Exchange Rate and Stock Prices in Japan
Tetsushi Homma
Yoshiro Tsutsui
Uri Benzion
Discussion Paper 00-14

September 2000
この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。
Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, Japan

EXCHANGE RATE AND S TOCK PRICES IN JAPAN*

Tetsushi Homma
Osaka University
Yoshiro Tsutsui
Osaka University and University of California, San Diego
Uri Benzion
Israel Institute of Technology

September, 2000

Correspondence :Yoshiro Tsutsui
Graduate School of International Relations and Pacific Studies,
University of California, San Diego
9500 Gilman Drive, La Jolla
CA 92093-0519 U.S.A.
Phone: +1-858-822-4314
Fax:+1-858-534-3939
e-mail: ytsutsui@ucsd.edu*

An earlier version of this paper was presented at MEW (Monetary Economics Workshop) and annual

meeting of Nippon Finance Association in June 2000. The authors are grateful to Takato Hiraki,
Andrew Coors and the participants of the workshop and the meeting for their comments.

Abstract
This paper explores whether investors carefully watch the export intensity and net foreign
position of Japanese firms, and whether this information is properly reflected in the stock prices.
By estimating a multi-factor model including the TOPIX, the call rate, the exchange rate andother variables representing the characteristics of individual firms, we find that investors doproperly respond to a change in the exchange rate when considering the firms’ foreign assets
and liabilities since 1992. Investors are also shown to be aware of the export intensity of firms
since 1985.

JEL Classification Number: G12; G14; G15.
Keywords: Exchange rate; Stock price; Market efficiency; Panel unit root test.

11. Introduction
In a modern world where trade between countries is extremely significant, exchange rates arevery important determinants of a firm’s profitability. In particular, the exchange rate is vital tothe profitability of both exporting and importing firms, as well as any multinational corporation.
For example, changing exchange rates most heavily impacts countries like Japan. In Japan
there has been a tendency of a significant balance of payment surplus and a large proportion of
the industrial production directed to export over the last two decades. For entities that haveassets or liabilities measured in foreign currency, the exchange rate directly determines thevalue of those operations, and therefore affects their stock prices, if investors evaluate themproperly.
Firms with a significant portion of their business involved in the export or import of goods, aswell as in foreign exchange positions are strongly influenced by changes in the exchange rate.
Thus one can consider the exchange rate as another major economic factor, in addition to theclassical market index. This approach leads to the application of a simple test of a multi-factor
model by running a multi-factor model which incorporates variables such as export intensity(export/sales) and the net position of the foreign assets and foreign liabilities for each firm.
The effect of exchange rate on stock prices has been investigated in line with the Arbitrage
Pricing Theory (APT), which examines whether the arbitrage condition is well explained whenthe exchange rate is added to common factors. Surprisingly, many studies deny the explanatory
power of the exchange rate for the U.S. 1 For Japan, Hamao (1988) does not find the exchange
rate significant, but Choi et al. (1998) and He et al. (1997) report that the exchange rate is an
important factor.
This paper considers export intensity and foreign net position as the principal channels

1

See Jorion (1991) among others for these results.

1

through which the exchange rate affects stock prices of individual firms. The effect of thesevariables differs depending on the level of export intensity and foreign net position of the firm.
For example, firms with positive net positions and firms with negative net positions see
opposite effects when there are changes in the exchange rate. Thus, grouping firms may
average out the effects of individual firms. This paper examines directly how the effect of
exchange rate on stock prices differs depending on the firms ’ characteristics. 2
Another feature of this paper is the use of daily data, while previous studies used monthly
data. The analysis is interesting from the viewpoint of testing the market efficiency of thesemi-strong form. 3 The amount of export and the foreign asset position is the type of public
information that a stock investor should watch. It is interesting to see whether or not investors
are alert to this information, which could be a valuable test of market efficiency.
In this paper, we examine whether the stock prices of the firms that export their products orhave a net position of foreign assets, are influenced by a change of exchange rate. The data
used contains over 330,000 observations of stock prices and other financial data of 114 major
Japanese firms, from January 5, 1983 to March 29, 1996.
The rest of the paper is organized as follows: Section 2 is a presentation of the model and thedata used for this analysis. Section 3 gives the estimation results, and examines whether themarket efficiency has changed over the sample period. Section 4 concludes the paper.

2

The effect of export intensity and foreign position is examined in Bodner and Gentry (1993) and He etal. (1997), but in more indirect way.
3

For details of market efficiency, see Roberts (1967), Fama (1970, 1991) and Grossman and Stiglitz(1980).

2

2. Model
We assume that a stock price of a firm is determined both by factors common to all firms, andthose attributed to the individual firm. As for the common factors that could relate to a stock
price for an individual firm we use TOPIX, the call rate and the yen-dollar exchange rate.
TOPIX is the stock price index of Tokyo Stock Exchanges, which may represent the price of themarket portfolio; however, many studies suggest that the market portfolio should be a broader
portfolio including various international financial assets, as well as other non-financial assets,such as human capital. Therefore, additional macroeconomic variables and indicators may be
important common factors. Interest rate is a good candidate of such a factor, since a rise in thecall rate will raise the expectation of the future interest rates, resulting in a fall of the stock
prices. The yen-dollar exchange rate may be a good variable represent ing foreign phenomenathat affect the stock prices. 4
As for individual factors, various characteristics of the sample firms are available. Amongthem, we put special emphasis on whether a firm exports its products and whether it has foreign
assets or liabilities. 5 Because depreciation of the yen relative to the currency of an importing
country tends to make export easier, firms which rely more on export are expected to benefit
more when a depreciation occurs. Thus, the effect of the change in the exchange rate should belarger for these firms. Depreciation of the yen implies a rise in the yen value of the foreign
assets and liabilities, denominated by the dollar. If a firm has more foreign assets than foreign
liabilities, so that the firm’s net foreign asset position is positive, depreciation of the yen resultsin larger firm value. Given these arguments, coefficients of the exchange rate are formalized as

4

It may be restrictive that only the yen-dollar rate is considered as the exchange rate in this paper.
However, because the U.S. is a particularly important trade partner for Japan, the analysis is appropriate
as a first approximation.
5
Although import is undoubtedly an important element as is export of a firm, such data are not available.
No one knows exactly how much a firm uses import goods as input, because a considerable portion ofimports may be used as an intermediate goods.

3

a function of export intensity and foreign net position, whose coefficients are expected to bepositive.
Other variables considered as individual factors are firm size and debt-asset ratio. While
there is no definite theory of how firm size effects stock prices, debt-asset ratio is considered tohave two opposite effects on stock prices. One effect is that the larger the debt, the smaller thefree cash flow, leading to the larger probability of bankruptcy.

This, in turn, results indisciplining the firm’s managers and higher stock prices (Jensen and Meckling, 1976). On theother hand it can be argued that there exists agency costs between debtors and stockholders
concerning the risks of projects, so that an increase of debt-asset ratio results in higher agency
costs and lower stock prices (Myers and Myluf, 1984). Which effect is stronger is not assumed
a priori.
Our basic equation is:

ris,t = α i + ( β1 + β 2 ⋅ xie,t + β3 ⋅ xin,t ) ⋅ rte + γ 1 ⋅ rt m + γ2 ⋅ rt c + δ1 ⋅ xis,t + δ2 ⋅ xid,t + ε%i ,t ,i = 1,..., N , t = 1,..., T ,(1)where ris,t ≡ ( SPi, t − SPi ,t −1 ) SPi,t −1 , rte ≡ ( ERt − ERt −1 ) ERt −1 , rtc ≡ ( CALLt − CALLt−1 ) CALLt−1 ,rtm ≡ ( TOPIX t − TOPIX t −1 ) TOPIXt −1 , xie,t ≡ ESi, t TS i,t , xin,t ≡ ( FAi ,t − FLi, t ) TAi,t ,
xis,t ≡ TAi, t( ∑ ∑ TA ) and x ≡ TL TA .
i

t

i ,t

d
i, t

i ,t

i ,t

Here, SPi,t stands for the stock price (closing price) of firm i at date t, ERt , exchange rate,
TOPIXt , the market portfolio index of Tokyo Stock Exchanges, CALLt , the call rate, ESi,t ,exporting sales, TSi, t , total sales, FAi,t , foreign assets, FLi,t , foreign liabilities, TAi, t , total
assets and TLi,t , total liabilities. The subscript t stands for the date, while the subscript i stands
4

for the i-th firm. As argued above, we expect β2 > 0 , β3 > 0 , γ 1 > 0 and γ 2 < 0 , but the signsof β1 , δ 1 and δ 2 have yet to be determined.
Equation (1) formalizes explicitly the channel of the possible effect of changes in theexchange rate on stock prices, by specifying the coefficient of exchange rate as a function ofexport intensity and net foreign position. To make estimation viable, β1, β2 , β3 , γ1 and γ 2 areassumed to be constants. Thus, the differences in stock price among individual firms are
explained by individual intercepts, α i , and individual factors, xie, t , xin, t , xis, t and xid, t .
To examine the robustness of the results of equation (1), we estimate a model where foreign
assets and foreign liabilities are separately adopted as explanatory variables rather than using
foreign net position. Since an increase in foreign assets should have the same effect on the firm
values as a decrease in foreign liabilities, we expect that the coefficient of foreign assets is
positive and that of foreign liabilities is negative, both with equal magnitudes.
The regression equation is

ris,t = α i + ( β1 + β 2 ⋅ xie,t + β 4 ⋅ x ia,t + β 5 ⋅ xil,t ) ⋅ rte + γ 1 ⋅ rtm + γ2 ⋅ rtc + δ1 ⋅ xis,t + δ2 ⋅ xid,t + ε%i,t ,i = 1,..., N , t = 1,..., T ,(2)where xia,t ≡ FAi, t TAi,t and xil,t ≡ FLi, t TAi ,t .
The data description is given in Table 1. The selection criterion of stock prices is that theclosing price is given on all days in the sample period. We must heed to the difference between
the frequency of individual factors and that of stock prices and common factors. The former isaccounted yearly, while the latter is daily. Thus, we assume that the individual factors take on
the same value each year. This assumption may not be awkward because these figures areannounced only annually in the financial statement, so that investors are informed of the change
5

of these variables at this frequency.
Because the export intensity and net foreign position are annual data, we are forced to change
the manner in which we characterize the test of market efficiency. We do not examine how fast
the information on the characteristics of firms are reflected in the stock prices, like many event
studies in corporate finance, but instead examine whether the daily change of exchange rate isproperly reflected in the stock prices. We conduct this test given the characteristics of thefirms’ export intensity and foreign net position. If investors pay adequate attention to theannual information of these characteristics, they should respond to the daily change of theexchange rate. Thus, this paper should provide a test of informational market efficiency of thesemi-strong form. The sample period is from January 5, 1983 to March 29, 1996. The data set
contains 114 individual firms, 2336 minimum time periods and 3067 maximum time periods,
summing to 335,392 observations of daily data.

3. Estimation Results
3.1. Panel Unit Root Test
Since the time series dimension of ris,t , rte , rtm and rtc is very large, we conduct unit-root tests
on them. To provide meaningful estimates these variables should be stationary. Since rte , rtm
and rtc do not have the cross-sectional dimension, we use the usual Augmented Dickey Fuller(ADF), weighted symmetric and Phillips-Perron tests on them (Dickey and Fuller, 1979, Parkand Fuller, 1993, Phillips and Perron, 1988).
As for ris,t , we use the panel versions of the unit root tests because the additional crosssectional dimension in the panel leads to better power properties of the panel tests, whencompared with the well-known low power of the standard individual-specific Dickey-Fuller

6

(DF) and Augmented Dickey-Fuller (ADF) tests. 6 The panel unit root tests differ from eachother by the degree of heterogeneity between individuals. Among them, tests suggested by Im,
Pesaran and Shin (1997), henceforth denoted IPS, are attractive in that they allow complete
heterogeneity between individuals. IPS developed panel unit root tests based on the average
across individuals of the Lagrange multiplier test (LM-bar) and the ADF τ statistic (t-bar). IPS
showed that the estimated test statistics ( ΓLM and Γt ) are asymptotically distributed as N ( 0,1) .
IPS also demonstrated that, in general, both the LM-bar and t-bar tests have better power
performance than the test suggested by Levin and Lin (1993), and that the t-bar test also tends toperform better than the LM-bar test. Hence we use the t-bar test on ris,t in this paper.
The results are shown in Tables 2A and 2B. The number of augmenting lags K is selected by
the Ljung-Box Q-statistics, Q ( p ) .

Our arbitrary choice of the maximum number of

autocorrelations for Q ( p ) is 23. For all stock prices, the selected K is the minimum number ofaugmenting lags in which we cannot reject the null hypothesis that Q ( p ) = 0 for allρ = 1,2,...,23 at a 10% significance level. The null hypothesis that each series of ri,t , rte , rtmsand rtc contains a unit root is resoundingly rejected. Therefore, the estimation of equation (1)and (2) should be immune from the problems associated with non-stationary series.

3.2. Results of the Basic Estimation
Estimation results of equation (1) are presented in Table 3. The first three columns show the
OLS, WITHIN (i.e., fixed-effects, FE, model specification) and GLS (i.e., random-effects, RE,
model specification) estimates respectively, under the assumption of no correlation between
individual-factor variables xie, t , xin, t , xia, t , xil, t , xis, t and xid, t , and the disturbance term, ε~i,t . In

6

On this subject, see, Quah (1994), Levin and Lin (1993), Pedroi (1995), Im, Pesaran and Shin (1997),

7

addition, the OLS and RE estimates assume no correlation between X i ,t and αi , and the OLS
estimates assume α i = α for all i. The next three columns of the table show the GMM,
FEGMM (i.e., GMM in FE model specification) and REGMM (i.e., GMM in RE model
specification) estimates respectively, which allow correlation between individual-factor
variables X i ,t and ε~i,t . As do the OLS and RE estimates, the GMM and REGMM estimatesalso assume no correlation between X i ,t and αi , and then the GMM estimates assume α i = α
for all i. These GMM estimates take into account the heteroskedasticity of an unknown form in
ε~i,t and autocorrelation, in which case we specify a second-order moving average process.

Bartlett kernels were specified for the kernel density to insure positive definiteness of thecovariance matrix of the orthogonal conditions, when the number of autocorrelation terms is
positive. We assume that stock prices and the individual factors are endogenous variables, sothat the common-factor variables on the current day and the individual-factor variables in theprevious account year, such as rte , xie,τ −1 ⋅ rte , xin,τ −1 ⋅ rte , rtm , rtc , xis,τ −1 and xid,τ −1 , are chosen as
instruments.
These six estimation methods bring about similar estimates, thus comparable conclusions
apply, independent of the estimation methods; however, it is still worth finding which are themost statistically valid results of these six estimates. Comparing the OLS and GMM estimatesusing a Hausman specification test (Hausman 1978), we test the null hypothesis that theindividual-factor variables, such as xie,τ ⋅ rte , xin,τ ⋅ rte , xis,τ and xid,τ , are uncorrelated with ε~i,t .
Because the specification test statistic is 16.44 which is distributed as χ62 under the null
hypothesis, we can reject the null hypothesis at a 5 % level, but we cannot reject it at a 1 %confidence level.
As for the comparison of the FE and FEGMM, and RE and REGMM estimates, we get the

McCoskey and Kao (1998) and Kao (1999).

8

same results as those in the comparison of the OLS and GMM estimates. The null hypothesis isrejected at a 5 % significance level, implying that the OLS, WITHIN and GLS estimators are
not consistent
Constructing the F statistic of the GMM and FEGMM estimates, we also test the null
hypothesis that the individual-effect parameters are identical (i.e., α i = α for all i ), and get theresult that the null hypothesis is not rejected at all significance levels. This result suggests thatthe individual-factor variables are adequate proxies for any individual-specific factor in eachstock return. This also support the assumption that γ 1 and γ 2 are independent of i. Taking
these results into account allows us to concentrate our attention on the GMM estimates.
An important consequence is that both the coefficients β2 of xie,t ⋅ rte and β3 of xin,t ⋅ rte are
significantly positive. This confirms our basic hypothesis that Japanese investors are alert tothe effect of the change in the exchange rate on the individual firm value. The result suggeststhat investors adequately consider the characteristics of the firms, such as their net foreign
position.
The other estimates also take on sensible values. The coefficient γ 1 of rtm is highly
significant and is close to unity. This suggests that rtm is a good variable representing the returnof the market portfolio. The coefficient γ 2 of rtc is negative, as is consistent with the notionthat a rise in the interest rate will raise the subjective discount rate of the future values. The
constant coefficient β1 of rte is negative, which implies a rise of the exchange rate tends tolower the stock prices through effects other than a firm’s exporting characteristics and foreign
net position. We are not certain why, but the following may be a reason for this result. We do
not include the amount of the import goods used as the input for production in explanatory
variables because of the data availability.

This information is not readily available to9researchers, but it may be accessible to investors. 7 If this is the case, then the value of firms
using more imported goods should see prices of the input factors falling when the yen isdepreciated. The negative impact on stock prices due to a devaluation is included in theinformation set controlling the constant coefficient β1 of the rte . In the current case, thevariable of import intensity is excluded, so that β1 takes on a negative value. 8
The coefficients δ 1 and δ 2 of the other individual firm variables, xis, t and xid, t , are notsignificant. The coefficient on the debt-asset ratio implies that the two effects explained above
have similar strength, so that they either offset each other, or that both effects are negligible.
The constant term α is insignificant, suggesting that there are no important variables missing
in equation (1) that have strong systematic explanatory power. Adjusted R2 is 0.285, which isvery high considering that the rate of change of stock prices is the dependent variable and thatthe sample size is quite large.

3.3. Effects of Foreign Assets and Liabilities
The estimates of equation (2) are presented in Table 4. Table 4 is different from Table 3 inthat both β4 and β5 are reported. In Table 3 as well, we report the OLS, FE, RE, GMM,
FEGMM and REGMM estimates. The GMM, FEGMM and REGMM estimates take accountof heteroskedasticity and autocorrelation by the same procedures as in Table 3.

7

The

As we argued in footnote 6, correct information is not available . However, we know that someindustries depend heavily on imported goods, e.g. oil.
8mAssume that true relation is not equation (1), but the one that includes the import intensity, xi, t , that is

ris,t = α i + ( β 1 + ... + β 6 ⋅ xim,t ) ⋅ rte + ... , where β6 < 0. Estimating this relation excluding the variable

xim,t ⋅ rte , the estimate of β1 has a bias of β6 ⋅  cov ( rte , xim, t ⋅ rte ) var ( rte )  (Green, 1997). Considering

(

)

( )

that xi, t is annual data and almost behaves like a constant, we get cov rte , xim,t ⋅ rte ≈ xim,t ⋅ var rte > 0 .
m

Therefore, the bias is negative.

10

instrumental variables are rte , xie,τ −1 ⋅ rte , xia,τ −1 ⋅ rte , xil,τ −1 ⋅ rt e , rtm , rtc , xis,τ −1 and xid,τ −1 .
The specification test statistics are respectively 17.58 (OLS vs. GMM), 17.58 (FE vs.
FEGMM) and 15.00 (RE vs. REGMM) which are distributed as χ72 , χ82 and χ52 under the null
hypothesis that the individual-factor variables, such as xie,τ ⋅ rte , xia,τ ⋅ rte , xil,τ ⋅ rt e , xis,τ and xid,τ ,are uncorrelated with ε~i,t . Thus we can reject the null hypothesis at a 5 % significance level.
This implies that the OLS, WITHIN and GLS estimators would be inconsistent. In addition, thenull hypothesis that the individual-effect parameters are identical is not rejected at any
significance levels. Hence, we focus on the GMM estimates.
Inspection reveals that all the coefficients except β4 and β5 are almost equivalent to those in
Table 3. Although β5 is negative and takes on a similar value to the coefficient β3 of xin, t , β 4is also negative and insignificant by GMM estimation, contradicting our expectations.

β4 could be negative for a couple of economic reasons : for example, there may be someelements common to the firms that have foreign assets or liabilities, which affect the stock
prices downward. The problem is that dummy variables cannot correct the sign of thecoefficient. Inclusion of a dummy variable representing the possession of foreign position does
not bring about a positive β4 .
The constant coefficient β1 of the exchange rate may represent the effect of the evaluation ofimporting firms. If so, β1 should be different between industries depending on their intensityof importing because investors know which industries are more import-intensive. Following
this idea, we adopt the industry dummies instead of constant coefficient. 9 The estimation

Industry numbers j ( = 1,… ,47 ) denote, respectively, livestock processing, beer and liquor, bread andconfectionery, other food, synthetic fiber, cotton, knitwear, pulp and paper making, printing, plastic ,other organic chemistry, petrochemistry, grease and soap, drug, other chemistry, glass, ceramic , carbon
steel, alloy steel, ferroalloy, bronze, smelting and refining of copper, lead and zinc, telegraph wire andcable, boiler and motor , office machine, general industry machine, electric power machine andequipment, computer, radio and television, sound machine and equipment, other electric machines and911results are presented in Table 5. Since the results of the specification test are essentially thesame as those in Tables 3 and 4, we concentrate our attention on the GMM estimates. 10
Considerable numbers of coefficients, β1, j ( j = 1,..., 47 ), of industry dummies,

DI j( j = 1,..., 47 ), are significant: 26 coefficients at a 10 % significance level, of which some takepositive and the other take negative values, suggesting that the specification is reasonable(estimates of industrial dummies are not shown in the Table to save space). The coefficients of
rtm and rtc are very similar to those in Tables 3 and 4, while that of export β2 gets smaller.

Most importantly, β4 takes on a positive value, though not significant, and β5 is negative. The
results confirm our supposition, even if there still remains a need of further investigation.
In the next section, we present the results of conducting analyses with divided sample periods.
These results clarify why β4 does not systematically show the sign we expect with the wholesamples.

3.4. Change of the Market Efficiency
Because of our rather long estimation period, it should seem reasonable to assume there areheterogeneous phases within the period. There is no reason to believe that the model structureis unchanged over the whole period, thus it is interesting to see whether the market efficiencychange s over this period.
Looking at the TOPIX t in Figure 1, there seems to exist three phases: normal period before
1986, the bubble period between 1986 and 1990, and a stagnant period thereafter. The stagnant

equipment, wagon, automobile parts, ship building and repairing, optical machine and equipment, clock
and it's parts, fishery, oil and natural gas, civil architecture, trading company and wholesale, other retail,real estate rental, installment sale , railroad, auto truck, air service, electric power supply , and town gas.
DI j ⋅ rt ( j = 1, …,47 ), xi,τ −1 ⋅ rt , xi,τ −1 ⋅ rt , xi,τ −1 ⋅ rt , rtm , rtc , xi ,τ −1 and xi ,τ −1 are chosen as
instruments in GMM estimation.
10

e

e

e

a

e

l

12

e

s

d

period may be divided at the point that the TOPIX t takes on the lowest value, that is August 4
1992. Before this date, TOPIX t rapidly fell, while it stayed at the low level thereafter. If one
looks at the transition of the exchange rate in Figure 1, it seems reasonable to divide the period
into three sub-periods. While the middle period from March 3 1985 to the Black Monday ischaracterized as a rapid appreciation of the yen, in the earlier period until March 3 1985 and thelater period after the Black Monday, the exchange rate is relatively stabilized.
In sum, the whole period is divided into the following five sub-periods.
Period Ⅰ: January 5, 1983 to March 8, 1985,
Period Ⅱ: March 11, 1985 to October 19, 1987,
Period Ⅲ: October 20, 1987 to December 12, 1989,
Period Ⅳ: January 4, 1990 to August 4, 1992,
Period Ⅴ: August 5, 1992 to March 29, 1996.
In Table 6, the GMM estimates of equation (1) are presented because they turned out to be
the best of six estimation methods with the same specification tests in the former section. Most
importantly, the estimates of the key coefficients, β2 and β3 , are drastically changed over theperiod. In period I, β2 is negative and β3 is not significant. They show expected only forperiods Ⅳ and Ⅴ. It is apparent that they increasingly show the correct signs in the later
periods.

This strongly suggests that the market efficiency of the semi-strong form has

improved throughout these fourteen years.
The R 2 ’s of periods Ⅰ and Ⅱ are quite low, taking the value of around 0.1; however, sinceperiod Ⅲ, they have been in excess of 0.3. This confirms that the multi-factor model applies
well after the Black Monday. Closer inspection of the estimates of period Ⅴ reveals that they
are very similar to those of the whole period, except that β3 more than doubles.
13

In Table 7, the results of equation (2) are presented. In this Table, the coefficients of foreign
assets and foreign liabilities, β4 and β5 , are shown instead of the coefficient of net foreign
position, β3 . Except for β4 and β5 , the results are quite similar to those in Table 6. The
coefficients β4 and β5 satisfy the correct signs only for periods Ⅳ and Ⅴ, though β4 is notvery significant in period Ⅳ. In period Ⅴ, both β4 and β5 are significant and they take on
similar values consistent with our expectation.
These results suggest that stock investors correctly evaluate the foreign asset position offirms and appropriately respond to a change in the exchange rate since 1992. In contrast, β2 ,the coefficient of the export intensity, takes on the correct positive sign since 1985, suggestingthat investors began to pay attention to exporting firms much earlier than to firms’ foreign
assets and liabilities.
Incidentally, the coefficient β2 of period Ⅱ is more than double of those in the later periods.
Noticing that the yen rapidly appreciated from 260 to 120 against the dollar during period Ⅱ
and kept that level thereafter, the result implies that the effect of the change of the exchange rateis larger during the volatile period than during the stabilized period. This makes intuitive sense;
investors should be more concerned with the exchange rate when there is high variation, rather
than when things are stable.

144. Concluding Remarks
In this paper, we have explored whether export intensity and net foreign position of theJapanese firms are carefully watched by investors and are properly reflected in the stock prices.
By estimating a multi-factor model including the TOPIX, the call rate, the exchange rate, andother variables representing the characteristics of individual firms, we have examined themarket efficiency of the Japanese stock market.
Our main results are as follows.
(i) Japanese investors adequately consider the characteristics of the firms, such as the exporting
behavior and net foreign position.
(ii) The market efficiency of the semi-strong form has been improved throughout the period.
(iii) Stock investors correctly evaluate firms’ foreign asset position and appropriately respondto the change of the exchange rate after 1992. In contrast, investors began to pay attention toexporting firms much earlier, that is, since 1985.
This paper has some limitations, while it retains some novelty.

First, the yen-dollar

exchange rate represents the various exchange rates for Japan in this paper. Consideration of
the other exchange rates is a necessary future task. Second, assumption of the constantcoefficients of common factors might be restrictive, though the estimation results suggest the
opposite. Third, we concentrate on investigating the effect of the exchange rate on stock prices
from the point of view of the market efficiency. From the perspective of the APT, our analysis
corresponds to an estimation of the data generating process. Examination of the validity of theinternational APT will be an interesting future work.

15

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Hausman, J., (1978) “Specification Test in Econometrics,” Econometrica 46, 1251-1272.
He, J., L. K. Ng, and X. Wu, (1997) “Foreign Exchange Exposure, Risk and the Japanese Stock
Market,” Working Paper of City University of Hong Kong.
Im, K., H. Pesaran, and Y. Shin, (1997) “Testing for Unit Roots in Heterogeneous Panels,”
Department of Applied Economics, University of Cambridge Working Paper.
Jensen, M. C., and W. Meckling, (1976) “Theory of the Firm: Managerial Behavior, Agency
Costs, and Capital Structure,” Journal of Financial Economics 3, 305-360.
Jorion, P., (1991) “The Pricing of Exchange Rate Risk in the Stock Market,” Journal of
Financial and Quantitative Analysis 26, 363-376.
Kao, C., (1999) “Spurious Regression and Residual Based Tests for Cointegration in PanelData,” Journal of Econometrics 90, 1-45.
Levin, A., and C. Lin, (1993) “Unit Root Tests in Panel Data: New Results,” Department of
Economics, University of California San Diego Working Paper.

16

McCoskey, S., and C. Kao, (1998) “A Residual-Based Test of the Null of Cointegration in
Panel Data,” Econometric Review 17, 57-84.
Myers, S. C., and N. S. Myluf, (1984) “Corporate Financing and Investment Decisions WhenFirms Have Information that Investors Do Not Have,” Journal of Financial Economics 5,
187-221.
Park, H. J., and W. A. Fuller, (1993) “Alternative Estimators for the Parameters of theAutoregressive Process,” unpublished manuscript, Iowa State University, Dept. of
Economics.
Pedroni, P., (1995) “Panel Cointegration; Asymptotic and Finite Sample Properties of Pooled
Time Series Tests with an Application to the PPP Hypothesis,” Indiana University Working
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Phillips, P. C. B., and P. Perron, (1988) “Testing for a Unit Root in Time Series Regression,”
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17

Table 1

Data Description
SymbolVariableSPi,tDefinition, frequency and Source

Stock price

Closing price; daily; Domestic Stock
Prices, Nomura Research InstituteExchange rate

Yen-dollar mean rate among banks; daily;
Call, Nikkei Inc.

Market portfolio index

Tokyo Stock Exchanges; daily; Call,

Common factor
ERt
TOPIX tNikkei Inc.
Secured overnight rate; daily; Call,

Call rate

CALLt

Nikkei Inc.

Individual factor

xie,t ≡ ESi, t TS i,tRate of exporting sales

Exporting sales / total sales; account year;
Financial Data of Corporations, Development Bank

xia,t ≡ FAi, t TAi,tRate of foreign assets

Foreign assets / total assets; account year;
Financial Data of Corporations, Development Bank

xil,t ≡ FLi, t TAi ,tRate of foreign liabilities

Foreign liabilities / total assets; accountyear; Financial Data of Corporations,
Development Bank

xin,t ≡ ( FAi ,t − FLi, t ) TAi,tRate of net foreign assets

xia,t − xil,t ; account year; Financial
Data of Corporations, Development Bank

xis,t ≡ TAi, t( ∑ ∑ TA ) Normalized total assets
i

t

i ,t

xid, t ≡ TLi ,t TAi,tSample period
Selection criterionof stock prices

Total assets / sum of all samples; accountyear; Financial Data of Corporations,
Development Bank
Debt / total assets; account year; Financial Data of Corporations, Development
Bank

Debt-asset ratio

January 5, 1983 to March 29, 1996
The closing price is bidden at all days in the sample period.

Number of individuals
Minimum time periods
Maximum time periods
Number of observations

114
2336
3067
33539218

Table 2A
Seriesris,tt-bar Test of Stock Prices
Test Statistic

t-bar Statistic

Γt(P-value )

tNT

3.524
(.00043)

CommonMean of t iT

CommonVariance of t iT

E ( tT α i = 1)

-16.508var ( tT αi = 1)

-16.713.385

Number ofAugmenting
Lags

K
10

Note:
1
N
t .
∑
i =1 iTN
2. E ( tT α i = 1) and var ( tT αi = 1) are computed via stochastic simulations with 50,000

1. The number of stock prices N is 114. The t-bar statistic is obtained by tNT ≡replications. tT is the t-statistic for testing α = 1 in the Augmented Dickey-Fuller
regression with time trend,
10
xt = α ⋅ xt−1 + β + γ ⋅ t + ∑ j=1δ j ⋅ ∆xt − j + ε t , t = 1,2,…,T; T = 3067 .
The observations xt are generated as xt = ε tx , ε tx : N ( 0,1) .

3. The test statistic is defined by Γ t ≡{} . Im, Pesaran and ShinN ⋅ tNT − E ( tT αi = 1)
var ( tT αi = 1)

(1997) showed that Γ t converge weakly to a standard normal distribution as N andT → ∞ . Hence the inference can be conducted by comparing the obtained Γ t statistic tocritical values from the lower tail of the N ( 0,1) distribution.

Table 2B

Unit Root Tests of Common Factors
Number ofAugmenting
Lags

Augmented
Dickey-Fuller(P-value )Weighted
Symmetric(P-value )Phillips-Perron(P-value )e-23.434(.000)-23.464(.000)-3157.020(.000)

rtm-25.056(.000)-25.085(.000)-2602.624(.000)

4rtc-17.782(.000)-17.818(.000)-2764.496(.000)

11

Seriesrt

19

K
5

Table 3

Estimation Results of Equation (1)ParametersOLS

α i ( = α ) -.734×10-4(-.56)

Fixed
effects(FE)―

Randomeffects(RE)

GMM-.613×10-4 -.660×10-4(-.43)(-.50)

Fixed effects
GMM

Randomeffects GMM(FEGMM)(REGMM)―.316× 10-5(.33× 10-1 )β1-.591×10-1(-8.75)-.592×10-1(-8.76)-.591×10-1(-8.75)-.649×10-1(-8.76)-.651×10-1(-8.78)-.650×10-1(-8.77)β2.680(22.30).680(22.30).680(22.30).746(21.08).746(21.06).746(21.08)β3.824(5.75).825(5.75).824(5.75)

1.131(4.83)

1.129(4.81)

1.130(4.82)

γ1

1.054(364.24)1.054(364.14)1.054(364.26)1.054(232.95)

1.054(233.66)1.054(232.97)γ2-.526×10-2(-4.53)-.525×10-2(-4.52)-.526×10-2 -.525×10-2(-4.53)(-4.37)-.523×10-2(-4.36)-.524×10-2(-4.37)

δ1-5.889(-.71)

32.105(.91)-5.169(-.57)-4.225(-.60)

55.053
(.29)

3.764
(.20)

δ2

.278× 10-3
(1.42)-.480×10-3(-.74).257× 10-3
(1.20).260× 10-3
(1.26)-.767×10-3(-.67×10-1 ).122× 10-3
(.69)

θ

―

―

.811

―

―

.815

AdjustedR-squared

.285

.285

.285

.285

.285

.285

LM
heteroskedasticity
Test

2496.60[.00]

2497.82[.00]

2496.39
[.00]

―

―

―

Durbin-Watson

2.10

2.10

2.10

2.10

2.10

2.10

Note: 1. The numbers in parentheses represent estimated t-ratios.
2. The numbers in brackets represent estimated p-values.
3. θ ≡ σW2 ( σW2 + TM ⋅σ B2 ) , where TM is the maximum number of time periods, σ W2 isthe variance of the basic error terms and σ B2 is the variance of the individual-specificerror terms. If θ = 1 , RE (or REGMM) is the same as OLS (or GMM). If θ = 0 , RE
(or REGMM) is the same as FE (or FEGMM).
4. F test for equality with the OLS and WITHIN (FE) estimatorswith the GMM and FEGMM estimators.23 [1.00].22 [1.00]

5. Hausman test of H 0 : RE vs. FE
H 0 : REGMM vs. FEGMM
H 0 : OLS vs. GMM
H 0 : FE vs. FEGMM
H 0 : RE vs. REGMM
20

3.39 [.85].54 [.97]
16.44 [.01]
16.44 [.02]
16.79 [.01]Table 4

Estimation Results of Equation (2)Parameters

OLS

Fixed
effects(FE)

α i ( = α ) -.698×10-4(-.54)―

Randomeffects(RE)

GMM-.576×10-4 -.618×10-4(-.40)(-.46)

Fixed effects
GMM

Randomeffects GMM(FEGMM)(REGMM)―.352× 10-5(.37× 10-1 )β1-.511×10-1(-7.30)-.511×10-1(-7.30)-.511×10-1(-7.30)-.561×10-1(-7.01)-.562×10-1(-7.02)-.561×10-1(-7.01)

β2.697(22.68).698(22.68).697(22.68).765(21.49).765(21.48).765(21.50)β4-.832(-2.04)-.839(-2.06)-.833(-2.05)-.595(-1.02)-.604(-1.03)-.599(-1.02)β5-.884(-6.13)-.885(-6.14)-.884(-6.13)-1.230(-5.21)-1.229(-5.20)-1.230(-5.20)γ1

1.054(364.13)1.054(364.03)

1.054(364.14)1.054(232.80)

1.054(233.52)1.054(232.83)γ2-.524×10-2(-4.51)-.523×10-2(-4.51)-.524×10-2 -.522×10-2(-4.51)(-4.35)-.521×10-2(-4.34)-.522×10-2(-4.35)δ1-5.879(-.72)

33.210(.94)-5.140(-.57)-4.227(-.60)

55.678
(.29)

3.818
(.20)

δ2

.273× 10-3
(1.40)-.484×10-3(-.74).252× 10-3
(1.18).254× 10-3
(1.23)-.780×10-3(-.69×10-1 ).121× 10-3
(.68)

θ

―

―

.810

―

―

.815

AdjustedR-squared

.285

.285

.285

.285

.285

.285

LM
heteroskedasticity
Test

2491.86[.00]

2493.08[.00]

2491.65
[.00]

―

―

―

Durbin-Watson

2.10

2.10

2.10

2.10

2.10

2.10

Note: 1. See the Notes of Table 3.
2. F test for equality with the OLS and WITHIN (FE) estimatorswith the GMM and FEGMM estimators.23 [1.00].22 [1.00]

3. Hausman test of H 0 : RE vs. FE
H 0 : REGMM vs. FEGMM
H 0 : OLS vs. GMM
H 0 : FE vs. FEGMM
H 0 : RE vs. REGMM

3.60 [.89].63 [.99]
17.58 [.01]
17.58 [.02]
15.00 [.01]21Table 5 Estimation Results of Equation (2) adopted the Industry Dummies instead of b1
Parameters

OLS

Fixed
effects(FE)

Randomeffects(RE)

GMM

Fixed effects
GMM

Random effects(FEGMM)(REGMM)GMM

α i ( = α ) -.210×10-4(-.16)―-.950×10-5 -.176×10-4(-.13)(-.67×10-1 )―.626× 10-5(.66× 10-1 )

β2

.109
(1.44).109(1.44).109(1.44).384(3.49).383(3.47).384(3.48)

β4.582(1.17).570(1.15).580(1.17)

1.078(1.28)

1.063(1.26)

1.070(1.27)

β5-.337(-2.18)-.339(-2.19)-.337(-2.18)-.405(-1.46)-.403(-1.45)-.404(-1.46)γ1

1.054(364.62)1.054(364.52)1.054(364.66)

1.054(233.15)1.054(233.87)1.054(233.17)γ2-.530×10-2(-4.57)-.529×10-2(-4.56)-.530×10-2(-4.57)-.529×10-2(-4.41)-.528×10-2(-4.39)-.529×10-2(-4.41)

δ1-7.331(-.90)

33.034(.93)-6.612(-.74)-5.921(-.86)

54.833
(.29)

1.902
(.10)

δ2

.206× 10-3
(1.05)-.535×10-3(-.82).186× 10-3(.87)

.195× 10-3(.95)-.783×10-3(-.69×10-1 ).126× 10-3
(.71)

θ

―

―

.817

―

―

.832

AdjustedR-squared

.286

.286

.286

.286

.286

.286

LM
heteroskedasticity
Test

2497.19[.00]

2497.78[.00]

2496.98
[.00]

―

―

―

Durbin-Watson

2.10

2.10

2.10

2.10

2.10

2.10

Note: 1. See the Notes of Table 3.
2. F test for equality with the OLS and WITHIN (FE) estimatorswith the GMM and FEGMM estimators.22 [1.00].20 [1.00]

3. Hausman test of H 0 : RE vs. FE
H 0 : REGMM vs. FEGMM
H 0 : OLS vs. GMM
H 0 : FE vs. FEGMM
H 0 : RE vs. REGMM

8.08 [1.00].16 [1.00]
333.78 [.00]
172.51 [.00]
380.58 [.00]22Table 6 Estimation Results of Equation (1) for divided periods
Parameters
GMM estimates
Ⅰ
Ⅱ
Ⅲ
Ⅳ
Ⅴ
83.1.5 85.3.11 87.10.20 90.1.4 92.8.5 85.3.8
87.10.19
89.12.29
92.8.496.3.29α-.501×10-3(-1.03)-.190×10-3(-.46)-.856×10-3(-2.63).897× 10-3
(3.13).548× 10-4
(.31)

β1

.424× 10-1
(1.18)-.112(-4.64)-.707×10-1(-4.29)-.431×10-1(-2.52)-.497×10-1(-4.62)β2-.479(3.61)

1.405(15.95).688(8.43).700(9.15).623(11.18)

β3

1.085
(.77)

.295
(.61)

.463
(1.00)

2.364(4.69)

2.115(3.42)

γ1.939(51.57).814(66.04)

1.056(82.37)1.142(143.69)1.061(144.68)γ2-.245×10-2(-.38)-.101×10-1(-2.25)-.107×10-1(-2.71)-.673×10-2(-1.36)-.220×10-2(-1.60)δ1-65.158(-2.40)30.032(1.10)-30.937(-1.75)-2.874(-.21)

5.407
(.56)

δ2

.690× 10-3(.98)

.790× 10-3
(1.30).173× 10-2
(3.39)-.829×10-3(-1.85)-.192×10-4(-.68×10-1 )Number of
Observations

45,53261,01657,501

72,672

98,671

AdjustedR-squared

.079

.105

.306

.430

.360

Note: The numbers in parentheses represent estimated t-ratios.

23

Table 7 Estimation Results of Equation (2) for divided periods
Parameters
GMM estimates
Ⅰ
Ⅱ
Ⅲ
Ⅳ
Ⅴ
83.1.5 85.3.11 87.10.20 90.1.4 92.8.5 85.3.8
87.10.19
89.12.29
92.8.496.3.29α-.482×10-3(-.99)-.104×10-3(-.25)-.858×10-3(-2.63).898× 10-3
(3.13).544× 10-4
(.31)

β1

-.588×10-3(-.97×10-2 )-.731×10-3(-2.92)-.535×10-3(-3.01)-.339×10-1(-1.82)-.512×10-1(-4.52)β2-.688(-3.26)1.581(17.28).735(8.83).708(9.23).624(11.20)β4

8.882(1.18)-7.876(-5.46)-1.543(-2.13)1.085(1.02)

2.578(1.88)

β5

.264
(.14)

-.585
(-1.21)-.704(-1.47)-2.483(-4.84)-2.074(-3.33)γ1.939(51.59).814(66.08)1.056(82.42)

1.142(143.68)1.061(144.67)γ2-.244×10-2(-.38)-.102×10-1(-2.27)-.107×10-1(-2.72)-.671×10-2(-1.36)-.220×10-2(-1.60)δ1-65.788(-2.42)28.380(1.04)-30.563(-1.73)-2.971(-.22)

5.412
(.56)

δ2

.665× 10-3(.94).672× 10-3
(1.11).173× 10-2
(3.39)-.829×10-3(-1.86)-.185×10-4(-.65×10-1 )Number of
Observations

45,53261,01657,501

72,672

98,671

AdjustedR-squared

.078

.105

.306

.430

.360

Note: The numbers in parentheses represent estimated t-ratios.

24

500

25
96-0306

95-1205

95-0905

95-061295-0313

94-1212

94-0909

94-0616

94-0316

2000

93-12152250

93-0914

Period V

Exchange Rate
Exchange Rate

140

TOPIX

160

1750
180

1500
200

TOPIX

220

1000
240

750
260

Yen-Dollar Exchange Rate

Period IV

93-062193-0319

92-1217

92-0918

92-0624

92-0326

91-1220

91-0924

91-0627

91-032990-1225

90-0925

Period III

90-062990-0402

89-1228

89-0929

89-0705

89-0406

89-01051250

88-0920

Period II

88-061588-0303

87-111987-081187-0507

87-0123

86-1009

86-0701

86-0318

Period I

85-112785-08132500

85-0501

85-011484-0927

84-061984-0307

83-11172750

83-0803

83-041983-0105

TOPIX

FIGURE 1 Exchange Rate and Stock Prices in Japan
1983.1-1996.3
80

100

120