KAFM-WF001

Corporate International Diversification and Stock Liquidity:
The Effect of the Informational Efficiency of Stock Price

郭チャリ・畠田敬
2020年 2月 27日

Corporate International Diversification and Stock Liquidity:
The Effect of the Informational Efficiency of Stock Price
Charee Kwak
Takashi Hatakeda

Abstract
This study examines the effect of corporate international diversification on stock
liquidity. Previous literature suggests that an improvement in the informational efficiencyof stock price increases liquidity of the stock, as it reduces risk of trading the stock. We
conjecture that the relationship between corporate international diversification and stock
liquidity depends on whether overseas expansion increases or decreases the informational
efficiency of stock price. If corporate international diversification enhances theinformational efficiency of stock price, it would improve liquidity of the stock, and viceversa. The results of empirical analysis using a sample of listed Japanese firms show that
corporate international diversification is associated with greater stock liquidity. In
addition, the effect of corporate international diversification on stock liquidity becomes
weaken and statistically insignificant, after controlling for the effect of the informational
efficiency of stock price. These results indicate that internationally diversified firms
would experience the greater stock liquidity and the greater liquidity is due to the effectof informational efficiency of their stock prices.

1. Introduction
This

study

investigatestherelationshipbetweencorporateinternational

diversification and stock liquidity. Stock liquidity has been received considerable
attention from the market microstructure and corporate finance literature, as it would be
one of matter of concerns of business management. Previous studies on stock liquidity
suggest that the level of stock liquidity influence stock return or firm value (e.g., Amihudand Mendelson, 1986; Amihud, 2002, Faucault el al., 2013). Thus, several studies havebeen identified the factors that influence the liquidity of stocks. However, the effects ofcorporate international diversification on stock liquidity has been unexplored. In thisstudy, we focus on the differences in stock liquidity due to firm’s overseas expansion. We
infer that there is a significant relationship between corporate international
diversification and stock liquidity, based on the relationship between informational
efficiency of stock price and stock liquidity.
Informational efficiency of stock price is positively related to stock liquidity. The
informational efficiency of stock price contributes to the better predictability of the stock
return, thereby reducing trading risk of the stock. Thus, the greater informational
efficiency of stock price improves liquidity of the stock. Previous studies suggest thedeterminants of stock liquidity on the basis of the relationship between the informational
efficiency of stock price and stock liquidity. The determinants suggested by these studies,such as informed investors, investment horizon, corporate governance, and productmarket power, have some effects on the degree of the informational efficiency of stock
price, thereby influencing liquidity of the stock. These studies argue that thedeterminants that enhance the informational efficiency of stock price have a positive
impact on stock liquidity. In contrast, if they lower the informational efficiency of stock
price, it would result in less liquidity of the stock.
Based on the positive relation between the informational efficiency of stock price andstock liquidity, we conjecture that corporate international diversification also affect stock
liquidity, as it is expected to have some effects on the degree of informational efficiency ofstock price. The expected effects of corporate international diversification on theinformational efficiency of stock price are conflicting. Firm’s overseas expansion may
increase the complexity in operations and information asymmetry between insiders andoutside investors, thereby decreasing the informational efficiency of stock price. However,
if managers of internationally diversified firms have the greater incentive to reduce the2information asymmetry between them and outside investors, they would release better
corporate disclosure which contributes to the greater stock liquidity. Therefore, weconstruct conflicting hypotheses about the relationship between corporate international
diversification and stock liquidity. The relationship would be positive or negative
depending on how the degree of the informational efficiency of stock price changes bycorporate international diversification.
To test our hypotheses, we examine how corporate international diversification isassociated with stock liquidity, with the sample of Japanese listed firms from 2004 to 2016.
We use the three measures of stock liquidity, Amihud’s relative illiquidity, the quoted
spread, and the effective spread. The measure of overseas business activities is the ratioof foreign subsidiaries to total subsidiaries. Our regression results show that firms withhigher foreign subsidiaries ratio exhibit lower stock liquidity measures, suggesting that
corporate international diversification is associated with greater stock liquidity. Thisfinding is robust to different estimation methods including fixed-effects regression andtwo-step efficient GMM. In addition, we find that the positive relation between corporate
international diversification and stock liquidity becomes weaken and statisticallyinsignificant, after controlling the effect of the informational efficiency of stock price. The
greater stock liquidity of internationally diversified firms results from the effect of
informational efficiency of their stock price rather than the independent effect of corporate
international diversification.
This study provides an empirical evidence for the difference in the level of stock
liquidity between Japanese multinational firms and domestic firms. This contributes tothe on-going debate on the effect of overseas business activities on various corporate
outcomes, because the relationship between multinational operations and stock liquidity
has received less attention. The finding of the greater stock liquidity of Japanesemultinational firms may imply that firms can enhance their stock liquidity by overseas
expansion. However, our additional finding suggests that the positive effect of corporate
international diversification on stock liquidity is almost derived from the effect of theinformational efficiency of stock price. This provides an implication that firms would haveto make an effort to improve the informational efficiency of their stock prices to increaseliquidity of their stocks, when they expand their business activities overseas.
The remainder of this paper is organized as follows. Chapter 2 reviews the literatureon the determinants of stock liquidity that explains based on the relationship between theinformational efficiency of stock price and stock liquidity. In Chapter 3, we establish the3hypotheses for the relationship between corporate international diversification and stock
liquidity with assumption about the effect of corporate international diversification on theinformational efficiency of stock price. Then, Chapter 4 describes the sample, thevariables, and the method for analysis and Chapter 5 presents the regression results.
Finally, Chapter 6 offers concluding discussions.

2. Literature Review
Informational Efficiency of Stock Price and Stock Liquidity
The informational efficiency of stock price is one of determinants of stock liquidity.
Informational efficiency of stock price is defined as the amount of private information
revealed in prices. Thus, the greater informational efficiency of stock price indicates thatthe price reflects more information about the security’s true value. As the informational
efficiency of stock price becomes greater, it improves the predictability of stock return andreduces uncertainty about future payoffs, thereby decreasing risk of trading the stock.
The reduction in trading risk results in an increase in trading liquidity (Mendelson and
Tunca, 2004). Therefore, the informational efficiency of stock price enhances liquidity ofthe stock. Previous studies determine several determinants of stock liquidity on the basisof the positive relationship between informational efficiency of stock price and liquidity ofthe stock. In this chapter, we introduce the determinants of stock liquidity suggested byprevious studies to understand how they are related to the informational efficiency ofstock price and to stock liquidity.
The presence of Informed investors is an important determinant of stock liquidity. The
effect of informed investor on liquidity depends on whether they enhance the level of
informational efficiency of stock price or not. Thus, informed investors influence liquidityof stocks in two ways. The presence of informed investors may cause information
asymmetry between informed and uninformed investors and adverse selection risk on
uninformed investors. The adverse selection risk reduces the motivation for uninformed
investors to trade, because they are afraid of monetary loss due to their uncertainty about
the true value of the stock. If the presence of informed investors increases information
asymmetry between informed and uninformed investors and adverse selection risk, it
would result in lower liquidity of the stock (higher bid-ask spread) (Glosten and Milgrom,
4

1985; Easley and O’Hara, 1987). On the other hand, competition among informed
investors improves the informational efficiency related to stock price, thereby reducing
the perceived uncertainty about the true value of the stock. This improvement in theinformational efficiency of stock price would lead to an increase in stock liquidity (Holden
and Subrahmanyam, 1992; Mendelson and Tunca, 2004).
Investment horizon also has significant effect on stock liquidity. Some previous studies
suggest that investment horizon is negatively associated with stock liquidity (Atkins andDyl, 1997; Vovchak, 2014). This negative relationship between investment horizon andliquidity can be explained by informational advantage of short-term investors. Wermers(2000) shows that high-turnover funds hold stocks with significantly higher average
returns than low-turnover funds and argues that the higher return level is due to thebetter stock-picking skills of managers of high-turnover funds. Consistent with Wermers(2000), Yan and Zhang (2009) demonstrate that short-term institutional ownership has
predictive power for future stock returns, contrary to no incremental predictive power oflong-term institutional ownership. They explain that short-term investors would be more
informed than long-term investors, because investors who possess superior information
about stock value are expected to trade more frequently to exploit their informational
advantage.
Better corporate governance is considered to have positive impact on stock liquidity,
according to agency theory. Corporate governance improves the firm’s financial andoperational transparency by enhancing the quality and frequency of information releasedby the firm. An increase in operational transparency provides better understating about
its operations to investors and reduces information asymmetries between insiders andoutside investors, as well as among outside investors. This decrease in information
asymmetry results in lower adverse selection risk which outside investors face andsmaller spread posted by liquidity providers (Chung et al. 2010). Thus, better corporate
governance enhances the informational efficiency of stock price, thereby increasingliquidity of the stock. In contrast, liquidity providers may post wider spread for stocks offirms with poor governance because they face greater adverse selection problems in thesestocks (Glosten and Milgrom, 1985).
Product market power is also one of the determinants of stock liquidity. As a firm withgreater market power can change a price of its product when it faces a productivity shockthat influences output, it has ability to pass on the productivity shock to its customers.
Thus, product market power reduces the volatility of the firm’s cash flow and stock returns.
5

The lower volatility of cash flow and stock returns enhance the precision of investor’s
information about stock price, resulting in lower price impact and better stock liquidity.
Based on this discussion, Peress (2010) suggests that greater product market power
improves stock liquidity. Kale and Loon (2011) also test Peress (2010)’s inference
empirically and provide an evidence of the positive relationship between product market
power and stock liquidity.

3. Hypotheses Development
3.1. Corporate International Diversification and Informational Efficiency of Stock PriceAs seen in previous chapter, the determinants of stock liquidity discussed above is
explained by the relationship between the informational efficiency of stock price and stock
liquidity. We infer that corporate international diversification also would influence stock
liquidity, if it gives rise to a change in the degree of informational efficiency of the firm’s
stock price. However, we expect that the relation between corporate international
diversification and stock liquidity is ambiguous because multinational operations haveconflicting aspects on the informational efficiency of stock price. To infer the relation
between corporate international diversification and stock liquidity, we consider theseconflicting effects of multinational operations on the informational efficiency of stock price.
Corporate international diversification may lower the informational efficiency of stock
price. Multinational firms are likely to experience a more complexity in their operations
compared with domestic firms. The greater complexity of multinational operations makes
it difficult to monitor management efficiently and increases information asymmetry
between managers and investors (Lee and Kwok, 1988; Riahi-Belkaoui and Picur, 2001).
Geographically diverse operations of multinational firms also may give rise to greater
information asymmetry between managers and investors, because greater geographic
diversity makes active monitoring of managerial decisions more difficult and expensive
in comparison to domestic firms (Doukas and Pantzalis, 2003). Besides, analysts’ lower
familiarity with language, cultural practices and governmental regulations of foreigncountries and more discretion for managers of multinational firms can be some of thereasons for greater information asymmetry between managers and analysts (Duru andReeb, 2002). Moreover, the challenge of communicating across borders and the principal6

agent relationship between the domestic parent and the foreign subsidiary may increase
information asymmetry between insiders and outside investors and among shareholders(Runyan and Smith, 2007). This greater information asymmetry between insiders andoutside investors and among outside investors would deteriorate the informational
efficiency of stock price.
On the other hands, it might be argued that multinational firms can experience ghegreater informational efficiency of their stock price than that of domestic firms. Valuemaximizing managers have incentives to reduce information asymmetry between
managers and investors, because it increases monitoring costs and the costs of externalfinancing (Myers and Majluf, 1984; Bartov and Bodnar, 1996). The greater complexity inoperations and information asymmetry arising from overseas business activities may
increase the incentives to reduce information asymmetry between managers andinvestors, in order to allow investors to understand about their activities (Luo, 2005).
Some previous studies provide an evidence for the positive relation between corporate
international diversification and the level of corporate disclosure. Lee et al. (2008) findthat multinational firms announce their earnings earlier than domestic firms. Cahan etal. (2005) show that global operations are related to the higher level of voluntary
disclosure. This improvement in the level of corporate disclosure of internationallydiversified firms would enhance the informational efficiency of their stock prices.
3.2. Corporate International Diversification and Stock Liquidity
As discussed above, corporate international diversification has two conflict effects on
stock liquidity. The greater complexity and information asymmetry between insiders andoutside investors and among investors of multinational firms would decrease theinformational efficiency of their stock prices. In contrast, multinational firms may release
the better corporate discloser that improves the informational efficiency of stock price,because managers of those firms are motivated to provide more information about theiroperations to reduce the increased information asymmetry between them and investors.
According to previous studies, the informational efficiency of stock price has a positiveeffect on stock liquidity. Thus, we can conjecture how corporate international
diversification affects liquidity of the firm’s stock based on the relationship between
informational efficiency of stock price and stock liquidity. If corporate international
diversification lowers the informational efficiency of their stock prices, it would decrease7

stock liquidity. However, if the effect of better corporate disclosure of multinational firmsthat outweighs the effects of the information asymmetries between managers andinvestors and among investors increases the informational efficiency of their stock price,
it would result in higher stock liquidity.
So, we build conflicting hypotheses as follows:
H1.a. Corporate international diversification is associated with higher stock liquidity.
H1.b. Corporate international diversification is associated with lower stock liquidity.

4. Data and Methodology
4.1. Sample Selection
We use two types of database: Nikkei Financial Quest and Nikkei Value Search. We
obtain the firm’s financial information from Nikkei Needs Financial Quest database, andinformation about foreign subsidiaries from Nikkei Value Search database. The sample
consists of all firms listed on the Japanese stock markets except for financial industry andutilities. The data set covers the period 2004 – 2016 because company’s data incorporated
by Nikkei Value Search database has become available since 2004. We exclude
observations with missing value of variables used in the analysis. Then, we winsorize all
the variables at the 1th and 99th percentiles to minimize the effects of outliers. The final
dataset consists of 3,804 firms and 34,291 firm-year observations.
4.2. Stock Liquidity Measures
We use three types of liquidity measures: Amihud(2002)’s illiquidity measure, quoted
spread and effective spread.
(1) The Amihud estimate
Amihud (2002) illiquidity measure (ILLIQ) is the average ratio of the daily absolute
return to the trading volume on that day. It is calculated as follows:

8

Dit

|R itd |1
ILLIQ it =∑Dit
VOLDit𝑑d=1where R itd is the return on stock i on day d of year t, VOLDitd is the respective dailyvolume in yen, and Dit is the number of days available to obtain data for stock i in year
t. ILLIQ is a rough estimate of the daily price impact of the order flow and measures how
much one yen of trading volume causes absolute price change. Then, we calculate theaverage relative illiquidity (RILLIQ) for each year. RILLIQ is the ratio of illiquidity
measure to the average market illiquidity across stocks in that year. It is calculated asfollows:

𝑅𝐼𝐿𝐿𝐼𝑄𝑖𝑡 =ILLIQ it
1 Nt∑
Nt t=1 ILLIQ it

where Nt is the number of stocks in year t. Since average illiquidity varies considerably
over the years, ILLIQ is replaced by RILLIQ, mean-adjusted value of ILLIQ (Amihud,2002). We use RILLIQ as the measure of illiquidity.
(2) Quoted spread
The quoted percentage spread of stock (QUOTED SPREAD) is defined as the difference
between ask price and bid price divided by the mid-price of the quotes. We calculate thequoted spread as follows:

𝑄𝑢𝑎𝑡𝑒𝑑_𝑆𝑝𝑟𝑒𝑎𝑑𝑖𝑡 =Ask it − Bidit(Ask it + Bidit )/2

where Ask it is the ask price for stock i at time t, Bidit is the bid price for stock i at timet. We compute the average spreads for each year. The quoted spread is the implicit trading
cost of market orders when a trade occurs in the quoted price with no price improvement(Chung et al., 2010).
(3) Effective spread
The effective percentage spread of stock (EFFECTIVE SPREAD) is defined as thetwice of the absolute value of the difference between the transaction price and the quote
mid-price divided by mid-price of the quote. We calculate the effective spread as follows:9𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣_𝑆𝑝𝑟𝑒𝑎𝑑𝑖𝑡 =|Pit − (Ask it + Bidit )/2| × 2(Ask it + Bidit )/2

where Pit is the transaction price for stock i at time t. The effective spread measures thecost of trading when it occurs in prices inside the posted bid and ask quotes (Chung et al.,2010).
4.3. International Diversification Measures
The degree of corporate international diversification is measured by the foreignsubsidiaries ratio (FSR). We calculate the foreign subsidiaries ratio by dividing thenumber of foreign subsidiaries of the firm to the total number of subsidiaries. For thenumber of subsidiaries, we collect the number of consolidated subsidiaries except for
associated companies from Nikkei Needs Financial Quest.

𝐹𝑆𝑅𝑖𝑡 =The number of foreign subsidiariesit
The total number of subsidiariesit

4.4. Control Variables
We include a number of control variables in empirical analysis to control the effects ofother determinants of liquidity. Following previous studies, we employ growth
opportunity (TOBIN_Q), profitability (EBITDA Ratio), firm size (SIZE1), leverage (LEV),
Investment (INV Ratio, R&D Ratio, Other INV Ratio 2 ), operational efficiency (NWC

Ratio3) and dividend payout dummy (Payout Dummy) as control variables. These areconsidered to have significant effects on stock market liquidity. For example, high-growthfirms are likely to have higher stock liquidity, because they can attract more attention
from investors. Larger firms may also have greater liquidity due to more available
information and smaller adverse selection risk.

1

SIZE is the natural logarithm of total assets.

2

INV Ratio is the ratio of investment in tangible fixed assets to total assets. R&D Ratio is the ratio of total
R&D to total assets. Other INV Ratio is the ratio increase in total assets excluding tangible fixed assets andliquid assets to total assets.
3 NWC Ratio is the ratio of net working capital to total assets.

10

4.5. Method
We examine the relationship between corporate international diversification and stock
market liquidity to test the hypotheses in previous chapter. We assume the baseline
equation as follows:(1)′
𝐼𝐿𝐿𝐼𝑄𝑖𝑡 = α + β × 𝐼𝐷𝑖𝑡 + γ × 𝑋𝑖𝑡−1+ 𝑌𝑡 + 𝐼𝑁𝐷𝑘 + 𝜀𝑖𝑡

where 𝐼𝐿𝐿𝐼𝑄𝑖𝑡 is stock market illiquidity of firm 𝑖 in fiscal year 𝑡, 𝐼𝐷𝑖𝑡 is the degree of′corporate international diversification, 𝑋𝑖𝑡−1
is a set of control variables, 𝑌𝑡 is a set ofyear fixed effects, absorbing time-varying shocks all firms face, 𝐼𝑁𝐷𝑘 is a set of industry
fixed effects, absorbing time-invariant unobservable characteristics that differ acrossindustries. Regarding the definition of industry fixed effects, we refer Nikkei Medium
Classification Industry Code.
In the baseline equation, the coefficient of interest is β, representing the corporate
international diversification sensitivity of stock market liquidity. If β has a negative sign,
Hypothesis 1a is supported that corporate international diversification is associated withhigher stock liquidity. This implies that overseas operations contribute to increase stock
market liquidity by enhancing the informational efficiency of stock price. In contrast, if β
is positive, Hypothesis 1b is supported.
4.6. Descriptive Statistics
Panel A of Table 1 presents summary of statistics of all the variables. The average
foreign subsidiaries ratio (FSR) is 0.28. The mean of the sample firms’ relative illiquidity(RILLIQ) is 0.965, and their average quoted spread (QUOTED SPREAD) and effective
spread (EFFECTIVE SPREAD) are 0.014 and 0.011, respectively.
In Panel B of Table 1, we divide firms into two groups, multinational firms deploying
international operations and domestic firms not deploying international operations, basedon whether the firm has foreign subsidiaries in such period. As shown in Panel B, themeans of liquidity measures of multinational firms are smaller than that of domestic
firms. This indicates that multinational firms in our sample have more liquid stocks
relative to domestic firms. Panel B also presents that, on average, multinational firms
have larger total assets and higher Tobin’s q ratio than domestic firms. Therefore,11multinational firms in our sample have greater firm size and growth opportunity
compared to domestic firms.
Table 2 shows the correlation coefficients between all variables. The stock liquidity
measures show strong positive pairwise correlations ranging from 0.612 to 0.987. Theinternational diversification measure (FSR) is negatively correlated with the stock
liquidity measures, suggesting that firms deploying more overseas activities areassociated with greater stock liquidity.

5. Results
5.1. The Relation Between Corporate International Diversification and Stock Liquidity
To investigate the relationship between corporate international diversification andstock liquidity, we estimate the regression model in equation (1) using the pooled crosssectional time-series data. Table 3 presents the ordinary least squares (OLS) regression
results of the models regressing the three different measures of stock liquidity (RILLIQ,QUOTED SPREAD, and EFFECTIVE SPREAD) on the international diversification
measure (FSR). The table reports the results of basic model in which the international
diversification variable is only a regressor, as well as comprehensive versions with thecomplete set of control variables. Year dummy variables and industry dummy variables
are included in all regressions.
In Table 3, the regression results of basic models (model 1, 3, and 5) show that
corporate international diversification has a significantly negative relation with stock
illiquidity. In model 1, which uses RILLIQ as the dependent variable for example, theestimated coefficient on FSR is -0.493 and statistically significant at the 1% level. It
implies that the higher degree of foreign subsidiaries ratio is associated with greater stock
liquidity. The consistent conclusion can be drawn from model 3 and 5 using QUOTEDSPREAD and EFFECTIVE SPREAD as the dependent variables, respectively. The
coefficients on FSR in model 3 and 5 are negative and statistically significant at the 5%
level.
Model 2, 4, and 6 provide the results of regressions including a set of control variables.
We confirm that the estimated coefficients on FSR do not change qualitatively, although
the coefficient on FSR in model 2 is attenuated because of the correlation between FSR
12and control variables. In model 2, which uses RILLIQ as the dependent variable, theestimated coefficient on FSR is -0.433 and statistically significant at the 1% level. The
results of the regression using QUOTED SPREAD (model 4) or EFFECTIVE SPREAD(model 6) are corresponding with the results of basic models indicating a negative
relationship between the stock liquidity measures and the international diversification
measure.
Table 3 shows that control variables have significant relations with stock liquidity as
well. Stock liquidity is higher for firms with larger Tobin Q, greater EBTIDA ratio, larger
firm size, and higher investment in fixed assets and NWC ratio. In contrast, we find that
more levered and paying dividend firms exhibit a lower liquidity of stocks. Larger firms
are related to greater stock liquidity, since the information about them is more available
due to greater media and analyst coverage (Stoll and Whaley, 1983). The positive effectsof Tobin Q and investment in fixed assets may be the reason of more attention of investors
about firms with greater growth opportunity. These results of control variables are
consistent with that of previous studies (e.g., Chung et al., 2010; Kale and Loon, 2011;
Prommin et al., 2014).
After controlling the other possible determinants of stock liquidity, the negative
relation between FSR and stock illiquidity remains unchanged. Thus, we conclude thatthe ratio of foreign subsidiaries is a positive effect on stock market liquidity, providing
support for the Hypothesis 1a that corporate international diversification is associatedwith higher stock liquidity.
5.2. Robustness
In this section, we check the robustness of results in previous section with respect todifferent estimation methods. We first use the fixed effects regression method to control
for unobserved firm-specific variables that differ across firms but are constant over time.
The method focuses on changes in the variables over time to estimate the effects of the
independent variables on the dependent variable. The estimated regression model of fixed
effects method is as follows:(2)′
𝐼𝐿𝐿𝐼𝑄𝑖𝑡 = α + β × 𝐼𝐷𝑖𝑡 + γ × 𝑋𝑖𝑡−1+ 𝑌𝑡 + 𝐹𝑖 + 𝜀𝑖𝑡

where 𝐹𝑖 is firm-specific fixed effects. The definitions of other variables are consistent13

with the equation (1).
Panel A of Table 4 presents the fixed effects regression model results. Consistent with
Table 3, we report the results of basic model in which an international diversification
variable is only a regressor, as well as comprehensive versions with the complete set ofcontrol variables. We find again that all stock liquidity measures are negatively andsignificantly related to the international diversification measure in both basic version
models and comprehensive versions, except for the model 4 which uses QUOTEDSPREAD as the dependent variable and includes other control variables. These results
reinforce our earlier observation that corporate international diversification is positively
associated with stock liquidity. The estimated coefficients on other control variables are
qualitatively similar to those in Table 3 with few exceptions.
Next, we rely on the two-step efficient generalized method of moments (GMM) for
robustness check. The model employs the instrumental variable to take into account theendogeneity concerns of regressor. To alleviate the endogeneity of FRS, we use some
instrumental variables4: FRS, the ratio of foreign sales, the number of subsidiaries,
firm age and the number of board members. The estimated regression model is asfollows:(3)

𝐼𝐷𝑖𝑡 = δ × 𝐼𝑉𝑖𝑡−1 + 𝜇𝑖𝑡(4)′
𝐼𝐿𝐿𝐼𝑄𝑖𝑡 = α + β × (δ × 𝐼𝐷𝑖𝑡−1 + 𝜇𝑖𝑡 ) + γ × 𝑋𝑖𝑡−1+ 𝑌𝑡 + 𝐹𝑖 + 𝜀𝑖𝑡

Where 𝐼𝑉𝑖𝑡−1 is the group of instrumental variables, and 𝜇𝑖𝑡 is the error term. The definitionsof other variables are consistent with the equation (1) and (2).
Panel B of Table 4 shows the results of the two-step efficient GMM regression. The
results indicate that the stock liquidity measures are significantly and negatively related
to international diversification measure. The estimated coefficients on FRS are negativeand statistically significant in all regression models. These results provide additional
evidence to support Hypothesis 1a that corporate international diversification isassociated with higher stock liquidity. Note again that the coefficients on the control
variables are qualitatively similar to those in Table 3 and Panel A of Table 4.

4 All the instrumental variables are 1 year lagged (t-1) variables.

145.3. Additional Analysis
In this paper, we infer that corporate international diversification may influence stock
liquidity on the basis of the effect of the informational efficiency of stock price on liquidityof the stock. If corporate international diversification enhances the level of informational
efficiency of the firm’s stock price, it would increase liquidity of the stock, and vice versa.
Our empirical results from various estimation methods show that firms withmultinational operations exhibit the greater stock liquidity. We can conjecture that thepositive relation between corporate international diversification and stock liquidity is due
to an improvement in the informational efficiency of the stock price. In this section, we
examine whether greater stock liquidity of internationally diversified firms is associatedwith the effect of the informational efficiency of stock price for additional analysis.
We first regress our stock liquidity measures on the measure of the informational
efficiency of stock price to control for its effect on stock liquidity, using the R-square
regarded as the measure of stock price informational efficiency.
(5)

𝐼𝐿𝐿𝐼𝑄𝑖𝑡 = λ + φ × 𝑅𝑖𝑡 + 𝜔𝑖𝑡

where 𝑅𝑖𝑡 is the R-square of CAPM for stock of firm i in year t, 𝜔𝑖𝑡 is the error term. In
the equation (5), the error term (𝜔𝑖𝑡 ), which is called the residual, can be interpreted as
the degree of stock liquidity excluding the effect of the informational efficiency of stock
price. We again estimate the two-step efficient GMM in the equation (4), replacing thestock liquidity measures with the residuals of each liquidity measures.
Table 5 presents the results of our additional analysis using the residuals of stock
liquidity measures as the dependent variables. We report the results of regression models
using our original stock liquidity measures in Panel B of Table 4, as well as the results ofadjusted regression models in which the residuals of each liquidity measures arealternative variables of the original stock liquidity measures, to compare the results. The
results indicate that the negative relation between stock liquidity measures andinternational diversification measure becomes weaken and statistically insignificant
when the stock liquidity measures are substituted by the residuals of them. The estimatedcoefficient on FSR in model 2 using the residual of RILLQ is -0.181 and statisticallysignificant at the 10% level, which is much smaller and less significant compared to thecoefficient in model 1 using the original RILLQ as the dependent variable. Furthermore,15

in model 4 and 6 where the residuals of QUOTED SPREAD and EFFECTIVE SPREAD
are the dependent variables, the estimated coefficients on FSR become smaller and evenstatistically insignificant. These results suggest that the positive impact of corporate
international diversification on stock liquidity is almost due to the effect of theinformational efficiency of stock price.

6. Conclusion
The relationship between corporate international diversification and stock liquidity
depends on how corporate international diversification influences the level of theinformational efficiency of stock price. Corporate international diversification has two
conflicting effects on the informational efficiency of stock price. The greater complexity inoperations and information asymmetry between insiders and outside investors andamong investors of multinational firms may lower the informational efficiency of theirstock prices. However, they may experience the higher informational efficiency of stock
prices in virtue of better corporate discloser by managers who have an incentive to reduce
information asymmetry between them and investors. If corporate international
diversification improves the information efficiency of their stock price, it would also
increase stock liquidity, and vice versa.
Our empirical results suggest that firms with internationally diversified operations
are associated with greater stock liquidity, after controlling for other determinants ofliquidity. All measures of stock illiquidity are negatively and significantly related toforeign subsidiaries ratio in various estimation model. These results confirm that firms
with multinational operations obtain an advantage of an improvement in their stock
liquidity. Next, for additional analysis, we estimate again with the adjusted liquidity
variables to clarify whether the relationship between corporate international
diversification and stock liquidity is due to the effect of the informational efficiency ofstock price. We find that the positive relation between corporate international
diversification and stock liquidity becomes weaken and statistically insignificant, after
controlling for the effect of the informational efficiency of stock price.
Likewise, internationally diversified firms exhibit greater stock liquidity than that ofdomestic firms. This implies that internationally diversified firms may enhance firm
value exploiting the effect of overseas activities on stock liquidity. Considering the results
16

of additional analysis, however, the positive relation between corporate international
diversification and stock liquidity seems to be derived from the effect of the informational
efficiency of stock price, not the independent effect of corporate international
diversification. Therefore, firms expanding their business overseas would better toaccompany an effort for an improvement in the informational efficiency of their stock price,
to increase their stock liquidity. What factors contribute to the difference in informational
efficiency of stock price for Japanese multinational firms and domestic firms remains aquestion. For example, if Japanese multinational firms are required to release better
disclosure or have more informed investors such as institutional investors compared todomestic firms, these might improve the information efficiency of their stock price,
thereby increasing stock liquidity. Further research is needed to clarify the details for thegreater informational efficiency of stock price for Japanese multinational firms, that
result in their greater stock liquidity.

17

<References>
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects.

Journal of financial markets, 5(1), 31-56.
Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of

financial Economics, 17(2), 223-249.
Atkins, A. B., & Dyl, E. A. (1997). Transactions costs and holding periods for common
stocks. The Journal of Finance, 52(1), 309-325.
Bartov, E., & Bodnar, G. M. (1996). Alternative accounting methods, information
asymmetry and liquidity: Theory and evidence. Accounting review, 397-418.
Cahan, S. F., Rahman, A., & Perera, H. (2005). Global diversification and corporate
disclosure. Journal of International Accounting Research, 4(1), 73-93.
Chordia, T., Roll, R., & Subrahmanyam, A. (2001). Common determinants of liquidity andtrading.
Chung, K. H., Elder, J., & Kim, J. C. (2010). Corporate governance and liquidity. Journalof Financial and Quantitative Analysis, 45(2), 265-291.
Dahlquist, M., & Robertsson, G. (2001). Direct foreign ownership, institutional investors,
and firm characteristics. Journal of financial economics, 59(3), 413-440.
Doukas, J. A., & Pantzalis, C. (2003). Geographic diversification and agency costs of debt
of multinational firms. Journal of Corporate Finance, 9(1), 59-92.
Duru, A., & Reeb, D. M. (2002). International diversification and analysts' forecast
accuracy and bias. The Accounting Review, 77(2), 415-433.
Dvořák, T. (2005). Do domestic investors have an information advantage? Evidence fromIndonesia. The Journal of Finance, 60(2), 817-839.
Easley, D., & O'hara, M. (1987). Price, trade size, and information in securities markets.

Journal of Financial economics, 19(1), 69-90.
Glosten, L. R., & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist
market with heterogeneously informed traders. Journal of financial economics,

14(1), 71-100.
Holden, C. W., & Subrahmanyam, A. (1992). Long‐lived private information and imperfectcompetition. The Journal of Finance, 47(1), 247-270.
Kale, J. R., & Loon, Y. C. (2011). Product market power and stock market liquidity.

Journal of Financial Markets, 14(2), 376-410.
Lee, K. C., & Kwok, C. C. (1988). Multinational corporations vs. domestic corporations:18International environmental factors and determinants of capital structure. Journalof International Business Studies, 19(2), 195-217.
Luo, Y. (2005). How does globalization affect corporate governance and accountability? A
perspective from MNEs. Journal of International Management, 11(1), 19-41.
Mendelson, H., & Tunca, T. I. (2004). Strategic trading, liquidity, and informationacquisition. Review of Financial Studies, 17(2), 295-337.
Michel, A., & Shaked, I. (1986). Multinational corporations vs. domestic corporations:
Financial performance and characteristics. Journal of international businessstudies, 17(3), 89-100.
Morck, R., & Yeung, B. (1991). Why investors value multinationality. Journal of Business,
165-187.
Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions whenfirms have information that investors do not have. Journal of financial

economics, 13(2), 187-221.
Peress, J. (2010). Product market competition, insider trading, and stock market efficiency.

The Journal of Finance, 65(1), 1-43.
Prommin, P., Jumreornvong, S., & Jiraporn, P. (2014). The effect of corporate governanceon stock liquidity: The case of Thailand. International Review of Economics &Finance, 32, 132-142.
Riahi-Belkaoui, A., & Picur, R. D. (2001). The impact of multinationality on theinformativeness of earnings and accounting choices. Managerial Finance, 27(12),82-94.
Reeb, D. M., Kwok, C. C., & Baek, H. Y. (1998). Systematic risk of the multinational
corporation. Journal of International Business Studies, 29(2), 263-279.
Uno, J., & Kamiyama, N. (2010). Ownership structure, liquidity, and firm value.

Unpublished manuscript, Waseda University.
Vovchak, V. (2014). Liquidity and Investment Horizon. Swiss Finance Institute Research

Paper, (14-02).
Wermers, R. (2000). Mutual fund performance: An empirical decomposition into stock‐pickingtalent,style,

transactions

costs,

andexpenses. The

Journal of

Finance, 55(4), 1655-1695.
Yan, X. S., & Zhang, Z. (2009). Institutional investors and equity returns: Are short-term
institutions better informed? Review of financial Studies, 22(2), 893-924

19

Table 1 Descriptive Statistics
Panel A: Summary Statistics
All firm - years
VariableN

Mean

RILLIQ

34,0420.965

QUOTED SPREAD

34,291

EFFECTIVE SPREADMedian

SD

Min

Max

0.125

2.130

0.00008

16.699

0.014

0.007

0.018

0.000

0.174

34,291

0.011

0.006

0.014

0.001

0.159

FSR

34,291

0.279

0.152

0.320

0.000

1.000

Tobin Q34,291

1.121

0.970

0.626

0.052

7.128

EBITDA Ratio

34,291

0.081

0.076

0.061

-0.333

0.298

SIZE

34,291

10.677

10.524

1.590

6.706

15.325

LEV

34,291

0.368

0.346

0.194

0.000

0.990

INV Ratio

34,158

0.033

0.024

0.035

-0.0850.201

R&D Ratio

34,291

0.013

0.004

0.021

0.000

0.127

Other INV Ratio

34,158

0.006

0.003

0.052

-0.3000.308

NWC Ratio

34,291

0.187

0.182

0.127

-0.112

0.668

Payout Dummy

34,291

0.916

1.000

0.277

0.000

1.000

RILLIQ=relative illiquidity measure, Quoted Spread=quoted spread, Effective Spread=effective spread,
FSR=foreign subsidiaries ratio, TOBIN Q=Tobin’q ratio, SIZE=a natural logarithm of total assets, LEV=leverage,
INV Ratio= tangible fixed assets ratio, R&D Ratio=the R&D expenditure ratio, Other INV Ratio= assets except
for tangible fixed assets ratio, NWC Ratio=net working capital ratio, Payout Dummy=dividend payout dummy

20Table 1 (Continued)
Panel B: Sample without Foreign Subsidiaries V.S. Sample with Foreign Subsidiaries
Firm - years without foreign subsidiaries (FSR ＝ 0)

Firm - years with foreign subsidiaries (FSR ＞ 0)

VariableN

MeanMedianRILLIQ

13,6391.468

QUOTED SPREAD

13,848

SD

Min

Max

N

Mean

Median

SD

Min

Max

0.371

2.606

0.000

16.69920,403

0.629

0.058

1.658

0.000

16.699

0.018

0.011

0.021

0.000

0.174

20,443

0.011

0.006

0.014

0.000

0.162

EFFECTIVE SPREAD 13,848

0.014

0.009

0.016

0.001

0.159

20,443

0.008

0.004

0.011

0.001

0.128

FSR

13,847

0.017

0.000

0.082

0.000

1.000

20,440

0.230

0.160

0.250

0.000

1.000

Tobin Q

13,848

1.106

0.958

0.632

0.078

7.128

20,443

1.130

0.980

0.621

0.052

7.128

EBITDA Ratio

13,848

0.074

0.068

0.062

-0.3330.298

20,443

0.086

0.081

0.060

-0.333

0.298

SIZE

13,84810.16610.087

1.397

6.706

15.32520,44311.02310.849

1.619

6.706

15.325

LEV

13,848

0.398

0.383

0.205

0.000

0.988

20,443

0.348

0.327

0.183

0.005

0.990

INV Ratio

13,801

0.029

0.020

0.035

-0.0850.201

20,357

0.036

0.028

0.035

-0.0850.201

R&D Ratio

13,848

0.006

0.000

0.014

0.000

0.127

20,443

0.018

0.010

0.024

0.000

0.127

Other INV Ratio

13,801

0.005

0.002

0.057

-0.3000.308

20,357

0.007

0.003

0.048

-0.3000.308

NWC Ratio

13,848

0.162

0.148

0.140

-0.1120.668

20,443

0.205

0.200

0.115

-0.112

0.668

Payout Dummy

13,848

0.893

1.000

0.309

0.000

1.000

20,443

0.932

1.000

0.253

0.000

1.000

21

Table 2 Correlation Matrix
[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[1]
[2]
[3]

RILLIQQUOTED SPREAD
EFFECTIVE SPREAD

1.000
0.612
0.624

1.000
0.987

1.000

[4]

FSR

-0.053-0.049-0.054

1.000[5]Tobin Q

-0.130-0.165-0.156

0.006

1.000

[6]

EBITDA Ratio-0.211-0.196-0.202

0.085

0.322

1.000

[7]

SIZE

-0.391-0.438-0.451-0.048-0.094

0.096

1.000

[8]

LEV

0.067

0.043

0.050

-0.2250.015-0.125

0.141

1.000

[9]

INV Ratio-0.072-0.099-0.102

0.087

0.082

0.307

0.110

0.10

1.000

[10] R&D Ratio-0.077-0.069-0.072

0.302

0.085

0.038

0.096

-0.16

0.089

1.000

[11] Other INV Ratio-0.060-0.091-0.093-0.016

0.140

0.138

0.050

-0.02

0.065

-0.0301.000[12] NWC Ratio

0.032

0.064

0.060

0.200

-0.081-0.057-0.096-0.02-0.1500.198-0.0551.000[13] Payout Dummy-0.124-0.103-0.1180.02-0.144

0.209

0.259

-0.14

0.071

-0.026

0.059

0.012

22

[13]

1.000

Table 3

OLS Regression Results
RILLIQQUOTED SPREAD

EFFECTIVE SPREAD

Variable

FSR

Model 1Model 2Model 3Model 4Model 5Model 6-0.493 ***-0.433 ***-0.004 **-0.004 ***-0.003 **-0.003 ***( 0.169 )Tobin Q

( 0.114 )( 0.002 )-0.498 ***( 0.001 )-0.009 **-0.007 **( 0.731 )( 0.004 )( 0.003 )1.135-0.006-0.006( 1.505 )( 0.008 )( 0.006 )-0.250

0.001

0.001

( 0.232 )( 0.001 )( 0.001 )-0.632 *-0.006 *( 0.314 )Payout Dummy

0.008 ***

( 0.002 )-0.168NWC Ratio( 0.000 )0.010 ***

( 0.253 )Other INV Ratio-0.004 ***( 0.000 )1.472 ***

R&D Ratio( 0.002 )-0.006 ***( 0.042 )INV Ratio-0.016 ***( 0.003 )-0.601 ***LEV( 0.000 )-0.018 ***( 0.395 )( 0.001 )-0.003 ***( 0.000 )-3.117 ***SIZE( 0.001 )-0.004 ***( 0.037 )EBITDA Ratio( 0.001 )-0.005 *( 0.004 )0.085( 0.003 )0.002 ***

( 0.062 )0.001 **

( 0.001 )( 0.000 )Industry_Dummies

Yes

Yes

Yes

Yes

Yes

Yes

Year_Dummies

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R2

0.023

0.233

0.091

0.321

0.096

0.328

N

34,042

33,911

34,291

34,15834,291

34,158*** p < 0.01; ** p < 0.05; * p< 0.10

23

Table 4 Regression Results from Alternative Estimation Methods
Panel A Results from Fixed Effects Method
RILLIQQUOTED SPREAD

EFFECTIVE SPREAD

Variable

FSR

Model 1Model 2Model 3Model 4Model 5Model 6-0.288 **-0.274 **-0.001 *-0.001-0.001 **-0.001 **( 0.118 )Tobin Q

( 0.115 )( 0.001 )-0.264 ***( 0.023 )EBITDA Ratio-4.096 ***-0.002 ***( 0.000 )0.003 ***
( 0.001 )-0.004 **( 0.002 )-2.345 **( 0.002 )-0.024 *( 1.031 )-0.022 **( 0.013 )( 0.009 )-0.001-0.001( 0.106 )( 0.001 )( 0.001 )-0.007

0.000

0.000

( 0.319 )( 0.002 )( 0.002 )-0.001

0.000

0.000

( 0.042 )( 0.000 )( 0.000 )-0.409 ***Payout Dummy( 0.002 )-0.004 *( 0.229 )NWC Ratio-0.019 ***( 0.001 )0.106

Other INV Ratio( 0.000 )0.003 **

( 0.151 )R&D Ratio( 0.000 )( 0.000 )0.433 ***INV Ratio

0.000-0.002 ***( 0.047 )( 0.000 )0.000( 0.003 )-0.326 ***LEV( 0.000 )-0.024 ***( 0.253 )SIZE( 0.001 )Industry_Dummies

Yes

Yes

Yes

Yes

Yes

Yes

Year_Dummies

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R2

0.003

0.037

0.175

0.186

0.188

0.2

N

34,042

33,911

34,291

34,15834,291

34,158*** p < 0.01; ** p < 0.05; * p< 0.10

24

Table 4 (Continued)
Panel B Results from Two-step Efficient GMM Method
RILLIQQUOTED SPREAD

EFFECTIVE SPREAD

Variable

FSR

Model 1Model 2Model 3Model 4Model 5Model 6

-0.464 ***-0.42 ***

-0.015 ***-0.012 ***-0.012 ***-0.009 ***( 0.141 )Tobin Q

( 0.132 )( 0.005 )-0.273 ***( 0.021 )EBITDA Ratio-4.065 ***-0.001 ***( 0.000 )0.002 ***
( 0.001 )-0.004 *( 0.002 )-2.388 **( 0.002 )-0.022 *( 0.948 )-0.021 **( 0.012 )-0.450 ***Payout Dummy( 0.002 )-0.004 *( 0.172 )NWC Ratio-0.022 ***( 0.001 )0.064

Other INV Ratio( 0.000 )0.002 **

( 0.140 )R&D Ratio( 0.000 )( 0.000 )0.369 ***INV Ratio

0.000-0.002 ***( 0.042 )( 0.003 )0.000( 0.002 )-0.323 ***LEV( 0.004 )-0.027 ***( 0.201 )SIZE( 0.004 )( 0.009 )-0.002 *-0.001( 0.091 )( 0.001 )( 0.001 )

0.130

0.000

-0.001( 0.266 )( 0.002 )( 0.002 )

0.000

0.000

( 0.000 )( 0.000 )0.052 *
( 0.028 )Industry_Dummies

Yes

Yes

Yes

Yes

Yes

Yes

Year_Dummies

Yes

Yes

Yes

Yes

Yes

Yes

N

33,812

33,68334,03933,90834,03933,908

j

6.05

6.07

6.10

5.54

5.36

5.04

idstat11.98 **

11.88 **

11.81 **

11.82 **

11.81 **

11.82 **

widstat

524.1

763.6

26.14

25.71

26.14

25.71

*** p < 0.01; ** p < 0.05; * p< 0.10

25

Table 5 Regression Results with Revision of Stock Liquidity Measure
RILLIQ

R_RILLIQQUOTEDSPREAD

Model 1Model 2Model 3Model 4Model 5Model 6-0.420 ***-0.181 *-0.012 ***-0.003-0.009 ***-0.002

Variable

FSR( 0.132 )Tobin Q

-0.273 ***( 0.021 )EBITDA Ratio-4.065 ***( 0.201 )SIZE-0.323 ***( 0.042 )LEV

INV Ratio

R&D Ratio

0.369 ***NWC Ratio( 0.019 )-2.872 ***( 0.214 )-0.168 ***( 0.027 )-0.141( 0.123 )0.064-0.151( 0.172 )( 0.149 )-2.388 **-0.450 ***( 0.091 )0.130( 0.266 )Payout Dummy-0.228 ***( 0.140 )( 0.948 )Other INV
Ratio

( 0.095 )0.052 *

-1.974 ***( 0.722 )-0.385 ***( 0.056 )0.277 **
( 0.117 )0.070 **

( 0.004 )0.000( 0.000 )-0.027 ***( 0.002 )-0.002 ***( 0.000 )0.002 **
( 0.001 )-0.004 *( 0.002 )-0.022 *( 0.012 )-0.002 *R_QUOTED EFFECTIVE R_EFFECTIVESPREADSPREADSPREAD( 0.004 )-0.001 ***( 0.000 )-0.017 ***( 0.001 )0.000 **
( 0.000 )0.000( 0.001 )-0.003( 0.002 )-0.005( 0.005 )-0.004 ***( 0.003 )0.000( 0.000 )-0.022 ***( 0.002 )-0.001 ***( 0.000 )0.002 ***
( 0.001 )-0.004 *( 0.002 )-0.021 **( 0.009 )-0.001( 0.003 )-0.001 ***( 0.000 )-0.014 ***( 0.001 )-0.001 ***( 0.000 )0.000( 0.000 )-0.003 *( 0.002 )-0.007 *( 0.004 )-0.003 ***( 0.001 )( 0.001 )( 0.001 )( 0.001 )

0.000

0.001

-0.0010.001( 0.002 )( 0.001 )( 0.002 )( 0.001 )

0.000

0.000

0.000

0.000

( 0.028 )( 0.033 )( 0.000 )( 0.000 )( 0.000 )( 0.000 )

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N

33,68330,16533,90830,38833,90830,388

j

6.07

5.59

5.54

4.58

5.04

5.40

idstat11.88 **

11.83 **

11.82 **

11.73 **

11.82 **

11.73 **

widstat

763.6

810.3

25.71

24.37

25.71

24.37

Industry_
DummiesYear_Dummies*** p < 0.01; ** p < 0.05; * p< 0.10

26