Asia Pacific Management Review (2007) 12(1), 1-12
The Critical Role of Monetary Policy in the Link between Business
Conditions and Security Returns in Taiwan
Ming-Hsiang Chena*, Su-Jane Chenb and Yi-Chi Kuoc
a
Department of Finance, National Chung Cheng University, Chia-Yi, Taiwan, ROC
b
Department of Finance, Metropolitan State College of Denver, Denver, USA
c
International Bills Finance Corporation, Taipei City, Taiwan, ROC
Accepted in October 2006
Available online
Abstract
This study examines the relationship between the business condition proxies and security returns and whether the relationship
varies across changing monetary policy environments in Taiwan. We test the explanatory power of business condition proxies,
namely, dividend yield (D/P), term spread (TERM), and growth rate of industrial production (GIP), on stock and bond returns in
Taiwan and then incorporate monetary policy action into the business condition model to investigate its impact on the ability of
business condition proxies in accounting for stock and bond return variation over time. Empirical results suggest that the influence of
D/P and TERM on security return behavior varies dramatically with monetary policy. Furthermore, the structural relation between
D/P and TERM endures significant changes across monetary environments. These findings underline the importance for central
bankers to closely monitor monetary policy. Investors and corporate managers, based on presented evidence, should also take into
account monetary policy when modeling security market returns for investment and financing decision-making purpose.
Keywords: Business condition; Monetary policy; Security returns; Taiwan
1. Introduction
(expansive vs. restrictive) are examined. Their findings
indicate that the variation of stock and bond returns over
time is linked to both business conditions and monetary
policy. In fact, it is shown that the significance of business conditions (proxied by dividend yields, term spread,
and default spread) to the volatility in stock and bond
returns varies across different monetary policy environments.
Monetarists claim that changes in monetary policy
affect the real economy. One of the fundamental financial theories states that the value of any financial asset is
equal to the present value of all future cash flows expected from holding the asset. Accordingly, monetary
policy changes should have an impact on the valuation
of financial assets through changes in both the anticipated level of future cash flows and the discount rate
employed in discounting these expected cash flows. If
the Federal Reserve pursues a more restrictive monetary
policy, growth in money supply is slowed and, holding
everything constant, interest rates tend to rise. This, in
turn, will drive up corporate financing cost and cause
future cash flows to fall. A secondary effect of a more
restrictive monetary policy lies on the discount rate used
to discount these cash flows. The hiked discount rate
makes these cash flows worth less in present terms.
Through this mechanism, monetary policy can be linked
to asset returns. Empirical studies support this link.
Following Jensen et al. (1996), the primary research
question investigated in this study is if monetary policy
affects non-U.S. financial markets in a pattern similar to
that in the U.S. financial market, which, needless to say,
possesses its own uniqueness in economic and financial
conditions. Hess (2001) replicates the research by Jensen
et al. (1996) by examining the relationship between
business condition proxies and security returns from the
United Kingdom and whether the relationship differs
across changing monetary policy conditions. Unlike
Jensen et al. (1996), no significant change in the connection between business condition proxies and security
returns across different monetary policy environments is
evidenced in Hess (2001). Given inconsistent empirical
findings between the two cited studies, this research
intends to find out if empirical findings generated in
Jensen et al. (1996) can be generalized to another
Jensen, Mercer, and Johnson (1996) study the effect
of monetary policy changes on the relationship between
business conditions and security returns in the United
States. Stock and bond returns against business conditions across different monetary policy environments
*
E-mail: finmhc@ccu.edu.tw
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Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
Kingdom. This finding underlines the importance for
central bankers to closely monitor their country’s
monetary policy, in light of its implications on business
conditions and in turn, financial markets. This study also
has important ramifications for security investors and
corporate decision makers, both domestically and internationally. When modeling security market returns
for investment and financing decisions, they should
consider monetary policy changes pertaining to the
country (countries) involved in their decision. Moreover,
the critical impact of monetary policy changes on the
association between business conditions and stock returns is found to be significant across all 19 industry
sectors as well, including not only interest rate sensitive
industries such as finance and construction but also
industries with significant export/import exposure such
as automobile and textile. This finding is consistent with
Nowak (1993).
non-U.S. financial market, namely, Taiwan. That is, this
study focuses on the investigation of the impact of
monetary policy changes on the interaction between
business conditions and security returns in Taiwan.
The research findings are significant in terms of
shedding some lights on several important, related
questions. First, are changing business conditions alone
sufficient to explain stock and bond return variations
over time? Or does the monetary policy add to the explanatory power of business conditions? Thus, this study
attempts to provide further empirical evidence on the
joint role of monetary policy and business conditions on
time-series variation of security returns. Also, does the
interrelationship between the three business condition
proxies adopted in this study persist over time or shift
with changes in the monetary environment? Moreover,
can results evidenced in Jensen et al. (1996) be replicated, using economic and financial data prevailing in
Taiwan? Recent empirical evidence supports the
well-accepted notion that the stock market in Taiwan has
been historically characterized by high volatility (see
Titman and Wei, 1999; Chen, 2003; Chen and Bidarkota,
2004). Thus, it is plausible that related empirical findings generated from the U.S. are not readily applicable to
Taiwan.
The next section provides a brief literature review. It
is followed by the coverage of data and methodology.
Section 4 presents empirical findings. The final section
concludes this study.
2. Literature Review
Waud (1970), Pearce, and Roley (1985), and Smirlock and Yawitz (1985) focus on the short-term security
market reaction to discount rate changes. An inverse
relationship between discount rate changes and stock
returns over the announcement period is documented in
these studies. Baker and Meyer (1980), Roley and Troll
(1984), and Cook and Hahn (1988) report effects of
monetary policy on various debt markets. Johnson,
Buetow, Jensen, and Reilly (2003) examine the relationship between U.S. monetary policy and intermediate-term and long-term returns on U.S. corporate and
government bonds. They show that corporate bond returns are strongly correlated to monetary policy. However, no significant return differences in intermediate-term or long-term Treasury or government indexes
between expansive and restrictive monetary policy periods are documented in the study. Brown (1981) and
Batten and Thornton (1984) suggest significant monetary policy influence on foreign exchange market.
Conovar, Jensen, and Johnson (1999) observe a significant association between stock returns in foreign
markets and both local and U.S. monetary environments.
Further, different from Jensen et al. (1996), we
examine the influence of changes in monetary condition
on the link between business conditions and stock returns in 19 industry sectors, in addition to the stock
market as a whole. Nowak (1993) discusses some implications of discount rate changes. For example, he
argues that monetary policy changes are likely to have a
strong impact not only on interest rate sensitive industries but also on those industries with a substantial export
or import component. Given Taiwan’s heavy economic
reliance on both exporting and importing business, this
study provides a valid tool to the verification of Nowak’s
(1993) assertion.
Using economic and return data from July 1991
through February 2004, we conclude that the influence
of business conditions on Taiwan security returns is
affected by the monetary policy. Consistent with Jensen
et al. (1996), this study suggests that the impact of
business conditions on security return behavior differs
dramatically across various monetary environments and
the structural relation between the three employed
business condition proxies is also significantly affected
by the monetary policy. Given the vast difference between the two countries, Taiwan and the U.S., in aspects
such as economic conditions, geographical location,
natural resources, political environment, depth and size
of financial markets, etc., the similarity of findings between the two studies is encouraging. It suggests that the
significance of monetary policy may be persistent across
borders, with some exceptions such as the United
Several studies also link long-term security return
patterns to prior changes in monetary policy. Jenson and
Johnson (1995) find that long-term monthly and quarterly stock returns during expansive monetary periods
are significantly higher than those during restrictive
periods. Patelis (1997) concludes that a shift in the
stance of monetary policy can account for the predictability in expected excess returns on stocks over long
horizons. Thorbecke (1997) shows that while the impact
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Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
ure bill rates, the 90-day commercial paper (CP) rates
taken from the TEJ database are used instead as the
proxy for risk-free rates.
of monetary policy shocks on stock returns is similar
across industries, returns on small firms (as opposed to
large firms) are significantly affected by the policy
shocks. Park and Ratti (2000) indicate that the restrictive
monetary policy causes significant negative movements
in inflation and expected real stock returns.
Monthly data of 10-year bond rates and 90-day CP
rates are available from January 1995 to February 2004
and June 1991 to February 2004, respectively. For the
TSE composite and construction, finance, foods, paper
and pulp, and textiles industries, monthly time series
data of price index cover the time period of June 1991 to
February 2004. Monthly data of price index on all other
industries run from July 1995 through February 2004.
We calculate real stock and bond returns to more accurately reflect true asset returns by subtracting the Consumer Price Index (CPI) growth rate from concurrent
monthly stock and bond returns. The time series CPI
data are obtained from the TEJ database.
Fama and French (1988, 1989) and Schwert (1990)
show that three business condition proxies (dividend
yield, default spread, and term spread) are able to explain a significant variation of security returns. Balvers,
Cosimano, and McDonald (1990) claim that security
return patterns are a result of predictable movements in
aggregate outputs. Several economic variables proposed
by Chen, Roll, and Ross (1986) are found to be significant in explaining stock returns. Asprem (1989) investigates the relationship between stock indices and macroeconomic variables in ten European countries. It is
shown that employment imports, inflation, exchange
rate, and interest rates are inversely related to stock
prices; expectations about future real activity measures
for money and the U.S. yield curve are positively related
to stock prices.
Table 1 presents summary statistics of real returns
over the entire sample period for 10-year bonds, TSE
composite, and all 19 industry sectors included in this
study. Generally speaking, stock market exhibits high
returns and high volatility. The mean bond return and its
volatility are much lower than those of stocks. The average monthly real stock returns (including the TSE
composite and 19 industry sectors) range from 1.95
percent in the paper and pulp industry to 7.40 percent in
the steel and iron industry. These stock returns are also
very volatile. The standard deviation is 9.43 percent for
the entire market. For the industry sectors, the volatility
varies from 8.36 percent in the electric and machinery
industry to 12.91 percent in the electronics industry. The
Sharpe ratio, the indicator of the relative risk-return
tradeoff, shows that the steel and iron industry has the
highest Sharpe ratio of 0.75 and the paper and pulp industry is the least risk-rewarding sector with a Sharpe
ratio of 0.15, which is even lower than that of bond
returns.
Closely related empirical work examines security
returns behavior across various stages of the business
cycle. Chen (1991) demonstrates that expected excess
returns are negatively related to the recent growth of
Gross National Product (GNP) and positively related to
the economic variable’s future growth. Patelis (1993)
discovers that expected stock returns are positively
correlated with expected macroeconomic conditions.
Many studies are able to establish a link between
monetary policy and business conditions. Laurent
(1988) demonstrates that changes in the Fed’s discount
rate serve as a signal to future monetary policy and real
output developments. McQueen and Roley (1993) find
that stock market response to macroeconomic news
varies, depending on business conditions. Jensen et al.
(1996) show that the impact of business conditions on
stock and bond returns differs across various monetary
policy environments.
Statistical test results for normality (Jarque and Bera,
1980) and zero autocorrelations (Ljung-Box Q-statistic)
are also reported in Table 1. The skewness measures the
asymmetry of the return data distribution about the mean,
while kurtosis in excess of three implies that it is fat
tailed. The CP rates and stock returns in three industries,
glass and ceramics, others, and wholesale and retail, are
negatively skewed. The CP rates and stock returns in the
composite, construction and finance exhibit fat tails. The
Jarque and Bera test rejects the normality hypothesis of
security returns for the CP rates and composite, construction, finance, paper and pulp, and steel and iron
industries. The Ljung-Box Q-statistics for the security
returns indicate that real stock returns in general have no
statistically significant sample autocorrelations. The
results, however, suggest that CP rates are autocorrelated. Based on the reported Q-statistics for the square
values of the security returns, the only industry with real
returns showing a possible nonlinear dependence and
3. Data and Methodology
3.1 Data
This study examines monthly real returns from July
1991 through February 2004. The time period in this
study is chosen due to limitations in data availability.
We obtain monthly stock returns (including dividends)
of the Taiwan Stock Exchange (TSE hereafter) composite and 19 industry sectors from the financial database of the Taiwan Economic Journal (TEJ hereafter).
Monthly 10-year bond returns are taken from the Grand
Cathy Government Bond Index in Taiwan. The total
bond returns include monthly price changes and accrued
interest. As a result of the limited availability of Treas3
Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
presence of autoregressive conditional heteroscedasticity (ARCH) is finance.
environments. There is no consensus among economists
about how to measure the size and direction of changes
in monetary policy. Following Jensen et al. (1996), we
classify monthly observations that follow an increase in
the discount rate set by the Central Bank of China (CBC
hereafter) as falling in a restrictive monetary environ
3.2 Monetary Policy: Discount Rate Changes
To facilitate the analysis in this study, we divide the
sample into two sub-samples based on monetary policy
Table 1. Summary Statistics of Real Monthly Security Returns
Industry
Mean
(%)
MAX
(%)
MIN
(%)
STD
(%)
Sharpe
ratios
Skewness
Kurtosis
JB
10-year bonds
0.61
5.04
-2.86
1.39
0.23
0.21
3.72
3.30
90-days CP
0.29
2.32
-3.26
0.93
---
-0.30
3.95
8.09
TSE composite
3.74
41.63
-16.83
9.43
0.37
0.85
4.77
38.58
Automobile
6.71
36.25
-11.81
9.58
0.67
0.50
3.32
4.77
Cement
3.67
34.21
-20.13
11.51
0.29
0.22
2.91
0.90
Chemicals
3.84
28.37
-18.67
9.36
0.38
0.13
2.83
0.45
Construction
4.09
46.65
-17.60
11.73
0.32
0.82
4.02
23.93
Electric and machinery
5.14
34.8
-13.35
8.36
0.58
0.38
3.79
5.22
Electrical appliance cable
3.81
38.08
-28.73
11.46
0.31
0.16
3.59
1.93
Electronics
6.05
38.03
-24.96
12.91
0.45
0.35
3.17
2.22
Finance
3.45
66.69
-17.36
12.17
0.26
2.10
11.14
532.32
Foods
3.67
29.67
-17.04
9.01
0.38
0.20
3.11
1.15
Glass and ceramics
4.26
26.73
-19.42
8.97
0.44
-0.12
2.85
0.37
Others
5.26
26.94
-19.53
8.79
0.57
-0.07
2.83
0.22
Paper and pulp
1.95
45.03
-23.33
11.15
0.15
0.54
3.71
10.65
Plastics
4.54
36.19
-19.85
10.93
0.39
0.43
3.57
4.66
Rubber
4.23
28.02
-19.18
10.68
0.37
0.11
2.40
1.75
Steel and iron
7.40
35.14
-10.98
9.48
0.75
0.71
3.42
9.54
Textiles
3.09
31.07
-16.36
9.97
0.28
0.38
2.84
3.87
Tourism
2.89
23.88
-25.00
9.25
0.28
0.14
3.48
1.33
Transportation
4.56
33.36
-26.75
9.95
0.43
0.33
3.83
4.82
Wholesale and retail
4.33
27.53
-18.26
8.37
0.48
-0.01
3.52
1.15
Q(6)
Q2(6)
3.17
2.87
14.59*
6.13
Q(12) Q(18)
Q2(12) Q2(18)
13.14
28.64
7.22 11.21
19.44
33.82*
7.84
11.00
8.72
4.19
4.42
3.59
1.34
6.62
3.60
4.09
7.03
2.99
4.90
2.84
6.42
7.35
8.72
10.07
4.82
1.29
4.78
7.28
2.27
8.18
5.11
5.59
5.30
1.88
2.10
8.51
3.24
7.58
3.89
5.56
4.21
6.09
1.78
11.95
2.54
5.55
3.62
8.96
13.76
17.11
7.82
6.75
6.95
14.25
18.10
19.90
11.50
14.91
23.09*
11.98
18.52
11.98
11.07
13.08
10.01
28.14*
12.21
12.13
4.85
9.83
7.76
14.21
13.07
7.82
15.35
11.77
13.46
14.76
5.95
11.65
9.84
21.28*
6.20
23.69*
9.07
8.17
6.05
12.23
Stock market
29.08*
18.81
14.70
9.20
10.49
18.19
25.89
26.93
18.83
22.64
29.00*
14.44
28.83
22.13
20.15
16.26
21.43
28.87*
20.38
23.59
10.67
25.93
18.57
17.95
27.13
15.37
21.72
14.95
17.95
17.39
14.12
15.56
21.47
25.83
14.71
27.81
10.83
16.98
19.48
19.14
Notes: Monthly time series data of 10-year bond rates and 90-day CP rates are available from January 1995 to February 2004 and June 1991 to
February 2004, respectively. For the TSE composite, construction, finance, foods, paper and pulp, and textiles industries, monthly time
series data of price index cover the time period of June 1991 to February 2004. Monthly time series data of price index on all other industries
run from July 1995 through February 2004. The standard errors for skewness and kurtosis shown in parentheses are (6 / T ) and (24 / T ) ,
respectively. T is the number of sample observations. J-B is the Jarque and Bera (1980) normality test under which the null hypothesis is that
the coefficients of skewness and kurtosis are equal to zero and three, respectively. JB is defined as [(T / 6)b + (T / 24)(b − 3) ] ~ χ , where T is the
number of sample observations, b1 is the coefficient of skewness and b2 is the coefficient of kurtosis (Jarque and Bera, 1980). The critical
value at the 5% significance level is 5.99. Q-statistic, Q(n), is the Ljung-Box (1978) Q-statistic at lag n, used to test whether a group of n
autocorrelations are significantly different from zero. Q(n) is distributed as χ . Critical values for n =6, 12, and 18 at the 5% significance
1/ 2
2
1
2
2
2
n
level are 12.59, 21.03, and 28.87, respectively. The asterisk (*) denotes statistical significance at the 5% significance level.
4
2
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Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
ment and observations that follow a decrease in the
discount rate as falling in an expansive monetary environment. In this classification scheme, the absolute level
of the discount rate is not the important parameter.
Rather, it is the direction of the last change that signals
future monetary policy.1
Table 2. Discount-Rate-Change Series: July
1991-February 2004
Monthly
observations in
series
1
D
1991/7/15
5
9
2
I
1992/5/9
1
4
3
D
1992/10/5
2
27
4
I
1995/2/27
1
4
5
D
1995/7/25
3
24
6
I
1997/8/1
1
12
7
D
1998/9/29
4
17
8
I
2000/3/24
2
8
9
D
2000/12/19
15
38
Notes: A series is identified as a sequence of consecutive rate change
that is in the same direction. A new monetary environment
starts when the direction of discount rate change is reversed
from that of the previous one. As a result, nine months during
which the first rate changes in a series occurred are eliminated
from the sample. The number of monthly observations in the
full sample equals 143, with 115 observations following rate
decreases and 28 observations following rate increases.
Series
To be more specific, a new monetary environment
starts when the direction of discount rate change is reversed from that of the previous one. Discount rate data
is collected from the CBC’s database. During the study
period, the CBC changed the discount rate 32 times: 5
increases and 27 decreases. These changes constitute
nine rate-change series, five decreases and four increases.
Rate-change series are considered because the CBC
is assumed to be under the same monetary policy (e.g.
expansive vs. restrictive) until a discount rate change in
the opposite direction is announced. The identified series
of rate changes should effectively represent fundamental
changes in the monetary environment. Following this
approach, the analysis has identified five expansive and
four restrictive monetary policy periods, respectively.
Nine months during which the first rate changes in a
series occurred are eliminated from the sample. These
months are omitted for two reasons. First, our objective
is to focus on the long-term relationship between
monetary condition and security returns, and thus we
eliminate any announcement-period effect. Second, the
returns associated with months that mark the initiation of
a new monetary environment would include both expansive and restrictive days. The total number of resulting observations is 143 months, of which 115 follow
discount rate decreases and 28 follow discount rate increases. Table 2 provides summary information on the
rate change series.
night interest rate. They represent a cross-section of
monetary base, interest rate level, and the CBC and
banking activity. All variables were taken directly from
the CBC’s web site (www.cbc.gov.tw).
Associated test results reported in Table 3 show that
significant differences exist in the medians and means of
each of the variables between the two monetary policy
periods (expansive vs. restrictive), except for the median
of excess reserve. Consistent with the contention that
decreasing rate series characterize expansive monetary
environment, the medians and means of the CBC credit
and excess reserve are significantly greater during decreasing rate series than during increasing rate series. On
the other hand, based on respective medians and means,
monetary base and overnight interest rate are significantly higher in restrictive periods than in expansive
periods. Thus, regardless of whether discount rate
changes signal or simply confirm monetary policy development, they are useful gauges of monetary stringency. The breath of the four variables examined reinforces the conjecture that rate-change series serve as an
effective indicator of the monetary environment.
To support the proposition that the series in Table 2
is an effective indicator of monetary stringency, statistical tests are conducted on monthly observations of four
macroeconomic variables. The attempt is to check if
these variables’ medians and means associated with the
expansive and restrictive monetary periods are significantly different from each other. Both parametric and
nonparametric tests are conducted for this purpose. We
utilize Wilcoxon rank sum test for our nonparametric
test. The four selected macroeconomic variables are
monetary base, CBC credit, excess reserve, and over
1
Increasing (I) or First rate change
Rate
Decreasing (D)
in series
changes in
series
3.3 Regression Models
Recent research on the relationship between security
returns and business conditions has focused on three
measures of business environment: dividend yield, default spread, and term spread. We use dividend yield
(D/P), term spread (TERM), and growth rate in industrial
production (GIP) as our measures for business conditions. The decision of replacing default spread with GIP
in this study as a proxy for business conditions results
from the lack of default spread data in Taiwan and is
well justified by empirical literature. (See Chen, 1991;
Note that the money supply and overnight rate, the latter of which is
similar to the Federal funds rate in the U.S., are another two common
money policy variables. However, the money supply and overnight
rate have not been widely used as a good indicator of different
monetary policy environment, due to the frequent changes in both
series (Jensen and Johnson, 1995; Jensen and Mercer, 2002; Johnson
and Jensen, 1998; Conovar et al., 1999; Johnson et al., 2003). Based
on the same reason, we have used changes in the discount rate, rather
than the money growth rates or changes in the overnight rates, as the
classifying factor for monetary policy environments.
5
Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
Table 3. Test of Effectiveness of Monetary Stringency (July 1991-February 2004)
Test
Monetary Base
Expansive
Wilcoxon
Sum Rank Test
t-test
CBC Credit
Restrictive
Median level
(100million NT$)
1,558,800
1,631,000
Excess Reserve
Expansive Restrictive Expansive Restrictive
Median level
(million NT$)
412,480
Median level
(million NT$)
366,372
6,447
5,908
Inter-Bank Overnight
Call-Loan Rate
Expansive Restrictive
Median level (%)
5.08
6.67
-2.35 (0.01)
Mean level
(100million NT$)
1,545,300
1,600,300
2.06 (0.03)
Mean level
(million NT$)
426,594
361,125
1.42 (0.15)
Mean level
(million NT$)
8,861
6,236
-3.79 (0.00)
Mean level (%)
-1.93 (0.02)
2.16 (0.01)
1.68 (0.04)
-4.01 (0.00)
4.69
6.29
Notes: Median and mean of monthly observations of monetary base, CBC credit, excess reserve, and inter-bank overnight call-loan rate
are calculated over expansive and restrictive monetary policy periods. All four macroeconomic variables are taken directly from
Central Bank of China. The p-values for Wilcoxon sum rank test and t-test, presented in parentheses, are for tests of differences
in levels of the four macroeconomic variables between expansive and restrictive monetary policy periods. Expansive (restrictive)
monetary policy periods are those involving series of discount rate decreases (increases).
ERt = α1 + α2 D Pt −1 + α3TREMt −1 + α4GIPt −1 + α5 DRt −1 + vt
Chen, 2005; Estrella and Hardouvelis, 1991; Fama and
French, 1989; Miffre, 2001).
, (2)
where DR, the discount rate change, is a binary variable,
which takes on a value of one if the previous discount
rate change was an increase and a value of zero if the
previous change was a decrease. To account for possible
presence of autocorrelation and heteroscedasticity and to
ensure consistent estimates in regression coefficients,
standard errors, and associated t-statistics, we follow
Newey and West (1987) by regressing security returns in
the form of Eqs. (1) and (2), respectively.2
To obtain D/P, this study first computes dividends
(including stock dividend and cash dividend) of all firms
listed on the TSE. Respective total dividends are then
divided bythe total market value of the TSE market
index and TSE industry indices for the calculation of
D/Ps. For the bond market, D/P is replaced by current
yield, which is total coupon payment (including accrued
interest) divided by the total market value of government
bonds. D/P and GIP data are collected from TEJ database. Taken from the CBC database, TERM is measured
as the difference between the 10-year bond yield and
90-day CP rate.
4. Empirical Results
4.1 A preliminary Examination
Following the approach of Fama and French (1989)
and Jensen et al. (1996), we use excess real returns associated with the bond index, the stock market, and TSE
industry indices as dependent variables in a linear multiple regression model expressed in Eq. (1):
Tables 4 and 5 present the means of the variables
used in this study over the full sample period as well as
during expansive and restrictive monetary environments.
It also presents the test for the equality of the means of
the variables under the two monetary policies.
ERt = β1 + β 2 D Pt −1 + β 3TERM t −1 + β 4 GIPt −1 + et ,
As shown in Table 4, the term spread (TERM), a
measure of forward rates and containing information for
expected bond returns (Fama and Bliss, 1987), is significantly higher in the expansive monetary environment
than in the restrictive environment. The two components
of term spread, 10- year government bond and 90-day
CP, in Table 4 show that the TERM variable is driven by
difference in the two associated rates across the two
environments. We also find that the mean growth rate of
industrial production (GIP) is significantly lower in the
(1)
where ER t is the real excess return of interest in month t,
D/Pt-1 is the dividend yield of the index of interest in
month t-1, TERM t −1 is the 10-year government bond
yield over the 90-day CP rate in month t-1, GIPt −1 is the
growth rate of industry production and et is the residual
term of month t. To obtain real excess returns, we subtract contemporaneous monthly real returns on the
90-day commercial papers from monthly real rates of
return. Also, because the analysis focuses on expected
returns, this study lags the independent variables, D/P,
TERM, and GIP, relative to the dependent variable of
excess real returns, by one period.
2
We then examine the influence of monetary environment on security returns in Eq. (2) by adding a
dummy variable, DR, to Eq. (1):
6
The Pearson correlation coefficient matrix of the independent variables over the full sample period indicates that the correlation coeffi
cient is -0.15 (p-value= 0.12) between D/P and TERM, -0.17
(p-value= 0.11) between D/P and DR, -0.37 (p-value= 0.01) between
TERM and DR, 0.07 (p-value= 0.28) between GIP and TERM, -0.03
(p-value= 0.91) between D/P and GIP, 0.03 (p-value= 0.91) between
GIP and DR. With the exception of correlation between D/P and DR,
correlations appear to be, at most, modest. Thus, multicollinearity
should not present a serious concern.
Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
Table 5. Security Monthly Mean Excess Real Returns
Table 4. Mean Value on Proxies for Business Conditions
under Different Monetary Conditions
Business condition
proxies
D/P (TSE)
TERM
GIP
10-year
government bond
90-day CP
Full
sample
3.39
0.42
0.20
Expansive Restrictive
period
period
3.41
3.34
0.60
-0.19
0.02
3.34
Index
t-statistics
(p-value)
0.33 (.36)
4.11 (.00)***
-1.78 (.07)*
5.01
4.64
6.24
-4.86 (.00)***
4.59
4.88
6.43
-5.41 (.00)***
Bond market
Stock market
TSE composite
Automobile
Cement
Chemicals
Construction
Electric and machinery
Electrical
appliance cable
Electronics
Finance
Foods
Glass and ceramics
Others
Paper and pulp
Plastics
Rubber
Steel and iron
Textiles
Tourism
Transportation
Wholesale and retail
Notes: Term spread (TERM) equals the difference between 10-year
government bond and 90-day commercial paper yield.
10-year government bond and 90-day CP rates are the
monthly average of daily rates taken form the Central Bank of
China. The t-statistics test the null hypothesis that variable
means are equal between expansive and restrictive monetary
policy periods. The asterisks (*), (**), (***) denote statistical
significance at the 10%, 5%, and 1% significance levels, respectively. The data excludes months of changes in monetary
policy.
expansive monetary environment than in the restrictive
environment. The observed pattern in the term structure
and industrial production growth is consistent with the
monetary effects of a CBC stabilization policy, which
tends to be restrictive when the economy and interest
rates are rising and expansive when the economy and
interest rate are declining. Since the CBC policy is
linked to changing economic conditions, both monetary
policy and business conditions are likely to contribute to
the observed pattern in TERM. On the other hand, the
result shows no significant difference in the mean of
dividend yield of the TSE index between the two
monetary policy periods, expansive vs. restrictive.
Full Expansive Restrictive
sample
period
period
0.31
0.31
0.28
t-statistics
(p-value)
0.13 (.88)
3.59
6.20
3.47
3.28
3.80
4.71
3.33
4.99
6.96
4.22
4.83
5.27
5.86
5.02
-2.17
3.23
0.57
-2.76
-2.25
0.23
-3.25
6.83 (.00)***
1.56 (.06)*
1.29 (.10) *
3.38 (.00)***
3.05 (.00)***
2.73 (.00)***
2.97 (.00)**
5.94
3.27
3.42
3.81
4.82
1.87
4.17
3.81
7.13
2.80
2.53
4.13
3.91
8.02
4.48
4.24
4.31
6.10
3.48
5.72
5.30
8.10
4.41
3.45
5.05
5.22
-2.15
-1.70
0.08
1.86
-0.14
-4.73
-1.87
-1.96
3.32
-3.83
-1.03
0.55
-1.81
3.28 (.00)***
2.39 (.00)***
2.18 (.01)***
1.07 (.14)
2.92 (.00)***
3.55 (.00)***
2.83 (.00)***
2.76 (.00)***
2.01 (.02)**
4.05 (.00)***
1.93 (.02)**
1.78 (.03)**
3.14 (.00)***
Notes: The t-statistics test the null hypothesis that variable means are
equal between expansive and restrictive monetary policy periods. The asterisks (*), (**), (***) denote statistical significance
at the 10%, 5%, and 1% significance levels, respectively. The
data excludes months of changes in monetary policy.
ering full sample, expansive, and restrictive periods,
respectively.
Panel A of Table 6 displays the regression results
over the full sample period for each industry. We observe that dividend yield (D/P) has a significantly positive coefficient (at the 10% significance level) on 15 (the
TSE composite and 14 industry indices) out of the 20
stock indices but hasno explanatory power for bond
index. This panel of the table shows, for example, that a
1% increase in the TSE market dividend yield is associated with a 3.19% increase in TSE market excess real
return. On the other hand, the coefficients of the TERM
variable are statistically significant for 7 out of 21 security indices and the coefficients of the GIP variable are
only statistically significant for the automobile, others,
rubber, textiles and transportation industries.
As illustrated in Table 5, the means of excess real
returns for all stock indices, except for the glass and
ceramics industry, are statistically significantly higher in
the expansive monetary environment than in the restrictive environment. This finding is consistent with Rozeff’s (1974) and Jenson and Johnson (1995) in the sense
that a significant association between monetary developments and contemporaneous stock returns is documented in all three studies. The result in Table 5 shows
no significant difference for bond market between the
two monetary policy periods, though. This is in line with
Johnson, Buetow, Jensen, and Reilly (2003), which finds
that returns in intermediate-term or long-term Treasury
or government indexes do not differ significantly between expansive and restrictive monetary policy periods.
Panel B of Table 6 lists regression results of security
excess real returns on business conditions associated
with the expansive monetary policy period. The findings
are similar to those of Panel A. Dividend yield retains its
positive significance on returns for 14 of the 20 industry
indices but again fails to demonstrate its influence on the
bond index. However, the impact of TSE market dividend yield on excess real returns of TSE composite
during the expansive monetary policy period is not statistically significant. Also, the coefficients of the TERM
variable are statistically significant for 4 of 21 security
indices. However, the GIP variable is strongly related to
4.2 Regression Results
Table 6 reports the empirical relationship between
business conditions and excess real returns. It is generated from Eq. (1). The sample period is divided into
expansive and restrictive monetary periods to investigate
whether or not the association between business conditions and security returns varies with monetary environments. Thus, Table 6 consists of three panels, cov
7
Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
Table 6. Regression Results: Security Excess Real Returns on Dividend Yield, Term Spread and Industrial Production Growth Rate
ERt = β1 + β 2 D Pt −1 + β3TREMt −1 + β 4GIPt −1 + et
Index
Bond market
D/P
TERM
GIP
R
2
Panel
A
-0.01
(.95)
Panel
B
0.04
(.86)
Panel
C
-3.99
(.45)
Panel
A
0.17
(.20)
Panel
B
0.26
(.13)
Panel
C
-0.41
(.02)**
Panel
A
-0.01
(.80)
Panel
B
-0.01
(.54)
Panel
C
5.98
(.34)
Panel
A
-0.01
Panel
B
0.02
Panel
C
-0.05
3.19
(.01)***
0.42
(.30)
1.15
(.03)**
2.14
(.03)**
1.08
(.23)
2.28
(.01)***
1.15
(.05)**
2.28
(.00)***
1.70
(.03)**
0.46
(.39)
1.75
(.00)***
1.10
(.34)
1.63
(.01)***
3.88
(.00)***
0.99
(.41)
0.73
(.10)*
2.78
(.02)**
1.64
(.01)***
2.36
(.00)***
1.51
(.05)**
1.53
(.37)
0.33
(.48)
2.09
(.01)***
1.93
(.03)**
2.21
(.04)**
2.91
(.01)***
0.98
(.14)
0.81
(.30)
1.68
(.05)**
0.51
(.49)
1.84
(.00)***
1.78
(.05)**
1.10
(.10)*
3.07
(.01)***
3.22
(.06)*
0.91
(.04)**
2.45
(.06)*
1.58
(.01)***
2.38
(.00)***
0.52
(.48)
2.52
(.63)
9.19
(.02)**
1.25
(.08)*
2.26
(.42)
2.58
(.33)
-3.30
(.38)
-1.26
(.57)
-1.02
(.14)
-0.11
(.96)
5.04
(.26)
3.37
(.06)*
0.33
(.01)***
0.05
(.95)
-0.57
(.80)
1.12
(.11)
-0.56
(.41)
-0.84
(.64)
2.67
(.02)**
0.44
(.90)
-1.42
(.10)*
0.62
(.51)
2.67
(.04)**
2.49
(.02)**
0.38
(.68)
1.48
(.32)
0.26
(.87)
1.58
(.17)
0.26
(.92)
0.50
(.62)
0.93
(.44)
1.71
(.04)**
0.73
(.47)
1.74
(.12)
2.16
(.01)**
1.30
(.24)
1.91
(.04)**
1.56
(.10)*
-0.32
(.79)
1.68
(.08)*
0.40
(.63)
0.22
(.86)
1.83
(.18)
4.47
(.00)***
0.58
(.60)
3.88
(.11)
1.33
(.14)
2.14
(.18)
-1.69
(.43)
0.47
(.76)
1.62
(.27)
2.16
(.05)**
0.77
(.48)
1.88
(.19)
1.68
(.13)
2.23
(.06)*
1.91
(.15)
2.07
(.07)*
-0.68
(.67)
1.20
(.32)
-0.16
(.87)
-0.65
(.03)**
-1.06
(.01)***
-0.44
(.02)**
-0.21
(.35)
-0.23
(.09)*
-0.67
(.00)***
-0.48
(.03)**
-0.37
(.00)**
-0.43
(.04)**
-0.37
(.02)***
-0.29
(.04)**
-0.01
(.67)
-0.32
(.01)***
-0.40
(.00)***
-0.21
(.04)**
-0.55
(.02)**
-0.42
(.04)**
-0.41
(.00)***
-0.09
(.70)
-0.43
(.07)*
-0.07
(.38)
-0.12
(.10)*
-0.13
(.26)
-0.08
(.36)
-0.06
(.57)
-0.11
(.35)
-0.14
(.20)
-0.11
(.35)
-0.00
(.98)
-0.14
(.17)
0.01
(.91)
-0.12
(.08)*
-0.09
(.35)
-0.08
(.32)
-0.16
(.10)*
-0.06
(.48)
-0.14
(.10)*
-0.08
(.42)
-0.11
(.10)*
-0.04
(.72)
-0.01
(.81)
-0.07
(.36)
-0.05
(.61)
-0.06
(.56)
0.01
(.93)
-0.14
(.04) **
-0.07
(.51)
-0.06
(.58)
0.09
(.50)
-0.01
(.94)
-0.03
(.71)
-0.11
(.21)
-0.02
(.87)
-0.06
(.54)
-0.12
(.27)
-0.02
(.87)
-0.06
(.56)
-0.07
(.55)
-0.05
(.45)
0.07
(.34)
-7.47
(.48)
-13.62
(.03)**
-5.56
(.14)
-0.58
(.91)
-0.49
(.93)
-1.67
(.78)
-0.64
(.91)
-0.06
(.10)*
-3.66
(.26)
-6.72
(.17)
-3.79
(.19)
-0.25
(.59)
-1.07
(.42)
-2.84
(.52)
-0.92
(.52)
-1.24
(.70)
-1.74
(.63)
-6.89
(.02)**
-1.36
(.45)
0.37
(.90)
0.08
0.00
0.12
0.08
0.00
0.28
0.05
0.05
0.10
0.08
0.02
-0.13
0.03
0.03
-0.06
0.08
0.11
0.26
0.06
0.02
-0.03
0.08
0.01
0.17
0.02
0.00
-0.02
0.01
0.00
0.00
0.06
0.06
0.12
0.03
0.05
0.34
0.07
0.01
-0.03
0.14
0.08
0.08
0.04
0.05
-0.01
0.06
0.05
-0.04
0.07
0.02
-0.02
0.06
0.05
0.27
0.09
0.07
-0.18
0.01
-0.03
0.02
Stock market
TSE composite
Automobile
Cement
Chemicals
Construction
Electric and
machinery
Electrical
appliance cable
Electronics
Finance
Foods
Glass and
ceramics
Others
Paper and pulp
Plastics
Rubber
Steel and iron
Textiles
Tourism
Transportation
Wholesale
and retail
Notes: Panel A is entire sample period, Panel B is expansive monetary period, and Panel C is restrictive monetary period. Figures in the
parentheses are Newey and West’s (1987) corrected p-values, which take into account the moving average created by the
overlapping of forecasting horizons and conditional heteroskedastcity. The asterisks (*), (**), (***) denote statistical significance
at the 10%, 5%, and 1% significance levels, respectively. R 2 is the adjusted R square. The data excludes months of changes in
monetary policy.
stock return only in the electric and machinery industry
under expansive monetary condition.
cluding both the bond index and the TSE market), except
for chemicals, others, and transportation. More importantly, regression results in Panels B and C suggest that
sensitivity of security returns to business conditions
varies, depending on the state of monetary stringency.
The D/P variable plays a significant role in explaining
security returns variation in full and expansive monetary
environment, while the TERM variable is significant for
The response of the security returns to business
conditions during restrictive monetary period is reported
in Panel C of Table 6. While the D/P and GIP variables
are significant in only 6 and 3 of the 21 security indices
respectively, the coefficient of the TERM variable is
statistically significantly negative for all indices (in-
8
Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
sive and restrictive monetary environments, respectively.
There should be little difference in the two R 2 s if the
model captures similar variation in returns, independent
of monetary policy. However, as Table 7 shows, there
are substantial differences in the implied R 2 s in most
regressions. This suggests that the model expressed in
Eq. (1) fails to account for the significant impact of the
monetary policy on the ability of business condition
proxies in explaining security returns.
all but three security indices in restrictive period. To
fixed income investors, while dividend yield is not an
important factor to consider for investment decision-making purpose, term spread is worth paying attention to, especially during restrictive monetary policy
period when interest rates are steadily increasing.
As explained in the section of Data and Methodology, a dummy variable, DR, is created in Eq. (2) to
further examine the influence of monetary environment
on security returns. The associated regression results
based on Eq. (2) are presented in Table 7. Consistent
with evidence displayed earlier in Table 6, the results,
according to the significantly negative coefficient loaded
on DR for most stockindices (14 out of 20), indicate that
stock returns in general are significantly higher during
expansive monetary policy periods than during restrictive monetary policy periods. In contrast, there is no
evidence that bond return is significantly different between the two monetary environments. Again, this
finding is consistent with the results of Johnson, Buetow,
Jensen, and Reilly (2003). Their study suggests that
none of the return differences in intermediate-term or
long-term Treasury or government indexes are significant between expansive and restrictive monetary policy
periods.
Fama and French (1989) suggest that the relations
between the proxies for business conditions are consistent over time and driven by their common link with
changing business conditions. However, respective
Pearson correlation coefficients between, for example,
D/P and TERM calculated in this study for the entire
sample, expansive, and restrictive monetary policy periods portray a different picture. The correlation coefficients over the three periods between the two variables
are -0.15, -0.26, and -0.03, respectively, with corresponding p-values of 0.12, 0.01, and 0.91. Thus, in
contrast to Fama and French (1989), we find that, as far
as Taiwan financial market is concerned, the interrelationship between the business condition proxy variables
is affected by monetary stringency and therefore is not
purely driven by business conditions. Clearly, monetary
environment affects the structural relation between
business condition proxy variables over various business
cycles. As a result, monetary policies have an impact on
the relation between business conditions and security
returns. Once again, the finding suggests that both investors and corporate managers should take into account
not only business conditions but also monetary policies
when modeling market returns for investment and/or
financing decision making purpose.
The results in Table 7 also show that the addition of
the dummy variable for monetary stringency alters the
explanatory power of business conditions, D/P and
TERM, on stock and bond indices. In particular, the
significant explanatory power of D/P observed in Panel
A of Table 6 has disappeared in Table 7 for TSE composite, chemicals, electronic and machinery, electrical
appliance cable, electronics, textiles, and wholesale and
retail industry indices. On the other hand, as with Panel
C of Table 6, the regression coefficient on TERM for
bond index is statistically significant at the 3% significance level. This reinforces our earlier suggestion that
fixed income investors must take both term spread and
monetary policy into consideration for their investment
decisions. The persistent significance of TERM in accounting for security return variation during restrictive
monetary environment suggests that business and
monetary conditions do not mirror each other perfectly.
5. Discussions and Conclusion
This study constructs measures of business condition proxies, namely dividend yield (D/P), term spread
(TERM), and growth rate in industrial production (GIP),
to test the relationship between business condition
proxies and security returns and whether the relationship
varies across changing monetary policy conditions in
Taiwan. This study, unlike Jensen et al. (1996), investigates the impact of changes in monetary condition on
the link between business conditions and stock returns of
19 industry sectors as well as returns of the stock market
as a whole. As Nowak (1993) suggested, changes in
monetary policy are likely to significantly affect both
interest rate sensitive industries and industries with a
substantial export or import component. Taiwan is well
known for its heavy economic reliance on both exporting
and importing business. Thus, this study of the Taiwan
financial market provides an excellent avenue to verify
Nowak’s (1993) assertion.
Because monetary condition affects the role of
business condition proxies in explaining security returns,
we examine the quality of the functional specifications
by estimating implied R 2 in expansive vs. restrictive
monetary
environment.
The
implied
R2s
2
2
( Re s and Rr s ) are also presented in Table 7. They are
calculated as (1- SSEe / SSTe ) or (1- SSE r / SSTr ), where
SSEe and SSEr are the respective error sum-of-squares
in expansive and restrictive monetary environments,
derived from Eq. (1) for each industry. Accordingly,
SSTe and SSTr are the total sum-of-squares in expan-
9
Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
Table 7. Regression Results: Security Excess Real Returns on Term Spread, Dividend Yield, Industrial Production
Growth Rate and Discount Rate Change Dummy
ERt = α1 + α2 D Pt −1 + α3TREMt −1 + α4GIPt−1 + α5 DRt−1 + vt
Index
Bond market
Stock market
TSE composite
Automobile
Cement
Chemicals
Construction
Electric and machinery
Electrical appliance cable
Electronics
Finance
Foods
Glass and ceramics
Others
Paper and pulp
Plastics
Rubber
Steel and iron
Textiles
Tourism
Transportation
Wholesale and retail
D/P
TERM
**
0.01 (.97)
0.27 (.03)
1.47 (.37)
0.43 (.31)
1.19 (.03)**
1.33 (.11)
0.73 (.40)
1.74 (.12)
0.82 (.25)
1.19 (.14)
1.39 (.06)*
0.41 (.43)
1.79 (.00)***
0.54 (.52)
1.17 (.07)*
3.42 (.00)***
0.69 (.47)
0.78 (.08)*
1.71 (.13)
1.53 (.01)***
2.42 (.00)***
0.61 (.41)
-0.67 (.55)
1.99 (.13)
2.66 (.04)**
-0.34 (.74)
0.70 (.72)
0.03 (.97)
0.76 (.61)
-2.10 (.29)
-0.06 (.96)
0.96 (.50)
1.74 (.10) *
-0.12 (.91)
0.85 (.54)
1.26 (.10)*
0.25 (.85)
1.32 (.30)
0.50 (.68)
-0.79 (.57)
1.18 (.26)
-0.51 (.76)
Re2
Rr2
Re − R r
2
GIP
DR
-0.01 (.42)
0.25 (.26)
-0.00
-0.01
-0.11
0.10
-0.02 (.77)
-0.11 (.19)
-0.06 (.56)
-0.05 (.58)
0.01 (.97)
-0.14 (.03)**
-0.08 (.41)
-0.07 (.56)
0.06 (.58)
0.09 (.38)
-0.03 (.73)
-0.10 (.18)
-0.07 (.45)
-0.06 (.53)
-0.11 (.26)
-0.04 (.65)
-0.08 (.38)
-0.07 (.52)
-0.07 (.28)
0.04 (.61)
-6.48 (.01)***
-0.92 (.67)
-2.12 (.45)
-6.52 (.03)**
-7.40 (.02)**
-5.17 (.07)*
-5.35 (.10)*
-8.78 (.05)**
-5.64 (.01)***
-1.96 (.41)
-0.35 (.87)
-5.24 (.05)**
-6.57 (.04)**
-4.24 (.10)*
-6.46 (.03)**
-3.36 (.20)
-6.49 (.02)**
-3.23 (.12)
-3.79 (.07)*
-4.87 (.01)***
0.11
0.03
0.02
0.07
0.03
0.08
0.06
0.10
0.04
0.00
0.05
0.04
0.10
0.13
0.03
0.06
0.09
0.06
0.08
0.03
-0.01
0.00
0.04
0.02
0.03
0.11
0.02
0.01
-0.00
-0.00
0.04
0.05
0.00
0.07
0.05
0.05
0.02
0.05
0.07
-0.03
0.12
0.16
0.10
-0.08
-0.07
0.22
-0.03
0.11
0.07
0.03
0.12
0.19
0.15
0.10
-0.01
-0.05
-0.03
0.18
-0.07
0.02
0.13
0.16
0.06
0.10
0.10
0.11
0.05
0.10
0.07
0.03
0.08
0.14
0.15
0.03
0.06
0.10
0.05
0.13
0.14
0.05
R
2
2
Notes: DR is the discount rate change dummy variable. It takes on a value of one in restrictive monetary policy periods and a value of zero in expansive monetary policy periods. Implied R2s （Re2s and Rr2s）are calculated as (1-SSEe/SSTe) or (1-SSEr/SSTr), where SSEe and SSEr are
the respective error sum-of-squares in expansive and restrictive monetary environments derived from the model estimated over the full
sample period of July 1991-February 2004. SSTe and SSTr are the total sum-of-squares in expansive and restrictive monetary environments,
respectively. Figures in the parentheses are Newey and West’s (1987) corrected p-values, which take into account the moving average created
by the overlapping of forecasting horizons and conditional heteroskedastcity. The asterisks (*), (**), (***) denote statistical significance at the
10%, 5%, and 1% significance levels, respectively. R 2 is the adjusted R square. The data excludes months of changes in monetary policy.
Empirical results in this study reveal that the connection between business conditions and security returns
varies significantly across changing monetary policy
environments, which is in line with the results reported
in Jensen et al. (1996) for the U.S. case, but different
from the findings in Hess (2001) for the case of U.K.
While the critical role of monetary policy in the association between business conditions and security returns
is identified in both the U.S. and Taiwan, the link between business conditions and security returns under
various monetary policy environments in Taiwan behaves differently from that in the U.S. Fama and French
(1989) and Jensen et al. (1996) show that expected returns on U.S. stocks and bonds are related to all three
business condition proxies, D/P, TERM, and default
premium. In contrast, we find that, over the entire sample
period, expected stock returns of the Taiwan composite
are significantly related to D/P only and expected returns
on bond are not related to any of the three business
condition proxies. For industry sectors, 14, 7, and 5 (out
of 19) industry sectors are significantly related to D/P,
TERM, and GIP, respectively.
by the use of discount rate set by the Central Bank of
China. We observe that the coefficient of D/P is statistically significantly positive in 16 out of 20 stock indices
in expansive period, while the coefficient of TERM is
significant only in 5 cases during the same period. On the
other hand, during restrictive period, we observe that
returns of 18 out of 21security indices (including bond)
are statistically significantly negatively related to TERM.
It appears that the impact of business condition proxies
on expected security returns in Taiwan depends on
whether monetary period is classified as expansive or
restrictive.
Empirical findings from the examination of effects
of monetary policy changes on the relationship between
business conditions and industry stock returns also support Nowak’s (1993) assertion that monetary policy
changes are likely to have a strong influence on interest
rate sensitive industries. The finance industry is commonly considered as an interest rate sensitive industry
since changes in interest rates resulted from monetary
policy changes usually have direct impact on financial
firms’ cost of funds and hence their profit and earning
performance. The construction industry is another interest rate sensitive industry because construction ac-
We also divide the entire sample into two sub- samples, based on monetary policy environments identified
10
Ming-Hsiang Chen et al./Asia Pacific Management Review (2007) 12(1), 1-12
tivities are usually financed with borrowed funds and
thus the profit margins fluctuate with interest rate
changes. We find that excess stock returns in finance and
construction industries are significantly related to D/P
under expansive monetary condition and to TERM under
restrictive monetary condition (see Table 7).
Batten, D. S. and Thornton, D. L. (1984). Discount rate-changes and
the foreign exchange market. Journal of International Money and
Finance, 3, 279-292.
Brown, K. H. (1981). Effects of changes in the discount rate on the
foreign exchange value of the dollar: 1973-1978. Quarterly
Journal of Economics, 95, 551-558.
Chen, M. H. (2003). Risk and return: CAPM and CCAPM. Quarterly
Test results in Table 7 also confirm Nowak’s (1993)
findings that monetary policy changes have a strong
impact on industries with a substantial export or import
exposure, due to the influence of monetary policy
changes on the foreign exchange. For instance, excess
stock returns in textile industry are significantly related
to both D/P and TERM in expansive monetary period,
but only significantly related to TERM in restrictive
monetary period. Excess stock returns in automobile
industry are significantly related to both D/P and TERM
in restrictive monetary period, but not in expansive
monetary period. As a matter of fact, our study further
indicates that the significant effect of monetary policy
changes on the connection between business conditions
and stock returns is evident not only in interest rate sensitive industries and industries with a substantial export
or import component, but also across all 19 industries.
Review of Economics and Finance, 43, 369-393.
________ (2005). Stock returns and changes in the business cycles.
Asia Pacific Management Review, 10, 321-327.
________ and Bidarkota, P. V. (2004). Consumption equilibrium asset
pricing in two Asian emerging markets. Journal of Asian Economics, 15, 305-319.
Chen, N. (1991). Financial investment opportunities and the macroeconomy. Journal of Finance, 46, 529-554.
_______, Roll, R. and Ross, S. A. (1986). Economic forces and the
stock market. Journal of Business, 59, 383-403.
Conovar, C. M., Jensen, G. R. and Johnson, R. R. (1999). Monetary
environments and international stock returns. Journal of Banking
and Finance, 23, 1357-1381.
Cook, T. and Hahn, T. (1988). The information content of discount rate
announcement and their effect on market interest rates. Journal of
Money, Credit and Banking, 20, 167-180.
Estrella, A. and Hardouvelis, G. A. (1991). The term structure as a
The results also show that the introduction of the
variable of monetary policy causes the explanatory
power of D/P on major stock indices to disappear, while
its inclusion enhances the significance of TERM in explaining bond returns. Thus, business conditions play a
substantially different role in explaining variation in
stock and bond returns, depending on monetary environments.
predictor of real economic activity. Journal of Finance, 46,
555-576.
Fama, E. F. and Bliss, R. R. (1987). The information in long-maturity
forward rates. American Economic Review, 77, 680-692.
________ and French, K. R. (1988). Dividend yields and expected
stock returns. Journal of Financial Economics, 22, 3-25.
________ and French, K. R. (1989). Business condition and expected
returns on stocks and bonds. Journal of Financial Economics, 25,
23-49.
All presented evidence supports that both the behavior of business condition proxies and their influence
on security return variations are significantly affected by
the monetary sector. The results suggest that monetary
environment plays a crucial role in security markets.
They underline the importance for central bankers to
closely monitor monetary policy. Furthermore, all assets
pricing models should take into account the effect of
monetary policy. Thus, investors and corporate managers, for investment and financing decision-making purposes, should incorporate monetary policy into their
security market return modeling process.
Hess, M. (2001). Business condition, monetary policy, and expected
security returns in the United Kingdom. Working Paper, International Investments.
Jarque, C. M. and Bera, A. K. (1980). Efficient tests for normality,
homoscedasticity and serial independence of regression residuals.
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Jensen, G. R. and Johnson, R. R. (1995). Discount rate-changes and
security returns in the U.S.: 1962-1991. Journal of Banking and
Finance, 19, 79-96.
________ and Mercer, J. M. (2002). Monetary policy and the
cross-section of expected stock returns. Journal of Financial
Research, 25, 125-139.
________, Mercer, J. M. and Johnson, R. R. (1996). Business condition,
monetary policy, and expected security returns. Journal of Fi-
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