The intertemporal relation between U.S. and Japanese stock markets

BEBR

m

FACULTY WORKING
PAPER NO. 89-1595

The Intertemporal Relation Between
U. S. and Japanese Stock Markets

Kent G. Becker
Joseph E. Finnerty
Manoj Gupta

College of Commerce and Business Administration
Bureau of Economic and Business Research
University of Illinois Urbana-Champaign

BEBR

FACULTY WORKING PAPER NO. 89-1595

College of Commerce and Business Administration

University of Illinois at Urbana- Champaign

September 1989

The Intertemporal Relation Between U. S
and Japanese Stock Markets

Kent G. Becker
Temple University

Joseph E. Finnerty
University of Illinois

Manoj Gupta
University of Illinois

Department of Finance

ABSTRACT

This paper finds a high correlation between the open to close returns
in U. S. stocks in the previous trading day and the performance in the
Japanese equity market in the current period. In contrast, the Japan-
ese market has only a small impact on the U. S. return in the current
period. High correlations among open to close returns are a violation
of the efficient market hypothesis; however, in trading simulations, the
excess profits in Japan vanish when transactions costs and transfer taxes
are included.

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THE INTERTEMPORAL RELATION BETWEEN THE U.S.
AND JAPANESE STOCK MARKETS

The two largest stock markets in the world in terms of capitaliza-
tion, volume, and shares listed are the Tokyo Stock Exchange (TSE) and
the New York Stock Exchange (NYSE). Because Tokyo is 14 hours ahead
of New York, there is an eight hour difference between the close of
the TSE and open of the NYSE. Since there is no overlap between the
two markets, traders or technical analysts may look to the TSE as a
predictor of market movement on the NYSE and/or examine changes on the
NYSE as indicators of performance on the TSE.

, As shown in Figure 1, the TSE opens at 7:00 p.m. Eastern Standard
Time (EST) and closes at 1:00 a.m. EST. The NYSE opens at 11:00 p.m.
Japanese time (9:00 a.m., EST) and closes at 5:00 a.m. Japanese time
(3:00 p.m., EST). Thus, there is no common time interval in which
both markets are open.

Insert Figure 1 about here

High correlations between the respective open to close returns are
a violation of the efficient market hypothesis because public infor-
mation about the performance in one market could be used to profitably

-2-

trade in another market. If the markets are efficient, information
about the open to close performance in one market (for example, the
U.S. return in period t-1) will be fully reflected in the open price
in the other market (Japan in period t, for example). Since new in-
formation flows randomly into the market, subsequent price changes
should be random and the open to close returns in Japan will be uncor-
rected with the U.S. returns. Thus, the performance in the U.S.
should affect the open price in Japan and the correlation between the
open to close returns in the two markets will be zero.

Early research on the synchronization among stock prices across
countries [Grubel (1968), Levy and Sarnat (1970), Agraon (1972), Ripley
(1973), Lessard (1976), Panton, Lessiq, and Joy (1976), and Hilliard

(1979)] focused on the benefits of international diversification in

2
reducing systematic portfolio risk. Most of the studies used weekly

or monthly return data for a number of years and found that correla-
tions across countries were statistically insignificant or very low.

Recent research on this topic investigated international equity
market linkages. Using daily closing market prices for five coun-
tries, Jaffe and Westerfield (1985a) found that correlations between
the U.S. and the other market returns for each day of the week were
generally positive and significant. Schollhammer and Sand (1985) and
Eun and Shinr (1989) employed daily market closing data in the 1980s
for several countries and discovered a substantial amount of inter-
dependence among national stock markets.

Bennett and Kelleher (1988), Dwyer and Hafer (1988), Goodhart
(1988), King and Wadhwani (1988), Neumark, Tinsley, and Tosini (1988),

-3-

and Roll (1988) investigated international equity market linkages
around the October 1987 crash. King and Wadhwani (1988) and Goodhart
(1988) used hourly data for the New York, London, and Tokyo markets.
They found strong cross-exchange linkages after the crash. Neumark,
Tinsley, and Tosini (1988) focused on U.S. stocks that were also
traded in London and Tokyo. Using opening and closing prices in New
York and closing prices in London and Tokyo for eight months after the
crash, they discovered that the predictive ability of after-hours
pricing in foreign equity markets was strong after the crash, but de-
clined sharply in later months.

This paper employs opening and closing data for market averages
in the U.S. and Japan for a longer time period, from 1985 to 1988, to
study the synchronization of stock price movements. Therefore, our
focus is not the transmission of stock prices and volatility during
the crash. There are two advantages of opening and closing prices
over only closing data. First, direct tests of market efficiency can
be conducted in which a simulated trader in Japan buys or sells at the
opening price, depending on the performance in the U.S. market the
previous day. Second, the influence of the daily return in one market
on the overnight return of the other market can be investigated.

The results indicate that the performance in the U.S. greatly in-
fluences open to close stock returns in Japan the next day and the
change in the TSE has only a slight impact on the NYSE performance the
same day. Large movements in the U.S. predict open to close returns
in Japan the next day remarkably well. However, when Japanese trans-
actions costs and taxes are included, the excess returns from following

-4-

the U.S. vanish. In addition, the overnight return in Japan is
greatly affected by the U.S. performance. In contrast, the Japan open
to close return does not have an impact on the U.S. overnight return.

I. DATA AND METHODOLOGY

Daily opening and closing data for the Nikkei Index, S&P 500, and
the yen/dollar exchange rate from October 5, 1985 to December 28, 1988
were obtained. It is believed that this period is more meaningful
than a longer time period because of the structural changes in both
the U.S. and Tokyo markets. Data for the Nikkei Index, which is a
price-weighted index of 225 stocks on the TSE , were acquired from
Nihon Keizai Shimbun (Japan Economic Journal). Opening and closing
spot rates for the yen were gathered from the International Monetary
Market Yearbooks. Arithmetic returns for the Nikkei Index and S&P 500
are calculated on a local and common currency basis. Common currency
returns are computed by converting the opening and closing S&P 500
levels to yen equivalents.

October 1987 was a very unusual period in the recent history of
the stock market. To ensure that the results are not being driven by
the data from the crash, two data sets are used in this study: the
first with the crash month, October 1987, and the second without.

Correlations between the open to close returns are computed for
(1) TSE o-c and S&P o-c, which tests whether the Japanese market leads
the U.S., and (2) TSE o-c and S&P , o-c, a test of the U.S. equities
leading the Japanese. To determine how the open to close result in
one market relates to the close to open in the other, the following

-5-

correlations are calculated: (1) S&P , o-c and TSE c-o to determine

how the performance in the U.S. market affects the TSE close to open

3

returns and (2) S&P c-o and TSE o-c.

Regressions are estimated to determine the relation between the
two markets. As a test of the Japan market leading the U.S., a re-
gression is estimated with TSE o-c as the independent variable and
S&P o-c as the dependent variable. As a test of the U.S. leading
Japan, a regression is run with S&P , o-c as the independent variable
and TSE o-c as the dependent variable.

Thus, the correlations and regressions are calculated on the local
and common currency returns, with and without October 1987.

In addition, simulated trading strategies are implemented on the
data set without October 1987. In the simulation, a trader buys in
the Japanese market when the local S&P 500 increases by .5%, 1%, 1.5%

or 2%, the previous day and sells when the index decreases by the same

4
percentages. The positions are closed at the end of the day. Re-
turns with round-trip transactions costs of 0%, .50%, and 1% are cal-
culated. Profitable trading days are counted along with mean returns.

II. EMPIRICAL RESULTS

The results show that the performance in the U.S. strongly in-
fluences Japanese returns while the Japan market has only a slight
impression on the S&P 500. Tables 1 and 2 present the regressions and
correlations between the open to close returns. The correlation be-
tween the Nikkei and S&P 500 return in the current period, which is a
test of the Japanese market leading the U.S., is significant for the

-6-

local returns with and without October 1987, and the common currency
returns for the whole data set. The correlations range from .0463 to
.1171. Thus, the Japanese performance in the current period explains
only about one percent of the fluctuations in the U.S. returns.

Insert Tables 1 and 2 about here

In contrast, the performance in the U.S. in the previous trading
day has a major impact on the Japanese return in the current day. All
correlations between the lagged U.S. return and the current Nikkei re-
turn, which range from .2667 to .4963, are significant at a 1% level.
Again, the correlations for the common currency returns are slightly
lower than the local currency returns. In addition, the correlations
for the entire data set are much higher than for the data set without
the crash month.

As expected, the open to close returns in the U.S. affect the
close to open in Japan. From Tables 3 and 4, correlations between the
lagged U.S. return open to close and the Nikkei close to open returns
are all significant at a 1% level, ranging from .3407 to .4205. In
addition, the Japanese open to close performance does not have an im-
pact on the U.S. overnight return. This result is surprising, since
the same Japan daily return has a slight impact on the subsequent
daily U.S. return. In effect, information which affects the Japanese
market has little or no influence on the U.S. market.

Insert Tables 3 and 4 about here

-7-

For the data sets without the crash month, the lagged U.S. return
has more impact on the overnight Japanese return than on the following
open to close return. When October 1987 is included, the correlation
between the lagged U.S. performance and the Tokyo daily return is
higher.

The simulated trading strategies, presented in Table 5, reveal
that, in the absence of transactions costs, the filter rules do a
remarkable job of predicting up and down returns in Japan. The up
triggers predict profitable trading days 72% to 81% of the time.
Looser up triggers are better able to predict profitable Japanese
trading days, with the exception of the 2% method.

Insert Table 5 about here

The down triggers foretell negative returns the next day with
slightly less precision, 59% to 75%. Similarly, the looser down
triggers are more effective at detecting negative returns. Whatever
method is implemented, the resulted demonstrate that next day market
performance in Japan is predicted by the U.S. market performance.

However, the presence of transactions costs and taxes eliminate
the profits and predictive ability of the filters. Trading costs are
higher in Japan than in the U.S., over 1% round-trip for large insti-
tutions when commissions and taxes are included. Table 5 presents
mean returns for the various triggers; following the U.S. is not
profitable when transactions costs are 1%, with profitable trading days
well below 50%. When transactions costs are .5%, the percentage of
profitable trading days is about 50% for the up 1.5% and 2% triggers.

-8-

Although the lagged U.S. return predicts performance in Japan re-
markably well, it is impossible to profit from following the U.S. be-
cause of the high trading costs in Japan.

III. CONCLUSION

From October 5, 1985 to December 28, 1988, the performance of the
U.S. market had a great impact on Japanese equities. The S&P 500 re-
turns in the previous day explain from 7-25% of the fluctuations in
the Nikkei Index open to close returns the next day, demonstrating
that the U.S. market greatly influences Japan. In addition, the per-
formance in the U.S. in the previous day explains between 11-18% of
the fluctuations in the Japan overnight returns.

In contrast, the Japanese market has a small impact on U.S.
equities, explaining only one percent of the fluctuations in U.S.
open to close returns. Although this result is statistically sig-
nificant, it is probably not high enough to profitably trade on in
the U.S. In addition, there is no relation between the performance
of the Japanese market and the close to open return in the U.S.

Trading simulations are performed on the Japanese market based on
U.S. performance. Various filters are implemented; all are successful
in selecting profitable Japanese trading days with great regularity.
However, high trading costs in Japan prevent Japanese arbitrageurs
from profiting from this strategy.

-9-

FOOTNOTES

The TSE takes a lunch break from 11:00 a.m. to 1:00 p.m. Tokyo
time.

2
See Madura (1985) for a review of literature dealing with the

co-movement of international stock prices, particularly in an equity

portfolio context.

3
When prices could not be obtained for a lagged or current trading

day due to a closed market in one country, the observation is deleted

from the sample. For example, assume that both markets are open

Thursday, Friday, and Monday, and the Japanese market is open Saturday

For the test of Japan leading the U.S., returns are taken from

Thursday, Friday, and Monday. For the test of the U.S. leading Japan,

observations are taken from Friday and Saturday. A Monday return

could not be calculated because the U.S. market was not open Saturday.

For the U.S. affecting Japanese overnight returns, observations are

taken for Friday and Saturday. Only the Friday U.S. overnight return

is obtained for the test of Japan on the U.S. close to open returns.

4
Inclusion of the crash month would have yielded more dramatic re-
sults because the S&P 500 open to close return decreased by 20.43% on
October 19, followed by a fall of 14.90% in Japan the next day. The
U.S. return increased 5.34% on October 20 and the Nikkei Index fol-
lowed by rebounding 9.29%.

After the S&P 500 declined by 20.43% on October 19, 1987, the
overnight return in Japan was -.0066%. This outlier affects the re-
sults. If only this return is deleted, the correlation between the
lagged common currency U.S. returns and the Japanese open to close is
.3846 and .3945 for the local currency U.S. returns.

For the time period of this study, 56.6% of the U.S. open to
close returns were up (464 U.S. up trading days and 356 down). In
Japan, 58.5% of open to close returns were up (522 returns greater
than zero and 371 down).

-10-

The scale of commission rates established by the TSE is set out
below:

Value of Transaction (in Yen)

less than 1,000,000 Yen
1,000,001 to 3,000,000
3,000,001 to 5,000,000
5,000,001 to 10,000,000
10,000,001 to 30,000,000
30,000,001 to 50,000,000
50,000,001 to 100,000,000
100,000,001 to 1,000,000,000
over 1,000,000,000

One

Way % (of

Commission Rate

highest value)

1.2%

1.20%

1.00% + 2,000 Yen

1.07%

.80% + 5,000

.90%

.75% + 12,500

.88%

.60% + 27,500

.69%

.40% + 87,500

.58%

.25% + 182,500

.41%

.20% + 212,500

.22%

.15% + 712,500

.17% for

3 Billion Yen

In addition, a transactions tax between .18% to .50% is imposed on the
seller.

-11-

REFERENCES

Aderhold, Robert, Christine Cumming, and Alison Harwood, 1988, Inter-
national linkages among equities markets and the October 1987
market break, Federal Reserve Bank of New York Quarterly Review,
34-46.

Agmon, Tamir, 1972, The relations among equity markets: A study of
share price co-movements in the United States, United Kingdom,
Germany, and Japan, Journal of Finance 27, 839-855.

Bennett, Paul and Jeanette Kelleher, 1988, The international transmis-
sion of stock price disruption in October 1987, Federal Reserve
Bank of New York Quarterly Review, 17-33.

Dwyer, Gerald P. and R. W. Hafer, 1988, Are national stock markets
linked? Federal Reserve Bank of St. Louis Review, 3-14.

Eun, Cheol S. and Bruce G. Resnick, 1986, Estimating the correlation
structure of international share prices, Journal of Finance 41,
313-330.

Eun, Cheol S. and Bruce G. Resnick, 1988, Estimating the dependence
structure of share prices: A comparative study of the United
States and Japan, Financial Review 23, 313-330.

Eun, Cheol S. and Sangdal Shim, 1989, International transmission of
stock market movements, Journal of Financial and Quantitative
Analysis 24, 241-256.

Finnerty, Joseph E. and Thomas Schneeweis, 1979, The comovement of
international asset returns, Journal of International Business
Studies 10, 66-78.

Goodhart, Charles A. E., 1988, The international transmission of
asset price volatility, Financial Market Volatility (Federal
Reserve Bank of Kansas City) .

Grubel, Herbert G., 1968, Internationally diversified portfolios:
Welfare gains and capital flows, American Economic Review 58,
1299-1314.

Hilliard, Jimmy E., 1979, The relationship between equity indices on
world exchanges, Journal of Finance 34, 103-114.

Jaffe, Jeffrey and Randolph Westerfield, 1985a, The week-end effect
in common stock returns: The international evidence, Journal of
Finance 40, 433-454.

-12-

Jaffe, Jeffrey and Randolph Westerfield, 1985b, Patterns in Japanese
common stock returns: Day of the week and turn of the year
effects, Journal of Financial and Quantitative Analysis 20,
261-272.

King, Mervyn A. and Sushil Wadhwani , 1988, Transmission of volatility
between stock markets, London School of Economics Financial
Markets Working Paper.

Lessard, Donald R., 1976, International diversification, Financial
Analysts Journal 32, 32-38.

Levy, Haim and Marshall Sarnat, 1970, International diversification of
investment portfolios, American Economic Review 60, 668-675.

Madura, Jeff, 1985, International portfolio construction, Journal of
Business Research 13, 87-95.

Neumark, David, P. A. Tinsley, and Suzanne Tosini, 1988, After-hours
stock prices and post-crash hangovers, Federal Reserve Board
Working Paper.

Panton, Don B., V. Parker Lessiq, and Maurice Joy, 1976, Co-movement
of international equity markets: A taxonomic approach, Journal
of Financial and Quantitative Analysis 11, 415-432.

Ripley, D., 1973, Systematic elements in the linkage of national stock
market indices, Review of Economics and Statistics 55, 536-361.

Roll, Richard, 1988, The international crash of October 1987,
Financial Analysts Journal 44, 19-35.

Schollhammer , H. and 0. Sand, 1985, The interdependence among the

stock markets of major European countries and the United States:
An empirical investigation of interrelationships among national
price movements, Management International Review 25, 17-26.

D/363

MIDNIGHT

Figure 1
Trading Hours for TSE and NYSE

JAPAN DAY 1

6 a.m. NOON 6 p.m.

******** ********

TSE OPEN

MIDNIGHT

10 a.m.
********

4 p.m.

U.S. DAY 1
10 p.m.

4 a.m,

10 a.m.
********

NYSE OPEN

MIDNIGHT

JAPAN DAY 2

6 a.m. NOON 6 p.m.

******** ********

TSE OPEN

MIDNIGHT

Table 1

Regression and Correlation Results-Local Currency Returns

T Value in Parentheses

S&P o-c = oc + BJSE o-c + £j

Oct. 87 Included

Oct. 87 Excluded

INTERCEPT (a )

.0007
(1.34)

.0010***
(2.58)

TSEt (B )

.1475***
(3.27)

.1000**
(2.28)

F VALUE
R SQUARE
C0RR

10.71
.0137
.1171***

5.18

.0069

.0829**

TSEt0"c = aus + euss&pt-i°"c + eus

Oct. 87 Included

Oct. 87 Excluded

INTERCEPT (ous)

.0009**
(2.51)

.0010***
(3.29)

S&Pt (Bus)

.3894***
(15.9)

.2270***
(7.87)

F VALUE
R SQUARE
CORR

253.60

.2453

.4963***

61.89
.0759
.2756***

*** Significant at a 1% level
** Significant at a 5% level
* Significant at a 10% level

Table 2

Regression and Correlation Results-Common Currency Returns

T Value in Parentheses

S&P o-c = a, + BTTSE o-c + e „
t J J t J

Oct. 87 Included

Oct. 87 Excluded

INTERCEPT (a )

.0003
(0.48)

.0006
(1.39)

TSEt <Bj>

.1226**
(2.49)

.0642
(1.27)

F VALUE
R SQUARE
C0RR

6.20

.0080

.0893**

1.61

.0021

.0463

TSE o-c = aTTO + 8TTOS&P , o-c + e

~US wUS"~~t-l
Oct. 87 Included

US
Oct. 87 Excluded

INTERCEPT (a )

U o

.0010***
(2.86)

.0011***
(3.51)

s&pt (SUS)

.3404***
(15.1)

.1885***
(7.60)

F VALUE
R SQUARE
CORR

229.49

.2280

.4775***

57.83
.0711
.2667***

*** Significant at a 1% level
** Significant at a 5% level
* Significant at a 10% level

Table 3

Regression and Correlations Results-Local Currency Returns

T Value in Parentheses

S&P o-c = ctT + STTSE o-c + e

INTERCEPT (a )

TSE (0 )

I— «J

F VALUE
R SQUARE
CORR

~ ~ „j K

'J " t

J

Oct. 87 Included

Oct. 87 Exc

-.0001

-.0002

(1.33)

(1.33)

.0097

.0001

(0.18)

(0.05)

.034

.000

.000

.000

.0071

.0002

TSEto-c = aus + SusSSPt_l0-c + c^

Oct. 87 Included

Oct. 87 Excluded

INTERCEPT (aus)

.0001
(6.00)

.0001
(5.62)

s&pt (eus)

.0154***
(9.87)

.0258***
(12.4)

F VALUE
R SQUARE
CORR

97.55
.1161
.3407***

154.41

.1768

.4205***

*** Significant at a 1% level
** Significant at a 5% level
* Significant at a 10% level

Table 4

Regression and Correlations Results-Common Currency Returns

T Value in Parentheses

S&P o-c - a, + 6TTSE o-c + eT
t J J t J

Oct. 87 Included

Oct. 87 Excluded

INTERCEPT (a.)

-.0003
(-1.35)

-.0003
(-1.28)

TSEt (8 )

.0071
(0.35)

.0338
(1.24)

F VALUE
R SQUARE
CORR

.119

.0002

.0133

1.54

.0024

.0486

TSE o-c = aTTO + |3Tt_S&P.. .o-c + e

~US " "US*""t-l
Oct. 87 Included

US

Oct. 87 Excluded

INTERCEPT (a )

U O

.0001
(6.32)

.0001
(6.17)

s&pt (8US)

.0142***
(9.94)

.0213***
(11.7)

F VALUE
R SQUARE
CORR

98.86
.1174
.3427***

136.9
.1600
.4000***

*** Significant at a 1% level
** Significant at a 5% level
* Significant at a 10% level

Table 5

Performance of Nikkei Index in Day t Inclusive

of Round Trip Transactions Costs (TC)
After S&P 500 Local Return Trigger in Day t-1
(Data Without October 1987)

0%

ROUND TRIP TC
.50%

1%

S&Pt-! UP by .5% (226 TIMES)
MEAN TSEt RETURN
% UP

S&Pt_i DOWN by .5% (151)
MEAN TSEt RETURN
% DOWN

S&Pj--! UP by 1% (114)
MEAN TSEt RETURN
% UP

S&Pt-i DOWN by 1% (83)
MEAN TSEt RETURN
% DOWN

S&Pt-! UP by 1.5% (53)
MEAN TSEt RETURN
% UP

S&Pt-! DOWN by 1.5% (46)
MEAN TSEt RETURN
% DOWN

S&Pt-! UP by 2% (25)
MEAN TSEt RETURN
% UP

S&Pt-! DOWN by 2% (20)
MEAN TSEt RETURN
% DOWN

.341
72%

-.160
37%

-.658
17%

.234
59%

-.267
28%

-.769

18%

.507
78%

.006

-.493
24%

.350
63%

-.150
35%

-.652
25%

.665
81%

.163
60%

-.337

30%

.535
70%

.036
43%

-.465
30%

.733
76%

.230

60%

-.270
36%

.699
75%

.201
40%

-.299
30%