The Changing Role of Foreign Investors inTokyo Stock Price Formation
Kentaro Iwatsubo
Clinton Watkins
February , 2021
Discussion Paper No.2106

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANThe Changing Role of Foreign Investors inTokyo Stock Price Formation?
Kentaro Iwatsuboa , Clinton Watkinsa,b,∗aGraduate School of Economics, Kobe University, 2-1 Rokkodai-cho, Nada, Kobe 657-8501, Japanb
Akita International University, Yuwa, Akita-City 010-1292, Japan
February, 2021

Abstract
Recent research suggests that foreign investors improve the informational efficiency of national
stock markets. We examine how foreign investors contribute to efficiency. We model the influence of domestic and foreign investors’ trades on the efficient price of Japanese stocks over
39 years. Our results show that foreign investors trade at an information advantage over domestic investors, and this advantage has increased since around 2000. We find this is due to a
substantial increase in the importance of global stock price and exchange rate information instock price formation. Our results suggest that foreign investors have made more use of global
information in trading Japanese equities as the influence of international financial factors on thedomestic market has increased over recent decades.
Keywords: Efficiency, Equities, Foreign investors, Information content of trade, Investor
behavior, Market microstructure
JEL: C32, G14, G15?

This study is conducted as part of the project “JSDA Capital Market Forum” undertaken by the Japan securities
Dealers Association (JSDA). We are grateful for financial support from the JSDA for this study. We appreciate theconstructive comments of the Editor-in-Chief Robert Faff and an anonymous reviewer. We thank Takeshi Yamada,
Masahiro Yamada, Zsuzsa Reka Huszar, Kenichi Ueda, Shigeyuki Hamori and participants at the Nippon Finance
Association 1st Fall Conference 2019 at Osaka University, the 3rd RESSU Conference at Shiga University, the 3rd
Sydney Banking and Financial Stability Conference 2019 at the University of Sydney Business School, the Spring2020 Meeting of the Japanese Economic Association, a seminar at Akita International University, the JSDA CapitalMarket Forum for helpful discussions.
∗
Corresponding author
Email address: clinton-watkins@aiu.ac.jp (Clinton Watkins)

1. Introduction
International stock market integration and financial liberalisation have facilitated an increasein foreign portfolio investment over recent decades. Local equity markets provide foreign investors with opportunities for exposure to international risk premia and diversification. Foreign
investors are now important participants in most local markets. Researchers have examined theimpacts of greater foreign equity portfolio investment on local economies, such as the real effects on invested firms and macroeconomic output (Henry, 2000a; Bekaert et al., 2005, 2011),
and on the characteristics of local stock markets, including liquidity (Bekaert et al., 2007; Rhee
and Wang, 2009), return volatility (Bae et al., 2004; Li et al., 2011) and the cost of capital(Bekaert and Harvey, 2000; Henry, 2000b). Recent research has found that foreign investors
improve the informational efficiency of local stock markets (Kacperczyk et al., 2019; He et al.,2013; He and Shen, 2014; Kang et al., 2016). Foreign ownership or trading of local stocks
appears to be positively related to efficiency, which suggests that foreign investors may posses
value-relevant information.
However, exactly how foreign investors contribute to the efficiency of local markets andwhether they trade at an informational advantage is less well understood. Foreign investors
may trade at an information advantage or disadvantage to locals1 . Some studies show foreigners
are disadvantaged regarding local information (Kang and Stulz, 1997; Coval and Moskowitz,
1999). Other research suggests foreigners have an advantage in global information (Bae et al.,2012; Kang et al., 2016). Foreign investors outperform locals in some studies (Grinblatt and
Keloharju, 2000; Karolyi, 2002; Kamesaka et al., 2003; Bae et al., 2006), but underperform inothers (Timmermann and Blake, 2005; Shukla and van Inwegen, 1995; Choe et al., 2005).
In this study, we investigate whether foreign investors trade at an informational advantage
to domestic investors in the Japanese stock market over a long period of 39 years. We find that
foreign investors do indeed trade at an informational advantage. We also examine what types ofvalue-relevant information within foreign investors’ trades contribute to that advantage and how
the information in foreign investors’ trades has changed as national stock markets have become
more internationally integrated over the last four decades.
The Tokyo Stock Exchange (TSE) offers an interesting setting to investigate the role offoreign investors in price formation. Foreign portfolio investors have become important par1

The survey of Japanese and US investors’ expectations in Shiller et al. (1996) shows that domestic and foreign
investors had very different views on future stock prices around the time that the Japanese stock bubble burst at theend of 1989.

2

ticipants on the TSE over the last 40 years. Unique for a market classified as developed since
19692 , the shares of Japanese total stock ownership and trading volume attributed to foreign
investors have increased dramatically since the 1980s. Increased foreign participation has coincided with a substantial unwinding of large strategic cross-shareholdings by domestic financial
institutions and corporate investors3 . Figure 1 shows the ownership share of various investor
groups in Panel (a) and the share of trading volume in Panel (b). The investor groups includedin the figure are non-resident investors (Foreign), non-financial business corporations (Corporation), financial institutions including city, regional and trust banks, life and non-life insurers(Financial), individual or retail investors (Individual) and investment trusts that may be considered the same as mutual funds (Trust). Financials and corporations once dominated stock
holdings. However, ownership by foreign investors has increased from less than 10 percent in
the 1980s to around 30 percent in the current decade. Meanwhile, foreign investors’ transaction
volumes have grown from around 10 percent to over 60 percent. The turnover share of othergroups has declined, particularly individuals who were the largest traders up to the early 1990s.
The presence and importance of foreign investors in the market have grown substantially since
the late-1989 peak in stock prices.
We analyse the TSE for several reasons. In addition to the substantial increase in foreign
investors’ presence, the Japanese market is one of the largest national markets by capitalisationand shares many characteristics with other developed markets. It has similar regulation andinstitutional investors are the dominant players. Our findings have general implications forother developed markets and complement the evidence from analyses of emerging markets.
The TSE is the only developed market exchange that provides long-term and consistent data on
stock transactions classified by investor type. This allows us to investigate the evolution of theinformation content of foreign and domestic investors’ trades over a 39-year period.
Hasbrouck (1991a,b) introduces a bivariate vector autoregression (VAR) of trades and returns that isolates the random-walk and stationary components of price. The former is considered the efficient price and the latter mispricing. Changes in the efficient price can be decomposed into a component that is attributed to trades and a component that is not. In the spiritof Hasbrouck’s approach, we specify multivariate VAR models to identify the influence of for2

Japan has been classified as a developed market by MSCI since 1969 (MSCI, 2012).
Miyajima et al. (2015) provide a detailed discussion of the transition in ownership shares of domestic insiders
and foreign investors. Cross-shareholdings were unwound starting in the 1990s, mostly between banks and corporations. Since the mid-2000s there has been limited renewed interest in strategic cross-shareholdings between
corporations.
33

Figure 1: Investor Participation on the Tokyo Stock Exchange(a) Ownership (annual, percent share)
40

Foreign
Corporation
Financial

Individual
Trust

30

20

10

0
1980

1990

2000

2010

(b) Trading Volume (annual, percent share)
Foreign
Corporation
Financial

60

Individual
Trust

40

20

0
1980

1990

2000

2010

Note: Trading volume for each group includes purchases plus sales.

eign and various types of domestic investors’ TSE trades on the efficient price, as well as theimportance of both domestic and global public information. Investors who have a substantial
influence on the efficient price and a positive long-run price impact of trade are considered informed. Similarly, public information that has a substantial influence on the efficient price and apositive long-run price impact is regarded as value-relevant information for the TSE stock price.
We begin with a benchmark model that contains the trades of foreign, financial, investment trust and individual investors in Japanese First Section stocks on the TSE. We represent
the return on First Section stocks by the return on the TOPIX index4 . The benchmark model
demonstrates the information shares attributable to the trades of each investor group and to do4

The TOPIX is a free-float adjusted capitalisation-weighted index of all domestic common stocks in the TSEFirst Section.

4

mestic public equity information. We compare the benchmark model with an extended modelthat includes returns on the S&P 500 index to represent global public equity information andreturns on the USD/JPY exchange rate to proxy global public foreign exchange information.
We examine how the information shares to each investor groups’ trades and to domestic public
information change with the introduction of global public information.
The benchmark model shows that foreign investors are informed and that their influence
over the efficient price of Japanese stocks has increased since around 2000. Domestic financial, trust and individual investors are uninformed. However, the extended model suggests that
global stock prices and the exchange rate have become more important since around 2000 and2010, respectively. When we control for global public information, foreign investors’ information share is substantially lower than that for global public information in the same period.
Using the time-varying conditional correlation analysis, we show that the dynamic correlation
between foreign investors’ trading and global public information increased at the same time as
the influence of global public information on the efficient price of Japanese stocks increased.
Our results suggest that foreign investors have made more use of global information in trading
Japanese equities as the influence of international financial factors on the domestic market has
increased in recent decades. We also examine the role of foreign investors in the evolution of
informational efficiency over time using Hasbrouck’s (1993) inefficiency measure. Althoughforeign investors’ influence over the efficient price is small relative to that of global information, we confirm that foreign trading has contributed to efficiency even when global information
is taken into account.
Our focus is on transactions rather than the holdings used in He and Shen (2014), Luo et al.
(2014) and others, as transactions are more closely aligned with efficiency and the impoundingof information in prices than holdings. Boehmer and Kelley (2009) find that both the transactions and holdings of institutions matter and transactions are more important where stocks are
priced less efficiently. The long period over which we conduct our study allows us to examine
how the information content of trade has evolved, from when foreign investors were minor participants in the early 1980s, to recent years in which they dominate both ownership and trading
activity. He and Shen (2014) use the absolute change in foreign ownership as a proxy for the
trading activity of foreign investors, but this is an indirect measure at best.
Our paper is closely related to Bae et al. (2012) and Kang et al. (2016). Bae et al. (2012)suggest that foreign investors improve efficiency by incorporating global market information in
emerging stocks more quickly. Stocks with greater investibility by foreigners incorporate global

5

information faster than stocks with less investibility. Returns on more investible stocks leadthose of less investible stocks. Kang et al. (2016) find that foreign trading in Korean stocks reduces the time taken for global information dissemination, and that the effect is more significant
for global firms with high levels of foreign investor trading. Foreign investors have an informational advantage regarding global information. Our paper is also related to research showingthat greater international market integration over recent decades has increased local return correlations, suggesting a greater role for foreign or global information in domestic stock markets(Longin and Solnik, 1995; Connolly and Wang, 2003; Christoffersen et al., 2012; Chevallier
et al., 2018). Our approach differs from previous research in a number of respects. We use a
different measure of efficiency by examining the long-run impact of a trade shock and using arandom walk to proxy for the efficient price. We also specify the form of public domestic andforeign information. Further, we examine a developed market and demonstrate similarities withthe results obtained in emerging markets. Our results have general implications regarding therole of foreign investors in price formation for developed market stocks, particularly following
the late 1990s ‘Big Bang’ financial liberalisation in Japan.
Our main contributions to the literature are fourfold. First, we show that foreign investors
possess an informational advantage over domestic investors, analysed in terms of the efficient
price of Japanese stocks. Second, we demonstrate that foreign investors’ trades have increasingly reflected global stock price and exchange rate information over the past four decades.
Third, we establish the timing of changes in the information imparted by foreign investors’
trades and show that these changes are unique to the foreign investor group. Foreign investors
have increasingly relied on global financial information simultaneously with global information
becoming more influential over Japanese stock returns. Fourth, we describe the evolution of theinformation imparted by foreign investors’ trades over a lengthy sample of 39 years spanning1980 to 2019, using a consistent set of transactions and returns data.
The article proceeds as follows. We survey the relevant literature in Section 2. Section 3
explains our VAR model and the transactions and returns data used for estimation. We discuss
our empirical results and supporting evidence in Section 4. Section 5 provides the implicationsof our research for policy-makers and investors, and concludes.
2. Relevant Literature
Several recent studies examine the whether foreign investors contribute to the efficiency ofequity markets. Kacperczyk et al. (2019) build a cross country database for over 23 thousand6

firms in 40 countries and document that stocks with higher foreign institutional ownership havemore informative prices. Similarly, He et al. (2013) demonstrate a positive relationship between ownership by large foreign investors and price informativeness in 40 markets. He andShen (2014) show that Japanese stock prices deviate less from a random walk for stocks witha large change in foreign ownership. The participation of foreign investors in many emerging
markets has been increasing. Vo (2017) finds that foreign ownership improves stock price informativeness in the Vietnamese market and Lim et al. (2016) find that foreign investors accelerate
the incorporation of both local and global information in Malaysian stocks. Using data forover four thousand stocks in twenty one emerging markets, Bae et al. (2012) find that greater
investibility reduces price delay to global market information and infer that financial liberalisation yields efficiency improvements. On the other hand, Qin and Bai (2014) find emerging
market stocks that are fully investible by foreigners exhibit stronger long-run price momentum
than those which are non-investible. Hao et al. (2015) find that foreign investors decrease efficiency in the Taiwan stock index futures market due to their use of passive limit orders, despite
a substantial increase in both domestic and foreign trading volume.
A related area of literature pertains to the information asymmetry between domestic and foreign traders, and their relative investment performance. Evidence published to date suggests that
foreigners may trade at an advantage or disadvantage to locals. Numerous studies suggest thatdomestic investors have superior local information, and for that reason they have a performanceadvantage over foreign investors. Kang and Stulz (1997) show that foreign investors hold more
stocks of large firms than small firms in Japan, and suggest this may indicate foreign investors
have a greater information disadvantage in trading small stocks. Foreign investors may be ata disadvantage due to their distance from a firms’ headquarters (Coval and Moskowitz, 1999,
2001) or language difference (Grinblatt and Keloharju, 2001)5 . Looking over 32 countries,
Ferreira et al. (2017) find foreign institutional investors are disadvantaged relative domestic institutions in countries with difficult investment environments, and during market downturns or
periods of high market uncertainty. Consistent with the research suggesting domestic investors
have superior information on local stocks, several studies of investment performance suggestthat domestic investors outperform foreigners. Foreign investors have been found to be poor
market timers (Timmermann and Blake, 2005), underperform due to informational disadvan5

MSCI (2019a) notes that a lack of company and market information available in English, and a relatively poor
general level corporate governance for a developed market, as well as unconventional dividend practices, constituteaccess impediments for foreign investors in the Japanese stock market.

7

tages (Shukla and van Inwegen, 1995) or experience greater price slippage when they tradecompared with domestic investors (Choe et al., 2005). Dvorak (2005) shows locals are more
profitable than foreigners and perform better in short-term trades, while foreigners are relatively
good at longer investment horizons.
On the other hand, several studies provide evidence that foreign investors have superior information or generate higher trading profits than domestic investors. Seasholes (2000) showsthat foreign investors time Taiwanese firms’ earnings announcements well, and Grinblatt and
Keloharju (2000) provides evidence from the Finnish equity market that suggests foreigners are
better stock pickers than domestic investors. In a study of the Japanese stock market over 1995
to 2001, Karolyi (2002) found foreign investors were profitable and appeared to be good market
timers, while domestic investors performed poorly. Similarly, Kamesaka et al. (2003) foundforeign investors in Japanese stocks made profits while domestic retail investors did not. Baeet al. (2006) also demonstrates that foreigners have superior market timing ability in Japanesestocks. Albuquerque et al. (2009) provides a model and evidence suggesting that that US investors exploit superior global information when they invest in foreign markets.
Another relevant literature investigates the question of whether stocks are priced internationally or locally. If foreign investors play an important role in incorporating global information
into stock prices, this supports the case for global pricing. Karolyi and Stulz (2003) discuss
strong evidence suggesting national equity risk premia are determined internationally. For instance, a number of papers demonstrate that exchange rates influence country stock market risk
premia (Dumas and Solnik, 1995; De Santis and Gérard, 1998). On the other hand, Karolyi
and Stulz see less of an established case for the influence of international factors on the thecross-section of expected returns, and they note that home bias increases the local influence on
asset prices.
Our work is also related to the literature on the time-varying international dependence between equity markets. Developments such as greater financial openness and international capital flows, improvements in information and communications technology, increases in international economic linkages through trade in goods and services often involving complex supply
chains, greater multinational operation of listed firms, and the trend toward globalisation havecontributed to increased international financial market integration over time. With greater integration, an increase in the comovement of national market stock returns would seem a reasonable hypothesis.
Longin and Solnik (1995) find an increase in international correlation between monthly de-8veloped market stock returns, including Japan, between 1960 and 1990. Berben and Jansen(2005) show that the correlations between the German, the UK and US markets doubled between 1980 and the late 1990s, although those with the Japanese market remained the same.
Morana and Beltratti (2008) find greater interlinkages between the markets of Germany, Japan,
the UK and the US over the period 1973 to 2004. Christoffersen et al. (2012) show that thecorrelations among 16 developed and 17 emerging markets have increased markedly between
1973 and 2009. Okimoto (2014) demonstrates increased asymmetric dependence between thestock markets of France, Germany, the UK and US over the period 1973 to 2008. Connolly
and Wang (2003) present results for the US, UK and Japan that suggest foreign market returns
are more important for domestic returns than domestic economic fundamentals. Chevallier
et al. (2018) provide evidence that cross-market linkages have become stronger in the PacificBasin region between 1993 and 2014, that the United States is the single greatest contributor toshock spillover in the region, although spillover effects from the Japanese market are limited. A
common finding is that national equity market correlations rise during, or following, periods ofhigh volatility or financial crisis (Chakrabarti and Roll, 2002; Johansson, 2011; Koch and Koch,
1991; King et al., 1994; Liu et al., 1998; Longin and Solnik, 1995; von Furstenberg and Jeon,
1989)6 .
3. Methodology and Data
3.1. VAR Model of Transactions and Returns
We estimate multivariate VAR models of the form:
Yt = Φ0 + Φ1 Yt−1 + · · · + ΦP Yt−P + t(1)

where Yt contains the trade ratios and returns variables, and the Φi are coefficient matrices
for lag i = 1 . . . P included in the VAR7 . We estimate a benchmark model including traderatios for four investor groups (foreign, financial, trust and individual) and TOPIX returns. The
construction of the trade ratios is explained in Section 3.2. We also estimate an extended modelthat includes S&P 500 and USD/JPY exchange rate returns to reflect global information. We
6

Also see articles from the contagion literature such as Bekaert et al. (2011) who suggest crisis periods lead tothe international transmission of information as described by the “wake-up call hypothesis”. Longin and Solnik(2001) find correlations rise during bear markets
7
Following the approach in the literature, the block-recursive identification framework is used. The number oflags to be included in each VAR model is determined using Akaike’s Information Criterion (AIC) and the models
were estimated using ordinary least squares.

9

adapt Hasbrouck’s (1991a,b) “information content of trade” approach to estimate the influenceof the four investor groups’ trades and return variables on the efficient price.
The price of a security, pt , may be decomposed into the efficient price, mt , and mispricing,st :pt = mt + st(2)

where mt follows a random walk process, st is a mean-zero covariance stationary process, andlimh→∞ E (st+h ) = 0.
The permanent component of price, or efficient price, can be modelled as:
mt = mt−1 + ωt

(3)



where ωt ∼ N 0, σ2ω , and E (ωt ω s ) = 0 for t , s.
The efficient price, mt , is driven by the previous period’s efficient price and an innovationthat reflects new fundamental information, ωt , which is incorporated in the efficient price attime t. The efficient price innovation, ωt , has a permanent influence on the security price, while
the mispricing shock, st , is has only a temporary effect. The st component represents transitory
effects on the security’s price, or mispricing, arising from non-information based microstructure
effects, liquidity provision and noise trading. The variance of the efficient price, σ2ω , measuresthe variation in the permanent component of the price related to fundamental information.
The VAR model shown in equation (1) can be inverted to the Vector Moving Average (VMA)
representation:
Yt = I + θ1 L + θ2 L2 + θ3 L3 + . . . t = θ (L) t(4)

where L is the lag operator, the θi are N × N matrices of coefficients for N variables in the model,and t is a white noise error process with E (t ) = 0 and Var (t ) = Ω.
The variance of the shock to the permanent component of the security’s price is estimatedfrom the VMA representation in (4) as:
σ2ω = [θ (1)]N Ω [θ (1)]0N(5)where [θ (1)]N denotes the N th row of [θ (1)] that corresponds to the returns equation, and[θ (1)] = I + θ1 + θ2 + . . . .

10The variance of the trade-related component for an investor group, g, is:0σ2ω,xg = θ? (1) N Ω θ? (1) N(6)where θ? represents θ from the VMA with the coefficients related to all other investor groups
and the nontrade-related coefficients set to zero. Similarly, the variance of the non-trade-related
component σ2ω,r is calculated by setting θ? to the θ from the VMA with zeros for the coefficients
related to all investor groups.
As the trading behaviour of the different investor groups is correlated, we expect Ω to be anon-diagonal covariance matrix. Accordingly, we use Cholesky factorisation to extract σ2ω . SetΩ = F 0 F, where where F is the upper triangular Cholesky factor, and let d = [θ (1)]N F 0 . Thenthe variance of the permanent shock is the sum of the squares of the elements of d:
σ2w =

X

di2

(7)

We can obtain the variance due to trade-related, σ2ω,xg , and non-trade-related, σ2ω,r , components using this procedure, and express these relative to the total variance of the permanent
component σ2ω . The relative trade-related variance for an investor group provides a measure of
the relative influence of that group’s trades on the efficient price. The relative non-trade-related
variance gives a measure of the influence of public information on the efficient price.
3.2. Transactions and Returns Data
We use weekly data on the yen value of trading by different types of investors on the TSEfrom the first week of January 1980 to the last week of December 2018. The transactions are
for Japanese stocks that are listed on the First Section of the Exchange, which contains large
firms8 . The transactions data is collected by the TSE from its member firms (general trading
participants) on the exchange who have a minimum capital of three billion yen. It includes
all the member firms’ proprietary and client initiated brokerage transactions. We exclude themember firms’ proprietary trades given that these trades include liquidity provision for client
orders as well as trades on their own book. The client brokerage transactions represent around
80 percent of all trades on TSE. The transactions data set was obtained from the Nikkei Financial
Quest database.
8

The TSE First Section contains 2150 large firms, as of 16 August 2019. The minimum capitalisation for First
Section listing is currently two billion yen.

11

Figure 2: Transactions Data (weekly, trillions of yen, sales shown as negative)(a) Foreign(b) Financial
2

Purchases
Net
Sales

10

0

Purchases
Net
Sales

0

−10

−2
1980

1990

2000

2010

2020

1980

1990

(c) Individual

2000

2020

(d) Trust

Purchases
Net
Sales

5

2010

Purchases
Net
Sales
0.5

0

0.0

−0.5
−5

1980

1990

2000

2010

2020

1980

1990

2000

2010

2020

The transactions are broken down into the value of purchases and sales by different typesof investor9 . We include four investor groups in our analysis: foreign, individual, trust, and financial. Foreign investors are defined as all non-resident investors. While the category includes
both foreign institutional and individual investors, the vast majority of trades are by institutions.
Financial is composed of various types of domestic financial institutions, including life and
9

The TSE data contains the following breakdown. Total trading value is divided into proprietary trading andclient initiated brokerage trading. Client initiated brokerage transactions are divided into institutions; individuals;
foreigners; and securities companies. Institutions are decomposed into investment trusts; business corporations;other corporations; and financial institutions. Financial institutions are further divided into life and non-life insurers; city and regional banks; trust banks; and other financials.

12Figure 3: Equity Index and Exchange Rate Data (weekly)(a) TOPIX and S&P 500
3000(b) USD/JPY Exchange Rate

TOPIX
S&P 500

250

2000

200
150

1000

100
0
1980

1990

2000

2010

2020

1980

1990

2000

2010

2020

non-life insurers, city and regional banks, trust banks and others. We exclude the trades of corporations and securities firms from our analysis. Both groups constitute a small share of trades,
and corporate transactions reflect, in part, changes in strategic cross-shareholdings rather thanprofit motivated trades.
Plots of the purchases, sales (shown as negative values) and net purchases for each investor
group are shown in Figure 2. Note the different vertical axis scales in each panel. The plots
give a clear visualisation of when the four investor groups have been relatively active traders,by the yen value of their transactions. Foreign investors’ trading activity increased substantiallyin the 2000s and 2010s. Individuals have also been relatively active over the last two decades.
Financials and trusts were relatively active during the 1980s bull market in Japan.
Using the TSE transactions data we calculate the trade ratio, xg,t , for each investor group,g = 1, . . . , 4, as:Bg,txg,t = log
S g,t

!
(8)

where Bg,t and S g,t represent the yen value of stock purchases and sales by investor group g attime t, respectively10 .
We also use weekly logarithmic returns on the TOPIX, S&P 500 and the USD/JPY exOur trade ratio is approximately equal to (Bg,t −S g,t )/S g,t if Bg,t/S g,t is near 1. We considered two alternative traderatio indicators: (i) the difference between each group’s purchases and sales, and (ii) the difference between eachgroup’s purchases and sales divided by purchases plus sales. Linear dependence between the trade ratios may be
induced by (i) since all buys equal all sells, thus the trade indicator must be standardised in some way. Appendix
A shows the results using (i) as the trade indicator. Standardisation by (ii) and our trade ratio are both invariant toscale. For example, each indicator will be the same for buys equal to 10 thousand and sells equal to 9 thousand
as for buys equal to 10 million an sells equal to 9 million. The logarithm of purchases divided by sales has themildest variation over the sample and generates similar results to (ii), which we will make available on request.
10

13

change rate. TOPIX returns represent the return on domestic stocks11 . S&P 500 returns areused as a proxy for foreign or global equity returns. The USD/JPY return is used to represent
information from the currency market. Returns on the TOPIX and S&P 500 are calculated using the weekly close index value, while the USD/JPY return is calculated using the New York
close rate. TOPIX is obtained from the Nikkei Financial Quest database while the S&P 500 and
USD/JPY are taken from Bloomberg. Plots of the equity indices and exchange rate are shownin Figure 3.
We divide our 39 year sample into four approximately equal length subsamples to studychanges in the information content of trades. One of the advantages of our study is the ability
to examine these changes over a very long horizon using consistent time series of weekly transactions data with sufficient observations for modelling. The subsamples span the first week inJanuary to the last week in December for the following periods: 1980 to 1989 (referred to as the
1980s), 1990 to 1999 (1990s), 2000 to 2009 (2000s) and 2010 to 2018 (2010s). The number ofweekly observations in each subsample is 520, 521, 522 and 470 for the 1980s, 1990s, 2000s
and 2010s, respectively. The vertical lines in Figures 1, 2 and 3 delineate the subsamples.
Our decision to divide the sample into four equal periods rather than search for potential
break points in the series was based on two considerations: (i) consistency with the periods ofderegulation that improved foreign investors’ access to the domestic equity market; and (ii) thepracticality of having enough observations in each subsample to estimate the VAR model.
The broad trends in Japanese stock prices during the “bubble era” of the 1980s and thecollapse of the 1990s appear relatively independent of those in global stock prices, while there
is a much more obvious comovement between domestic and global stock prices in the 2000s
and 2010s12 . The 1980s saw a series of important financial liberalisations affecting the domestic
equity market that were implemented as the bubble in Japanese asset prices began to inflate. In
December 1980, the government amended the Foreign Exchange and Foreign Trade Control
Law of 1949, changing the regulatory regime for international capital flows from a “prohibition
in principle” to a “permission in principle” approach for many types of transactions (Takagi,
11

We follow the approach employed by Karolyi (2002) and Kamesaka et al. (2003) under which the trade ratio is
calculated using aggregated transactions in individual First Section stocks and returns are calculated on a relevantmarket index. We argue this approach is justified given that we are interested in the information investors exploit
in timing the market. Returns on the TOPIX are preferable to those on the NIKKEI 225 because the TOPIX is
calculated using all First Section stocks matching the stocks included in the transactions data, while the NIKKEI
225 includes only a subset of relatively large firms.
12
Our sub-samples also approximately correspond with the timing of major equity market shocks: the Japanese
asset price bubble burst at the end of the 1980s, the Asia crisis in the late 1990s and the Global Financial Crises in
the late 2000s.

14

2009). At the same time, the government also abolished the Foreign Investment Law of 1950
that controlled the acquisition of domestic stocks by non-residents. Several further policy andregulatory reforms were implemented in the mid-1980s with the aim of internationalising the
yen and domestic financial markets. These included formation of the Japan-U.S. Yen-Dollar
Committee, abolition of a requirement that forward exchange transactions must correspond tobona fide transactions, liberalisation of euroyen transactions and the Plaza Accord. The late1980s also saw the introduction of stock futures, stock index futures and index options tradingin Japan. The next round of major reforms occurred in the late 1990s within an entirely different
context of macroeconomic stagnation and declining asset prices. Japan’s Big Bang liberalisationthat aimed to make domestic financial markets and institutions more competitive and efficient
was announced in November 1996 (Takagi, 2009). The most important component was a new
Foreign Exchange and Foreign Trade Law that came into force in 1998. This provided forthe full deregulation of foreign exchange transactions and abolished the system of authorised
foreign exchange banks and designated securities firms. Other measures implemented in thelate 1990s improved access for foreign investors and removed several remaining impediments
to their transactions in domestic financial markets. These reforms coincided with an increasein foreign participation in the domestic stock market and and the partial unwinding of crossshareholdings among Japanese listed firms. Aronson (2011) notes that the Big Bang led to anincrease in the importance of equity relative to debt finance for Japanese non-financial firms
between 1995 and 2005.
3.3. Summary Statistics
Summary statistics for the equity transactions and returns data are provided for each subsample in Tables 1 and 2. We show summary statistics for the purchases, sales and net transactionsof foreign, financial, trust and individual investors. Also included are statistics for total transactions, which includes proprietary trades and client initiated brokerage trades for all investor
groups in the TSE data set. There are differences in the net transactions of the four investor
groups over the subsamples. Foreign investors are net sellers in the 1980s and net buyers thereafter. Individuals are net sellers in all subsamples. Financials are net buyers in the 1980s and1990s, and net sellers in the 2000s and 2010s. Trusts are net buyers in the 1980s, 2000s and2010s, and sellers in the 1990s. Foreign investors have the largest average purchases and sales
in all subsamples, except the 1980s when individuals are the largest traders as a group. Purchases and sales generally show high autocorrelation, while net purchases are somewhat less15

Table 1: Summary Statistics for the 1980s and 1990s
Mean

Med

Max

Min

Sum

S.D

Skew

Kurt

ρ1

ρ2

ρ3

Total purchases

2084.851050.03

9778.3446.30

1084.122058.27

1.33

3.84

0.88

0.83

0.82

Total sales

1980s Equity Transactions

2073.33

1040.869793.5343.43

1078.132050.81

1.33

3.85

0.89

0.83

0.82

Net purchases

11.52

8.70

109.93-142.02

5.99

25.47

0.24

7.84

0.43

0.23

0.18

Foreign purchases

189.16134.97881.95

3.02

98.36

153.75

1.21

4.10

0.69

0.54

0.51

Foreign sales

224.06150.781226.625.09

116.51

196.79

1.20

4.24

0.88

0.82

0.81

Foreign net purchases-34.90-11.86203.05-1045.39-18.15

94.21

-4.05

34.84

0.85

0.80

0.77

Financial purchases

358.0784.20

2087.08

5.03

186.20468.55

1.39

3.99

0.90

0.86

0.85

Financial sales

326.6561.77

1969.98

1.88

169.86443.75

1.46

4.24

0.89

0.85

0.84

Financial net purchases

31.43

12.15

394.24-182.94

16.34

66.98

1.44

7.59

0.56

0.39

0.38

Trust purchases

142.3449.28

857.57

3.02

74.02

168.94

1.62

5.02

0.89

0.86

0.86

Trust sales

132.0748.85

764.97

1.15

68.67

157.57

1.69

5.16

0.89

0.86

0.85

Trust net purchases

10.28

1.73

415.08-212.28

5.34

49.37

1.87

16.78

0.51

0.19

0.14

Individual purchases

595.13407.91

2464.1716.12

309.47493.63

1.39

4.26

0.84

0.76

0.75

Individual sales

631.47431.552477.8218.71

328.36518.85

1.35

4.15

0.84

0.76

0.75

Individual net purchases-36.34-19.60623.10-434.08-18.90

65.94

0.55

25.50

0.48

0.28

0.22

1980s Equity and Exchange Rate Returns
TOPIX

0.34970.3461

6.9552-12.1678

1.8468

-0.57

7.77

0.00

0.06

0.07

S&P 500

0.23000.46048.4617-13.0071

2.2416

-0.61

6.42

0.01

0.04

-0.04

USD/JPY exchange rate

-0.09430.0373

6.3120-8.6974

1.5251

-0.64

5.73

0.09

0.09

0.04

Total purchases

1804.39

1584.915415.19125.43940.09928.42

1.32

4.78

0.80

0.70

0.64

Total sales

1793.86

1570.32

5405.68

121.51

934.60929.53

1.33

4.81

0.80

0.70

0.64

1990s Equity Transactions

Net purchases

10.52

11.37

133.24-146.73

5.48

20.62

-0.77

16.76

0.43

0.30

0.13

Foreign purchases

422.40346.241860.58

19.42

220.07266.30

1.95

7.92

0.83

0.75

0.71

Foreign sales

376.88320.111465.84

13.67

196.36220.95

1.69

6.99

0.87

0.79

0.76

Foreign net purchases

45.52

30.84

719.86-519.35

23.72

126.99

0.65

6.76

0.62

0.52

0.40

Financial purchases

306.64285.93952.35

12.64

159.76161.18

1.12

4.79

0.75

0.63

0.57

Financial sales

293.38242.741044.815.75

152.85190.53

1.30

4.67

0.77

0.65

0.59

Financial net purchases

13.26

19.23

476.02-543.45

6.91

109.08

-0.38

5.37

0.63

0.48

0.43

Trust purchases

103.41

70.81

689.14

5.74

53.88

99.26

2.22

8.95

0.86

0.81

0.76

Trust sales

114.3183.61

644.84

1.95

59.55

97.45

1.91

6.94

0.86

0.81

0.76

Trust net purchases-10.90-9.84

280.44-210.18

-5.68

45.90

0.93

10.99

0.59

0.37

0.26

Individual purchases

325.45220.771629.2121.00

169.56264.91

1.84

6.38

0.83

0.72

0.65

Individual sales

343.49246.581408.1724.19

178.96251.76

1.64

5.54

0.82

0.72

0.66

Individual net purchases-18.04-19.29384.58-264.93

-9.40

68.53

1.20

9.15

0.48

0.38

0.20

1990s Equity and Exchange Rate Returns, Forward Earnings Per Share
TOPIX-0.0956-0.0381

10.8383-11.9169

2.7857

-0.03

4.74

-0.01

0.08

0.04

S&P 500

0.27410.3456

7.0619-6.8626

1.8624

-0.05

3.77

-0.12

0.07

0.01

MSCI Kokusai

0.24300.22388.2359-5.4495

1.6368

0.00

4.22

-0.03

0.06

0.05

FEPS

-0.0777-0.1236

149.6206-76.5629

13.0387

5.25

70.59

-0.01

-0.09

0.04

USD/JPY exchange rate

-0.0650

0.10945.9878-14.9794

1.7284

-1.48

13.56

-0.04

0.05

-0.01

The summary statistics are the mean (Mean), median (Med), maximum (Max), minimum (Min), sum (Sum), standard deviation (S.D.),
skewness (Skew), excess kurtosis (Kurt), and the autocorrelation coefficients for one (ρ1 ), two (ρ2 ) and three (ρ3 ) lags. Transactions
are in billions of yen per week, except for the sum which is stated as trillions of yen for the whole subsample. Returns are in percent
per week. The 1980s sample consists of 520 weekly observations spanning the first week in January 1980 to the last week in December 1989. The 1990s sample consists of 521 weekly observations spanning the first week in January 1990 to the last week in December
1999. Forward Earnings Per Share (FEPS) data starts from 1994.

16

Table 2: Summary Statistics for the 2000s and 2010s
Mean

Med

Max

Min

Sum

S.D.

Skew

Kurt

ρ1

ρ2

ρ3

Total purchases

6655.495507.85

18911.57

170.153474.163712.40

0.81

2.74

0.88

0.82

0.82

Total sales

6651.60

5496.66

18907.18

169.553472.143713.76

0.81

2.75

0.88

0.82

0.82

3.88

4.15

84.70

-87.00

2.03

20.27

-0.20

4.33

0.20

0.08

0.03

Foreign purchases

2699.852033.32

9921.18

61.52

1409.32

1795.67

0.94

2.93

0.90

0.85

0.85

Foreign sales

2638.04

1930.6310122.76

61.90

1377.06

1794.86

1.04

3.26

0.90

0.84

0.83

Foreign net purchases

61.81

48.65

932.43-911.7332.27

224.04

-0.03

4.15

0.39

0.31

0.27

Financial purchases

504.76481.611341.30

18.86

263.49185.05

0.78

4.83

0.62

0.48

0.42

Financial sales

525.80494.051424.84

5.86

274.47211.07

0.85

4.42

0.64

0.46

0.43

Financial net purchases-21.03-18.64438.73-692.50-10.98131.90

0.12

4.90

0.62

0.48

0.42

Trust purchases

130.32115.94410.57

2.23

68.03

75.89

0.69

2.72

0.82

0.78

0.76

Trust sales

120.6699.21

431.36

1.45

62.98

74.17

0.89

3.32

0.83

0.78

0.77

2000s Equity Transactions

Net purchases

Trust net purchases

9.66

6.08

226.39-197.84

5.04

33.82

0.60

9.25

0.45

0.39

0.26

Individual purchases

1196.721010.394859.0430.65

624.69825.24

1.31

5.17

0.89

0.84

0.82

Individual sales

1233.221078.944534.8628.43

643.74

831.54

1.16

4.47

0.89

0.85

0.83

Individual net purchases-36.51-32.87587.79-691.38-19.06173.81

0.03

4.29

0.16

0.10

0.04

2000s Equity and Exchange Rate Returns, Forward Earnings Per Share
TOPIX-0.1227

0.10269.2469-22.0185

2.9393

-0.94

8.72

-0.06

0.04

-0.03

S&P 500-0.0528

0.095811.3559-20.0837

2.7847

-0.86

10.07

-0.06

0.06

-0.09

MSCI Kokusai-0.0300

0.202412.6402-23.0701

2.8129

-1.19

13.38

-0.03

0.07

-0.07

FEPS

-0.2671-0.0758

254.4334-100.0000

17.9571

8.43

124.67

0.41

0.00

-0.06

USD/JPY exchange rate

-0.01860.02084.5521-7.5236

1.4609

-0.37

4.42

-0.07

0.06

-0.03

Total purchases

10170.49

10462.93

23617.75

2048.794780.133936.67

0.16

2.42

0.77

0.68

0.68

Total sales

10167.24

10466.92

23664.30

2047.384778.60

3934.44

0.16

2.43

0.77

0.69

0.68

3.25

2.11

89.91

-80.64

1.53

17.38

0.40

7.88

0.33

0.12

0.06

Foreign purchases

5961.646160.4114896.54

1029.232801.972545.00

0.14

2.20

0.80

0.73

0.72

Foreign sales

5930.586214.4715535.88

844.972787.372574.39

0.20

2.33

0.81

0.74

0.73

2010s Equity Transactions

Net purchases

Foreign net purchases

31.06

17.93

1535.62-1193.50

14.60

289.11

0.23

6.27

0.44

0.30

0.18

Financial purchases

417.12402.151229.0250.77

196.05149.76

1.02

6.05

0.48

0.30

0.27

Financial sales

421.62406.181179.05

49.61

198.16155.32

0.99

5.68

0.47

0.37

0.37

Financial net purchases

-4.49

-6.39

487.89-543.13-2.11

116.46

-0.14

5.15

0.61

0.45

0.37

Trust purchases

196.11

187.37527.98

21.28

92.17

88.32

0.56

2.97

0.69

0.62

0.59

Trust sales

195.97184.74537.65

19.70

92.11

91.58

0.71

3.46

0.71

0.61

0.60

Trust net purchases

0.14

0.74

205.39-223.45

0.07

51.88

-0.38

6.16

0.40

0.17

0.07

Individual purchases

1677.211660.686855.67

351.73788.29775.06

1.46

8.95

0.78

0.69

0.66

Individual sales

1742.05

1699.226456.86

303.72818.76819.34

1.29

7.37

0.77

0.68

0.65

Individual net purchases-64.83-47.90746.69-1124.51-30.47237.41

-0.47

5.03

0.31

0.11

0.01

0.00

-0.06

2010s Equity and Exchange Rate Returns, Forward Earnings Per Share
TOPIX

0.10280.37888.4911-13.4842

2.6198

-0.62

4.91

0.03

S&P 500

0.17450.28357.1284-7.46031.9800

-0.60

4.93

-0.11

0.00

-0.05

MSCI Kokusai

0.10390.23538.1464-9.3374

2.0441

-0.70

5.67

-0.07

-0.05

-0.04

FEPS

0.36700.022229.0549-12.2419

2.8723

5.63

51.30

0.32

0.36

0.10

USD/JPY exchange rate

0.03270.06354.2261-4.8477

1.3253

-0.10

3.54

-0.02

-0.01

0.00

The summary statistics are the mean (Mean), median (Med), maximum (Max), minimum (Min), sum (Sum), standard deviation (S.D.), skewness (Skew), excess kurtosis (Kurt), and the autocorrelation coefficients for one (ρ1 ), two (ρ2 ) and three (ρ3 ) lags. Transactions are in billionsof yen per week, except for the sum which is stated as trillions of yen for the whole subsample. Returns are in percent per week. The 2000s
sample consists of 522 weekly observations spanning the first week in January 2000 to the last week in December 2009. The 2010s sample
consists of 470 weekly observations spanning the first week in January 2010 to the last week in December 2018.

17

autocorrelated, particularly in the latter half of the sample.
3.4. Order of Variables in the VAR
The order in which variables enter a VAR can influence the orthogonalized impulse responses. Variables should be ordered from most to least exogenous. We assume that global
stock returns are the most exogenous and thus should enter the VAR first, followed by returns
on the USD/JPY exchange rate. The daily close of trade on TSE occurs prior to the NYSE
open. However, on a weekly basis it is reasonable to expect that the US equity market influences Tokyo more than the other way around, consistent with the finding of Chevallier et al.
(2018) that the Tokyo market receives more shocks than it transmits. The market capitalisationof NYSE is far greater than that of TSE and the bulk of global market moving economic andcorporate news is released during European and US trading hours, influencing US equity indices before Tokyo. We order the remaining variables as foreign, financial, trust and individual
trade ratios followed by TOPIX, thus we assume that TSE transactions are more exogenous than
TOPIX returns. Appendix C demonstrates that our main conclusions are robust to an alternative
variable order.
4. Empirical Results and Discussion
4.1. VAR Empirical Results
Our benchmark model is specified in the spirit of Hasbrouck’s (1991a,b) “information content of trade” approach, which includes a trade indicator for a security and its return as endogenous variables in a VAR system. Our specification includes the trade ratio of transactions in
TSE First Section stocks for each investor group and TOPIX returns as the endogenous variables. Table 3 shows the results for the benchmark model.
Panel (a) provides the long-run price impact of trade for each investor group, which is the
cumulative impulse response to a one standard deviation unanticipated increase in the investor
group’s trade ratio, evaluated at 30 weeks ahead. The long-run price impact of a shock to TOPIX
returns is also provided in the table. A positive (negative) long-run price impact of trade for aninvestor group shows that an unanticipated purchase is expected to be followed by a cumulative
increase (decrease) in price over the following 30 weeks. An investor group may be informed if
the long-run price impact of their trades is positive, and uninformed if the long-run price impact
is zero or negative.

18

Table 3: Benchmark Model
1980s

1990s

2000s

2010s

(a) Long-Run Price Impact of Trade
Foreign

1.27

1.89

2.07

1.82

Financial

-0.17

-0.44

0.06

0.04

Trust

0.27

-0.10

-0.36

-0.17

Individual

-0.45

-1.05

-1.02

-1.52

TOPIX

1.71

2.39

2.09

1.53

(b) Variance Decomposition of the Efficient Price (%)
Foreign

33.23

33.84

43.74

41.63

Financial

0.57

1.83

0.04

0.02

Trust

1.49

0.09

1.33

0.34

Individual

4.21

10.45

10.60

28.80

TOPIX

60.49

53.79

44.29

29.20

516

521

522

470

4

3

4

2

Observations
Lags

The order in which the variables were included in the VAR is Foreign, Financial, Trust, Individual and TOPIX.

The variance decomposition of the efficient price in percent share is given in Panel (b).
This shows the extent to which an exogenous shock to each variable in the model explains thevariance of the efficient price shock. The share for each investor group reflects the proportion ofefficient price variation attributed to their unanticipated trades, and represent the trade-related
components of the efficient price. The variance decomposition share for the TOPIX represents
the nontrade-related component of the efficient price, which is the efficient price variation due
to unanticipated shocks in stock prices. Investors with a positive long-run impact and relatively
large trade-related information share are considered to be more informed.
In the benchmark model, foreign investors appear to be informed. Foreigners have a positive long-run price impact in Table 3 Panel (a) and a relatively large information share for all
subsamples in Panel (b). The influence of foreign investors’ trades on the efficient price increased substantially in the 2000s and remained high in the 2010s as indicated by the sustained
increase in information share from the 2000s. At the same time, the influence of TOPIX on theefficient price falls in the 2000s and again in the 2010s. Financials, trusts and individuals are
uninformed. Financials and trusts have mixed positive and negative long-run price impacts thatare relatively small in absolute value, and their information shares are low suggesting they have19

almost no influence on the efficient price. Individual investors have negative long-run price impacts in all four subsamples. Their information share does increase over time, but the negative
long-run price impact suggests their trades supply liquidity to other investors.
The foreign investors’ positive long-run price impact of trade and relatively large information share suggests they possess an informational advantage over the other investor groups.
However, the benchmark model does not tell the full story. Next, we examine the information
content of foreign investors’ trades controlled for global public information.
Table 4: Extended Model Including S&P 500 and USD/JPY Exchange Rate Returns
1980s

1990s

2000s

2010s

(a) Long-Run Price Impact of Trade
Foreign

0.66

1.60

1.09

0.78

Financial

-0.01

-0.44

0.12

0.15

Trust

0.18

-0.06

-0.20

-0.18

Individual

-0.20

-1.15

-0.64

-0.73

TOPIX

1.47

2.23

1.55

1.21

S&P 500

0.98

1.45

2.25

1.78

USD/JPY

-0.27

-0.52

0.48

1.40

(b) Variance Decomposition of the Efficient Price (%)
Foreign

11.81

22.35

12.68

7.83

Financial

0.00

1.67

0.17

0.30

Trust

0.92

0.04

0.45

0.40

Individual

1.08

11.63

4.45

6.91

TOPIX

58.43

43.64

25.65

18.89

S&P 500

25.75

18.35

54.16

40.62

USD/JPY

2.00

2.33

2.44

25.04

Observations

518

521

522

470

2

2

2

1

Lags

The order in which the variables were included in the VAR is S&P 500, USD/JPY, Foreign, Financial, Trust, Individual and TOPIX.

As discussed in Section 2, numerous studies have shown that international equity market
and exchange rate information is important for local stock market risk premia. International
equity market comovement and cross market linkages have increased (Berben and Jansen, 2005;
Chevallier et al., 2018; Christoffersen et al., 2012; Karolyi and Stulz, 2003; Longin and Solnik,
1995; Okimoto, 2014) which suggests global equity information is important. Karolyi and Stulz

20(2003) suggest that national equity risk premia are internationally determined. Exchange rates
have been shown to influence local stock market risk premia (Dumas and Solnik, 1995; De
Santis and Gérard, 1998). To examine the influence of foreign investors’ trades on the efficient
price of Japanese stocks in the presence of global information, we add two variables to thebenchmark model and refer to this as the extended model. We include the return on the S&P 500
index in the model to proxy for global equity market information and the USD/JPY exchange
rate to capture information in the foreign exchange market. Inclusion of these variables in the
VAR allows us to assess the informational advantage of foreign investors controlled for global
public information.
Table 4 provides the estimates of the long-run price impact of trade and the variance decomposition of the efficient price for the extended model. The long-run price impact of unanticipated S&P 500 shocks on the efficient price of Japanese stocks is positive as would be expected.
Good news for global stocks is good news for Japanese stocks. The long-run price impact of
the USD/JPY exchange rate is negative in the 1980s and 1990s, but positive in the 2000s and2010s. During the 2010s, unexpected depreciation of the Japanese yen is associated with higher
Japanese stock prices.
Global stock price shocks have an important influence over the efficient price of Japanesestocks during each subsample in Table 4, and in particular for the 2000s and 2010s. During
the 2000s and 2010s, global equity market shocks account for around half the variance in theefficient price of Japanese stocks. The USD/JPY exchange rate has a substantial influence on theefficient price during the 2010s, but a negligible impact in the 1980s, 1990s and 2000s. Global
equity and exchange rate information together account for around two thirds of the variance inthe efficient price of Japanese stocks in the 2010s, up from just over one quarter in the 1980s.
Relative to the benchmark model, the influence of TOPIX returns on the efficient price inthe extended model is lower in the 1990s, and substantially lower in the 2000s and 2010s. Controlling for global public financial information, the importance of domestic equity market news
has also declined over time. In the extended model, TOPIX returns account for around 60 percent of the variance of the efficient price in the 1980s, but under 20 percent in the 2010s. Global
rather than local information has become relatively important in the formation of Japanese stock
prices, particularly in the 2000s and 2010s.
Compared with the benchmark model, foreign investors’ information share is substantiallylower in all subsamples under the extended model. Whereas under the benchmark model, foreign investors have a higher information share in the 2000s and 2010s than the 1980s and 1990s,

21this is no longer the case when we control for global stock prices and the exchange rate. We
argue that the majority of the information attributed to the trades of foreign investors in thebenchmark model is related to international financial factors, as proxied by S&P 500 and exchange rate returns in the extended model. Interestingly, foreign investors have a relatively
large information share during the difficult 1990s period compared with all other decades when
we control for international financial factors, while at the same time global and domestic stock
prices together have the lowest information share of all decades. Our results are robust to using the MSCI Kokusai index as a broader alternative proxy for global stock price information(Appendix B) and the main conclusions remain unchanged for an alternative ordering of thevariables in the VAR models (Appendix C).
Table 5: Correlations Between Trade Ratio and Returns Series
For

Fin

Tru

Ind

TOP

SP5

For

1980s

1990s

Fin

-0.24***-0.49***

Tru-0.37***

0.01

Ind

-0.43***

-0.04

TOP

0.33***-0.21***

0.01-0.29***

SP5

0.25***-0.15***

0.03-0.19***

0.37***

JPY-0.10**

0.12***-0.04

0.14***-0.16***-0.20***

-0.04

2000s

Fin

Tru

Ind

TOP

-0.27***

0.15***-0.29***-0.14***

0.06

0.24***-0.30***-0.10**-0.26***

0.11***-0.13***-0.05-0.17***

0.33***

0.02

0.00

-0.04

-0.04

-0.07

SP5

0.11**

2010sFin-0.55***

Tru-0.25***

0.30***-0.45***

Ind-0.56***

0.17***

TOP

0.48***-0.18***-0.03-0.64***

SP5

0.25***-0.12***

0.00-0.34***

0.52***

JPY

-0.04

-0.01

-0.02

-0.09*

0.22***

0.07*

0.22***-0.21***

0.43***-0.63***

0.16***

0.19***

0.51***-0.10**-0.11**-0.75***

0.28***

-0.06

-0.06

-0.49***

0.56***

0.30***-0.09*-0.12***-0.44***

0.57***

0.28***The trade ratios are foreign (For), financial (Fin), trust (Tru) and individual (Ind). The returns are TOPIX (TOP), S&P 500 (SP5) and USD/
JPY (JPY). Significant at 1% shown by *** , at 5% shown by ** , at 10% shown by * .

We provide supporting evidence for our argument in the form of: (i) correlations between
the data series (the trade ratios for the four investor groups and the TOPIX, S&P 500 and USD/
JPY exchange rate returns) in Table 5; and (ii) estimated time-varying conditional correlations
between the residuals of each equation in the extended model, as shown in Figure 4. We estimated the time-varying conditional correlations using a restricted version of diagonal Baba,
Engle, Kraft and Kroner (BEKK) model (Baba et al., 1985) as defined in Engle and Kroner(1995)13 . We estimated four diagonal BEKK models, one for each of the four subsamples, us13

We selected diagonal BEKK as the most appropriate multivariate model for estimating time-varying condi-

22

Figure 4: Dynamic Correlations Estimated for the Residuals of the Extended Model(a) Foreign and TOPIX(b) Foreign and S&P500

1.0

1.0

0.5

0.5

0.0

0.0

Cor(Foreign, S&P500)

Cor(Foreign, TOPIX)

−0.5

−0.5

30−week Moving Average

1980

1990

2000

2010

2020

30−week Moving Average

1980

1990

(c) Foreign and USD/JPY Exchange Rate

2000

2010

2020

(d) TOPIX and S&P500

1.0

1.0
Cor(Foreign, JPY)
30−week Moving Average

0.5

0.5

0.0

0.0

−0.5

−0.5

Cor(TOPIX, S&P500)

1980

1990

2000

2010

2020

30−week Moving Average

1980

1990

2000

2010

2020

(e) TOPIX and USD/JPY Exchange Rate
1.0
Cor(TOPIX, JPY)
30−week Moving Average

0.5

0.0

−0.5
1980

1990

2000

2010

2020

ing the residuals of the extended model. Recall that the residuals for the trade ratio equations
may be interpreted as unanticipated trades while those for the TOPIX, S&P 500 and USD/JPY
equations are return innovations. Overall, the two correlation analyses show that foreign investors increased their reliance on global information to trade Japanese stocks simultaneouslywith the increase in the influence of international financial factors on the domestic market.
tional correlations given the limitations of similar models discussed in Caporin and McAleer (2013).

23

Table 5 shows that foreign investors’ trades are positively and significantly correlated with
TOPIX and S&P 500 returns in all subsamples. Foreign trades and TOPIX returns are substantially more correlated in the 2000s and 2010s than in the 1980s and 1990s, while foreign trades
and both stock return series have relatively low correlations in the 1990s. Similarly, the conditional correlation between unanticipated foreign trades and TOPIX innovations (Figure 4 Panel(a)) shows a stable positive relationship around the 0.5 level during the 2000s and 2010s, but notin the 1980s and 1990s. Unanticipated foreign trades are also more consistently related to global
stock return innovations throughout the 2000s and 2010s compared with the 1980s and 1990s(Figure 4 Panel (b)) suggesting a stronger relationship between global information and foreign
investors’ trades in Japanese stocks. Foreign trades and USD/JPY exchange rate are positively
and significantly correlated during the 2010s, but not in the 1980s, 1990s or 2000s. Similarly,
Figure 4 Panel (c) shows a stable positive relationship between unanticipated foreign trades andexchange rate return innovations for the 2010s only. The correlation analyses demonstrate thatthe increase in the correlations between foreign investors’ trades and global financial variables
coincides with the increase in the influence of global information on the efficient price shownin Table 4.
Table 5 also suggests that foreign investors’ trading behaviour is quite different to that ofdomestic investors. The domestic investor groups’ trades have low or negative and significant
correlations with both domestic and foreign equity information. Domestic investors’ trades are
generally positively correlated with exchange rate returns in the 1980s and negatively correlated
in the 2010s. The correlations between foreign and domestic investor groups’ trades are negativeand significant in all subsamples. This is consistent with the conclusion drawn from Tables 3
and 4 that only foreign investors’ trades are related to global information.
Both Table 5 and Figure 4 Panel (d) illustrate the closer relationship between Japanese andglobal stock returns in the latter half of our sample. TOPIX and S&P 500 returns have higher
positive correlations in the 2000s and 2010s than the 1980s and 1990s. Conditional correlations
during the 2000s and 2010s are greater, on average, than in the 1980s and 1990s. TOPIX andthe exchange rate show a high correlation in the 2010s, both in terms of the return correlationsin Table 5 and the conditional correlation of innovations in Figure 4 Panel (e). The conditionalcorrelation increased over the 2000s, prior to which there was no stable relationship between
the TOPIX and USD/JPY innovations. These results are consistent with the hypothesis thatinternational financial factors have become more important for the Japanese equity market overrecent decades.

24

The extended model shows that the influence of global stock price and exchange rate information on the efficient price of Japanese stocks has increased over time. Global information has
had a substantial permanent impact on domestic stock prices, particularly during recent decades.
At the same time, the correlation between foreign investors’ trading and global information has
also increased. The timing of the increase in correlation between foreign investors’ trading andglobal financial variables coincides with the increase in the influence of global information on
the efficient price of Japanese stocks. As the comovement between Japanese and international
stock returns increased over time, the trade strategies of foreign investors appear to exploit moreglobal equity price and exchange rate information. In contrast, the trading of the domestic investor groups does not show a similar pattern. Their trades are negatively related or unrelated
to global public information.
A number of interpretations are possible. Foreign investors may use global public information to trade Japanese stocks on the knowledge that international factors have become more
important for domestic stock returns. Foreign investors may also possess private information
related to global public information and use this to trade Japanese stocks. An alternative interpretation is that the increase in foreign trading activity in Japanese stocks may have led to theincrease in correlation between domestic returns and global information. A limitation of ourstudy is that we are unable to determine the extent to which foreign investors exploit public or
private global information in their trading, or to identify whether causality runs from increased
foreign activity to higher international return correlation or the other way around.
4.2. Foreign Investors and Efficiency
Our benchmark and extended models provide estimates of the relative influence of foreign
investors on the efficient price compared with the other trade- and nontrade-related components
and show that foreign investors’ information share declines from the 1990s. However, do thetrades of foreign investors contribute to the informational efficiency of the Japanese stock market? Has the market become more efficient over time with greater foreign investor participation?
To examine these questions, we estimate measures of informational inefficiency that can be derived from our benchmark and extended VAR models, based on the approach of Hasbrouck(1993).
Equation (2) specifies the price of a security, pt , as the sum of the efficient price, mt , andmispricing, st . Boehmer and Kelley (2009) interpret the standard deviation of st , σ s , as a mea-

25

Table 6: Informational Inefficiency Measures
1980s

1990s

2000s

2010s

σs
σ s /σω

2.43
1.10

2.67
0.82

1.75
0.56

0.59
0.21

σ s /σω, Foreign
σ s /σω, Financial
σ s /σω, T rustσ s /σω, Individual
σ s /σω, T OPIX

1.92
14.57
9.03
5.38
1.42

1.41
6.08
26.74
2.54
1.12

0.84
27.66
4.84
1.72
0.84

0.33
14.76
3.59
0.39
0.39

(a) Benchmark Model(b) Extended Model Including S&P 500 and USD/JPY Exchange Rateσsσ s /σωσ s /σω, Foreign
σ s /σω, Financial
σ s /σω, T rustσ s /σω, Individual
σ s /σω, S&P 500
σ s /σω, US D/JPY
σ s /σω, T OPIX

1.99
1.03

2.67
0.79

1.17
0.38

0.48
0.17

3.00
175.20
10.76
9.92
2.03
7.29
1.35

1.67
6.11
41.33
0.34
1.84
5.18
1.20

1.08
9.39
5.73
1.82
0.52
2.46
0.76

0.61
3.15
2.71
0.65
0.27
0.34
0.40

sure of market inefficiency because it shows the extent to which pt deviates from mt over time14 .
Hasbrouck (1993) provides a method to estimate the lower bound for σ s from VARs of transactions and returns, such as our benchmark and extended models. For simplicity, we use thenotation σ s to refer to this lower bound from this point onward. We examine the evolutionof both market inefficiency and inefficiency relative to fundamental information over the four
decades from the 1980s to the 2010s.
We calculate inefficiency divided by the standard deviation of the efficient price, σ s /σω ,from the benchmark and extended model estimates. This is a measure of inefficiency relative tofundamental information, including both the trade- and nontrade-related components. We calculate σ s /σω,xg which is inefficiency per standard deviation of the trade-related contribution to
the efficient price by each investor group, where g = 1, . . . , 4, denotes the four investor groups,
foreign, financial, trust and individual. These measures represent inefficiency relative to eachtrade-related fundamental information source. We use these measures to demonstrate the extent
As st has a zero mean, σ s , represents the magnitude of mispricing over time. Specifically, Boehmer and Kelley(2009) call σ s an (inverse) measure of market efficiency, while Hasbrouck (1993) uses σ s as a measure of market
quality.
14

26

to which market inefficiency decreases over time relative to each investor group’s contribution to
the efficient price through trades. Similarly, σ s /σω,rh is inefficiency per standard deviation of thenontrade-related contribution to the efficient price by each return variable, where h = 1, . . . , 3,denotes the three returns series S&P 500, USD/JPY and TOPIX.
We calculate σ s /σω,xg as:
σ s /σω,xg =σ s /σωσω,xg /σω(9)where σ s /σω (inefficiency per standard deviation of the efficient price) and σω,xg /σω (squareroot of the variance decomposition for each investor group) are obtained from the variance decomposition of the efficient price for the benchmark and extended models. σ s /σω,rh is calculated
in a similar fashion.
Table 6 provides the estimates of σ s and the associated ratios to fundamental information,
σ s /σω , for the benchmark and extended models. Both models show that informational efficiency has improved since the 1990s. The 1990s is the least efficient subsample, coinciding
with the decade following the collapse of the Japanese asset price bubble. σ s /σω declines ineach decade following the 1980s, suggesting that fundamental information has contributed tothe improvement in efficiency of the Japanese stock market. According to both the benchmark
and extended models, σ s /σω, Foreign also declines continuously from decade to decade, and isrelatively small compared with the ratios for the other investor groups. Foreign investors’ trades
have improved the efficiency of the Japanese stock market, even when controlling for the informational contribution of global stock prices and the exchange rate.
4.3. Forward Earnings Information
The influence of foreign investors’ trades on the efficient price of Japanese stocks may reflect their use of fundamental information on Japanese stocks. Foreign investors’ trades may
exploit forecasts of Japanese stock price fundamentals. Global stock or exchange rate information may also be related to domestic stock price fundamentals. To examine whether the
influence of foreign investors trades and/or the global information variables reflect fundamental
information, we include a measure of earnings forecasts for TSE First Section stocks in the extended model. The Nikkei newspaper produces a forecast price-earnings ratio for the TSE First
Section, calculated as the market capitalisation of the First Section divided by the newspaper’s
forecast of total First Section earnings for the next twelve months. This data is available from27

199415 . We calculate the forecast earnings per share (FEPS) for First Section companies bydividing the TOPIX by the newspaper’s price-earnings ratio. We use the percentage change of
FEPS in our model, both for consistency with the percent change units of the other variables,and because the level of FEPS is non-stationary. Summary statistics for the percentage change
in FEPS appear in Tables 1 and 2.
Table 7: Extended Model Including S&P 500, USD/JPY Exchange Rate Returns Plus FEPS
1990s

2000s

2010s

(a) Long-Run Price Impact of Trade
Foreign

1.17

1.08

0.81

Financial

-0.66

0.15

0.12

Trust

-0.06

-0.19

0.00

Individual

-0.81

-0.66

-0.66

TOPIX

1.57

1.51

1.05

S&P 500

1.11

2.27

1.93

USD/JPY

0.04

0.55

1.30

FEPS

0.08

-0.06

-0.45

(b) Variance Decomposition of the Efficient Price (%)
Foreign

22.14

12.45

8.37

Financial

6.99

0.25

0.18

Trust

0.05

0.40

0.00

Individual

10.70

4.57

5.58

TOPIX

39.80

24.14

14.14

S&P 500

20.17

54.91

47.57

USD/JPY

0.02

3.24

21.53

FEPS

0.11

0.04

2.64

Observations

299

513

470

1

2

2

Lags

The order in which the variables were included in the VAR is S&P 500, USD/JPY, FEPS, Foreign, Financial,
Trust, Individual and TOPIX. The 1990s sample begins in 1994.

Table 7 provides the results for the extended model with FEPS. The variance decompositionof the efficient price suggests that FEPS has a negligible influence over the efficient price. Thus
including Japanese fundamental equity information in the extended model does not change our
15

There are three gaps in the Nikkei newspaper price-earnings ratio data, from 28 February to 21 May 1999, 24
March to 10 May 2002, and 3 May to 5 May 2009.

28

main results. This suggests that the information in foreign investors’ trades is not related toforecasts of Japanese corporate earnings16 . Note that the long-run price impact of unanticipatedrevisions to FEPS is negative in the 2010s, indicating that an unexpected increase in earnings
would be followed by lower stock prices. This suggests that earnings forecast revisions havenot been a good signal for Japanese stock prices during the most recent decade.
4.4. Discussion
Our benchmark model demonstrated that foreign investors trade at an information advantage
to domestic investors, and that their information advantage increased since around 2000. When
we control for global public information using the extended model, we find that the influenceof global stock prices on the Japanese market increased since the 2000s and the influence of theexchange rate increased during the 2010. Together, these sources of public information accountfor the increase in foreign investors’ information advantage implied by the benchmark model.
The extended model shows that local public information, represented by TOPIX returns, has
the largest information share in the 1980s and 1990s, while global public information has thelargest information share in the 2000s and 2010s. We also show that foreign investors’ trades
become more correlated with both global public information variables at the same time as thesevariables exert greater influence over Japanese stock prices. However, why has global stock
price information influenced Japanese stocks to a greater extent since 2000 and why has theexchange rate influenced Japanese stock prices only since 2010?
Previous research on national equity market comovement provides evidence consistent with
our finding that global stock price information was of greater importance for the Japanese market during the 2000s and 2010s than the 1980s and 1990s. Berben and Jansen (2005) conclude
that the Japanese market was comparatively disconnected from global market developments between 1980 and 2000, potentially reflecting the severe financial problems faced by the Japanese
economy at the time. Morana and Beltratti (2008) note that while international linkages continued to strengthen over the last decade of their 1973 to 2004 sample for the German, UK and
US markets, this was not the case for Japan. They suggest that the protracted macroeconomic
stagnation of the 1990s resulted in a more idiosyncratic Japanese market, that is, domestic
fundamentals dominated the trend toward greater international comovement of equity prices.
Christoffersen et al. (2012) note that Japanese stocks have a relatively low return correlation
16

It is possible that foreign investors forecast earnings ahead of the NIKKEI newspaper forecast, which may
mute the influence of FEPS on the efficient price.

29

with other developed markets from 1973 to 2000, after which the correlations increase.
We would argue that the relatively low influence of global public stock price information
during the 1980s and 1990s is likely due to the Japanese market’s idiosyncratic asset pricebubble during the latter half of the 1980s and dealing with the fallout of an idiosyncratic bust formuch of the 1990s. The “bubble era” of the 1980s saw asset prices, particularly real estate andstocks, rise substantially and broadly independently from asset prices in other countries17 . The
collapse of the bubble saw a prolonged period of macroeconomic stagnation, falling asset prices,
lingering zombie corporations, an intensifying non-performing loan problem and a banking
crisis in 1997 (Tsuruta and Miyasaka, 1999). In contrast, a bull market in stocks developed
from the mid-1990s in the US.
The late 1990s also saw Japan’s Big Bang period of financial deregulation during which
numerous restrictions on financial trading and cross-border transactions that persisted throughthe 1980s and 1990s were lifted, as discussed in Section 3.2. Significant liberalisation aimed
to increase price competition and efficiency in financial services. Deregulation and efforts tointernationalise the Japanese yen resulted in accelerated financial integration with the rest of thedeveloped world and a highly financially open economy (Takagi, 2009). Financial globalisation
as a result of the Big Bang deregulations is likely to have contributed to the relatively greater
influence of international financial factors on Japanese stock prices in the 2000s and 2010s.
Our results show that the exchange rate only influences Japanese stock prices during the2010s, and not before. The 2010s saw substantially greater monetary easing from the Bank ofJapan (BoJ). This began with “Comprehensive Monetary Easing” in October 2010, followed by
the Prime Minister’s announcement of a new policy to overcome deflation and adopt unlimitedmonetary easing in November 2012. In 2013 the BoJ adopted an aggressive and unconventionalmonetary policy stance, including a two percent “Price Stability Target” in January, followed by
the introduction of “Qualitative and Quantitative Monetary Easing” (QQE) in April. QQE involves very large scale asset purchases, far in excess of previous episodes of quantitative easingin Japan. The Bank expanded QQE in 2014, and enhanced the program with “Quantitative andQualitative Monetary Easing with a Negative Interest Rate” and “Quantitative and Qualitative
Monetary Easing with Yield Curve Control”, both in 2016. These policies sharpened global
investor focus on the relatively accommodative monetary stance in Japan.
Several studies show that the BoJ’s unconventional monetary policy has been associated17

Outward investment from Japan did elevate asset prices in some smaller economies (Reinhart and Rogoff,2009).

30

with yen depreciation and higher domestic stock prices (Rogers et al., 2014; Fukuda, 2015;
Kawai, 2015; Ryou et al., 2019), spillover effects to other Asian stock markets (Kawai, 2015;
Fukuda, 2018; Sugimoto and Matsuki, 2019) and economies (Ryou et al., 2019)18 . Yen depreciation itself should be favourable for Japanese stock returns on higher yen-denominated earnings
from international operations and greater export sales, particularly where pricing-to-market occurs. Rogers et al. (2014) note that BoJ asset purchase announcements have a larger effect on
the exchange rate and stock prices than other monetary policy announcements. Fukuda (2015)
finds that nighttime traders, assumed to be foreign, purchased Japanese stocks and sold the yenmore aggressively than daytime traders, assumed to be domestic, in response to Japanese unconventional monetary policy news between November 2012 and October 2014. The effects of
the BoJ’s unconventional monetary policy stance over the 2010s corresponds with our findingthat exchange rate information influences stock prices over the same period.
We have demonstrated that foreign investors have possessed an informational advantage
over domestic investors since the 1980s. We have also shown that their informational advantage
is primarily related to global public information since the 2000s. Why has the behaviour offoreign investors changed since the 2000s? The timing coincides with the increase in correlation between Japanese stock returns and global stocks returns, during the 2000s and 2010s, andexchange rate returns, during the 2010s. At the same time, foreign investors have contributed
to the efficiency of the Japanese market. As international financial factors have become more
influential over Japanese stock returns, foreign investors have made greater use of global public
information. In contrast, domestic investors have not made profitable use of global public information. The trades of foreign investors have been the conduit for incorporating global financial
information in Japanese stock prices as the TSE has internationalised. In this context, foreign
investors are key players in the Japanese stock market.
5. Conclusion
In this paper, we examined whether foreign investors trade at an informational advantage
to domestic investors, what types of information within foreign investors’ trades contribute
18
Although the BoJ pioneered quantitative easing between March 2001 and March 2006, long-term bond purchases were limited to less than the amount of bank notes in circulation by the Bank’s self imposed “bank noterule”. Asset purchases during this period were small in comparison to those implemented from 2013 onward whenthe Bank ceased observing the bank note rule (Rogers et al., 2014). Using a VAR, Honda et al. (2013) showthat quantitative easing shocks during the 2001 to 2006 period lead to a statistically insignificant yen depreciation
response.

31

to stock prices, and how the information in foreign investors’ trades has changed as national
stock markets have become more integrated overtime. Our empirical analysis investigated theinformation content of foreign and domestic investors’ trades on the TSE over a long sample
period of 39 years. The sample was divided into four approximately decade long subsamples toreveal the changing role of foreign investors in stock price formation.
To summarise our results, we show that (i) foreign investors possesses an informational
advantage over domestic investors; (ii) foreign investors’ trades predominantly contain global
information related to global stock prices since the 2000s and the exchange rate since the 2010s;(iii) foreign investors’ trades are not related to fundamental information on forecast future earnings of domestic firms; (iv) foreign investors’ trades increasingly influence Japanese stock prices
over time in a way which coincides with the increasing importance of global public information in stock prices, and (v) foreign investors have consistently contributed to the informational
efficiency of the Japanese stock market over the last 39 years even when we control for global
stock price and exchange rate information.
Our research has three main implications for policy-makers and investors. First, from an informational efficiency perspective, our research supports policies that provide access for foreign
investors to domestic markets. Foreign investors contribute global information to, and improvethe efficiency of, local markets. Our research is generalisable to other developed markets, andto emerging markets where the participation of foreign investors may increase from a low level
as it did in Japan. Second, although we are unable to infer causality, foreign investors’ tradingof Japanese stocks using global information has increased the comovement between Japaneseand U.S. stock market returns. While the efficiency of the Japanese market has improved asa result of foreign investors’ trading, the increased comovement is detrimental to global stock
diversification opportunities for investors, at least at the country level. Our results suggest thebenefits of diversification across national stock markets have declined since the 2000s. Third,
Japanese domestic investors may over-rely on local information and under-rely on global information, given the greater importance of global information during the 2000s and 2010s andtheir poor investment performance.
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Appendix A. Robustness to Alternative Trade Indicator
Table A.8 provides the empirical results for the extended model where the trade indicator foreach investor group is defined as the yen value of stock purchases minus sales. The results arevery similar to those in Table 4 in Section 4.1.
Table A.8: Extended Model Including S&P 500 and USD/JPY Exchange Rate Returns
1980s

1990s

2000s

2010s

(a) Long-Run Price Impact of Trade
Foreign

0.86

1.83

1.12

0.83

Financial

0.19

-0.13

0.14

0.31

Trust

0.11

-0.09

-0.04

-0.14

Individual

0.02

-1.11

-0.67

-0.75

TOPIX

1.53

2.20

1.44

1.15

S&P 500

1.15

1.44

2.24

1.76

USD/JPY

-0.31

-0.55

0.47

1.38

(b) Variance Decomposition of the Efficient Price (%)
Foreign

16.46

28.26

13.88

9.07

Financial

0.75

0.13

0.20

1.25

Trust

0.28

0.07

0.02

0.27

Individual

0.00

10.44

5.04

7.24

TOPIX

51.37

41.06

22.83

17.17

S&P 500

29.00

17.53

55.59

40.08

USD/JPY

2.14

2.51

2.43

24.92

Observations

517

521

522

470

3

2

2

1

Lags

The order in which the variables were included in the VAR is S&P 500, USD/JPY, Foreign, Financial, Trust, Individual and TOPIX.

38

Appendix B. Robustness to Alternative Proxy for Global Equity Prices
The S&P 500 index is often used as a proxy for global large-cap stock prices given the size andimportance of the US equity market. We examine whether our results are robust to a broader
proxy for global stock prices by estimating the extended model using the MSCI Kokusai index
in place of the S&P 500. The MSCI Kokusai index is the MSCI World index ex-Japan and waslaunched on 31 March 198619 . Summary statistics for MSCI Kokusai returns appear in Tables
1 and 2. We conduct the robustness check over the 1990s, 2000s and 2010s subsamples. Table
B.9 suggests our results are robust to the broader proxy for global equity prices.
Table B.9: Model Including MSCI Kokusai and USD/JPY Exchange Rate Returns
1990s

2000s

2010s

(a) Long-Run Price Impact of Trade
Foreign

1.50

1.02

0.69

Financial

-0.41

0.14

0.18

Trust

-0.04

-0.26

-0.19

Individual

-1.11

-0.64

-0.67

TOPIX

2.15

1.51

1.20

Kokusai

1.74

2.33

1.78

USD/JPY

-0.32

0.51

1.44

(b) Variance Decomposition of the Efficient Price (%)
Foreign

19.74

10.96

6.14

Financial

1.48

0.22

0.43

Trust

0.02

0.71

0.49

Individual

10.77

4.26

5.85

TOPIX

40.55

23.97

18.88

Kokusai

26.55

57.18

41.43

USD/JPY

0.89

2.71

26.78

Observations

520

521

470

2

2

1

Lags

The variable order is Kokusai, USD/JPY, Foreign, Financial, Trust, Individual, TOPIX.

19

MSCI Kokusai contains large- and mid-cap firms in 22 developed markets (Japan is excluded), has 1,326
constituents and covers approximately 85 percent of the free float-adjusted market capitalisation in each country(MSCI, 2019b). Securities or portfolios tracking the MSCI Kokusai index are commonly used by Japanese investors to take exposure to global stocks.

39Appendix C. Robustness to Alternative Order of the Variables in the VAR
Table C.10 provides the results for the extended model with the variables in the following order:
financial, trust, individual, foreign, USD/JPY, S&P 500, and TOPIX. The results presented inTable 4 in Section 4.1 are robust to different variable orders.
Table C.10: Extended Model Including S&P 500 and USD/JPY Exchange Rate Returns
1980s

1990s

2000s

2010s

(a) Long-Run Price Impact of Trade
Foreign

0.66

0.89

0.50

0.21

Financial

-0.37

-1.34

-0.80

-0.35

Trust

0.13

-0.27

-0.24

-0.34

Individual

-0.75

-1.67

-1.78

-2.20

TOPIX

1.47

2.23

1.55

1.21

S&P 500

0.61

0.90

1.47

0.69

USD/JPY

-0.12

-0.44

0.79

0.86

(b) Variance Decomposition of the Efficient Price (%)
Foreign

11.74

6.87

2.72

0.59

Financial

3.66

15.59

6.93

1.54

Trust

0.45

0.62

0.63

1.51

Individual

15.32

24.53

34.15

61.99

TOPIX

58.43

43.64

25.65

18.89

S&P 500

10.00

7.07

23.23

6.09

USD/JPY

0.40

1.68

6.70

9.39

Observations

518

521

522

470

2

2

2

1

Lags

The order in which the variables were included in the VAR is Financial, Trust, Individual, Foreign, USD/JPY,
S&P 500, and TOPIX.

40