The influence of cultural distance on the volatility of the international stock market

Accepted Manuscript The influence of cultural distance on the volatility of the international stock market Xiaoguang Zhou, Yadi Cui, Shihwei Wu, Weiqing Wang PII:

S0264-9993(18)31262-8

DOI:

https://doi.org/10.1016/j.econmod.2018.10.005

Reference:

ECMODE 4747

To appear in:

Economic Modelling

Received Date: 3 September 2018 Revised Date:

10 October 2018

Accepted Date: 13 October 2018

Please cite this article as: Zhou, X., Cui, Y., Wu, S., Wang, W., The influence of cultural distance on the volatility of the international stock market, Economic Modelling (2018), doi: https://doi.org/10.1016/ j.econmod.2018.10.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

The influence of cultural distance on the volatility of the international stock market Xiaoguang Zhoua,*, Yadi Cuia, Shihwei Wub, Weiqing Wanga

b

Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083,China

RI PT

a

Department of Business Management, National Taipei University of Technology, Taipei 10608, Taiwan

Declarations of interest: none

Acknowledgments

M AN U

E-mail address: [email protected] (Y. Cui); [email protected] (S. Wu); [email protected] (W. Wang).

SC

∗ Corresponding author, [email protected] (X. Zhou). Address: 30 Xueyuan Road, Haidian District, Beijing, China, zip code 100083, Tel: 8610-13691483603.

AC C

EP

TE D

This work was supported by "the National Natural Science Foundation of China (No. 71771023)".

ACCEPTED MANUSCRIPT The influence of cultural distance on the volatility of the international stock market

RI PT

Abstract: Along with the development of cultural dimensions and cultural distance, the influence of cultural variables on the stock market is attracting more and more attention. In this study, we propose an improved gravity model to examine the relationship between culture and the volatility of the international stock market. Firstly, based on Hofstede’s cultural dimensions theory, a model of the impact of cultural dimensions on the volatility of the national stock market is presented. Secondly, cultural distance is incorporated into the extended gravity model. Then, models of the impact of cultural distance on fluctuations in the international stock market and on foreign securities investment are proposed. Finally, the results of case studies using samples of national stock market indices indicate that different cultural dimensions have different influences on the volatility of national stock markets. The smaller the cultural distance between countries, the more similar the level of volatility in those countries’ stock markets. Greater cultural similarity promotes increased securities investment between countries. Key Words: Cultural distance; International stock market; Stock market volatility; Cultural dimension; Gravity model JEL classification: C30, C50, G15

Introduction

SC

1.

2.

Literature review

TE D

M AN U

The international stock market provides an extensive trading platform for investors and fund seekers, and is closely related to global economic fluctuations. With the continuous growth of the international stock market, scholars at home and abroad are paying more attention to factors affecting the volatility of the international stock market. In studies of economic, financial, political, and other factors, the impact of geographical distance has attracted the most attention. However, with the development of the Internet and the popularization of computerized trading systems, the influence of geographical distance on the volatility linkage between national stock markets has gradually reduced. Since the concepts of cultural dimension and cultural distance were presented, the impact of cultural distance on the volatility of the international stock market has gained increasing attention from scholars. Most previous studies have focused on traditional cultural variables, such as religion and language, as well as some of the cultural dimensions, while the scopes of these researches have mostly been regional stock markets. The six cultural dimensions outlined in Hofstede's cultural dimensions theory are selected, and other representative variables in economics, politics, finance, and geography are also considered to enable a comprehensive analysis of the relationship between culture and the volatility of the international stock market. In addition, the study is extended to global stock markets instead of regional stock markets using the improved gravity model. Models examining the impact of cultural dimensions on the volatility of national stock markets, the relevance of cultural distance on the difference in volatility between national stock markets, and the impact of cultural distance on the scale of foreign securities investment are proposed. Finally, empirical analyses of the national stock indices are undertaken.

AC C

EP

The factors affecting fluctuations in the international stock market can be categorized into macro factors and micro factors. Macro factors mainly include the currency supply, interest rate, exchange rate, and government performance. For example, Rozeff (1974) studied the effect of changes in monetary policy on the volatility of the stock market, while Campbell and Ammer (1993) discussed the influence of the inflation rate and interest rates on the return of stocks. Bernanke and Kuttner (2003) found that changes in interest rates had a significant effect on the stock market, while Asaolu and Ogunmuyiwa (2011) pointed out that a change in the exchange rate is a key reason for abnormal fluctuations in the stock market. Ulku and Baker (2014) analyzed the linkage between stock markets and macroeconomics based on the stock market beta (sensitivity of the national stock market index to the world index) and macroeconomic beta (sensitivity of national output and inflation to the world output and inflation). Countryman and Narayanan (2017) explored whether specific tariffs could reduce the volatility of price. Boadi and Amegbe (2017) used a fixed effect model to examine the relationship between the government’s performance and stock market returns in 23 countries. Micro factors mainly include internal factors relating to the stock market and investors, such as the size of the stock market, the number of investors, and the behavior of investors. Long et al. (1990) discussed the influence of investor types and investment scales on the volatility of the stock market, while Irfan et al. (2002) analyzed the effects of dividend yield, dividend payouts, and leverage rates on the volatility of stock returns on the Karachi Stock Exchange. Schuppli and Bohl (2010) explored the effect of foreign institutional investors on the stability of the Chinese stock market, while Chang and Lin (2015) examined the impact of investment decisions on the international stock market, pointing out that herd behavior by investors affects fluctuations on the international stock market. Pineiro-Chousa (2017) analyzed the impact of social media on the activities of investors and their impact on the Chicago Stock Exchange Volatility Index. Previous studies have shown that there are links between the volatility of various stock markets. Jaffe and Westerfield (1985) studied the stock markets of five developed countries and found the same “weekend effect” in each country. Fleming et al. (1998) discussed volatility correlations among stocks, bonds and the currency market, 1

ACCEPTED MANUSCRIPT

3.

AC C

EP

TE D

M AN U

SC

RI PT

and Fleischer (2003) further investigated volatilities among the stocks, currencies, and bond markets of Australia and the United States. Pretorius (2002) explored the dynamic connection among ten emerging stock markets and pointed out that the linkage between stock markets in the same region is closer to that between other stock markets. Gupta and Donleavy (2009) also conducted an empirical study of the linkage between yields in emerging stock markets. Sabri (2002) argued that the number of listed multinational companies and the degree of openness of the stock market had a positive impact on the linkages between the volatility of stock markets in various countries. Liu (2013) studied the correlations among 40 stock markets based on the gravity model and pointed out that the degree of similarity of the industrial structure has a significant influence on differences in volatility between developed markets and other markets. Junior et al. (2015) used national stock market index samples to show that there are links between stock markets in various countries from the perspective of information flow. Culture is defined as the norms of behaviors and values of specific countries or groups, and can affect investors’ psychology and behavior. Cultural values and distances change over time. However, they will not change in a short period of time. Walti (2005) found that common language played a positive role in the linkage of stock markets, while an increase in geographic distance led to a reduction in correlations between stock markets. Chan et al. (2005) argued that institutional investors mainly invest in markets that have a similar culture to their own. Drogendijk and Slangen (2006) pointed out that culture influenced the decision-making process of foreign direct investment. Guiso et al. (2008) discussed the impact of differences in the level of trust on financial transactions between European countries, especially bilateral investment activities. Aggarwal and Goodell (2010) analyzed the relationship between culture and foreign securities investments, and pointed out that culture affected the distribution of international securities investments. Lucey and Zhang (2010) found that countries with smaller cultural distances between them exhibit higher levels of linkage between their stock markets. Anderson et al. (2011) studied the impact of cultural distance on international diversification, while Tung (2013) pointed out that stock market volatility was higher in countries in which investors were more individualistic and less likely to avoid uncertainty. Contractor et al. (2014) argued that when acquisitions involve a shorter distance in terms of power or a greater distance in terms of uncertainty avoidance, there is a greater probability of most or a total acquisitions. Flavin et al. (2010) incorporated an overlapping open-time variable and a common linguistic variable into an extended gravity model to explore the relevance of international stock markets. Cai et al. (2014) analyzed the influence of cultural factors on price aggregation and price resistance in the Chinese stock market. Dutta and Mukherjee (2015) investigated the correlation between the cultural characteristics of the country and the development of the stock market, and found that trust and individualism had a positive influence on the development of the stock market, while uncertainty avoidance had a negative influence. Rothonis et al. (2016) explored whether cultural proximity would increase the volatility of the international stock market, and found an inverse relationship between cultural distance and the volatility of the international stock market. Singh et al. (2017) proved that similarities in terms of cultural characteristics increase the fluctuation between national stock markets. Wijayana and Gray (2018) investigated a sample of 30 countries and found that the response of the stock market to company announcements was significantly related to the cultural characteristics of the country. Previous studies contain several deficiencies in the study of the impact of cultural distance on the volatility of the international stock market. Firstly, there are few studies on the impact of a country’s culture on the fluctuations in its stock market, and there are few dimensions for describing national cultural characteristics. Secondly, the relationship between cultural distance and the volatility of the national stock market is generally analyzed using the gravity model, without considering other relevant factors. Thirdly, there have been few studies on the influence of cultural distance on foreign securities investment and the linkage between foreign securities investment and the volatility of national stock markets. Thus, the scope of this study is extended to the global stock market, and the six cultural dimensions of Hofstede are adopted to describe the characteristics of national cultures. Using an improved gravity model, we examine the impact of national cultural characteristics on the volatility of national stock markets, the impact of cultural distance on the correlation between national stock markets in terms of volatility, and the impact of cultural distance on the scale of foreign securities investments.

Preliminary theories

3.1. Gravity model

The gravity model is based on Newton’s law of universal gravitation and is widely used as a mathematical model of spatial interaction. The attractive force variables, the repulsive force variables, and the format of the gravity model differ in various fields, but the model is often applied to international trade, where its basic form is given by Eq. (1): (1) M i , j = KYi β Y jγ Diδ, j , where Mi,j denotes the volume of imports by country i from country j, Yi and Yj indicate the gross domestic product (GDP) of country i and country j, respectively, Di,j denotes the distance between the capitals of country i and country j, and K is the coefficient. The linear form of the model is shown in Eq. (2): (2) log( M i,j ) = α + β log(Yi ) + γ log(Y j ) + δ log( Di , j ) + ei , j . Since the level of trade between various countries or regions is affected by other factors, relevant control variables and virtual variables are added to establish a more practical extended gravity model. 2

ACCEPTED MANUSCRIPT 3.2. Cultural distance

6

(3)

SC

CDi , j = KSi , j = ∑ [( I i ,d − I j ,d ) 2 / Vd ] / 6 ,

RI PT

Cultural dimensions theory was first proposed by the Dutch psychologist Hofstede (1984) as a way of measuring the cultural differences between countries. Culture is the psychological manifestation of people in a specific environment, and distinguishes one group of people from another (Sondergaard, 1980). Hofstede categorized cultural differences into six dimensions: power distance index (PDI), uncertainty avoidance index (UAI), individualism index (IDV), masculinity index (MAS), long-term orientation index (LTO), and indulgence index (IVR). These dimensions help to explain the values of specific cultural dimensions and their influence on the workplace, the organization, and investors (Sorge, 1980). Investors’ investment psychology, response to information, and investment behavior are all influenced by the national culture. Investors tend to invest in an overseas market that is close to their own market, while they are less interested in allocating funds to markets that are both culturally and geographically distant (Beugelsdijk and Frijns, 2010). Culture can influence the volatility of the stock market by affecting the national information environment and the behavior of investors. Countries that are culturally closer are usually similar in the stock markets (Eun et al., 2015). Cultural distance is a measure of the cultural differences between two countries or regions. As cultural will not change in a short period of time, we assume that cultural characteristic and cultural distance will not change in this study. The comprehensive cultural distance index (KSI) is adopted to measure cultural distance. According to the method proposed by Kogut and Singh (1988), the KSI is based on the six cultural dimensions identified by Hofstede, and is measured using Eq. (3): d =1

M AN U

where KSi,j denotes the cultural distance between country/region i and country/region j, Ii,d and Ij,d indicate the values of country/region i and j on cultural dimension d, and Vd denotes the variance of the samples on dimension d. 3.3. Measurement index for the volatility of the stock market

TE D

The index measuring the volatility of stock market prices can be divided into two parts. One is the market yield index, and the other is the market volatility index. The index measuring the volatility relevance of the stock market can also be divided into two parts. One is the market yield relevance index, and the other is the market volatility relevance index. (1) Stock market yield Daily data from the main indices of various national stock markets are used, and trading days that are not common to all countries are excluded. The daily logarithmic yield is calculated to obtain better statistical characteristics, as shown in Eq. (4): (4) Rt ,i = ln Indext ,i − ln Indext −1,i ,

EP

where Rt,i denotes the logarithmic yield of national stock market index i on the tth trading day, Indext,i indicates the closing price of the national stock market index i on the tth trading day, and Indext-1 denotes the closing price of the national stock market index i on the (t-1)th trading day. (2) Stock market volatility Stock market volatility measures the volatility of stock prices, which is a measure of the uncertainty of yields. The historical volatility within a specific period is calculated using Eq. (5): SSS (5) VOLi = ⋅T , N −1

AC C

Where VOLi denotes the historical volatility of the national stock market index i, SSS =

n

∑

j =1

( Ri , j − R)2 , Ri,j

indicates the jth logarithmic yield of the national stock index i, R denotes the mean of the logarithmic yield, and T indicates the number of trading days. (3) Yield relevance index The relevant coefficient of the daily logarithmic yield of two national stock market indices is used to measure the degree of relevance of the two stock markets. This is calculated using Eq. (6): (6) ∑ Rmt Rnt , RORinkm,n,t = 2 2 ∑ Rmt ∑ Rnt where RORinkm,n,t denotes the degree of relevance between the stock market yields in country/region m and country/region n in tth year, and Rmt and Rnt indicate the daily logarithmic yields of the stock market index in country/region m and country/region n in tth year, respectively. (4) Volatility relevance index First, the monthly historical volatility is calculated. Then, the relevance coefficient of the monthly historical volatility of two national stock market indices is calculated to measure the volatility relevance of the two stock markets in that year using Eq. (7): (7) ∑VmtVnt , VOLinkm,n,t = 2 2 ∑Vmt ∑Vnt 3

ACCEPTED MANUSCRIPT where VOLinkm,n,t denotes the degree of volatility relevance between the stock markets in country/region m and country/region n in tth year, and Vmt and Vnt indicate the monthly historical volatility of the stock market index in country/region m and country/region n in tth year, respectively. The range of RORinkm,n,t and VOLinkm,n,t is (0, 1). Generally, the closer the absolute values of RORinkm,n,t and VOLinkm,n,t to 1, the stronger the volatility correlation between the two stock markets.

4.

The influence of cultural distance on the volatility of the international stock market

4.1. The influence of culture on the volatility of the national stock market

SC

RI PT

4.1.1. Basic model Inspired by existing researches (Irfan et al., 2002; Gupta and Donleavy, 2009; Flavin et al., 2010; Asaolu and Ogunmuyiwa, 2011; Ulku and Baker, 2014; Rothonis et al., 2016), multiple factors influencing the stock price are taken into account in this study, including economic (GDP per capita), political (political stability), financial market (size of the stock market), cultural (cultural dimensions proposed by Hofstede), and other factors (population and corporate information disclosure). Taking the historical volatility of the national stock market index as the explained variable, GDP per capita, the political stability index, the size of the national stock market, population, and corporate information disclosure as the control variables, and the six cultural dimensions proposed by Hofstede as the explanatory variables, the volatility–cultural dimensions model is presented, as shown in Eq. (8): ln VOLi ,t = α + β1 ln GDPi ,t + β 2 ln POPi ,t + β 3 ln MAKTi ,t + β 4 ln GPSi ,t + β 5 ln INFORM i ,t +

11

∑

,

(8)

M AN U

j =6

β j CULi , d ,t + µi ,t

where VOLi,t denotes the historical stock volatility of the ith country in the tth year, GDPi,t indicates GDP per capita of the ith country in the tth year, POPi,t denotes the total population of the ith country in the tth year, MAKTi,t indicates the total market value of the ith national stock market in the tth year, GPSi,t denotes the political stability index of the ith country in the tth year, INFORMi,t indicates the corporate information disclosure level of the ith country in the tth year, and CULi,d,t denotes the value of the dth cultural dimension of the ith country in the tth year. The data sources and expected effects are shown in Table 1. Table 1 Variables in the volatility–cultural dimensions model. Interpretation

Data source

VOL

Historical volatility of national stock market index

Wind Info

GDP

Gross domestic product per capita

World Bank

The expected symbol is "+". The greater the gross domestic product per capita, the higher the probability that people will invest in the stock market. The indicator has a positive effect on the volatility of the stock market.

POP

Population

World Bank

The expected symbol is "+". The larger the national population, the greater the number of investors. The indicator has a positive effect on the volatility of the stock market.

World Bank

The expected symbol is "–". The larger the scale of the national stock market, the greater the stability of the stock market. The indicator has a negative effect on the volatility of the stock market.

EP Stock market value

AC C

MAKT

Expected effect

TE D

Variable

GPS

Political stability index

WGI (worldwide governance indicators)

INFORM

Corporate information disclosure index

World Bank

CUL

The six cultural dimensions

Hofstede’s official website

The expected symbol is "–". The more stable national political environment, and the better performance of the government, the more stable stock market. The indicator has a negative effect on volatility of the stock market.

the the the the

The expected symbol is "–". The index ranges from 0 to 10. The larger the value, the higher the disclosure level and the more stable the stock market. The expected symbol is unknown.

4.1.2. Sample selection and data processing We used data from 29 countries in the Americas, the Asia-Pacific region, Europe, and the Middle East, namely the United States, Canada, Argentina, Brazil, Mexico, Chile, China, Japan, Korea, Australia, Singapore, Thailand, India, Indonesia, the Philippines, Malaysia, Russia, Germany, France, Spain, Portugal, the Netherlands, Switzerland, Greece, Ireland, Austria, Belgium, Hungary and Poland. We used daily data on major stock market indices of the selected countries from 2010 to 2016, such as the Shanghai Composite Index in China, the Standard & Poor’s 500 Index in the United States, the Paris Cotation Assistée en Continu 40 Index in France, etc. The historical volatilities of the 29 countries from 2010 to 2016 were calculated. Firstly, the daily logarithmic returns of national stock market indices were calculated using Eq. (4). Then, the annual historical volatilities of 4

ACCEPTED MANUSCRIPT each country were obtained using Eq. (5). The descriptive statistics for the sample data are shown in Table 2. Table 2 Descriptive statistics for the sample data in the volatility–cultural dimensions model. Standard Variable Mean Median Maximum Minimum Skewness deviation 0.716460 0.687440 1.718855 -0.186953 0.366057 0.335198 lnVOL

Kurtosis 2.955211

lnGDP

9.872588

10.00977

11.38980

7.204722

1.022156

-0.716540

2.738231

lnPOP

17.63583

17.56595

21.04438

15.33287

1.476647

0.533684

2.736047

26.98270

27.11122

30.93982

23.39833

1.525532

0.014390

3.026232

lnGPS

3.887947

4.128585

4.600359

1.650580

0.647635

-1.095640

3.254390

lnINFORM PDI

1.855888 59.53571

1.945910 61.5

2.302585 104

0.693147 11

0.456010 21.5334

-1.573044 -0.054617

4.650293 2.552442

IDV

49.89286

47

91

14

23.57618

0.155011

1.723540

MAS

55.67857

56

95

14

17.07917

0.012953

3.284357

RI PT

lnMAKT

66.75

67.5

112

8

25.32532

-0.257384

2.239818

LTO

52.92857

49.5

100

20

23.50424

0.255034

1.831546

IVR Number of observations

49.53571

47

97

20

17.70846

0.390198

2.900753

Number of sections

29

203

SC

UAI

M AN U

4.1.3. Regression analysis (1) Stationarity test To avoid spurious regression, stationarity tests were conducted including the LLC test, Breitung test, Hadri test, IPS test, and Fisher-ADF test. The original hypothesis of the LLC test, IPS test, and Fisher-ADF test is that the sequence has a unit root, and thus is nonstationary. The original hypothesis of the Breitung test is that there is no unit root, and thus the sequence is stationary. The results of stationarity tests are shown in Table 3. T (Trend) denotes that the sequence contains a trend term, I (Intercept) indicates that the sequence contains an intercept term, and N (None) denotes that neither type of term is included.

AC C

EP

TE D

Table 3 Results of stationarity tests for the variables in the volatility–cultural dimensions model. Test Variable I and T N I lnVOL 0.66557 (0.7472) -9.12309 (0.0000) -12.4618 (0.0000) lnGDP 6.78779 (1.0000) -10.4372 (0.0000) -10.4087 (0.0000) lnMAKT 0.78837 (0.7848) -7.06876 (0.0000) -11.9035 (0.0000) LLC lnGPS -0.81400 (0.2078) -3.80326 (0.0001) -9.86607 (0.0000) lnPOP 7.84729 (1.0000) -25.1278 (0.0000) -15.0194 (0.0000) lnVOL -2.32820 (0.0100) lnGDP 3.91898 (1.0000) lnMAKT -1.89285 (0.0292) Breitung lnGPS 4.66957 (1.0000) lnPOP 9.51591 (1.0000) lnVOL -2.53455 (0.0056) -2.54074 (0.4131) lnGDP -2.43217 (0.0075) -2.48859 (0.4459) lnMAKT -1.79647 (0.1897) -2.34686 (0.5362) IPS lnGPS -1.76235 (0.2240) -2.35997 (0.5278) lnPOP -7.73605 (0.0000) -2.37885 (0.5158) lnVOL 42.2608 (0.9401) 93.5810 (0.0021) 68.0750 (0.1717) lnGDP 61.0926 (0.3655) 95.9385 (0.0013) 65.6803 (0.2281) lnMAKT 37.2214 (0.9846) 73.5214 (0.0806) 58.8338 (0.4448) Fisher-ADF lnGPS 53.4911 (0.6434) 82.9905 (0.0174) 73.3261 (0.0846) lnPOP 139.981 (0.0000) 166.771 (0.0000) 93.6860 (0.0021)

It can be seen from Table 3 that at the 0.05 confidence level, the logarithmic variables lnVOL, lnGDP, lnPOP, lnMAKT, lnGPS, and lnINFORM are all stationary sequences. Although the volatility of stock market index, GDP per capita and other variables have non-stationary original sequences, they are stationary sequences after logarithmic transformation and can be used for regression analysis. (2) Cointegration test We used the Pedroni test and the Kao test, and the results are shown in Table 4. Table 4 Results of cointegration tests for the variables in the volatility–cultural dimensions model. Test method

Statistics

Statistical value (P-value)

Kao test

ADF

-1.425494 (0.0770)

Panel v-Statistic

-2.871452 (0.9980)

Pedroni test (containing T and I)

Panel rho-Statistic

5.623989 (1.0000)

Panel PP-Statistic

-47.54677 (0.0000)

Panel ADF-Statistic

-4.813440 (0.0000)

Group rho-Statistic

8.597601 (1.0000)

Group PP-Statistic

-46.61653 (0.0000)

Group ADF-Statistic

-12.77646 (0.0000)

5

ACCEPTED MANUSCRIPT It can be seen from Table 4 that although three results of the Pedroni test support the hypothesis that there is no cointegration relationship, the results of other four tests and the Kao test reject the hypothesis. Thus, on the basis of the results of the Pedroni test and the Kao test, it can be inferred that there is a cointegration relationship between volatility and the other panel variables. (3) Regression analysis The volatility–cultural dimensions model shown in Eq. (8) can be developed into Eq. (9) for regression analysis: ln VOLi , t = α + β1 ln GDPi ,t + β 2 ln POPi , t + β3 ln MAKTi ,t + β 4 ln GPSi ,t (9) + β ln INFORM + β PDI + β IDV + β MAS + β UAI . 5

i,t

6

i,t

7

i,t

8

i,t

9

i,t

RI PT

+ β10 LTOi ,t + β11IVRi , t + µi ,t The panel data cover only seven years, while there are large numbers of country samples and variables. Therefore, it is considered that the model has individual effect rather than time effect. The results of the regression analyses of the individual fixed effect and the random effect are shown in Table 5. It can be seen that the R-squared of the individual fixed effect is approximately equal to the random effect, even though the value of the F-statistic is much larger. Table 5 Results of the regression analyses of the individual fixed effect and the random effect. Cross-section fixed Cross-section random Adjusted R-squared

0.545935

Durbin-Watson stat

1.296803

F-statistic

176.2004

Prob(F-statistic)

0.000000

0.548176

SC

0.549051

0.507862 1.296803

627.8099 0.000000

M AN U

R-squared

We used the likelihood ratio test to determine whether there were cross-sectional and time effects. In addition, the Hausman test was performed to further examine whether there was an individual fixed effect or an individual random effect. The results of the likelihood ratio test and the Hausman test are shown in Table 6. The results of the likelihood ratio test show that at the 0.05 confidence level, there is no fixed effect on cross-section and time. The results of the Hausman test show that at the 0.05 confidence level, the original hypothesis that the random effect model is effective is rejected. Thus, a mixed model should be adopted. Chi-Sq. d.f. 28

Prob. 1.0000

Period F

26.217774

6

0.0005

27.341411

5

0.0000

Hausman test

TE D

Table 6 Results of the likelihood ratio test and the Hausman test. Test summary Effect test Chi-Sq. statistic Cross-section F 0.0000000 Likelihood-ratio Cross-section random

The model was analyzed using the generalized least squares method (GLS). Since the sample data represent the case in which the number of sections is much larger than the number of time series, the panel corrected standard errors (PCSE) method is used for the estimation. The results of the regression analysis are shown in Table 7. Table 7 Results of the GLS regression analysis of the volatility–cultural dimensions model. Coefficient

t-Statistic

Prob.

C

-1.627264

-16.70168

0.0000

lnGDP

0.630719

63.56868

0.0000

lnPOP

0.390767

60.69998

0.0000

lnMAKT

-0.355081

-66.27246

0.0000

AC C

EP

Variable

lnGPS

-0.333349

-28.68482

0.0000

lnINFORM

-0.033167

-3.866202

0.0001 0.0000

PDI

0.001994

8.016169

IDV

0.001303

9.256929

0.0001

MAS

0.004039

17.92455

0.0000

UAI

-0.000964

-3.982691

0.0001

LTO

-0.000900

-5.160957

0.0000

IVR

-0.003578

-13.92793

0.0000

R-squared

0.583446

Mean dependent var

0.798743

Adjusted R-squared

0.558544

S.D. dependent var

0.418888

S.E. of regression

0.251381

Sum squared resid

11.62745

F-statistic

23.42906

Durbin-Watson stat

1.414585

Prob(F-statistic)

0.000000

It can be seen from Table 7 that the R-squared is 0.583446 and the adjusted R-squared is 0.558544, which shows that the model explains about 58.34% of the historical volatility of stocks. The value of the F-statistic is 23.42906, and the corresponding P-value is approximately zero, indicating that the model has a good degree of fit.

6

ACCEPTED MANUSCRIPT To ensure the accuracy and significance of the coefficient of each cultural dimension, the GLS estimation was performed six times for the six cultural dimensions and volatility. The results of these regression analyses are shown in Table 8. Table 8 Results of six GLS regression analyses of the volatility and cultural dimensions. Variable

Coefficient

t-Statistic

Prob.

1

PDI

0.002431

14.03415

0.0000

2

IDV

0.001696

11.48174

0.0000

3

MAS

0.003004

13.50548

0.0000

4

UAI

-0.002354

-12.43344

0.0000

5

LTO

-0.004176

-19.51272

0.0000

6

IVR

0.000729

4.599995

RI PT

Regression analysis

0.0000

M AN U

SC

Table 7 and Table 8 show that, except for the variable IVR, the coefficients of other cultural dimensions are consistent, and the corresponding P-values of the variables are all less than 0.05, indicating that the variables have a significant relationship with the volatility of the stock market. Of the control variables, GDP and POP have a positive influence on VOL, while MAKT, GPS, and INFORM have a negative influence, which is consistent with the expected effects. It can be inferred from the results of the regression analyses that an increase in GDP per capita and the total population will increase the level of fluctuations in the national stock market, while an increase in the scale of the stock market, a higher level of corporate information disclosure, and a more stable political environment can reduce fluctuations in the national stock market. Of the cultural dimensions, PDI, IDV, and MAS have a positive effect on VOL, while UAI and LTO have a negative effect. The variable IVR has an inconsistent impact on the volatility of the national stock market in Table 7 and Table 8, so we conducted a comparative analysis to determine the relationship between IVR and the volatility of the national stock market. (4) Comparative analysis Above we used the GLS method and Eviews software to estimate the Eq. (9). In order to test the robustness of the proposed volatility–cultural dimensions model, we replaced the measurement method by the Maximum Likelihood Estimation (MLE) and used the Stata software to estimate the Eq. (9). The results of the MLE regression analysis are shown in the second column in Table 9. Table 9 Results of the MLE regression analysis of the volatility–cultural dimensions model.

C

Times of regression analysis

1

2

-0.7470752

-0.1279831

0.4766822

lnPOP

0.2989701

lnMAKT

-0.2779688

-0.2547085

0.3922792

0.3800061

0.3800123

0.4179322

0.2563442

0.2561324

0.2540012

0.2725013

-0.2573230

-0.2533637

-0.2557440

-0.2546195

-0.2465906

-0.2500202

-0.2550487

PDI

0.0012874

IDV

-0.2367763

-0.2254841

-0.2386526

-0.2020597

-0.2225296

-0.2273099

-0.0792489

-0.0741516

-0.0963592

-0.0718579

-0.0893369

-0.0813073

-

-

-

-

-

0.0008129

-

0.0013041

-

-

-

-

0.0028872

-

-

0.0022891

-

-

-

-0.0016087

-

-

-

-0.0084188

-

-

-0.0008329

-

-

-

-

-0.007163

-

-

-

-

-

-

-0.0036715

AC C IVR

7

-0.1276354

0.4074711

-0.0745012

LTO

6

-0.1084188

0.2711606

lnGPS

UAI

5

-0.1996185

0.409442

lnINFORM

MAS

4

-0.1778898

0.2668467

EP

lnGDP

3

TE D

Variable

-0.0032085

0.0017227

It can be seen from Table 9 that the coefficients of the variables are consistent with that of the GLS estimation method. Specifically, the signs and confidence levels of the variables are similar. To ensure the accuracy and significance of the coefficient of each cultural dimension, the MLE estimation was performed six times for the six cultural dimensions and volatility. The results of these regression analyses are shown in Table 9. All the coefficients of the variables are significant at the 0.05 confidence level, and the variable IVR has a negative impact on the volatility of the national stock market in the seven regression analyses. Therefore, according to the results of GLS and MLE regression analyses, PDI, IDV, and MAS have a positive effect on VOL, while UAI, LTO, and IVR have a negative effect. (5) Sensitivity analysis A sensitivity analysis can be used to understand the uncertainty of variables, and is an important step for improving the model. For a long period of time, some variables may change in different situations. To test the sensitivity of each cultural dimension in the volatility–cultural dimensions model, we conducted the following sensitivity analysis. The corresponding coefficients of variables in the volatility-culture dimensions model are acquired from Table 7, and thus Eq. (10) is obtained.

7

ACCEPTED MANUSCRIPT ln VOLi ,t = −1.627264 + 0.630719 ln GDPi ,t + 0.390767 ln POPi , t − 0.355081ln MAKTi ,t − 0.333349 ln GPSi ,t − 0.033167 ln INFORMi , t + 0.001994PDIi , t + 0.001303IDVi ,t + 0.004039MASi ,t − 0.000964UAIi ,t (10) − 0.000900LTOi , t − 0.003578IVRi , t .

M AN U

SC

RI PT

The average data of the 29 countries from 2010 to 2016 were used as the sample data. A single factor analysis method was performed. We varied the value of each cultural dimension from -25% to 25% (5% step), and observed how the VOL changed. The other inputs remained the same. The sensitivity of each cultural dimension is shown in Figure 1.

Fig. 1 Sensitivity analysis of cultural dimensions in the volatility–cultural dimensions model.

AC C

EP

TE D

It can be seen from Figure 1, the sensitivity of cultural dimensions from high to low is: MAS, IVR, PDI, IDV, UAI, LTO. The corresponding sensitivity coefficients of cultural dimensions are 2.310961, 1.732232, 1.221656, 0.646232, 0.629669 and 0.465170, respectively. It is obvious that VOL is more sensitive to the change of MAS and less sensitive to the change of LTO. The following conclusions can be drawn from the previous analysis. In countries with higher PDIs, the uneven distribution of power is more readily accepted by the residents, indicating that they acknowledge the social inequity and usually tend to pursue excess returns in an attempt to assert their independence, which increases the volatility of the stock market. Therefore, countries with higher PDIs, such as the Philippines (94), Russia (93), China (80), Indonesia (78), and India (77) tend to experience higher levels of volatility in their national stock markets. In countries with higher IDVs, the residents tend to pursue individual ideals and freedom, and the collective constraint upon individuals is lower. Residents in such countries usually appear to be overconfident, and thus tend to underestimate the risks involved in investing. Therefore, countries with higher IDVs, such as Australia (90), Hungary (80), the Netherlands (80), Belgium (75), and France (71) tend to experience higher levels of volatility in their national stock markets. In countries with higher MASs, the residents tend to pursue personal achievement and wealth, with lower expectations regarding their quality of life. Residents in such countries are usually able to bear higher risks and remain highly competitive. On the contrary, in countries with lower MASs, people pay more attention to caring about others and pursuing a higher quality of life. The results of the regression analysis indicate that countries with higher MASs, such as Japan (95), Hungary (88), Austria (79), and Switzerland (70) tend to experience higher levels of volatility in their national stock markets. In countries with higher UAIs, the residents are more prepared for uncertainty and ambiguity. Residents in such countries are strongly aware of the need for risk avoidance. In contrast, in countries with lower UAIs, the residents tend to ignore uncertainty, and thus show a higher risk preference. Hence, their investments often aggravate the volatility of the stock market. Therefore, countries with lower UAIs, such as China (30), Ireland (35), India (40), the Philippines (44), and Indonesia (48) tend to experience higher levels of volatility in their national stock markets. In countries with higher LTOs, the residents prefer to adopt a conservative investment strategy in pursuit of long-term value, while in countries with lower LTOs, the residents are more interested in short-term returns and usually adopt an aggressive investment strategy, which further intensifies the volatility of the stock market. Therefore, countries with lower LTOs, such as Australia (21), Ireland (24), the Philippines (27), Portugal (28), Thailand (32), and Poland (38) tend to experience higher levels of volatility in their national stock markets. In countries with higher IVRs, the residents have less self-restraint and are usually keener on material enjoyment and emotional indulgence. The results of the regression analysis indicate that countries with the lower IVRs, such as Russia (20), China (23), India (26), Hungary (31), and Indonesia (38) tend to experience higher levels of volatility in their national stock markets. 4.2. The influence of cultural distance on the volatility relevance of the international stock market 4.2.1. Basic model This study considers the influence of cultural differences in various countries on the volatility relevance of the 8

ACCEPTED MANUSCRIPT stock markets to examine whether the cultural similarity intensifies the volatility relevance of the national stock market. Based on the standard gravity model and following the approach of He et al. (2016), the cultural distance is chosen as the repulsive force variable and the scale of the national stock market is chosen as the attractive force variable. The proposed gravity model of volatility relevance–cultural distance is shown in Eq. (11): VOLinki , j ,t = β 0 + β1 ln CDi , j ,t + β 2 ln MAKTi ,t + β 3 ln MAKTj ,t , (11) n + β k Controlled V ariablei , j ,m + µi , j

∑ k =4

Table 10 Variables in the volatility relevance–cultural distance model.

VOLinki,j,t

CDi,j,t

Data source Wind Info

Hofstede’s official website

The market value of the stock markets of country i and country j, respectively

Wind Info

GDPi,j,t

Difference in the growth rate of GDP per capita between country i and country j

World Bank

GDi,j

Geographical distance between country i and country j

CEPII Database

LANG

Official language of country i and country j

CEPII Database

CONT

Border of country i and country j

CEPII Database

TE D

MAKTi,t MAKTj,t

EP

Expected effect

SC

Interpretation Correlation coefficient of the volatility relevance of the national stock markets of country i and country j The comprehensive cultural distance between country i and country j

The expected symbol is unknown.

M AN U

Variable

RI PT

where VOLinki,j,t denotes the volatility relevance between the national stock markets of country i and country j, CDi,j indicates the comprehensive cultural distance between country i and country j, MAKTi,t and MAKTj,t denote the size of the national stock markets of country i and country j, respectively, and Controlled Variablei,j,m indicates the mth control variable. The following control variables were used: culture (common language), distance (geographical distance, shared border), and finance (GDP growth rate). The data sources and expected effects of the variables are shown in Table 10.

The expected symbol is "+". The size of the national stock market plays a positive role in the model. The greater the value of the stock market, the closer the volatility linkage between the national stock markets. The expected symbol is "–". The smaller the difference in the growth rate of GDP per capita between the two countries, the more similar the economic structure of the two countries, and the closer the volatility linkage between the two stock markets. The expected symbol is "–". Geographical distance is the repulsive factor in the traditional gravity model. The larger the geographical distance between the two countries, the weaker the connection between the two national stock markets. The expected symbol is "+". If the official language of the two countries is the same, the value is 1; otherwise, the value is 0. A common language can enhance economic connection and promote similar investor preferences. The expected symbol is "+". If the two countries have a shared border, the value is 1; otherwise, the value is 0. Stock markets in the same area might have higher volatility relevance.

AC C

4.2.2. Regression analysis (1) Sample selection and data processing The correlation coefficients of the yields and volatilities of the 29 countries were calculated using Eq. (6) and Eq. (7), respectively. We used the daily logarithmic return to calculate the degree of the yield relevance and used the monthly volatility to calculate the degree of volatility relevance. The final sample included data for 406 country pairs from 2010 to 2016, with a total of 2,842 group observations. Eq. (3) was used to calculate the comprehensive cultural distance index using data for the Hofstede cultural dimensions. Descriptive statistics for the sample data are shown in Table 11. Table 11 Descriptive statistics for the sample data in the volatility relevance–cultural distance model. lnCD lnGD lnMAKTI lnMAKTJ lnGDP VOLink 2.00000 3.769087 26.9136 27.05222 4.163751 0.454261 Mean 0.433825 1.84689 3.934029 27.34711 26.93865 4.358139 Median 0.984574 5.236637 4.288860 29.73369 30.93982 4.939289 Maximum 0.000482 0.020553 2.238130 23.39833 23.39833 0.631223 Minimum Standard 0.208540 1.053515 0.429091 1.465134 1.573668 0.563822 deviation 0.274491 0.560593 -1.300544 -0.468531 0.386742 -1.434046 Skewness 2.546547 2.957322 4.050351 2.300453 3.411706 5.602580 Kurtosis

(2) Regression analysis Stationarity tests were performed for all variables in the volatility relevance–cultural distance model and it was 9

ACCEPTED MANUSCRIPT

RI PT

found that all variables belonged to stationary series at the 0.05 confidence level. Then, cointegration tests were carried out. The panel v-statistics and group rho-statistics supported the original hypothesis, while the results of other tests rejected the hypothesis. It can be inferred that there is a cointegration relationship among the variables, and the regression analysis can be conducted. The sample data is panel data with a time span of seven, and the number of sections is 406 country pairs. Overall, the sample data has a short time span but a large number of sections. The P-value of the likelihood ratio test is far less than 0.05, which rejects the hypothesis of the hybrid model. The P-value of the Hausman test is far less than 0.05, which rejects the original hypothesis that the random effect model is effective. Therefore, the individual fixed effect model should be adopted. Since the number of sections is larger than the number of series in the sample data, the heteroscedasticity of different sections should be taken into account for the regression analysis. Therefore, the PCSE method is applied. The results of the regression analysis using GLS estimation are shown in Table 12. Table 12 Results of regression analysis of the volatility relevance–cultural distance model. Variable

Coefficient

t-Statistic

C lnCD

0.328912

3.172925

-0.009105

-2.931271

lnMAKTI

0.001125

2.347877

0.005009

2.42858

lnGDP

-0.003366

-2.248738

0.0034

0.0058

0.0052

0.0069

SC

lnMAKTJ

Prob.

0.0015

lnGD

-0.002626

-2.622587

0.0043

LANG

0.033140

-1.994718

0.0462

CONT

0.001942

-1.178515

0.0858

0.469436

Mean dependent var

0.454156

0.424463

S.D. dependent var

0.208579

S.E. of regression

0.158237

Sum squared resid

65.57677

F-statistic

10.43809

Durbin-Watson stat

1.200559

Prob(F-statistic)

0.000000

M AN U

R-squared Adjusted R-squared

TE D

It can be seen that the R-squared of the volatility relevance–cultural distance model is 0.469436, the value of the F-statistic is 10.43809, and the P-value is approximately 0, indicating a good fit for the model. Except for the coefficients of the two dummy variables for common language and shared boundary, all variables are highly significant. It can be inferred from the signs of the coefficients that countries with large stock markets have strong volatility correlation. A reduction in cultural distance and geographical distance and a similar economic structure could enhance the volatility relevance between national stock markets. (3) Robustness test To test the robustness of the volatility relevance–cultural distance model, we replaced the volatility relevance by the yield relevance. The proposed yield relevance–cultural distance model is given by Eq. (12): RORinki, j ,t = β 0 + β1 ln CDi , j ,t + β 2 ln MAKTi,t + β 3 ln MAKTj ,t . (12) + β 4GDPi , j ,t + β5GDi, j + β 6 LANG + β 7CONT + µi , j

EP

Regression analysis was undertaken for Eq. (12) and the results are shown in Table 13. Table 13 Results of regression analyses of the yield relevance–cultural distance model. Coefficient

t-Statistic

Prob.

C

0.309200

2.993859

0.0028

AC C

Variable lnCD

-0.008982

-2.90403

0.0037

lnMAKTI

0.004114

2.344728

0.0303

lnMAKTJ

0.004233

2.103493

0.0355

lnGDP

-0.003585

2.331257

0.0183

lnGD

-0.001861

1.531379

0.0595

LANG

0.033140

1.838328

0.0401

CONT

0.001942

1.224540

0.0822

R-squared

0.468649

Mean dependent var

0.454156

Adjusted R-squared

0.424049

S.D. dependent var

0.208579

S.E. of regression

0.158294

Sum squared resid

65.67401

F-statistic

10.50778

Durbin-Watson stat

1.198917

Prob（ （F-statistic） ）

0.000000

It can be seen that the R-squared of the yield relevance–cultural distance model is 0.468649, the value of the F-statistic is 10.50778, and the P-value is approximately 0, indicating a good fit for the model. The regression results in relation to yield relevance are basically consistent with those for volatility relevance, and the coefficients of the control variables are also aligned with the expected symbols and level of significance. This indicates that the 10

ACCEPTED MANUSCRIPT

RI PT

volatility relevance–cultural distance model has a good robustness, and cultural distance has a significant negative effect on volatility relevance among national stock markets. In other words, a reduction in the cultural distance between countries increases the volatility relevance of their national stock markets. The greater the cultural proximity, the higher the volatility correlation between national stock markets, and the closer the national stock markets are. (4) Sensitivity analysis To test the sensitivities of variables in the volatility relevance–cultural distance model, we conducted a sensitivity analysis. The corresponding coefficients of variables in the volatility relevance–cultural distance model are acquired from Table 12, and thus Eq. (13) is obtained. VOLinki , j ,t = 0.328912 − 0.009105 ln CDi , j ,t + 0.001125 ln MAKTi ,t + 0.005009 ln MAKT j , t (13) − 0.003366GDPi , j ,t − 0.002626GDi , j + 0.033140LANG + 0.001942CONT

M AN U

SC

The average data of the 29 countries from 2010 to 2016 were used as the sample data. A single factor analysis method was conducted. We varied the value of each variable from -25% to 25% (5% step), and observed how the VOLink changed. The other inputs remained the same. The sensitivity of each variable (except for the virtual variables) is shown in Figure 2.

Fig. 2 Sensitivity analysis of variables in the volatility relevance–cultural distance model.

AC C

EP

TE D

It can be seen from Figure 2, the sensitivity of variables from high to low is: CD, MAKTJ, GDP, GD, MAKTI. The corresponding sensitivity coefficients of variables are 0.203081, 0.112502, 0.075288, 0.058758 and 0.025219, respectively. It is obvious that VOLink is more sensitive to the change of CD and less sensitive to the change of MAKTI. 4.2.3 The influence of market liquidity on the relationship between volatility and cultural distance We also examined whether the influence of cultural distance might be weakened by certain factors, such as the liquidity of the stock market. The liquidity of the national stock market is measured by the ratio of the annual trade volume to the total market value. We divided the 29 national stock markets into two groups based on the level of liquidity. Active markets had a liquidity ratio of 0.9 or greater, including South Korea, the United States, China, Germany, Japan, India, Switzerland, Greece, Spain, France, Hungary, and the Netherlands, while passive markets had a liquidity ratio of less than 0.9, including Mexico, Malaysia, Australia, Austria, Belgium, Brazil, Indonesia, Ireland, Argentina, the Philippines, Poland, Portugal, Russia, Singapore, Canada, Chile, and Thailand. The data for the active markets include a total of 462 group observations of 66 country pairs from 2010 to 2016, while the data for the passive markets include a total of 952 group observations of 136 country pairs from 2010 to 2016. GLS regression analysis was performed for each group, and the results are shown in Table 14. It can be seen from Table 14 that compared with the active market, the absolute values of the coefficients of the cultural distance variables are smaller in the passive market but the absolute values of the coefficients of the geographical distance variables are larger. This implies that cultural distance plays a more important role in active markets than in passive markets, while geographical distance plays a more important role in passive markets. Further, cultural distance is significant in both regression results. Therefore, regardless of the liquidity of the stock market, a smaller cultural distance could enhance the volatility linkage between national stock markets. Specifically, if the liquidity of the national stock markets is high, the influence of cultural distance on the volatility relevance between the national stock markets would be strengthened, while that of geographical distance would be weakened. 4.3. The influence of cultural distance on foreign securities investment 4.3.1. Basic model Previous studies have shown that both international securities investment and international currency exchange have an effect on the volatility relevance between national stock markets. International securities investment is closer to the international stock market. Thus, we examined the relationship between national culture and 11

ACCEPTED MANUSCRIPT international securities investment. Table 14 Results of regression analyses of active and passive markets. Variable

Active market

C

0.9182799

Passive market 0.9493781

lnCD

-0.0101230

-0.0057059 0.0045705

lnMAKI

0.0034831

lnMAKTJ

0.0018251

0.0040226

lnGDP

-0.0218466

-0.0219446

-0.00099857

-0.0031865

0.0192672

0.0209874

CONT

0.0189612

0.0059207

R-squared

0.428248

0.412171

Adjusted R-squared

0.399116

0.392517

Durbin–Watson stat

1.353183

1.421361

F-statistic

18.43089

14.34257

Prob (F-statistic)

0.000000

0.000000

RI PT

lnGD LANG

M AN U

SC

The proportion of total Chinese foreign securities investment that is invested in the country is selected as the explanatory variable, comprehensive cultural distance is chosen as the repulsive force variable, and geographical distance, the corporate tax rate, and political stability of the country in which the investment is made are chosen as the control variables. The proposed gravity model of foreign securities investment–cultural distance is given by Eq. (14): (14) ln(1 + FPI ch,i ) = α + β1 ln KS ch,i + β 2 ln(GDPch ⋅ GDPi ) + β 3 ln TAX i + β 4 ln GPSi + µ , where FPIch,i denotes the proportion of total Chinese foreign securities investment that is invested in the ith country, KSch,i indicates the cultural distance between China and country i, GDPch and GDPi denote the GDP of China and country i, respectively, TAXi indicates the corporate tax rate of the ith country, and GPSi denotes the political stability index of the ith country. The data sources and expected effects of the variables are shown in Table 15.

AC C

EP

TE D

Table 15 Variables in the foreign securities investment–cultural distance model. Variable Interpretation Data source Expected effect The proportion of total Chinese foreign securities investment that Wind Info FPIch,i is invested in the ith country. Comprehensive cultural distance World Bank The expected symbol is unknown. KSch,i between China and country i. The expected symbol is “+”. GDP always plays a GDP of China World Bank GDPch positive role in this model. The expected symbol is “+”. Generally, the higher GDP of country i World Bank the GDP, the greater the level of Chinese securities GDPi investment in the country. The expected symbol is “–”. The higher the Corporate tax rate of country i World Bank corporate tax rate, the lower the level of Chinese TAXi securities investment in the country. Official website The expected symbol is “+”. The more stable the Level of political stability of of WGI (global political situation and the better the government’s GPSi country i governance performance, the greater the level of Chinese indicator) securities investment in the country.

4.3.2. Sample selection and regression analysis This study examines how cultural distance affects the scale of Chinese securities investment in other countries. We selected a total of 24 countries, that is, the United States, Chile, Austria, Hungary, the Philippines, Russia, Poland, Thailand, Mexico, India, Belgium, Brazil, Spain, Indonesia, Korea, the Netherlands, Ireland, Switzerland, Canada, Singapore, France, Germany, Australia and Japan, as the sample countries and analyzed data on Chinese securities investment in those countries in 2016. The descriptive statistics for the sample data are shown in Table 16, where lnGDPMU denotes the product of the GDP of China and that of the country in which investments were made. Table 16 Descriptive statistics for the sample data in the foreign securities investment–cultural distance model. ln(1+FPI) lnKS lnGDPMU lnGD lnGPS 0.034948 0.364146 155.8871 7807.775 1.448562 Mean 0.007652 0.378267 156.4413 7808.242 1.561960 Median 0.667734 0.540884 171.6772 19079.88 1.865600 Maximum 0.000761 0.168992 144.9355 955.6511 0.586587 Minimum Standard 0.134230 0.104766 6.296403 4324.775 0.349580 deviation 4.491840 –0.287257 0.476074 0.925758 –0.865416 Kurtosis 21.47462 2.138791 2.987892 3.989461 2.788687 Skewness

The least squares method was performed for the regression analysis, and we tested for heteroscedasticity. The 12

ACCEPTED MANUSCRIPT results are shown in Table 17.

0.0008 0.0072 0.0000 0.0264 0.0417 0.0742

RI PT

Table 17 Results of heteroscedasticity tests. Heteroskedasticity Test: Breusch-Pagan-Godfrey 7.059944 Prob. F (5,18) F-statistic 15.89489 Prob. Chi-Square (5) Obs*R-squared 41.85128 Prob. Chi-Square (5) Scaled explained SS Heteroskedasticity Test: Harvey 3.33247 Prob. F (5,18) F-statistic 11.53691 Prob. Chi-Square (5) Obs*R-squared 10.03831 Prob. Chi-Square (5) Scaled explained SS Heteroskedasticity Test: Glejser 12.48068 Prob. F (5,18) F-statistic 18.62709 Prob. Chi-Square (5) Obs*R-squared 25.81075 Prob. Chi-Square (5) Scaled explained SS Heteroskedasticity Test: White 47.5584 Prob. F (20,3) F-statistic 23.92454 Prob. Chi-Square (20) Obs*R-squared

0.0000 0.0023 0.0001

0.0043 0.2457

Table 18 Results of WLS regression analysis.

SC

It can be seen from Table 17 that there is heteroscedasticity in the residual sequence. Therefore, the weighted least squares (WLS) method is employed to correct for heteroscedasticity of the cross-sectional data. The results of the WLS regression analysis are shown in Table 18.

Coefficient

t-Statistic

Prob.

C

-4.894607

-114.9698

0.0000

lnKS

-0.059826

-4.037593

0.0037

M AN U

Variable

lnGDPMU

0.036947

94.85277

0.0000

lnTAX

-0.501111

-30.94289

0.0000

lnGPS

0.032877

3.100420

0.0147

R-squared

0.999417

Mean dependent var

0.117394

Adjusted R-squared

0.999126

S.D. dependent var

0.401087

S.E. of regression

0.009790

Akaike info criterion

-6.131156

Sum squared resid

0.000767

-5.913868

-6.175818

3429.558

Durbin-Watson stat

1.008042

0.000000

Weighted mean dep.

0.247136

TE D

Schwarz criterion

Hannan-Quinn criter.

Log likelihood

44.85251

F-statistic

Prob(F-statistic)

AC C

EP

It can be seen from Table 18 that the R-squared, F-statistic, and corresponding P-value of the model are 0.999417, 3429.558, and approximately 0, respectively, which indicates that the regression equation has a good degree of fit. The P-values of all of the variables are approximately 0, indicating that the significance of all of the variables is high. In terms of the signs of the coefficients of the variables, cultural distance and corporate tax rate have a negative effect on Chinese international securities investment, while GDP and political stability have a positive effect. In summary, China is more willing to invest in countries with high cultural proximity, a low corporate tax rate, and high political stability. The above analysis shows that cultural distance has a significant negative impact on foreign securities investment and volatility relevance among national stock markets. Table 19 shows the top ten countries among the 24 sample countries in which China invested in 2016 in terms of volatility relevance and proportion of Chinese foreign securities investment. Table 19 The top ten countries in terms of proportion of Chinese foreign securities investment and volatility relevance. Ranking Proportion of Chinese foreign securities investment Volatility relevance United States Germany 1 Japan Spain 2 Australia Mexico 3 Germany India 4 Singapore Holland 5 Canada South Korea 6 Switzerland Japan 7 Ireland United States 8 Holland Singapore 9 South Korea Russia 10

The United States, Japan, Germany, Singapore, Holland, and South Korea are among the top ten countries in terms of both volatility relevance and proportion of Chinese securities investment, which indicates the close relationship between the scale of securities investment and volatility relevance, and further demonstrates the 13

ACCEPTED MANUSCRIPT negative impact of cultural distance on international securities investment and volatility relevance between national stock markets.

5.

Conclusions

M AN U

SC

RI PT

Culture is defined as the norms of behaviors and values of specific countries or groups. Investors’ investment psychology, response to information, and investment behavior are all influenced by the national culture. In this study, a volatility–cultural dimensions model is proposed to examine the influence of national culture on the volatility of the national stock market, and a volatility relevance–cultural distance model is proposed to explore the influence of cultural distance on the volatility relevance of the international stock market. The following conclusions can be drawn. (1) National culture has a significant impact on the volatility of the national stock market. Specifically, PDI, IDV, and MAS have a positive impact on the volatility of the national stock market, while UAI, LTO and IVR have a negative impact. In summary, countries with a high PDI, IDV, and MAS, and a low UAI, LTO and IVR experience more volatile national stock markets. (2) Cultural distance is an important factor affecting the volatility relevance between national stock markets, and has a negative impact. In other words, decreasing cultural distance enhances the correlation between national stock markets in terms of volatility. This is mainly because a reduction in cultural distance can reduce the transaction costs and information coordination costs of investors, while investors from countries with high cultural proximity have similar values, risk preferences, and information processing styles. Further, the impact of cultural distance on the correlations in terms of volatility on the international stock market does not disappear with an increase in the liquidity of the stock market. Cultural distance exerts a more significant impact than geographical distance on the volatility linkages between national stock markets with high liquidity. (3) Cultural distance is an important factor influencing international securities investment, and increasing cultural distance will inhibit the level of investment. When investing in the international securities market, investors prefer to choose countries with greater cultural proximity to their own country. There is also a close relationship between the scale of international securities investment and the volatility of stock markets. This indicates that cultural distance has a negative impact on both international securities investment and the volatility relevance in the stock market. In future research, we will further investigate the relationship between culture and investor sentiment, the relationship between culture and herd effect, etc.

References

AC C

EP

TE D

Aggarwal, R., Goodell, J.W., 2010. Financial markets versus institutions in European countries: Influence of culture and other national characteristics. International Business Review, 19(5), 502-520. Anderson, C.W., Fedenia, M., Hirschey, M., Skiba, H., 2011. Cultural influences on home bias and international diversification by institutional investors. Journal of Banking and Finance, 35(4), 916-934. Asaolu, T.O., Ogunmuyiwa, M.S., 2011. An econometric analysis of the impact of macroecomomic variables on stock market movement in Nigeria. Asian Journal of Business Management, 3(1), 72-78. Bernanke, B.S., Kuttner, K.N., 2003. What Explains the Stock Market's Reaction to Federal Reserve Policy? Journal of Finance, 60(3), 1221–1257. Beugelsdijk, S., Frijns, B., 2010. A cultural explanation of the foreign bias in international asset allocation. Journal of Banking and Finance, 34(9), 2121–2131. Boadi, I., Amegbe, H., 2017. The link between quality of governance and stock market performance: International level evidence. European Journal of Government and Economics 6(1), 78-101. Cai, B.M., Cai, C.X., Keasey, K., 2014. Influence of cultural factors on price clustering and price resistance in china's stock markets. Accounting and Finance, 47(4), 623-641. Campbell, J.Y., Ammer, J., 1993. What moves the stock and bond markets? A variance decomposition for long-term asset returns. Journal of Finance, 48(1), 3-37. Chan, K., Covrig, V., NG, L., 2005. What determines the domestic bias and foreign bias? Evidence from mutual fund equity allocations worldwide. The Journal of Finance, 60(3), 1495-1534. Chang, C.H., Lin, S.J., 2015. The effects of national culture and behavioral pitfalls on investors' decision-making: Herding behavior in international stock markets. International Review of Economics and Finance, 37, 380-392. Contractor, F.J., Lahiri, S., Elango, B., Kundu, S.K., 2014. Institutional, cultural and industry related determinants of ownership choices in emerging market FDI acquisitions. International Business Review, 23(5), 931-941. Countryman, A.M., Narayanan, B.G., 2017. Price volatility, tariff structure and the special safeguard mechanism. Economic Modelling, 64, 399–408 Drogendijk, R., Slangen, A., 2006. Hofstede, Schwartz, or managerial perceptions? The effects of different cultural distance measures on establishment mode choices by multinational enterprises. International Business Review, 15(4), 361-380. Dutta, N., Mukherjee, D., 2015. Cultural traits and stock market development: an empirical analysis. Journal of Entrepreneurship and Public Policy, 4(1), 33-49. Eun, C.S., Wang, L., Xiao, S.C., 2015. Culture and R 2. Journal of Financial Economics, 115(2), 283-303. Flavin, T.J., Hurley, M.J., Rousseau, F., 2010. Explaining stock market correlation: A gravity model approach. Manchester School, 70(S1), 87-106. 14

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Fleischer, P., 2003. Volatility and information linkages across markets and countries. Australian Journal of Management, 28(3), 251-272. Fleming, J., Kirby, C., Ostdiek, B., 1998. Information and volatility linkages in the stock, bond, and money markets. Journal of Financial Economics, 49(1), 111-137. Guiso, L., Sapienza, P., Zingales, L., 2008. Does culture affect economic outcomes? Research of Institutional Economics, 20(2), 23-48. Gupta, R., Donleavy, G.D., 2009. Benefits of diversifying investments into emerging markets with time-varying correlations: An Australian perspective. Journal of Multinational Financial Management, 19(2), 160-177. He, Q., Korhonen, I., Guo, J., Liu, F., 2016. The geographic distribution of international currencies and RMB internationalization. International Review of Economics and Finance, 42(4), 442-458. Hofstede, G.H., 1984. Culture's consequences: International differences in work-related values. Sage Publications. Irfan, C.M., Nishat, M., Sharif, H. 2002. Key fundamental factors and long-run price changes in an emerging market—A case study of Karachi Stock Exchange. Pakistan Development Review, 41(4), 517-533. Jaffe, J., Westerfield, R., 1985. The week‐end effect in common stock returns: the international evidence. Journal of Finance, 40(2), 433-454. Junior, L., Mullokandov, A., Kenett, D., 2015. Dependency relations among international stock market indices. Journal of Risk and Financial Management, 8(2), 227-265. Kogut, B., Singh, H., 1988. The effect of national culture on the choice of entry mode. Journal of International Business Studies, 19(3), 411-432. Liu, L., 2013. International stock market interdependence: are developing markets the same as developed markets? Journal of International Financial Markets Institutions and Money, 26(1), 226-238. Long, J.B.D., Shleifer, A., Summers, L.H., Waldmann, R.J., 1990. Positive feedback investment strategies and destabilizing rational speculation. Journal of Finance, 45(2), 379-395. Lucey, B.M., Zhang, Q.Y., 2010. Does cultural distance matter in international stock market comovement? Evidence from emerging economies around the world. Emerging Markets Review, 11(1), 62-78. Pineiro-Chousa, J., 2017. Influence of social media over the stock market. Psychology and Marketing, 34(1), 101-108. Pretorius, E., 2002. Economic determinants of emerging stock market interdependence. Emerging Markets Review, 3(1), 84-105. Rothonis, S., Tran, D., Wu, E., 2016. Does national culture affect the intensity of volatility linkages in international equity markets? Research in International Business and Finance, 36, 85-95. Rozeff, M.S., 1974. Money and stock prices: Market efficiency and the lag in effect of monetary policy. Journal of Financial Economics, 1(3), 245-302. Sabri, N.R., 2002. Increasing linkages of stock markets and price volatility. Finance, 16, 349–374. Schuppli, M., Bohl, M.T., 2010. Do foreign institutional investors destabilize China’s A-share markets? Journal of International Financial Markets Institutions and Money, 20(1), 36-50. Singh, V., Li, B., Roca, E., 2017. How pervasive is the effect of culture on stock market linkages? Evidence across regions and economic cycles. Applied Economics, 49(42), 1-22. Sondergaard, M., 1994. Research note: Hofstede's consequences: A study of reviews, citations and replications. Organization Studies, 15(3), 447-456. Sorge, A., 1980. Culture's consequences: international differences in work-related values by Geert Hofstede. Social Science Electronic Publishing, 36(1), 129-130. Tung, N.T., 2013. Conflicts and multicultural team: Developing competencies for managers. Lahden Ammattikorkeakoulu, 1-45. Ulku, N., Baker, S., 2014. Country world betas: the link between the stock market beta and macroeconomic beta. Finance Research Letters, 11(1), 36-46. Walti, S., 2005. The macroeconomic determinants of stock market synchronization. Journal of International Banking Law, 11(10), 436–441. Wijayana, S., Gray, S.J., 2018. Capital market consequences of cultural influences on earnings: the case of cross-listed firms in the U. S. stock market. International Review of Financial Analysis, 57, 134-147. Yeganeh, H., 2011. A generic conceptualization of the cultural distance index: Application to Schwartz's and Hofstede's frameworks. Journal of Strategy and Management, 4(4), 325-346.

15

ACCEPTED MANUSCRIPT Highlights • The impact of cultural dimensions on the volatility of the national stock market is explored. • The influence of cultural distance on the volatility relevance of the international stock market is

AC C

EP

TE D

M AN U

SC

RI PT

examined. • The influence of cultural distance on foreign securities investment is discussed.