor model is unable to capture variations (in average returns) related to profitability and investment (e.g., see Novy-Marx (2013); Titman, Wei, and Xie (2004)). In this challenging context, Fama and French (2015) proposed a five-factor model by adding profitability and investment factors to the three-factor model. They documented that five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns performs better than the three-factor model. Not surprisingly, recent studies investigated and tested the significance of this five-factor model. Fama and French (2017) found that average stock returns for North America, Europe, and Asia Pacific increase with the book-to-market ratio and profitability and are negatively related to investment. Meanwhile, the relation between average returns and book-tomarket ratio is strong, but average returns show little relation to profitability or investment in Japan. Guo, Zhang, Zhang, and Zhang (2017) tested the five-factor model in China and found strong size, value and profitability patterns in average returns, but weak investment pattern. They also notice that the profitability factor significantly improves the description of average return; however, the investment factor made marginal contributions for portfolios. Lin (2017) also studied in Chinese stock market and found that the five-factor model consistently outperforms the three-factor model in the Chinese equity market. In addition, Lin found that both value and profitability factors are important, while the investment factor is found to be redundant for describing average returns in contrast to the findings in Fama and French (2015). Precisely, Zaremba and Czapkiewicz (2017) documented that the five-factor model best explains the returns of anomaly portfolios and verify its superiority over the other models including the capital asset-pricing model (Sharpe, 1964), the three-factor model (Fama & French, 1993), the four-factor model (Carhart, 1997). Although these empirical studies supporting the five factor Fama-French model, the creditability of this model is still under investigation due to some problems. Precisely, Fama and French (2015) and Fama and French (2017) emphasize that their five-factor model's major problem is that it cannot capture the low average returns on small stocks. In this direction, Lin (2017) finds that the main problem with the five-factor model is its failure to fully capture the high average returns of stocks whose returns perform like those of growth firms that invest conservatively due to low profitability. According to Preis, Moat, and Stanley (2013), the changes in the volume of particular Google search term can suggest the
_ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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changes in the levels of researchyattention and, therefore, decision making. More precisely, if individual searches for a term in Google, he is undoubtedly paying attention to it, thus the changes in the search volume suggests the changes in the attention of individuals on the issues on a whole population.2 In this context, aggregate search frequency in Google is a direct and unambiguous measure of attention (Da, Engelberg, & Gao, 2011). Bank, Larch, and Peter (2011) explained that search volume on Google not only serves as an intuitive proxy for overall firm recognition, but also captures the interest of investors for a particular stock. Working on the German markets, they found that an increase in search queries is associated with a rise in trading activity and stock liquidity with search volume primarily measures attention from uninformed investors. Moreover, they found evidence that an increase in Google search volume is associated with temporarily higher future returns. Bollen, Mao, and Zeng (2011) showed that the Down-Jones Industrial Average (DJIA) predictions can be significantly improved by integrating the search on Twitters (they found an accuracy of 86.7% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%). In the same vein, Joseph, Wintoki, and Zhang (2011) examined the ability of online ticker searches to influence abnormal stock returns and trading volumes. They found that over a weekly horizon that online search intensity can predict abnormal stock returns and trading volumes, and that the sensitivity of returns to this search is positively related to the difficulty of a stock being arbitraged in a sample of S&P 500 firms over the period 2005e2008. Bordino et al. (2012) showed that daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily queries related to the same stocks and, in many cases peaks, queries anticipate of trading by one day or more. However, the authors added that the queries volume dynamics emerged from a collective behaviour without any coordination between users. In their comprehensive study, Preis et al. (2013) suggested that massive data generated by human interactions on Internet pave the way to a new perspective on the study of financial markets. For instance, they found patterns that they called ‘‘early warning signs’’ of stock market moves by analyzing changes in Google query volumes for search terms related to finance. Mondria, Wu, and Zhang (2010), Kristoufek (2013) and Curme, Preis, Stanley, and Moat (2014) recorded the same kind of observations by analyzing data related to search engine on Google, online encyclopedia Wikipedia and Amazon Mechanical Turk. They showed an evidence of link between Internet searches relating to politics or business and subsequent stock market moves. Specifically, they found that an increase in search volume for politics and business tends to precede stock market falls. Veiga, Ramos, and Latoeiro (2013) provided more detailed evidences that an increase in web searches for stocks on
Google engine is followed by a temporary increase in volatility, the volume and a drop in cumulative returns. In addition, an increase for web search queries for the market index leads to a decrease in the index returns and in the stock index futures generating an increase of the implied volatility. Veiga et al. (2013) also showed that investors' attention interacts with behavioral biases explaining that the predictability of web searches for the return and the liquidity is enhanced when firm prices (and market prices) hit a 52-week high level; and it diminished when the market hits a 52-week low. Interestingly, Kim, Lucivjanska, Molnar, and Villa (2019) examine the impacts of Google searches on stock market activity in Norway. They find that Google searches have not have correlations with contemporaneous and future abnormal returns, but the increases in Google searches could lead to the increase in volatility and trading volume. It is worth mentioning that all these previous studies have not been done in a context of asset pricing models and more precisely in a context of FamaFrench model. In this article, we integrate this Google search indicator in the asset pricing models issue. Next section presents the methodology and data of our study. 3. Methodology and data From the basic model of financial literature, Markowitz (1952) with portfolio theory suggests that investors invest in a portfolio of asset to minimize risk which is valued by standard deviation of portfolio return. Then, Sharpe (1964) develops CAPM basing on portfolio theory that presents the expected return of stock i (ri) is the function of the free risk rate (rf), market return (rm), and the beta of stock i that proxy for the market risk of stock i (b). Due to the lack of explanatory power of CAPM and its extensions since it just accounts for the market risk that cannot perfectly explain for stock returns (Fama & French, 1996), Fama and French (1993) propose three-factor model, which add the two factors accounted for the firm size as the difference in the return of small stock and big stock (SMB), and the market value to book value of stock (HLM ). In 2015, Fama and French (2015) added two parameters (firm profitability and firm investment) to their previous three factor model. Our methodology starts with the theoretical model developed by Fama and French (2015) to explain stock returns as the function of several parameters: risk free rate, market return, the firm size, the firm market value, the firm profitability, and the firm investment activities. This study also extends the five-factor model by adding the leverage factor to proxy for the risk factor in explaining stock return differences (see George and Hwang (2010); Obreja (2013)). More precisely, we use the dynamic panel data model to catch the inertial trend in the stock returns. rit ¼ rit1 þ
5 X j¼1
2
While, Google accounted for over 77% of all search queries performed on the global stance in 2017.
3
bj Mjt þ
5 X k¼1
ak Fkt þ g1 IAt þ
5 X
sp Zpt þ εit
p¼1
ð1Þ
_ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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where: rit denotes the return of stock i in year t; Mi denotes the vector of macroeconomic factors that explain the change in return of stock i including variables presenting stock market, economic growth, and inflation; Fj denotes a vector of firm characteristics that explain the differences in return of stock i including firm size, firm profitability, leverage of firm, and liquidity of firm i at year t; IAt denotes the Google indicator characterizing the Google requests for stock i at year t, that we normalize by using the logarithms of yearly average of google searches for the ticker of each stock; Zp denotes a vector of interactions of investors' interest and firm characteristics in vector Fk to investigate the effects of investor's attention in the associations with firm characteristics; b, a, g and s are the estimated coefficients; 3 is error term. Data definitions are presented in Table 1. Firm's characteristics and stock prices were collected from the Orbis database for all firms operating in the countries we selected for our sample (Thailand, Vietnam, Malaysia, Indonesia and Philippines). Macroeconomic data came from the IMF database for each country; the Google search for stock ticker is hand-collected for each firm from Google trend of Google Inc. website. In which, the global total number of Google search for the ticker of each stock in both English and local language are recruited for monthly and then sum up to form the yearly Google search. The total number of Google search for each stock ticker is then taken in logarithms to normalize the data. We also use the yearly difference of the logarithms of google searches as an alternative measure of investor attention for a robustness check.3 The aim of this article is to explore the potential influence of a Google indicator on the explaining dimension of the Fama-French Model for emerging countries. Finding a positive correlation between a Google indicator and stock returns is not a sufficient condition to be used in the asset pricing model since such kind of link does not allow us to distinguish whether Google searches have an impact on stock returns or the stock returns have an impact on Google searches. With this objective to handle this issue, we tried to perform the panel Granger causality test based on the method developed by Dumitrescu and Hurlin (2012). However, our data period is too short to perform this test (since a minimum of 9 years is recommended for this test, Lopez and Weber (2018)). We deal with this particular issue by estimating the unconditional correlations between stock return and Google searches. Except for the case of Malaysia, our results (see Table 2) exhibited no significant correlation between these variables implying a potential weak mutual causality between them. Although the estimations of our relationship between Google searches and stock returns is still approximate; we can, based on our results, reasonably consider that the first can meaningfully be used in a pricing model as we will discuss our results in the following section.
Table 1 Data definitions. Variable Dependent var.
Independent var. Firm characteristics
Macroeconomic characteristics
Investor attention
Code
Definitions
Stock return (%)
R
[Log (PtePt-1)]*100, P is the year-end price of stock
Firm's size Firm's financial leverage (%) Firm's liquidity (%)
Asset Lev
Firm's profitability (%) Inflation (%) Economic growth (%) Market return (%)
Roa
Logarithms of Total asset The ratio of Liabilities to Total asset The ratio of cash and short-term investment to Total asset Return on Asset (after tax) The CPI inflation GDP real growth rate
Mr
Inflation (%) Google search
Inf IA
Liq
Inf eg
[Log (Pmt-Pmt-1)]*100, Pm is the year-end price of market index The CPI inflation Logarithms of Total google search for stock ticker
Note: the data of firm's characteristics and price of stock are collected from the Orbis database for each firm; the data of macroeconomic characteristics are collected from IMF database for each country; the google search for stock ticker is hand-collected from Google trend of Google Inc. for each ticker.
3.1. The Table 3 presents the data description After dropping listed firms that are in lack of data, our sample includes 319 Indonesian firms, 625 Malaysian firms, 152 Philippian firms, 404 Thailand firms, and 229 Vietnamese firms. In the period of 2009e2016, the average stock returns were positive for Indonesia, Malaysia, Philippines, and Thailand it shows that in Vietnam, these returns are, on average, negative. Vietnam is a special case with the average liquidity ratio at higher level than the rest of other markets. In addition, it is worth mentioning that the averages of return of asset (ROA) are positive in all markets, meanwhile the debt almost accumulates a haft of capital structure of all firms. 4. Results and discussion For the estimation of the eq. (1), we applied the dynamic panel techniques for unbalance panel data of each market, whereas System Generalised Method of Moments (Sys-GMM) (Arellano and Bond (1991); Arellano and Bover (1995); and Blundell and Bond (1998) was used. Actually, in addition to dealing with potential endogeneity problems due to the reverse
Table 2 Unconditional correlation between stock return and Google search (in log form). Correlation between Indonesia Malaysia Philippines Thailand Vietnam
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Thank you reviewer for this great suggestion.
R and AI p-value
0.0292 0.2028
0.0318 0.0468
0.0462 0.1451
0.0128 0.041 0.5111 0.1199
_ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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_ C.P. Nguyen et al. / Borsa Istanbul Review xxx (xxxx) xxx Table 3 Data description. Variables Indonesia R Asset Lev Liq Roa Inf Eg Mr IA Malaysia R Asset Lev Liq Roa Inf eg Mr IA Philippines R Asset Lev Liq Roa Inf eg Mr IA Thailand R Asset Lev Liq Roa Inf eg Mr IA Vietnam R Asset Lev Liq Roa Inf eg Mr IA
Obs
Mean
Std. Dev.
Min
Max
2006 2419 2418 2418 2417 2552 2552 2233 2357
6.3774 26.5476 48.7925 11.6412 6.5862 5.2851 5.4385 10.0991 4.2481
53.3233 3.6186 21.0199 12.4366 11.2957 1.0017 0.5973 12.2858 1.2951
241.0799 13.9108 0.0000 0.0050 71.0000 3.5258 4.6289 13.6357 0.0000
383.2896 33.1917 98.4722 95.0000 68.5200 6.4134 6.2239 26.6812 7.0344
4019 4853 4847 4843 4846 4375 5000 5000 4752
4.0852 19.8061 39.1963 14.4431 4.6586 4.2638 4.3920 2.0777 4.6743
36.1167 1.5817 18.9392 13.2251 10.1122 8.7183 2.7301 0.7922 1.1883
176.2201 14.9787 0.0000 0.0064 82.3300 6.3208 2.5258 0.5833 0.6931
223.8047 25.5908 99.6167 100.0000 94.9200 19.8438 6.9810 3.2000 7.0309
1018 1209 1209 1208 1208 1064 1216 1216 1181
7.3027 22.6445 40.4531 14.5434 5.8062 11.5266 5.6650 3.2663 5.4125
43.9494 2.2942 22.9847 15.4785 9.3898 13.9315 2.0385 1.0893 0.9816
306.0652 15.0964 0.0000 0.0043 95.9600 3.9277 1.1500 1.4300 1.3863
218.8898 27.8934 99.7700 100.0000 66.4100 31.9336 7.6300 4.6500 7.0121
2658 3174 3174 3166 3172 2821 3226 3226 3201
8.6727 22.1358 44.7874 11.0453 6.8063 10.6029 3.0894 1.5737 5.2706
43.6412 1.5661 20.7668 12.4262 9.9802 17.3806 2.7634 1.7409 0.9236
291.7771 18.1196 0.0000 0.0093 80.9800 15.0805 0.6907 0.8950 0.6931
279.5553 28.4195 99.6500 97.9167 64.0400 34.0746 7.5136 3.8098 6.9847
1441 1811 1811 1811 1811 1603 1832 1832 1827
7.4864 27.8069 49.7800 59.2115 6.6677 4.2214 5.9375 7.5625 4.2439
48.1933 1.4669 21.7092 23.4562 8.2937 16.2956 0.5074 5.0111 1.1354
242.0368 21.3696 0.1981 0.0000 64.5500 32.1096 5.2000 0.6000 1.0986
172.9724 34.4861 96.6925 99.8996 78.3700 19.8612 6.7000 18.6000 6.9518
causality thus the Sys-GMM model with the advantage of addressing the bias associated with the fixed effects and endogeneity in short panels is recruited as our case with the period of 2009e2016. All results are summarized in Tables 4e8, the estimators are not subject to serial correlation of order two when we have un-significant AR (-2) test and the instruments used are valid since we have un-significant result for the Hansen test. At the first step, we estimate eq. (1) without Google search factor to examine the main drivers in Fama-French model for
5
each country. The results in Table 4 show interesting findings. The positive effects of inflation on stock returns are found in Malaysia, Philippines, Thailand, while inflation has negative impacts on stock returns in Indonesia and Vietnam. However, these impacts of inflation on stock return are only significant in the case of Philippines and Thailand that have average inflation over 10% in the period of 2009e2016.4 The economic growth has a positive impact on stock returns in four countries (Indonesia, Malaysia, Philippines, and Thailand) while a higher economic growth has negative impact in the case of Vietnam. Vietnam has witnessed a medium-term period of high economic growth in 2000e2007, which then boosted many sectors in the national economy. The higher economic growth requires more capital for economic activities which can crowd out the capital going to the stock market, and thus impacts negatively on stock returns. Regarding to the Fama-French factors, the positive impact of market return on stock return shows that the stock return has a positive beta on average and that they are almost marketcyclical stocks. In terms of microeconomic factors, the size of firms (proxy by logarithm of total asset) have negative impacts on stock returns in Malaysia, Philippines, Thailand, and Vietnam cases, which are significant and consistent with the financial theory and Fama-French's expectations. This means that the smaller firms require higher premium in expected returns. Let us mention that the case of Indonesia might suggest that investors prefer larger firms on this market. The profitability of firms (return on asset) has a positive impact on stock return that are significant and consistent with theory and empirical literature on the topic. This result also implies that investors in these emerging markets focus on the profitability of firms as the major determinants of their investment decisions.5 Finally, the insignificant positive impact of liquidity on stock return may add more insight for this finding. As Fama-French model forecasts, a higher liquidity of firm means a better financial condition, thus the stock is expected to have a higher return. However, investors may associate a higher liquidity with a lower profitability of firms simply because these firms keep too much cash (and then short-term investment with lower expected returns). Just to notice that a higher liquidity is in line with a lower stock return in Malaysia. The factors identified in the Fama-French model offer an interesting framework for estimating returns in the five emerging markets selected in this study. However, by integrating previous comments and studies mentioned earlier, we plan to focus on the interaction between firms' profitability and 4
In fact, the period of 2009e2016 includes the 2008 global financial crisis which affected and slow downed economic activities. In this context, an increase of inflation might be the signal of a higher demand which then positive impacts on the firm's activities. 5 The positive impact of firm leverage on stock return also contributes to this finding. In fact, the investors focus on firms' profitability, is usually approximate by return on asset. However, profitability can also be captured through the return on equity that is truly the implied profit of the investors in investing into stock. Therefore, the higher leverage boosts the return on equity of firms through financial leverage that increases the investor's expectation on stock return.
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6 Table 4 Stock return and Fama-French factors. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1) Inf Eg Mr Asset Lev Liq Roa C N No. of firm No. of IVs AR (2) testa Hansen testa
0.3236** [0.1432] 1.4743 [1.8465] 10.2022* [5.3951] 0.9051*** [0.1225] 1.4161*** [0.5018] 0.3308** [0.1293] 0.3676 [0.3254] 1.1261*** [0.2106] 114.0995** [44.3563] 1686 314 19 0.591 0.165
0.5633 [0.5378] 2.7475 [3.8929] 5.2512* [3.0396] 1.6371*** [0.4052] 2.6322*** [0.8688] 0.1492*** [0.0563] 0.1961* [0.1060] 0.6579** [0.3311] 10.5126 [21.6455] 3369 625 14 0.361 0.267
0.2118* [0.1186] 6.5601** [2.8800] 3.6253 [2.3326] 0.5515*** [0.1544] 1.6474* [0.9368] 0.1960 [0.1280] 0.5117 [0.4744] 1.0310*** [0.2767] 23.1600 [25.6976] 865 152 14 0.709 0.657
0.3919* [0.2352] 4.3525** [1.8191] 3.8313*** [0.8220] 0.3538 [0.3517] 2.4012*** [0.6744] 0.1159** [0.0560] 0.0123 [0.0965] 1.4018*** [0.1941] 26.9959* [14.4586] 2253 402 12 0.118 0.699
0.2445*** [0.0802] 0.9174 [1.7484] 25.7977*** [8.0189] 0.8343* [0.4481] 2.9086*** [0.8254] 0.2572*** [0.0880] 0.0558 [0.0573] 1.3290*** [0.3999] 218.7586*** [64.2370] 1199 229 10 0.131 0.233
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively.
Table 5 Stock return and Google search. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1) Inf Eg Mr Asset Lev Liq Roa IA C N No. of firm No. of IVs AR (2) testa Hansen testa
0.3330*** [0.1178] 1.0097 [1.7006] 9.0150* [4.6303] 0.9656*** [0.1142] 1.0351** [0.5026] 0.2225* [0.1210] 0.2015 [0.1342] 1.1625*** [0.2133] 4.1982 [3.1916] 111.0517*** [42.5839] 1611 301 29 0.496 0.113
0.1437 [0.4926] 5.0418 [3.5331] 5.7801** [2.6875] 1.3811*** [0.3531] 3.5979*** [1.0641] 0.1132** [0.0475] 0.1695* [0.0934] 0.8552*** [0.3282] 2.9269** [1.3870] 10.9812 [19.4620] 3302 625 15 0.756 0.147
0.0477 [0.1118 4.4055 [3.6839] 3.9201 [4.6311] 0.7318*** [0.2081] 2.1167** [1.0025] 0.1982 [0.1524] 0.5122 [0.5144] 0.9977*** [0.2966] ¡2.8390* [1.5653] 4.7760 [41.1810] 849 150 15 0.452 0.853
0.3533 [0.2353] 4.1263** [1.8050] 3.8535*** [0.8343] 0.4014 [0.3516 1.6904** [0.6923] 0.1507*** [0.0578] 0.0395 [0.0993] 1.3719*** [0.1953] ¡4.8690*** [1.4515] 34.8113** [14.0060] 2243 402 13 0.150 0.722
0.2404*** [0.0795] 1.0073 [1.7383] 26.3420*** [8.0178] 0.7992* [0.4452] 2.5329*** [0.9108] 0.2391*** [0.0890] 0.0406 [0.0602] 1.3296*** [0.3981] ¡2.3196* [1.1943] 221.8864*** [64.0270] 1199 229 11 0.125 0.230
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively.
Table 6 Stock return, Fama-French factors and Google search. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1) Inf Eg Mr Asset Lev Liq Roa IA IA*Asset IA*Lev IA*Liq IA*Roa C N No. of firm No. of IVs AR (2) testa Hansen testa
0.3888*** [0.0897] 0.1260 [1.4952] 10.3768*** [3.9961] 0.9384*** [0.0934] 15.9599*** [4.9149] 0.9644 [0.8335] 0.4796 [1.7061] 2.4871* [1.2896] 107.0987*** [32.3377] 3.5918*** [1.1536] 0.1549 [0.1967] 0.0376 [0.3945] 0.3640 [0.3207] 555.2186*** [139.7061] 1611 301 63 0.206 0.171
0.6432 [0.4359] 11.9119*** [3.4807] 10.8418*** [3.7831] 1.3865*** [0.3917] 151.5992*** [32.6978] 2.0373** [0.8957] 6.6050*** [2.2080] 3.2315** [1.3945] 635.0539*** [130.5902] 32.1258*** [6.6430] 0.4882** [0.1982] 1.2821*** [0.4717] 0.9711*** [0.2991] 3088.5190*** [644.3008] 3299 623 19 0.227 0.888
0.0494 [0.1279] 6.0976 [4.3791] 6.0003 [5.8369] 0.7033*** [0.2333] 48.9500* [26.2387] 1.7254 [2.9313] 3.9402 [5.4529] 4.1591 [2.6176] 174.1231* [93.1039] 8.5668* [4.6642] 0.2754 [0.5214] 0.5822 [0.8441] 0.5715 [0.4662] 917.9753* [498.7550] 846 150 19 0.352 0.896
0.1089 [0.2343] 2.2513 [1.8152] 3.9503*** [1.1773] 0.7581** [0.3418] 63.5611*** [22.8692] 6.7093*** [2.5071] 2.6511* [1.4308] 4.8967*** [1.6557] 183.0021** [75.5990] 11.1106*** [4.1385] 1.2167** [0.4768] 0.4859* [0.2758] 0.7033** [0.3249] 1040.6870** [423.1403] 2243 402 15 0.667 0.693
0.2319*** [0.0666] 0.8294 [1.6414] 21.2499** [9.4041] 1.0457* [0.5563] 18.1119** [8.2441] 2.1519* [1.2072] 1.9085 [1.3546] 4.5198*** [1.6546] 96.5072* [58.3503] 3.4442* [1.9035] 0.4276 [0.2739] 0.4343 [0.3176] 0.6709* [0.3666] 607.5203** [238.7289] 1199 229 15 0.126 0.392
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively.
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Table 7 Changes in Google search and Stock returns. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1)
0.3197** [0.1418] 1.0510 [1.9017] 8.8176* [5.2353] 0.9984*** [0.1180] 1.3096*** [0.5021] 0.3009** [0.1239] 0.2379* [0.1376] 1.1522*** [0.2162] 6.7365 [4.2254] 103.75** [43.9268] 1611 301 20 0.607 0.226
0.0574 [0.4684] 5.7999* [3.4133] 6.8371*** [2.5221] 1.5141*** [0.2808] 3.2883*** [0.8602] 0.1157** [0.0459] 0.1475 [0.0932] 0.8848*** [0.3079] 10.3871** [4.0613] 11.232 [18.4871] 3302 625 15 0.861 0.161
0.0658 [0.1107] 3.6637 [3.5258] 2.7764 [4.4791] 0.7497*** [0.2049] 0.8102 [0.8169] 0.1534 [0.1413] 0.5378 [0.5197] 0.9578*** [0.2859] ¡1.4188 [6.4523] 29.341 [43.6696] 849 150 15 0.496 0.788
0.4001* [0.2399] 4.4086** [1.8499] 3.8520*** [0.8283] 0.3448 [0.3570] 2.3618*** [0.6680] 0.1113** [0.0560] 0.0076 [0.0965] 1.4017*** [0.1936] 3.8017 [2.9953] 26.386* [14.4065] 2251 402 13 0.109 0.690
0.2476*** [0.0796] 0.8702 [1.7412] 25.6701*** [8.0373] 0.8513* [0.4480] 2.9017*** [0.8233] 0.2578*** [0.0884] 0.0556 [0.0574] 1.3284*** [0.4000] 1.8273 [3.5276] 217.60*** [64.3604] 1199 229 11 0.137 0.239
Inf Eg Mr Asset Lev Liq Roa DIA C N No. of firm No. of IVs AR (2) testa Hansen testa
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively. Table 8 Changes in Google search, Fama-French Model and Stock returns. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1)
0.3034** [0.1490] 0.8907 [1.8299] 6.1657 [5.2492] 1.0830*** [0.1316] 0.9484* [0.5216] 0.1797** [0.0837] 0.2135 [0.1375] 1.1148*** [0.2178] 98.949*** [34.56] ¡4.1704*** [1.3067] 0.2983* [0.1553] 0.3873 [0.2756] 0.2541 [0.2860] 74.79* [44.62] 1611 301 21 0.666 0.937
0.3130 [0.5052] 9.7422** [4.2739] 10.6310** [4.4234] 1.5682*** [0.3771] 6.5667*** [1.4694] 0.5836*** [0.2205] 0.4618* [0.2526] 1.0823*** [0.3413] 226.72** [106.71] ¡23.121** [10.29] 5.3840** [2.6273] 2.2543* [1.1532] 1.8162** [0.7882] 21.66 [32.67] 3229 623 19 0.681 0.622
0.0199 [0.1247] 4.0589 [3.8670] 3.1845 [5.1726] 0.7536*** [0.2245] 0.4243 [0.9526] 0.1375 [0.1542] 0.5756 [0.5098] 0.9481*** [0.2891] ¡53.205 [125.49] 5.8835 [5.2457] ¡0.7381 [1.0228] ¡3.5139 [6.1879] ¡1.8129* [0.9453] 41.88 [47.15] 846 150 19 0.392 0.790
0.4319* [0.2291] 4.4741** [1.7926] 4.1093*** [0.8496] 0.3113 [0.3429] 3.2287*** [0.8435] 0.3565*** [0.1264] 0.1425 [0.1274] 1.4765*** [0.2046] 61.236 [44.58] ¡2.3251 [2.1321] ¡0.0714 [0.1703] ¡0.0848 [0.2424] ¡0.2533 [0.4293] 32.48** [15.51] 2251 402 17 0.059 0.639
0.0595 [0.1381] 4.2389 [2.9813] 34.5569** [14.9044] 0.0651 [0.7904] 15.00** [6.5234] 0.3874 [0.2477] 0.2402 [0.1712] 2.0307*** [0.6258] 3084.9* [1662.5] ¡109.68* [58.57] 1.6885 [1.6547] ¡1.8837 [1.2038] 3.3872 [2.4109] 635.95*** [244.6] 1199 229 15 0.313 0.370
Inf Eg Mr Asset Lev Liq Roa DIA DIA*Asset DIA*Lev DIA*Liq DIA*Roa C N No. of firm No. of IVs AR (2) testa Hansen testa
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively. _ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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the investors' interest captured by the Google indicator. One of the major objectives of this study is to examine this indicator on the stock returns and its interaction with the factors used in the Fama-French model. First, the logarithm of Google search volume is used to proxy for investor attention as mentioned in our eq. (1). Table 5 presents the results. The negative significant impact of the Google indicator on stock returns in the case of Philippines, Thailand, and Vietnam mean that a higher investor interest generates a lower stock return. In accordance with Calvo (1999), this could indicate that investors in emerging markets are considered as less rationalyinformed than those operating in mature financial markets. Investors would therefore over-react negative signals in their financial choice. This study investigates further to investigate the association between the Google indicator and factors of the Fama-French model. The Table 6 shows hereafter the results of our analysis. The estimation indicates that the interaction between the Google indicator and the factors of the Fama-French model have an opposite sign than the coefficients of factors alone. This means that the Google requests influence and re-adjust the roles of Fama-French factors in explaining the stock returns. First, one can observe a positive significant relationship between the firms' assets and the stock returns in the case of Indonesia and Malaysia while this relationship is significantly negative for Philippines, Thailand, and Vietnam. We observe the same trend for other parameters such as financial leverage, liquidity, and return on assets. Interestingly, this observation might potentially be explained by the influence of Islamic Finance in Malaysia and Indonesia. Companies ruled by Islamic Finance are expected to spend their cash in more tangible (no speculative) assets. Especially, there are more significant coefficients when we put the interaction terms between investor attention and factors in Fama-French models into the regression meaning that the investors' interest plays a significant role in the Fama-French model. Tables 7 and 8 report the results with different measure of investor attention by using the difference of logarithms of google search. First, most of the cases the investor attentions appear with statistical insignificance excluding the case of Malaysia. Moreover, the results in Table 8 confirm these results. Only the case of Malaysia has statistical significance. It is interesting to notice the investor attention in Malaysia has positive impact on stock returns in Tables 5 and 6. The positive impacts of investor in Table 7 8 reaffirm only for the case of Malaysia. Secondly, the interaction terms between difference of logarithms of google search with the firm characteristics in Table 8 are not statistical significance in most of the cases, excluding the case of Malaysia. Notably, the results for the case of Malaysia are quietly consistent in most of interaction terms with results in Table 6. 5. Conclusion One of the major objectives of this study is to examine the impact of Google search on the stock returns and their interaction with the factors used in the Fama-French model. In our study focusing on five emerging markets (Indonesia, Malaysia, Philippines, Thailand, and Vietnam), we found that Fama-
French model is not always effective in capturing the market's trend. Therefore, we decided to integrate a Google indicator as a significant factor contributing to the effectiveness of the Fama-French model for emerging markets. We obtained interesting results. The negative significant impact of this Google indicator on stock returns in the case of Philippines, Thailand, and Vietnam mean that a higher investor interest generates a lower stock return. This suggests that investors might be more sensitive to bad news than good news in their investment decisions. When we take into account of the interaction between investors' attention and the factors of the Fama-French model, we observe these parameters have an opposite sign than the model coefficients alone. This means that our Google indicator influences and re-adjusts the roles of FamaFrench factors in explaining the stock returns. First, one can observe a positive significant relationship between the firms' assets and the stock returns in the case of Indonesia and Malaysia while this relationship is significantly negative for Philippines, Thailand, and Vietnam. Same trend for other parameters such as financial leverage, liquidity, and return on assets. Interestingly, this observation might potentially be explained by the influence of Islamic Finance in Malaysia and Indonesia. This article contributes to the existing literature under three aspects. First, by adding new factor in explaining stock returns of emerging markets. By using the Google search volume for the stock ticker as a Google indicator for a firm, this study examines the influences of Google search on stock returns. Furthermore, this article extends the empirical works using the Google search volume to asset pricing issues. Conflict of interest There is no conflict of interest. Acknowledgments This research is funded by the University of Economics Ho Chi Minh City, Vietnam. References Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277e297. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29e51. Bank, M., Larch, M., & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25(3), 239e264. Bijl, L., Kringhaug, G., Molnar, P., & Sandvik, E. (2016). Google searches and stock returns. International Review of Financial Analysis, 45, 150e156. https://doi.org/10.1016/j.irfa.2016.03.015. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115e143. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1e8. Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., & Weber, I. (2012). Web search queries can predict stock market volumes. PLoS One, 7(7), e40014.
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Kristoufek, L. (2013). Can Google Trends search queries contribute to risk diversification? Scientific Reports, 3(2713). Lin, Q. (2017). Noisy prices and the FamaeFrench five-factor asset pricing model in China. Emerging Markets Review, 31, 141e163. https://doi.org/ 10.1016/j.ememar.2017.04.002. Majid, M. S. A., Meera, A. K. M., Omar, M. A., & Aziz, H. A. (2009). Dynamic linkages among ASEAN-5 emerging stock markets. International Journal of Emerging Markets, 4(2), 160e184. https://doi.org/10.1108/ 17468800910945783. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77e91. Mondria, J., Wu, T., & Zhang, Y. (2010). The determinants of international investment and attention allocation: Using internet search query data. Journal of International Economics, 82(1), 85e95. Novy-Marx, R. (2013). The other side of value: The gross profitability premium. Journal of Financial Economics, 108(1), 1e28. Obreja, I. (2013). Book-to-market equity, financial leverage, and the cross-section of stock returns. Review of Financial Studies, 26(5), 1146e1189. Petkova, R. (2006). Do the FamaeFrench factors proxy for innovations in predictive variables? The Journal of Finance, 61(2), 581e612. Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684. Richards, A. (2009). Big fish in small ponds: The trading behavior and price impact of foreign investors in asian emerging equity markets. Journal of Financial and Quantitative Analysis, 40(1), 1e27. https://doi.org/10.1017/ S0022109000001721. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425e442. Tang, W., & Zhu, L. (2017). How security prices respond to a surge in investor attention: Evidence from Google Search of ADRs. Global Finance Journal, 33, 38e50. https://doi.org/10.1016/j.gfj.2016.09.001. Titman, S., Wei, K. J., & Xie, F. (2004). Capital investments and stock returns. Journal of Financial and Quantitative Analysis, 39(4), 677e700. Veiga, H., Ramos, S., & Latoeiro, P. (2013). Predictability of stock market activity using Google search queries (Retrieved from). Zaremba, A., & Czapkiewicz, A. (2017). Digesting anomalies in emerging european markets: A comparison of factor pricing models. Emerging Markets Review, 31, 1e15. https://doi.org/10.1016/j.ememar.2016.12.002.
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Google search and stock returns in emerging markets Canh Phuc Nguyen a, Christophe Schinckus b, Thai Vu Hong Nguyen c,* a b
School of Banking, University of Economics Ho Chi Minh City, 59C Nguyen Dinh Chieu, District 3, Ho Chi Minh City, Viet Nam School of Finance and Economics, Taylor's University, Lakeside Campus, 1 Jalan Taylors, 47500 Subang Jaya, Selangor, Malaysia c School of Business and Management, RMIT University Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Viet Nam Received 30 March 2019; revised 3 June 2019; accepted 11 July 2019 Available online ▪ ▪ ▪
Abstract The Fama-French model offers a framework explaining the stock return variability by capturing the size, value, profitability, and investment patterns of firms; but it fails in capturing the low average returns on small stocks. This article contributes to asset pricing models by investigating the role that the volume of Google search might play as augmented factor in explaining the stock returns. Through system-GMM estimations with the data in the period of 2009e2016 for 5-emerging markets (Indonesia, Malaysia, Philippines, Thailand, and Vietnam), we find that FamaFrench model is not always effective. The increases in Google search volume appear to have significant negative impacts on stock returns in the case of Philippines, Thailand, and Vietnam. This suggests that investors might be more sensitive to bad news than good news in their investment decisions. Furthermore, our Google indicator is found to have an influence on the Fama-French factors in explaining the stock returns. _ Copyright © 2019, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL classifications: D53; G12; O16 Keywords: Asset-pricing model; Google search; Stock returns
1. Introduction In the literature of finance, the three-factor Fama-French model is seen as the most important model for explaining stock returns in past three decades. Precisely, this model explains the differences in the evolution of stock returns by using the market risk and the differences in firm's characteristics (firm size and market-to-book ratio) (Fama & French, 1993). Many empirical studies showed that the three-factor FamaFrench model has a better explanatory power than other asset pricing models such as CAPM for instance (Gaunt, 2004). However, the three-factor Fama-French model also generates a lot of debates in the finance literature since its success is the
* Corresponding author. E-mail addresses: [email protected] (C.P. Nguyen), Christophe. [email protected] (C. Schinckus), [email protected] (T.V. Hong Nguyen). _ Peer review under responsibility of Borsa Istanbul Anonim S¸irketi.
one based on time-varying investment opportunities (Petkova, 2006). Petkova (2006) suggested that the evolution of stock returns can also be explained by other factors such as investment or firm profitability for example. Since then, new factors have been considered and tested in recent studies. Fama and French extended their model to a five-factor model (see Fama and French (2015), which includes firm profitability and firm investment. This new version of the model generates new room for debates and new empirical investigations. This article aims at contributing to these debates by integrating an indicator summarizing the volume of Google requests for a company in the Fama-French model context. Specifically, we argue that this indicator can actually contribute to the Fama-French model in our understanding of stocks returns evolution in emerging markets. Recently, one can observe an increasing numbers of empirical studies investigating the impacts of the Google requests on the dynamics of financial prices (e.g., see Bijl, Kringhaug, Molnar, and Sandvik (2016); Tang and Zhu
https://doi.org/10.1016/j.bir.2019.07.001 _ 2214-8450/Copyright © 2019, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). _ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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(2017)). However, the potential influence of these requests on asset pricing models is still under-investigated in the literature. This article study contributes to the literature by examining the effects of investors' attention on stock returns in the context of 5-emerging markets including Indonesia, Malaysia, Philippines, Thailand, and Vietnam. We selected here 5emerging markets which are new emerging markets with high dynamic and development in economies and stock markets in Asia Pacific area. The economies of Indonesia, Malaysia, Philippines, Thailand, and Vietnam are recovering from the impacts of the 2008 global financial crisis with the average growth rates at around 6% (see Fig. 1, IMF (2017)). The stock markets of these countries are mainly composed by domestic private investors while they gradually open to foreign institutional investors (see Richards (2009); Majid, Meera, Omar, and Aziz (2009)). Furthermore, it is worth mentioning that Indonesia, Philippines, Thailand, and Vietnam are 4 in the top 20 highest internet using country in the world,1 which is used widely by people through desktop, laptop, mobile, smart phone, etc. Therefore, these five markets are a good sample for investigating the contribution of Google search on financial prices. Precisely, we use the volume of Google search to proxy the investor attention and examine their effects on stock returns. The article is structured as follows. Section 2 reviews the literature dealing with asset pricing model and the Google search. Section 3 presents the methodology and data an emphasize on Google search volume as an indicator. The results are discussed in Section 4 and some implications are suggested in the final section. 2. Literature review Asset pricing models define a popular area of research in the financial literature. Among the most common models used in finance, one can mention the CAPM, the APT, or the FamaFrench models. Fama and French (1992), as the pioneers, examined the joint roles of market beta, firm size, leverage, book-to-market equity and earning to price ratio in the crosssection of average stock returns. They found that market beta has little ability to explain the average stock returns even used alone or in combination with other variables. However, the firm size and the book-to-market equity seem to absorb the apparent roles of leverage and the earning to price in average return when they use them in a combination. This observation suggests that an extended version of the CAPM integrating the firm size and the book-to-market equity offered a higher explanatory power than the classical CAPM. As a result, the model suggested by Fama-French has been labeled threefactor model (or the three-factor Fama-French model). Improving their research, Fama and French (1993) confirmed that portfolios constructed to mimic risk factors related to market, size, and value all help to explain the returns to well-diversified stock portfolios. In the same vein, Fama and
1
See http://www.internetworldstats.com/top20.htm until 3/May/2018.
French (1995) provide a deeper economic foundation for their three-factor pricing model by relating the return factors to earnings shocks. More recently studies, Gaunt (2004) confirmed that the Fama-French model provides significantly improved explanatory power over the CAPM on Australian stock market. Working on the Indian stock markets, Connor and Sehgal (2001, p. 379) showed that the returns and crosssectional mean returns can be explained through three factors Fama-French model (rather than the classical CAPM). However, there also exist some empirical evidences suggesting that the three-factor model is unable to capture variations (in average returns) related to profitability and investment (e.g., see Novy-Marx (2013); Titman, Wei, and Xie (2004)). In this challenging context, Fama and French (2015) proposed a five-factor model by adding profitability and investment factors to the three-factor model. They documented that five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns performs better than the three-factor model. Not surprisingly, recent studies investigated and tested the significance of this five-factor model. Fama and French (2017) found that average stock returns for North America, Europe, and Asia Pacific increase with the book-to-market ratio and profitability and are negatively related to investment. Meanwhile, the relation between average returns and book-tomarket ratio is strong, but average returns show little relation to profitability or investment in Japan. Guo, Zhang, Zhang, and Zhang (2017) tested the five-factor model in China and found strong size, value and profitability patterns in average returns, but weak investment pattern. They also notice that the profitability factor significantly improves the description of average return; however, the investment factor made marginal contributions for portfolios. Lin (2017) also studied in Chinese stock market and found that the five-factor model consistently outperforms the three-factor model in the Chinese equity market. In addition, Lin found that both value and profitability factors are important, while the investment factor is found to be redundant for describing average returns in contrast to the findings in Fama and French (2015). Precisely, Zaremba and Czapkiewicz (2017) documented that the five-factor model best explains the returns of anomaly portfolios and verify its superiority over the other models including the capital asset-pricing model (Sharpe, 1964), the three-factor model (Fama & French, 1993), the four-factor model (Carhart, 1997). Although these empirical studies supporting the five factor Fama-French model, the creditability of this model is still under investigation due to some problems. Precisely, Fama and French (2015) and Fama and French (2017) emphasize that their five-factor model's major problem is that it cannot capture the low average returns on small stocks. In this direction, Lin (2017) finds that the main problem with the five-factor model is its failure to fully capture the high average returns of stocks whose returns perform like those of growth firms that invest conservatively due to low profitability. According to Preis, Moat, and Stanley (2013), the changes in the volume of particular Google search term can suggest the
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changes in the levels of researchyattention and, therefore, decision making. More precisely, if individual searches for a term in Google, he is undoubtedly paying attention to it, thus the changes in the search volume suggests the changes in the attention of individuals on the issues on a whole population.2 In this context, aggregate search frequency in Google is a direct and unambiguous measure of attention (Da, Engelberg, & Gao, 2011). Bank, Larch, and Peter (2011) explained that search volume on Google not only serves as an intuitive proxy for overall firm recognition, but also captures the interest of investors for a particular stock. Working on the German markets, they found that an increase in search queries is associated with a rise in trading activity and stock liquidity with search volume primarily measures attention from uninformed investors. Moreover, they found evidence that an increase in Google search volume is associated with temporarily higher future returns. Bollen, Mao, and Zeng (2011) showed that the Down-Jones Industrial Average (DJIA) predictions can be significantly improved by integrating the search on Twitters (they found an accuracy of 86.7% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%). In the same vein, Joseph, Wintoki, and Zhang (2011) examined the ability of online ticker searches to influence abnormal stock returns and trading volumes. They found that over a weekly horizon that online search intensity can predict abnormal stock returns and trading volumes, and that the sensitivity of returns to this search is positively related to the difficulty of a stock being arbitraged in a sample of S&P 500 firms over the period 2005e2008. Bordino et al. (2012) showed that daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily queries related to the same stocks and, in many cases peaks, queries anticipate of trading by one day or more. However, the authors added that the queries volume dynamics emerged from a collective behaviour without any coordination between users. In their comprehensive study, Preis et al. (2013) suggested that massive data generated by human interactions on Internet pave the way to a new perspective on the study of financial markets. For instance, they found patterns that they called ‘‘early warning signs’’ of stock market moves by analyzing changes in Google query volumes for search terms related to finance. Mondria, Wu, and Zhang (2010), Kristoufek (2013) and Curme, Preis, Stanley, and Moat (2014) recorded the same kind of observations by analyzing data related to search engine on Google, online encyclopedia Wikipedia and Amazon Mechanical Turk. They showed an evidence of link between Internet searches relating to politics or business and subsequent stock market moves. Specifically, they found that an increase in search volume for politics and business tends to precede stock market falls. Veiga, Ramos, and Latoeiro (2013) provided more detailed evidences that an increase in web searches for stocks on
Google engine is followed by a temporary increase in volatility, the volume and a drop in cumulative returns. In addition, an increase for web search queries for the market index leads to a decrease in the index returns and in the stock index futures generating an increase of the implied volatility. Veiga et al. (2013) also showed that investors' attention interacts with behavioral biases explaining that the predictability of web searches for the return and the liquidity is enhanced when firm prices (and market prices) hit a 52-week high level; and it diminished when the market hits a 52-week low. Interestingly, Kim, Lucivjanska, Molnar, and Villa (2019) examine the impacts of Google searches on stock market activity in Norway. They find that Google searches have not have correlations with contemporaneous and future abnormal returns, but the increases in Google searches could lead to the increase in volatility and trading volume. It is worth mentioning that all these previous studies have not been done in a context of asset pricing models and more precisely in a context of FamaFrench model. In this article, we integrate this Google search indicator in the asset pricing models issue. Next section presents the methodology and data of our study. 3. Methodology and data From the basic model of financial literature, Markowitz (1952) with portfolio theory suggests that investors invest in a portfolio of asset to minimize risk which is valued by standard deviation of portfolio return. Then, Sharpe (1964) develops CAPM basing on portfolio theory that presents the expected return of stock i (ri) is the function of the free risk rate (rf), market return (rm), and the beta of stock i that proxy for the market risk of stock i (b). Due to the lack of explanatory power of CAPM and its extensions since it just accounts for the market risk that cannot perfectly explain for stock returns (Fama & French, 1996), Fama and French (1993) propose three-factor model, which add the two factors accounted for the firm size as the difference in the return of small stock and big stock (SMB), and the market value to book value of stock (HLM ). In 2015, Fama and French (2015) added two parameters (firm profitability and firm investment) to their previous three factor model. Our methodology starts with the theoretical model developed by Fama and French (2015) to explain stock returns as the function of several parameters: risk free rate, market return, the firm size, the firm market value, the firm profitability, and the firm investment activities. This study also extends the five-factor model by adding the leverage factor to proxy for the risk factor in explaining stock return differences (see George and Hwang (2010); Obreja (2013)). More precisely, we use the dynamic panel data model to catch the inertial trend in the stock returns. rit ¼ rit1 þ
5 X j¼1
2
While, Google accounted for over 77% of all search queries performed on the global stance in 2017.
3
bj Mjt þ
5 X k¼1
ak Fkt þ g1 IAt þ
5 X
sp Zpt þ εit
p¼1
ð1Þ
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4
where: rit denotes the return of stock i in year t; Mi denotes the vector of macroeconomic factors that explain the change in return of stock i including variables presenting stock market, economic growth, and inflation; Fj denotes a vector of firm characteristics that explain the differences in return of stock i including firm size, firm profitability, leverage of firm, and liquidity of firm i at year t; IAt denotes the Google indicator characterizing the Google requests for stock i at year t, that we normalize by using the logarithms of yearly average of google searches for the ticker of each stock; Zp denotes a vector of interactions of investors' interest and firm characteristics in vector Fk to investigate the effects of investor's attention in the associations with firm characteristics; b, a, g and s are the estimated coefficients; 3 is error term. Data definitions are presented in Table 1. Firm's characteristics and stock prices were collected from the Orbis database for all firms operating in the countries we selected for our sample (Thailand, Vietnam, Malaysia, Indonesia and Philippines). Macroeconomic data came from the IMF database for each country; the Google search for stock ticker is hand-collected for each firm from Google trend of Google Inc. website. In which, the global total number of Google search for the ticker of each stock in both English and local language are recruited for monthly and then sum up to form the yearly Google search. The total number of Google search for each stock ticker is then taken in logarithms to normalize the data. We also use the yearly difference of the logarithms of google searches as an alternative measure of investor attention for a robustness check.3 The aim of this article is to explore the potential influence of a Google indicator on the explaining dimension of the Fama-French Model for emerging countries. Finding a positive correlation between a Google indicator and stock returns is not a sufficient condition to be used in the asset pricing model since such kind of link does not allow us to distinguish whether Google searches have an impact on stock returns or the stock returns have an impact on Google searches. With this objective to handle this issue, we tried to perform the panel Granger causality test based on the method developed by Dumitrescu and Hurlin (2012). However, our data period is too short to perform this test (since a minimum of 9 years is recommended for this test, Lopez and Weber (2018)). We deal with this particular issue by estimating the unconditional correlations between stock return and Google searches. Except for the case of Malaysia, our results (see Table 2) exhibited no significant correlation between these variables implying a potential weak mutual causality between them. Although the estimations of our relationship between Google searches and stock returns is still approximate; we can, based on our results, reasonably consider that the first can meaningfully be used in a pricing model as we will discuss our results in the following section.
Table 1 Data definitions. Variable Dependent var.
Independent var. Firm characteristics
Macroeconomic characteristics
Investor attention
Code
Definitions
Stock return (%)
R
[Log (PtePt-1)]*100, P is the year-end price of stock
Firm's size Firm's financial leverage (%) Firm's liquidity (%)
Asset Lev
Firm's profitability (%) Inflation (%) Economic growth (%) Market return (%)
Roa
Logarithms of Total asset The ratio of Liabilities to Total asset The ratio of cash and short-term investment to Total asset Return on Asset (after tax) The CPI inflation GDP real growth rate
Mr
Inflation (%) Google search
Inf IA
Liq
Inf eg
[Log (Pmt-Pmt-1)]*100, Pm is the year-end price of market index The CPI inflation Logarithms of Total google search for stock ticker
Note: the data of firm's characteristics and price of stock are collected from the Orbis database for each firm; the data of macroeconomic characteristics are collected from IMF database for each country; the google search for stock ticker is hand-collected from Google trend of Google Inc. for each ticker.
3.1. The Table 3 presents the data description After dropping listed firms that are in lack of data, our sample includes 319 Indonesian firms, 625 Malaysian firms, 152 Philippian firms, 404 Thailand firms, and 229 Vietnamese firms. In the period of 2009e2016, the average stock returns were positive for Indonesia, Malaysia, Philippines, and Thailand it shows that in Vietnam, these returns are, on average, negative. Vietnam is a special case with the average liquidity ratio at higher level than the rest of other markets. In addition, it is worth mentioning that the averages of return of asset (ROA) are positive in all markets, meanwhile the debt almost accumulates a haft of capital structure of all firms. 4. Results and discussion For the estimation of the eq. (1), we applied the dynamic panel techniques for unbalance panel data of each market, whereas System Generalised Method of Moments (Sys-GMM) (Arellano and Bond (1991); Arellano and Bover (1995); and Blundell and Bond (1998) was used. Actually, in addition to dealing with potential endogeneity problems due to the reverse
Table 2 Unconditional correlation between stock return and Google search (in log form). Correlation between Indonesia Malaysia Philippines Thailand Vietnam
3
Thank you reviewer for this great suggestion.
R and AI p-value
0.0292 0.2028
0.0318 0.0468
0.0462 0.1451
0.0128 0.041 0.5111 0.1199
_ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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_ C.P. Nguyen et al. / Borsa Istanbul Review xxx (xxxx) xxx Table 3 Data description. Variables Indonesia R Asset Lev Liq Roa Inf Eg Mr IA Malaysia R Asset Lev Liq Roa Inf eg Mr IA Philippines R Asset Lev Liq Roa Inf eg Mr IA Thailand R Asset Lev Liq Roa Inf eg Mr IA Vietnam R Asset Lev Liq Roa Inf eg Mr IA
Obs
Mean
Std. Dev.
Min
Max
2006 2419 2418 2418 2417 2552 2552 2233 2357
6.3774 26.5476 48.7925 11.6412 6.5862 5.2851 5.4385 10.0991 4.2481
53.3233 3.6186 21.0199 12.4366 11.2957 1.0017 0.5973 12.2858 1.2951
241.0799 13.9108 0.0000 0.0050 71.0000 3.5258 4.6289 13.6357 0.0000
383.2896 33.1917 98.4722 95.0000 68.5200 6.4134 6.2239 26.6812 7.0344
4019 4853 4847 4843 4846 4375 5000 5000 4752
4.0852 19.8061 39.1963 14.4431 4.6586 4.2638 4.3920 2.0777 4.6743
36.1167 1.5817 18.9392 13.2251 10.1122 8.7183 2.7301 0.7922 1.1883
176.2201 14.9787 0.0000 0.0064 82.3300 6.3208 2.5258 0.5833 0.6931
223.8047 25.5908 99.6167 100.0000 94.9200 19.8438 6.9810 3.2000 7.0309
1018 1209 1209 1208 1208 1064 1216 1216 1181
7.3027 22.6445 40.4531 14.5434 5.8062 11.5266 5.6650 3.2663 5.4125
43.9494 2.2942 22.9847 15.4785 9.3898 13.9315 2.0385 1.0893 0.9816
306.0652 15.0964 0.0000 0.0043 95.9600 3.9277 1.1500 1.4300 1.3863
218.8898 27.8934 99.7700 100.0000 66.4100 31.9336 7.6300 4.6500 7.0121
2658 3174 3174 3166 3172 2821 3226 3226 3201
8.6727 22.1358 44.7874 11.0453 6.8063 10.6029 3.0894 1.5737 5.2706
43.6412 1.5661 20.7668 12.4262 9.9802 17.3806 2.7634 1.7409 0.9236
291.7771 18.1196 0.0000 0.0093 80.9800 15.0805 0.6907 0.8950 0.6931
279.5553 28.4195 99.6500 97.9167 64.0400 34.0746 7.5136 3.8098 6.9847
1441 1811 1811 1811 1811 1603 1832 1832 1827
7.4864 27.8069 49.7800 59.2115 6.6677 4.2214 5.9375 7.5625 4.2439
48.1933 1.4669 21.7092 23.4562 8.2937 16.2956 0.5074 5.0111 1.1354
242.0368 21.3696 0.1981 0.0000 64.5500 32.1096 5.2000 0.6000 1.0986
172.9724 34.4861 96.6925 99.8996 78.3700 19.8612 6.7000 18.6000 6.9518
causality thus the Sys-GMM model with the advantage of addressing the bias associated with the fixed effects and endogeneity in short panels is recruited as our case with the period of 2009e2016. All results are summarized in Tables 4e8, the estimators are not subject to serial correlation of order two when we have un-significant AR (-2) test and the instruments used are valid since we have un-significant result for the Hansen test. At the first step, we estimate eq. (1) without Google search factor to examine the main drivers in Fama-French model for
5
each country. The results in Table 4 show interesting findings. The positive effects of inflation on stock returns are found in Malaysia, Philippines, Thailand, while inflation has negative impacts on stock returns in Indonesia and Vietnam. However, these impacts of inflation on stock return are only significant in the case of Philippines and Thailand that have average inflation over 10% in the period of 2009e2016.4 The economic growth has a positive impact on stock returns in four countries (Indonesia, Malaysia, Philippines, and Thailand) while a higher economic growth has negative impact in the case of Vietnam. Vietnam has witnessed a medium-term period of high economic growth in 2000e2007, which then boosted many sectors in the national economy. The higher economic growth requires more capital for economic activities which can crowd out the capital going to the stock market, and thus impacts negatively on stock returns. Regarding to the Fama-French factors, the positive impact of market return on stock return shows that the stock return has a positive beta on average and that they are almost marketcyclical stocks. In terms of microeconomic factors, the size of firms (proxy by logarithm of total asset) have negative impacts on stock returns in Malaysia, Philippines, Thailand, and Vietnam cases, which are significant and consistent with the financial theory and Fama-French's expectations. This means that the smaller firms require higher premium in expected returns. Let us mention that the case of Indonesia might suggest that investors prefer larger firms on this market. The profitability of firms (return on asset) has a positive impact on stock return that are significant and consistent with theory and empirical literature on the topic. This result also implies that investors in these emerging markets focus on the profitability of firms as the major determinants of their investment decisions.5 Finally, the insignificant positive impact of liquidity on stock return may add more insight for this finding. As Fama-French model forecasts, a higher liquidity of firm means a better financial condition, thus the stock is expected to have a higher return. However, investors may associate a higher liquidity with a lower profitability of firms simply because these firms keep too much cash (and then short-term investment with lower expected returns). Just to notice that a higher liquidity is in line with a lower stock return in Malaysia. The factors identified in the Fama-French model offer an interesting framework for estimating returns in the five emerging markets selected in this study. However, by integrating previous comments and studies mentioned earlier, we plan to focus on the interaction between firms' profitability and 4
In fact, the period of 2009e2016 includes the 2008 global financial crisis which affected and slow downed economic activities. In this context, an increase of inflation might be the signal of a higher demand which then positive impacts on the firm's activities. 5 The positive impact of firm leverage on stock return also contributes to this finding. In fact, the investors focus on firms' profitability, is usually approximate by return on asset. However, profitability can also be captured through the return on equity that is truly the implied profit of the investors in investing into stock. Therefore, the higher leverage boosts the return on equity of firms through financial leverage that increases the investor's expectation on stock return.
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6 Table 4 Stock return and Fama-French factors. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1) Inf Eg Mr Asset Lev Liq Roa C N No. of firm No. of IVs AR (2) testa Hansen testa
0.3236** [0.1432] 1.4743 [1.8465] 10.2022* [5.3951] 0.9051*** [0.1225] 1.4161*** [0.5018] 0.3308** [0.1293] 0.3676 [0.3254] 1.1261*** [0.2106] 114.0995** [44.3563] 1686 314 19 0.591 0.165
0.5633 [0.5378] 2.7475 [3.8929] 5.2512* [3.0396] 1.6371*** [0.4052] 2.6322*** [0.8688] 0.1492*** [0.0563] 0.1961* [0.1060] 0.6579** [0.3311] 10.5126 [21.6455] 3369 625 14 0.361 0.267
0.2118* [0.1186] 6.5601** [2.8800] 3.6253 [2.3326] 0.5515*** [0.1544] 1.6474* [0.9368] 0.1960 [0.1280] 0.5117 [0.4744] 1.0310*** [0.2767] 23.1600 [25.6976] 865 152 14 0.709 0.657
0.3919* [0.2352] 4.3525** [1.8191] 3.8313*** [0.8220] 0.3538 [0.3517] 2.4012*** [0.6744] 0.1159** [0.0560] 0.0123 [0.0965] 1.4018*** [0.1941] 26.9959* [14.4586] 2253 402 12 0.118 0.699
0.2445*** [0.0802] 0.9174 [1.7484] 25.7977*** [8.0189] 0.8343* [0.4481] 2.9086*** [0.8254] 0.2572*** [0.0880] 0.0558 [0.0573] 1.3290*** [0.3999] 218.7586*** [64.2370] 1199 229 10 0.131 0.233
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively.
Table 5 Stock return and Google search. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1) Inf Eg Mr Asset Lev Liq Roa IA C N No. of firm No. of IVs AR (2) testa Hansen testa
0.3330*** [0.1178] 1.0097 [1.7006] 9.0150* [4.6303] 0.9656*** [0.1142] 1.0351** [0.5026] 0.2225* [0.1210] 0.2015 [0.1342] 1.1625*** [0.2133] 4.1982 [3.1916] 111.0517*** [42.5839] 1611 301 29 0.496 0.113
0.1437 [0.4926] 5.0418 [3.5331] 5.7801** [2.6875] 1.3811*** [0.3531] 3.5979*** [1.0641] 0.1132** [0.0475] 0.1695* [0.0934] 0.8552*** [0.3282] 2.9269** [1.3870] 10.9812 [19.4620] 3302 625 15 0.756 0.147
0.0477 [0.1118 4.4055 [3.6839] 3.9201 [4.6311] 0.7318*** [0.2081] 2.1167** [1.0025] 0.1982 [0.1524] 0.5122 [0.5144] 0.9977*** [0.2966] ¡2.8390* [1.5653] 4.7760 [41.1810] 849 150 15 0.452 0.853
0.3533 [0.2353] 4.1263** [1.8050] 3.8535*** [0.8343] 0.4014 [0.3516 1.6904** [0.6923] 0.1507*** [0.0578] 0.0395 [0.0993] 1.3719*** [0.1953] ¡4.8690*** [1.4515] 34.8113** [14.0060] 2243 402 13 0.150 0.722
0.2404*** [0.0795] 1.0073 [1.7383] 26.3420*** [8.0178] 0.7992* [0.4452] 2.5329*** [0.9108] 0.2391*** [0.0890] 0.0406 [0.0602] 1.3296*** [0.3981] ¡2.3196* [1.1943] 221.8864*** [64.0270] 1199 229 11 0.125 0.230
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively.
Table 6 Stock return, Fama-French factors and Google search. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1) Inf Eg Mr Asset Lev Liq Roa IA IA*Asset IA*Lev IA*Liq IA*Roa C N No. of firm No. of IVs AR (2) testa Hansen testa
0.3888*** [0.0897] 0.1260 [1.4952] 10.3768*** [3.9961] 0.9384*** [0.0934] 15.9599*** [4.9149] 0.9644 [0.8335] 0.4796 [1.7061] 2.4871* [1.2896] 107.0987*** [32.3377] 3.5918*** [1.1536] 0.1549 [0.1967] 0.0376 [0.3945] 0.3640 [0.3207] 555.2186*** [139.7061] 1611 301 63 0.206 0.171
0.6432 [0.4359] 11.9119*** [3.4807] 10.8418*** [3.7831] 1.3865*** [0.3917] 151.5992*** [32.6978] 2.0373** [0.8957] 6.6050*** [2.2080] 3.2315** [1.3945] 635.0539*** [130.5902] 32.1258*** [6.6430] 0.4882** [0.1982] 1.2821*** [0.4717] 0.9711*** [0.2991] 3088.5190*** [644.3008] 3299 623 19 0.227 0.888
0.0494 [0.1279] 6.0976 [4.3791] 6.0003 [5.8369] 0.7033*** [0.2333] 48.9500* [26.2387] 1.7254 [2.9313] 3.9402 [5.4529] 4.1591 [2.6176] 174.1231* [93.1039] 8.5668* [4.6642] 0.2754 [0.5214] 0.5822 [0.8441] 0.5715 [0.4662] 917.9753* [498.7550] 846 150 19 0.352 0.896
0.1089 [0.2343] 2.2513 [1.8152] 3.9503*** [1.1773] 0.7581** [0.3418] 63.5611*** [22.8692] 6.7093*** [2.5071] 2.6511* [1.4308] 4.8967*** [1.6557] 183.0021** [75.5990] 11.1106*** [4.1385] 1.2167** [0.4768] 0.4859* [0.2758] 0.7033** [0.3249] 1040.6870** [423.1403] 2243 402 15 0.667 0.693
0.2319*** [0.0666] 0.8294 [1.6414] 21.2499** [9.4041] 1.0457* [0.5563] 18.1119** [8.2441] 2.1519* [1.2072] 1.9085 [1.3546] 4.5198*** [1.6546] 96.5072* [58.3503] 3.4442* [1.9035] 0.4276 [0.2739] 0.4343 [0.3176] 0.6709* [0.3666] 607.5203** [238.7289] 1199 229 15 0.126 0.392
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively.
_ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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7
Table 7 Changes in Google search and Stock returns. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1)
0.3197** [0.1418] 1.0510 [1.9017] 8.8176* [5.2353] 0.9984*** [0.1180] 1.3096*** [0.5021] 0.3009** [0.1239] 0.2379* [0.1376] 1.1522*** [0.2162] 6.7365 [4.2254] 103.75** [43.9268] 1611 301 20 0.607 0.226
0.0574 [0.4684] 5.7999* [3.4133] 6.8371*** [2.5221] 1.5141*** [0.2808] 3.2883*** [0.8602] 0.1157** [0.0459] 0.1475 [0.0932] 0.8848*** [0.3079] 10.3871** [4.0613] 11.232 [18.4871] 3302 625 15 0.861 0.161
0.0658 [0.1107] 3.6637 [3.5258] 2.7764 [4.4791] 0.7497*** [0.2049] 0.8102 [0.8169] 0.1534 [0.1413] 0.5378 [0.5197] 0.9578*** [0.2859] ¡1.4188 [6.4523] 29.341 [43.6696] 849 150 15 0.496 0.788
0.4001* [0.2399] 4.4086** [1.8499] 3.8520*** [0.8283] 0.3448 [0.3570] 2.3618*** [0.6680] 0.1113** [0.0560] 0.0076 [0.0965] 1.4017*** [0.1936] 3.8017 [2.9953] 26.386* [14.4065] 2251 402 13 0.109 0.690
0.2476*** [0.0796] 0.8702 [1.7412] 25.6701*** [8.0373] 0.8513* [0.4480] 2.9017*** [0.8233] 0.2578*** [0.0884] 0.0556 [0.0574] 1.3284*** [0.4000] 1.8273 [3.5276] 217.60*** [64.3604] 1199 229 11 0.137 0.239
Inf Eg Mr Asset Lev Liq Roa DIA C N No. of firm No. of IVs AR (2) testa Hansen testa
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively. Table 8 Changes in Google search, Fama-French Model and Stock returns. Dep. Var: R
Indonesia
Malaysia
Philippines
Thailand
Vietnam
R (1)
0.3034** [0.1490] 0.8907 [1.8299] 6.1657 [5.2492] 1.0830*** [0.1316] 0.9484* [0.5216] 0.1797** [0.0837] 0.2135 [0.1375] 1.1148*** [0.2178] 98.949*** [34.56] ¡4.1704*** [1.3067] 0.2983* [0.1553] 0.3873 [0.2756] 0.2541 [0.2860] 74.79* [44.62] 1611 301 21 0.666 0.937
0.3130 [0.5052] 9.7422** [4.2739] 10.6310** [4.4234] 1.5682*** [0.3771] 6.5667*** [1.4694] 0.5836*** [0.2205] 0.4618* [0.2526] 1.0823*** [0.3413] 226.72** [106.71] ¡23.121** [10.29] 5.3840** [2.6273] 2.2543* [1.1532] 1.8162** [0.7882] 21.66 [32.67] 3229 623 19 0.681 0.622
0.0199 [0.1247] 4.0589 [3.8670] 3.1845 [5.1726] 0.7536*** [0.2245] 0.4243 [0.9526] 0.1375 [0.1542] 0.5756 [0.5098] 0.9481*** [0.2891] ¡53.205 [125.49] 5.8835 [5.2457] ¡0.7381 [1.0228] ¡3.5139 [6.1879] ¡1.8129* [0.9453] 41.88 [47.15] 846 150 19 0.392 0.790
0.4319* [0.2291] 4.4741** [1.7926] 4.1093*** [0.8496] 0.3113 [0.3429] 3.2287*** [0.8435] 0.3565*** [0.1264] 0.1425 [0.1274] 1.4765*** [0.2046] 61.236 [44.58] ¡2.3251 [2.1321] ¡0.0714 [0.1703] ¡0.0848 [0.2424] ¡0.2533 [0.4293] 32.48** [15.51] 2251 402 17 0.059 0.639
0.0595 [0.1381] 4.2389 [2.9813] 34.5569** [14.9044] 0.0651 [0.7904] 15.00** [6.5234] 0.3874 [0.2477] 0.2402 [0.1712] 2.0307*** [0.6258] 3084.9* [1662.5] ¡109.68* [58.57] 1.6885 [1.6547] ¡1.8837 [1.2038] 3.3872 [2.4109] 635.95*** [244.6] 1199 229 15 0.313 0.370
Inf Eg Mr Asset Lev Liq Roa DIA DIA*Asset DIA*Lev DIA*Liq DIA*Roa C N No. of firm No. of IVs AR (2) testa Hansen testa
Note: (a) these test results are presented with p-value; ***, **, * are significant levels at 1%, 5%, 10%, respectively. _ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001
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_ C.P. Nguyen et al. / Borsa Istanbul Review xxx (xxxx) xxx
the investors' interest captured by the Google indicator. One of the major objectives of this study is to examine this indicator on the stock returns and its interaction with the factors used in the Fama-French model. First, the logarithm of Google search volume is used to proxy for investor attention as mentioned in our eq. (1). Table 5 presents the results. The negative significant impact of the Google indicator on stock returns in the case of Philippines, Thailand, and Vietnam mean that a higher investor interest generates a lower stock return. In accordance with Calvo (1999), this could indicate that investors in emerging markets are considered as less rationalyinformed than those operating in mature financial markets. Investors would therefore over-react negative signals in their financial choice. This study investigates further to investigate the association between the Google indicator and factors of the Fama-French model. The Table 6 shows hereafter the results of our analysis. The estimation indicates that the interaction between the Google indicator and the factors of the Fama-French model have an opposite sign than the coefficients of factors alone. This means that the Google requests influence and re-adjust the roles of Fama-French factors in explaining the stock returns. First, one can observe a positive significant relationship between the firms' assets and the stock returns in the case of Indonesia and Malaysia while this relationship is significantly negative for Philippines, Thailand, and Vietnam. We observe the same trend for other parameters such as financial leverage, liquidity, and return on assets. Interestingly, this observation might potentially be explained by the influence of Islamic Finance in Malaysia and Indonesia. Companies ruled by Islamic Finance are expected to spend their cash in more tangible (no speculative) assets. Especially, there are more significant coefficients when we put the interaction terms between investor attention and factors in Fama-French models into the regression meaning that the investors' interest plays a significant role in the Fama-French model. Tables 7 and 8 report the results with different measure of investor attention by using the difference of logarithms of google search. First, most of the cases the investor attentions appear with statistical insignificance excluding the case of Malaysia. Moreover, the results in Table 8 confirm these results. Only the case of Malaysia has statistical significance. It is interesting to notice the investor attention in Malaysia has positive impact on stock returns in Tables 5 and 6. The positive impacts of investor in Table 7 8 reaffirm only for the case of Malaysia. Secondly, the interaction terms between difference of logarithms of google search with the firm characteristics in Table 8 are not statistical significance in most of the cases, excluding the case of Malaysia. Notably, the results for the case of Malaysia are quietly consistent in most of interaction terms with results in Table 6. 5. Conclusion One of the major objectives of this study is to examine the impact of Google search on the stock returns and their interaction with the factors used in the Fama-French model. In our study focusing on five emerging markets (Indonesia, Malaysia, Philippines, Thailand, and Vietnam), we found that Fama-
French model is not always effective in capturing the market's trend. Therefore, we decided to integrate a Google indicator as a significant factor contributing to the effectiveness of the Fama-French model for emerging markets. We obtained interesting results. The negative significant impact of this Google indicator on stock returns in the case of Philippines, Thailand, and Vietnam mean that a higher investor interest generates a lower stock return. This suggests that investors might be more sensitive to bad news than good news in their investment decisions. When we take into account of the interaction between investors' attention and the factors of the Fama-French model, we observe these parameters have an opposite sign than the model coefficients alone. This means that our Google indicator influences and re-adjusts the roles of FamaFrench factors in explaining the stock returns. First, one can observe a positive significant relationship between the firms' assets and the stock returns in the case of Indonesia and Malaysia while this relationship is significantly negative for Philippines, Thailand, and Vietnam. Same trend for other parameters such as financial leverage, liquidity, and return on assets. Interestingly, this observation might potentially be explained by the influence of Islamic Finance in Malaysia and Indonesia. This article contributes to the existing literature under three aspects. First, by adding new factor in explaining stock returns of emerging markets. By using the Google search volume for the stock ticker as a Google indicator for a firm, this study examines the influences of Google search on stock returns. Furthermore, this article extends the empirical works using the Google search volume to asset pricing issues. Conflict of interest There is no conflict of interest. Acknowledgments This research is funded by the University of Economics Ho Chi Minh City, Vietnam. References Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277e297. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29e51. Bank, M., Larch, M., & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25(3), 239e264. Bijl, L., Kringhaug, G., Molnar, P., & Sandvik, E. (2016). Google searches and stock returns. International Review of Financial Analysis, 45, 150e156. https://doi.org/10.1016/j.irfa.2016.03.015. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115e143. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1e8. Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., & Weber, I. (2012). Web search queries can predict stock market volumes. PLoS One, 7(7), e40014.
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_ Please cite this article as: Nguyen, C. P et al., Google search and stock returns in emerging markets, Borsa Istanbul Review, https://doi.org/10.1016/ j.bir.2019.07.001