DP2010-07

Ind ividual Stock Retur ns a nd Monetary
Pol icy: Evidenc e from Ja pa nese Data *Masahiko SHIBAMOTO
Minoru TACHI BANA
March 5, 2010

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Individual Stock Returns and Monetary Policy:
Evidence from Japanese Data∗
Masahiko Shibamoto†

Minoru Tachibana‡

May 2010

Abstract
This paper examines the eﬀects of Japanese monetary policy on equity markets usingﬁrm-level data. Our objective is to investigate whether there exist heterogeneous
responses of stock returns among ﬁrms, and if any, whether ﬁrm-speciﬁc characteristics
associated with the theories of monetary transmission mechanism can account for such
heterogeneity. We ﬁnd that a 1% surprise cut in the call-rate target increases stock
returns by 3% on average, but this eﬀect is signiﬁcantly larger for ﬁrms with highcapital intensity, low openness, high leverage, high interest payment burden, and low
working capital. It is also found that monetary policy had greater eﬀects on equity
markets in the recession period of the 1990s than the boom period of the late 1980s.

JEL Classiﬁcation: E52; E44.
Keywords: Monetary policy; Individual stock returns; Heterogeneity; Event study;
Monetary transmission mechanism.

∗

We would like to thank Ryuzo Miyao, Kazuo Ogawa, and seminar participants at the Japan Societyof Monetary Economics held at Kagawa University for many useful comments; Yuzo Honda and Yoshihiro Kuroki for providing some of the data series; and the Zengin Foundation (No.0714) and the JSPS
(No.21243027) for ﬁnancial support.
†
Research Institute for Economics and Business Administration, Kobe University, 2–1, Rokkodai, Nada,
Kobe, 657–8501, Japan. E–mail: shibamoto@rieb.kobe-u.ac.jp.
‡
School of Economics, Osaka Prefecture University, 1-1, Gakuen–cho, Naka–ku, Sakai, 599–8531, Japan.
E–mail: mtachi@eco.osakafu-u.ac.jp.

1

Introduction

It is well known that monetary policy inﬂuences macroeconomic variables such as output
and inﬂation, through its impacts on not only the bank lending market but also theﬁnancial and capital markets. Monetary policy shocks to short-term inter-bank markets(e.g., the federal funds market in the U.S. and the call market in Japan) cause changes
in longer-term interest rates and asset prices of the ﬁnancial and capital markets, which
in turn aﬀect real economic behavior. Among such ﬁnancial and capital markets, stock
market is one of the most important markets from the viewpoint of monetary policy
transmission, and thus some researchers have examined the eﬀects of monetary policy on
stock markets.1
There are at least three issues with regard to assessing monetary policy eﬀects on equity
markets. First, stock prices are inﬂuenced by various factors other than monetary policy,
for example, movements of GDP and inﬂation rate, and such factors may be correlatedwith monetary policy. Second, monetary authorities may simultaneously respond to stock
market movements.2 Third, stock markets have a forward-looking nature, i.e., market
participants anticipate in advance the direction and the extent of a policy change before
a central bank actually changes its policy stance.
These problems would cause the correlation between the policy rate and the error term
if simply regressing stock prices on monetary policy rates by OLS, which yields a bias in
the estimator. To eliminate this kind of bias, the literature has often taken an “eventstudy” approach in which a stock market response is estimated using data only on datesof the policy change. Furthermore, in order to address the third problem described above,the literature has decomposed policy rate movements into the expected and unexpected (orsurprise) components, and interpreted a stock price response to the surprise component
as a true eﬀect of monetary policy on equity market.3
1

Miyao (2002) and Honda et al. (2007) show the importance of stock markets for the monetary policy
transmission in Japan.
2
Rigobon and Sack (2003) ﬁnd a signiﬁcant policy response to the stock market in the U.S. Also,
Bernanke and Gertler (1999) argue that the Bank of Japan (BOJ) conducted monetary policy in responseto the behavior of stock prices in the 1990s.
3
The literature has adopted two alternative approaches to avoid the problems described above. One isbased on the vector autoregressive (VAR) method (see, for example Patelis, 1997; Thorbecke, 1997; Miyao,
2002; Honda et al., 2007). The other method, developed by Rigobon and Sack (2004), is an extension of
the event-study approach, which requires a weaker set of assumptions than needed in the event-study.

1Bernanke and Kuttner (2005) is one of the most inﬂuential work in the literature. Theygauge stock market responses to the surprise changes of U.S. monetary policy relying on
the event-study approach. The surprise component is deﬁned as the change in the federal
funds futures rate on the date of the policy change.4 They show evidence that a 1%
surprise cut in the federal funds rate target leads to a 4% increase in stock prices.
Honda and Kuroki (2006) apply the event-study approach to the Japanese economy. In
particular, they identify the dates of the call-rate target changes and the target levels (orranges) by carefully reading the articles in the Nikkei News Paper. Then, they decompose
the call-rate target changes into the expected and surprised components based on theinformation from the nearest-term three-month Euro-Yen futures market. They ﬁnd thata surprise decrease in the call rate target of 1% increases stock prices by 3%.5
In the above two studies, stock returns are regressed on both expected and surprisecomponents of the policy target-rate changes. They use as a measure of stock returns
the growth rates of the stock price indices which capture average movements of equity
markets. That is, what Bernanke and Kuttner (2005) and Honda and Kuroki (2006)
estimated is an average reaction of stock returns of all listed ﬁrms to a monetary policy
shock. However, if there are signiﬁcant diﬀerences in the stock price responses amongﬁrms, only estimating the average response is not enough to understand the eﬀects ofmonetary policy on equity markets. The purpose of this paper is therefore to investigate
whether there exist the heterogeneous responses of stock returns among ﬁrms, and if any,
what ﬁrm-speciﬁc characteristics can account for such heterogeneity. To this end, we will
adopt the event-study approach based on the Japanese ﬁrm-level data of stock prices,and divide stock price responses into the average and heterogeneous ones. We also try tolink the heterogeneity to the ﬁrm-speciﬁc characteristics associated with the theories ofmonetary transmission mechanism.
There are at least two theoretical reasons for an occurrence of the heterogeneous response. First, the heterogeneous response would be brought about through the traditional
interest rate (or monetary) channel. If demand or supply of a ﬁrm’s product is sensitive to4This method was ﬁrst employed by Kuttner (2001). He estimated reactions of the term structure ofinterest rates to monetary policy surprises.
5
Honda and Kuroki (2006) also estimated the eﬀects of monetary policy shocks on the term structureof interest rates in Japan.

2changes in the interest rate, stock returns of that ﬁrm would be more aﬀected by monetary
policy. Second, the heterogeneous response could be explained from the viewpoint of thecredit channel, particularly the balance sheet channel. In an environment of imperfect
information, the premium on external ﬁnance would exist reﬂecting the agency cost if
the external ﬁnance is not fully collateralized. Then it is more costly to raise funds byexternal ﬁnance than internal ﬁnance, implying that the ﬁrm faces ﬁnancial constraints.
Since the external ﬁnance premium depends inversely on the ﬁrm’s net worth and sincemonetary policy aﬀects the net worth, monetary policy would have larger eﬀects on theﬁnancially constrained ﬁrms than unconstrained ﬁrms. In this paper, we will consider
these two channels of monetary transmission mechanism as main sources of heterogeneity
in stock price reactions.
The most related work to ours is Ehrmann and Fratzscher (2004). They present
evidence that individual stock returns react in a highly heterogeneous fashion to U.S.
monetary policy shocks. In particular, the U.S. monetary policy aﬀects more strongly
the stock returns of ﬁrms with low cash ﬂows, small size, poor credit ratings, low debt
to capital ratios, high price-earnings ratios, or a high Tobin’s q. Our analysis parallels
Ehrmann and Fratzscher (2004) in that we extend the analysis of Honda and Kuroki (2006)
by using individual stock returns, instead of stock price indices, for the Japanese economy
while Ehrmann and Fratzscher (2004) is an extended version of Bernanke and Kuttner(2005) in the same way for the U.S. economy.
However, our study is diﬀerent from Ehrmann and Fratzscher (2004) in the sense that
our ﬁrm-characteristic variables, which are used in the regression analysis to account for
the heterogeneous response, seem to be more closely related to the concept of the interest
rate channel or the balance sheet channel. Firm-characteristic variables used in this paper
are the degree of capital intensity and openness of each ﬁrm as well as its ﬁnancial leverage,interest payment burden, short-term debt, working capital, and cash ﬂow. The ﬁrst twovariables are proxies for the determinant of the interest rate channel and the last ﬁvevariables for the determinant of the balance sheet channel. Most of our ﬁrm-characteristic
variables are also adopted by either Peersman and Smets (2005) or Dedola and Lippi (2005)
or both, although these two studies investigate the monetary policy eﬀects on real output(not on stock markets). Another diﬀerence from Ehrmann and Fratzscher (2004) is that3

we compare reactions of individual stock returns between boom and recession periods. As
will be described later, stock price responses are expected to be larger in recessions thanbooms, on the basis of the theory of either interest rate channel or balance sheet channel.
In this paper, we test if this prediction holds true for Japanese monetary policy even in
the situations of low interest rate and weak ﬁnancial system during the 1990s.
As mentioned above, this paper contributes to the literature, that examines monetary policy eﬀects on equity markets, by exploring heterogeneity in the individual stock
responses. Our study is also related to two strands of the empirical literature on theJapanese monetary policy. First, Miyao (2000), Fujiwara (2006), and Inoue and Okimoto(2008) show that there was a structural break around the mid 1990s for the Japanese
economy and that an impact of monetary policy on macroeconomy became weaker after
the break.6 This paper partially tests whether monetary policy was also ineﬀective forthe stock market during the period. Second, our study contributes to the literature on
the balance sheet channel for Japan from the standpoint of equity markets. Ogawa (2000)
and Hosono and Watanabe (2002) provide evidence that Japanese monetary policy aﬀects
ﬁrms’ ﬁxed investments through the balance-sheet channel. Suzuki (2004) shows that thequantity and price of bank loans and real output react to monetary policy shocks in sucha way as to support the balance-sheet view. Our research complements these previous
works by shedding light on the equity market.
The remainder of the paper is organized as follows. Section 2 explains an event-study
approach and presents a basic empirical model estimated in this paper. Section 3 describes
more detailed speciﬁcations of our econometric models, estimates those models by usingﬁrm-level data, and presents the results. More speciﬁcally, in Section 3.1 we ﬁrst estimate
an average response of stock returns to monetary policy surprises by using individual
stock returns, and then compare this estimated value with the one reported by Honda andKuroki (2006). Next we allow for the heterogeneous response of individual stock returns byindustries in Section 3.2 and by ﬁrm-speciﬁc characteristics in Section 3.3. Section 4 tests
whether the heterogeneous response of individual stock returns is symmetrical about theﬁrm-speciﬁc variables. Section 5 examines whether the eﬀectiveness of monetary policy is

6Inoue and Okimoto (2008) also found that a monetary base shock stimulated real output signiﬁcantly
even after the mid 1990s, whereas Fujiwara (2006) found no such signiﬁcant eﬀect.

4

diﬀerent between booms and recessions. Section 6 concludes the paper.

2

Event-Study and A Basic Empirical Model

The “event-study” approach focuses on the days of the policy change to gauge the policy
eﬀect. As described in the introduction, looking only at the policy-event days reduces
the bias associated with the omitted-variable and simultaneity problems. More formally,as shown by Rigobon and Sack (2004), the OLS estimate of the event-study analysis isconsistent as long as the variance of the monetary policy shock is inﬁnitely large comparedto the variances of the other shocks on the days of the policy event.7
Cook and Hahn (1989) ﬁrstly attempt to apply the event-study approach to the analysis
of monetary policy eﬀects. They investigate the ﬁnancial market responses to monetary
policy, by regressing changes in market interest rates on changes in the federal funds
rate target on the dates of the policy move. In an analogous way, when measuring thestock market response to monetary policy, one might rely on the event-study method andestimate the following equation:

ΔSt = α + βΔrt + t ,(1)

where Δrt is the change in the short-term interest rate manipulated by a central bank,and ΔSt is the rate of change in the stock market price index. The parameter β measures
an impact of monetary policy on equity market. Note that, in the event-study, t indicatesa date on which the central bank changes its policy rate.
As mentioned in the introduction, however, ﬁnancial and capital markets are forwardlooking in that market participants anticipate the policy change and respond according
to their expectations before the monetary authority actually changes its policy rate. For
this reason, the use of the raw policy rate rt as a policy indicator may fail to capture thetrue market response to monetary policy.
To avoid this problem, Kuttner (2001) separates changes in the federal funds rate
7

Rigobon and Sack (2004) suspect the satisfaction of a set of assumptions required under the eventstudy, and provide a new identiﬁcation approach requiring a much weaker set of assumptions than needed
under the event-study approach. This paper does not adopt their approach since monetary policy isunlikely to react to individual stock returns, which violates a speciﬁcation of Rigobon and Sack (2004).

5

target into expected and unexpected (or surprise) components, which are calculated fromthe federal funds futures market data. While Kuttner (2001) examines reactions of theyield curve, Bernanke and Kuttner (2005) highlight the stock market responses basedon the same decomposition approach. Speciﬁcally, the regression equation estimated by
Bernanke and Kuttner (2005)
ΔSt = α + β1 Δrte + β2 Δrts + t(2)

contains as independent variables both the expected and surprise components of the policy
rate changes, Δrte and Δrts , in place of the raw policy rate changes Δrt .
ΔSt in Eqs.(1) and (2) is the rate of change in the stock price index, which captures
market average moves of individual stock returns. This implies that we can see only theaverage impact of monetary policy on the equity market. However, monetary policy might
have diﬀerent eﬀects on stock markets across ﬁrms. Ehrmann and Fratzscher (2004) allow
for such heterogeneity in monetary policy eﬀects. They use ﬁrm’s individual data of stock
returns and estimate a panel data regression model. Following Ehrmann and Fratzscher(2004), we investigate heterogeneity in monetary policy eﬀects in the Japanese case. The
basic empirical model used in this paper is given by:Δsi,t = α + β1 Δrte + β2 Δrts + β3 zi,t Δrts + i,t ,(3)where Δsi,t is the rate of change in the stock price for ﬁrm i on the t-th policy event, andzi,t is the characteristic of ﬁrm i on the t-th policy event.
In Eq.(3), individual stock return Δsi,t is regressed on the expected change in thepolicy rate target Δrte , the surprise change Δrts , and the interaction term between thesurprise component and the ﬁrm-speciﬁc characteristic zi,t Δrts . Our focus of interest is
the estimate of β3 , which measures the degree of heterogeneity in monetary policy eﬀects.
As will be described in the next section, the basic model (3) is modiﬁed a bit in theestimations.

6

3

Heterogeneity in Monetary Policy Eﬀects

3.1

Average Impact of Monetary Policy

Before analyzing heterogeneity in monetary policy eﬀects, in this subsection we ﬁrst estimate an average impact of monetary policy on equity markets by using data of individual
stock returns. Then our results are compared with those reported by Honda and Kuroki(2006) in which two stock price indices, the Nikkei Stock Average and TOPIX, are considered. As will be described below, the comparison shows that individual stock prices andthe stock price index yield similar results for the average eﬀect of monetary policy. This
ensures that, in the subsequent subsections, we can investigate heterogeneity in monetary
policy eﬀects on the basis of ﬁrm-level data.
We use data on the surprise and expected components of policy target changes inJapan during the period from August 1989 to March 2001, which was compiled by Honda
and Kuroki (2006).8 According to Honda and Kuroki (2006), 55 policy changes occurred
during the period. Thus the number of time dimension in our panel analysis appears
to be enough to provide unbiased estimates. The sample period includes the prolonged
economic recession in the 1990s as well as a later stage of the bubble economy occurringin the middle 1980s.
We modify the regression models (1) and (2) as follows, respectively:Δsi,t = α + βΔrt+8τk M acroN ewsk,t +1998
ρs F iscalY ears,t + μi + i,t ,(4)

ρs F iscalY ears,t + μi + i,t ,(5)

s=1989k=1

Δsi,t = α + β1 Δrte + β2 Δrts+8

τk M acroN ewsk,t +1998

s=1989k=1

where, as deﬁned in the previous section, Δsi,t denotes the rate of change in the stock
price of ﬁrm i on the t-th policy event, Δrt the raw change in the target policy rate, Δrtethe expected component of Δrt , and Δrts the surprise component of Δrt .
8

The data after March 2001 cannot be constructed because the BOJ introduced quantitative easing
policies in March 2001 and its operating target was shifted from the call rate to the current accountbalances held at the bank.

7

MacroNews is the macroeconomic news dummy taking the value of one if news associated with a certain macroeconomic condition is released and zero otherwise. Honda andKuroki (2006) assert that there is a need to control for the eﬀects of macroeconomic news
on ﬁnancial and capital markets in the analysis of the Japanese case, since the BOJ usually
took several days to adjust the call rate toward a new policy target during the sample
period. Following Honda and Kuroki (2006), we include the same set of macroeconomic
news dummies in the regression models (4) and (5). The macroeconomic news dummies
are as follows: news associated with National Income Accounts (denoted by GDP), theBOJ’s Business Survey (Tankan), the Monthly Report on the Labor Force Survey (Unemployment), Indices of Business Conditions, the Index of Industrial Production (IIP), theConsumer Price Index (CPI), and the Wholesale Price Index (WPI). In addition, Honda
and Kuroki (2006) control for the possible impacts of announcements and statements
made by the governor of the BOJ about economic conditions. Therefore, we also add thisannouncement dummy variable to MacroNews.
Fiscal year dummies, FiscalYear, capture the eﬀects of macroeconomic trends or themarket’s moods in each of the ﬁscal years. The ﬁrm-speciﬁc intercept, μi , is contained tocontrol for the ﬁrm-speciﬁc eﬀects that remain stable over the sample period.
For the three types of the policy indicator, Δrt , Δrte , and Δrts , we use the data reported
on Table 1 in Honda and Kuroki (2006). The BOJ did not oﬃcially announce its call-ratetarget until 1998. Thus, Honda and Kuroki (2006) identiﬁed the data series for the callrate target changes, Δrt , from the articles in the Nikkei News Paper. And then, they used
the futures rate to capture the surprise component of the target-rate change Δrts , andcalculated the expected component Δrte from the values of Δrt and Δrts . Speciﬁcally, thefsurprise and expected components of the target-rate cahange are deﬁned as Δrts = rtf −rt−1fand Δrte = Δrt −Δrts , respectively, where rt−1
is the closing rate of the nearest-term Euro-Yen futures contract on the day before the starting date of the policy event, and rtf is theclosing rate on the end date of the policy event. Δrt , Δrte , and Δrts are all expressed interms of percentage change. As mentioned above, Honda and Kuroki (2006) constructed
these three variables for the period from August 1989 through March 2001 in which 55
policy shifts occurred.
Stock price data of individual ﬁrms are obtained from the stock price CD-ROM pub8

lished by Toyo Keizai Inc. Stock returns of ﬁrm i, Δsi,t , are calculated as the log diﬀerence
between the stock price of ﬁrm i on the end date of the t-th policy event and its stock
price on the day before the starting date of the policy event (multiplied by 100). Our
sample is selected from the ﬁrms listed on the ﬁrst or second section of the Tokyo Stock
Exchange as of March 2001. However, we exclude ﬁnance and insurance companies fromthe sample. Furthermore, we use only the observations of ﬁrms whose accounting period
ends in March each year, since we need to avoid a timing problem arising from diﬀerent
accounting periods. These procedures result in 56,774 observations of 1,525 ﬁrms in our
sample.9 We estimate Eqs.(4) and (5) by using the ﬁxed eﬀect estimation method with
unbalanced panel data.
The left column in Table 1 reports the estimation result for Eq.(4). The estimate of
the average impact of the raw call-rate change, β, has a negative sign and is signiﬁcant.
Almost all the dummy variables for macroeconomic news and ﬁscal year are signiﬁcant,
implying the need of controlling for these eﬀects on individual stock returns.
The right column in Table 1 presents the estimation result for Eq.(5). The average
eﬀect of the expected policy change, β1 , is statistically insigniﬁcant. On the other hand,the average eﬀect of the surprise component, β2 , is statistically signiﬁcant and its size ismuch larger than the impact of the raw call-rate changes. Furthermore, the magnitude ofthis estimate is close to those reported by Honda and Kuroki (2006) (−2.59 for the Nikkei
Stock Average and −2.90 for TOPIX). Therefore, we can replicate the results of Honda
and Kuroki (2006) by using individual stock prices, instead of stock price indices.

3.2

Industry Speciﬁc Eﬀects

In this subsection, as the ﬁrst step to examine heterogeneity in monetary policy eﬀects,
we allow for cross-industry diﬀerences in the stock price reaction. The empirical model isgiven by the following equation:

Δsi,t =

α + β1 Δrte +

32

j=1

+

8


β2j Industryj,i Δrts

τk M acroN ewsk,t +ρs F iscalY ears,t + μi + i,t ,(6)

s=1989

k=1
9

1998


We also exclude the observations of ﬁrms whose leverage ratios, deﬁned in Section 3.3, are below zero.

9

where Industryj,i is a dummy variable, which takes a value of one if ﬁrm i is classiﬁed inindustry j and zero otherwise. Eq.(6) includes the interaction terms, the product of thesurprise component and the industry dummy variable. Thus, the dispersion of coeﬃcientson the interaction terms, β2j , measures cross-industry heterogeneity in monetary policy
eﬀects on the stock market.
Industry classiﬁcation includes 32 industry groups, consisting of food, textiles, pulp
and paper, chemistry, medicine, oil, rubber, ceramic, steel, nonferrous metal, machine,
electric, shipbuilding, automobile, transport equipment, precision equipment, others manufacture, marine, mining, construction, trading, retail, real estate, railway and bus, landtransportation, marine transportation, air transportation, warehouse, communications,power, gas, and service industries.
Table 2 provides the empirical result for Eq.(6), sorting 32 industry groups by the estimated magnitude of monetary policy eﬀects. It is likely that there is a large cross-industry
diﬀerence in stock price reactions. In particular, we point out three remarkable features inthe estimation result. First, monetary policy surprises aﬀect stock returns very strongly
for the capital-intensive industries such as shipbuilding, railway and bus, power, and gas,
which is consistent with the view of the conventional interest rate (or capital cost) channel. Second, monetary policy eﬀects are relatively strong for real estate and constructionindustries. This may be because these industries had faced ﬁnancial constraints more
severely after the burst of the asset price bubble in the early 1990’s. Finally, it is likelythat monetary policy aﬀects individual stock returns in diﬀerent ways across the stage ofthe production process. The eﬀects are relatively stronger in the upstream industries suchas steel, rubber, chemistry, oil, mining, and ceramic, while they are weaker in downstream
industries or consumption goods industries such as communications, automobile, service,
precision equipment, and electric.

3.3

Firm Speciﬁc Eﬀects

This subsection presents an in-depth study of the heterogeneity in monetary policy eﬀects
by focusing on the characteristics of individual ﬁrms. As described in the introduction,monetary policy could have diﬀerent eﬀects on individual stock returns through either theinterest rate channel or the balance sheet channel. Therefore, we here examine whether
10the ﬁrm-speciﬁc characteristics associated with the interest rate or balance sheet channel
account for the heterogeneous responses in stock markets.
To this end, the following empirical model is estimated:
Δsi,t = α + β1 Δrte + β2 Δrts +
+

8


7

j=1

τk M acroN ewsk,t +β3j zi,j,t Δrts
1998
ρs F iscalY ears,t + μi + i,t ,(7)

s=1989k=1

where zi,j,t is the characteristic j of ﬁrm i at the t-th policy event. The coeﬃcient β3j on theinteraction term, the product of the monetary policy surprise and the ﬁrm characteristic
variable, measures the degree of heterogeneity in monetary policy eﬀects.
In this paper, we use seven types of ﬁrm-speciﬁc characteristics for the variable zi,j,t :capital intensity, openness, ﬁnancial leverage, interest payment burden, short-term debt,
working capital, and cash ﬂow.10 These variables are also employed by either Peersman
and Smets (2005) or Dedola and Lippi (2005) or both, except for cash ﬂow. Cash ﬂow isoften considered in the literature that examines the corporate investment with ﬁnancial
constraints (e.g., Fazzari et al. (1988) and Kaplan and Zingales (1997)). Capital intensity
and openness are related to the interest rate channel and the remaining variables to thebalance sheet channel, although some variables may capture both channels as described
below.
Capital intensity is measured as the ratio of tangible ﬁxed assets to the number of
employees. The expected sign of β3 is negative since ﬁrms that depend largely on capitalin its production process seem to be more aﬀected by monetary policy through changes in
the user cost of capital (i.e., through the interest rate channel).
Openness is measured as the ratio of exports to sales volume.11 It is not clear whatsign is expected for openness. Monetary policy would aﬀect the exchange rate throughthe interest rate channel, leading to greater eﬀects on more open ﬁrms. In this case,10

In this paper we do not consider the ﬁrm’s size indicators, such as total assets or the number of
employees, which are often used as proxies for the degree of ﬁnancial constraints in the literature. Thisis because our data includes only ﬁrms that are listed in equity markets, meaning that most of the ﬁrms
in our sample are very large. We also do not consider the durability dummy Peersman and Smets (2005)
and Dedola and Lippi (2005) employ as an indicator of demand sensitivity to the interest rate change,
because inclusion of nonmanufacturing industries in our sample makes the classiﬁcation between durable
goods and nondurable goods more diﬃcult.
11
Import data for each ﬁrm is not available in the Nikkei NEEDS data base.

11

we expect a negative sign for β3 . However, more open ﬁrms would be less aﬀected bymonetary policy, since it is less exposed to changes in the domestic economy. If this eﬀect
dominates the exchange rate eﬀect, then we would obtain a positive sign.
Financial leverage is measured as the ratio of total debt to total capital. It has two
opposite aspects as an indicator of the degree of ﬁnancial constraints. On the one hand, if
a high leverage ratio is considered as a relatively strong possibility of bankruptcy, it meansthat the frim faces severe ﬁnancial constraints. On the other hand, a high leverage ratio
may be a result of high indebtedness capacity, suggesting that it is easier for the ﬁrm toobtain additional funds from ﬁnancial markets. The sign of β3 is expected to be negative
for the former case, but the latter case requires a positive sign.
Interest payment burden is measured as the ratio of interest and discount expenses
to sales volume. Firms with a higher interest burden are more sensitive to monetary
policy through the usual interest rate channel. In addition, from the viewpoint of thebalance sheet channel, monetary policy inﬂuences the net worth of ﬁrms through changes
in interest payments, which induces ﬁrms with a higher interest burden to be more sensitive
to monetary policy. On the other hand, as with ﬁnancial leverage, high interest payments
may be a signal of high borrowing capacity. In this case, we expect ﬁrms with a higher
interest burden to be less aﬀected by monetary policy.
Short-term debt is measured as the ratio of short-term debt to total debt. Since longterm rates are not as controllable by monetary policy as short-term rates, ﬁrms with higher
short-term debt seem to be more sensitive to monetary policy changes. Furthermore, highshort-term debt may indicate low capacity to access long-term ﬁnancial markets if capital
markets are imperfect. Thus, based on the theory of balance sheet channel, monetary
policy would also have greater eﬀects on ﬁrms with higher short-term debt.
Working capital is measured as the ratio of working capital (current assets − [current
debt − short term loans]) to total assets. The sign to be expected is also not clear. On the
one hand, working capital is an important indicator of the ﬁrm’s default risk for creditors.
Hence, higher levels of working capital make the cost of external ﬁnance low, implying thatﬁrms with higher working capital are less inﬂuenced by monetary policy. On the other
hand, working capital is proxy for the short-term ﬁnancial requirement (see Peersman andSmets (2005)). In this case, monetary policy eﬀects are stronger for ﬁrms with relatively
12higher working capital.
Finally, cash ﬂow is measured as the ratio of cash ﬂow (proﬁt + depreciation) to total
assets. Firms that are more ﬁnancially constrained may accumulate higher cash ﬂow for
funds for corporate investments because of the diﬃculty in obtaining external funds.12
This means that monetary policy would have a larger inﬂuence on ﬁrms with higher cash
ﬂow. However, cash ﬂow may be correlated with ﬁrm proﬁtability of the future and thus
capture proﬁt expectations of investors. In this case, ﬁrms with higher cash ﬂow would
be less aﬀected by monetary policy.
Financial data of individual ﬁrms are drawn from the Nikkei NEEDS Financial dataset.
We construct the ﬁrm-speciﬁc variables z by subtracting the mean value of the entire observations. The use of demeaned variables makes the coeﬃcient β2 in Eq.(7) interpretable
as the monetary policy eﬀect for a ﬁrm with average values of all characteristics, sinceinteraction terms are all zero for such a ﬁrm. The ﬁrm-speciﬁc variables z are entered into
Eq.(7) with a lag of one ﬁscal-year in order to avoid an endogenous problem. For example,
for the ﬁrst policy event on 7-16 August 1989, we use the ﬁnancial data of individual ﬁrms
in the ﬁscal-year 1988.
Table 3 provides some summary statistics of the ﬁrm-speciﬁc variables (those before
subtracting mean values). We can conﬁrm enough dispersion for each ﬁrm-speciﬁc variable.
Table 4 presents the correlations between any two of the ﬁrm-speciﬁc variables. These
correlations are not large, except for the correlation between capital intensity and interest
burden. It is worth emphasizing that the correlation between short-term debt and working
capital is very low, implying that working capital hardly represents the short-term ﬁnancial
requirement in our sample.
Tables 5 and 6 show empirical results for the estimations of Eq.(7).13 In Table 5 eachof the interaction terms is included separately, while in Table 6 all the interaction terms
are entered jointly. The estimated coeﬃcients of the monetary policy surprise, β2 , range
from -3.1 to -3.3, which are all close to the value reported in Table 1. This suggests that a
12

Fazzari et al. (1988) and other many studies on corporate investments interpret the coeﬃciant on the
cash ﬂow varable, rather than cash ﬂow itself, as the degree of ﬁnancial constraint. Kaplan and Zingales(1997) theoretically and empirically show that higher investment sensitivity to cash ﬂow is not necessarily
interpreted as evidence that ﬁrms are more ﬁnancially constrained.
13
When we estimated Eq.(7) with outliers (observations above and below 3σ of each ﬁrm-speciﬁc variable)
excluded from our sample, similar results were obtained. These results are available from the authors uponrequest.

13

1% surprise cut in the call-rate target increases stock returns by about 3% for ﬁrms withthe ﬁrm-speciﬁc variable equal to its average.
We will see now what characteristics of ﬁrms account for the heterogeneous responses
of individual stock returns from the estimates of β3 in Tables 5 and 6. Capital intensity
has the expected negative sign and is signiﬁcant at a 1% level. This implies that ﬁrms
with larger capital are more sensitive to changes in monetary policy, being consistent withthe view of the conventional interest rate channel.
Openness has a positive sign and is signiﬁcant at a 1% level, suggesting that more
open ﬁrms are less aﬀected by monetary policy. This result supports the hypothesis that
more open ﬁrms are less exposed to changes in the domestic economy caused by monetary
policy.
Firms with a higher leverage ratio are more sensitive to monetary policy surprises.
This result contrasts with the ﬁndings of Dedola and Lippi (2005) and Ehrmann and
Fratzscher (2004), as well as Peersman and Smets (2005) for boom periods. However,
our result seems to be consistent with the Japanese case in the 1990s. Since many ﬁrms
suﬀered from excess debt in the period, high leverage might be understood as an indicatorof high risk of bankruptcy rather than an indicator of high borrowing capacity.
Firms with a higher interest burden are more aﬀected by monetary policy surprises.
This result does not support the hypothesis that a higher interest burden indicates greaterborrowing capacity. Rather, our result is consistent with the interest rate channel. It isalso consistent with the balance sheet channel in that monetary policy inﬂuences the net
worth of a ﬁrm with a higher interest burden through changes in the policy rate.
In Table 5, the estimate of β3 for short-term debt has the expected negative sign andis signiﬁcant at a 5% level. However, the estimate has the positive sign and is insigniﬁcantin Table 6. Hence, the evidence for short-term debt is not clear.
Firms with higher working capital are less sensitive to monetary policy surprises. This
result implies that working capital plays an important role as an indicator of the ﬁrm’s
default risk for creditors, rather than as an indicator of the short-term ﬁnancial requirement.
Finally, the estimate of β3 for cash ﬂow has the positive sign in both tables. However,
it is signiﬁcant at a 10% level in Table 5 and insigniﬁcant in Table 6. Thus, the evidence
14for cash ﬂow is rather weak.
In summary, a surprise decrease in the call-rate target of 1% leads, on average, toabout a 3% increase in stock prices, but this eﬀect is signiﬁcantly larger for ﬁrms withhigh capital intensity, low openness, high leverage, high interest payment burden, and low
working capital. It is possible to calculate the degree of such heterogeneous eﬀects basedon the result of Table 6. A 1% surprise cut in the call-rate target increases stock returns
by 3.3% for an average ﬁrm, while such a surprise cut increases stock returns further by
0.47% for a ﬁrm with capital intensity of one standard deviation above the average, 0.61%
for a ﬁrm with ﬁnancial leverage of one standard deviation above the average, and 0.79%
for a ﬁrm with interest burden of one standard deviation above the average. On the other
hand, the response of stock returns for a ﬁrm with openness of one standard deviation
above the average is 0.90% less than that of an average ﬁrm, and the response of stock
returns for a ﬁrm with working capital of one standard deviation above the average is
0.59% less than that of an average ﬁrm.

4

Asymmetric Heterogeneity

Eq.(7) in the previous section implicitly assumes that heterogeneity in monetary policy
eﬀects is symmetric around the average value of each ﬁrm-speciﬁc variable. However,Ehrmann and Fratzscher (2004) found asymmetric responses to monetary policy changes.
In this section, therefore, we relaxes the symmetric assumption and allows for asymmetricheterogeneity.
To this end, following Ehrmann and Fratzscher (2004), we divide our observations
into three groups according to the position in the cross-sectional distribution of each ﬁrmspeciﬁc variable. We then allow for diﬀerent coeﬃcients on the interaction terms among thethree groups. Speciﬁcally, we estimate for each ﬁrm-speciﬁc characteristic j the following
regression model:
Δsi,t = α + β1 Δrte +
p=l,m,h+8β2p z dumpi,j,t Δrts

τk M acroN ewsk,t +1998

s=1989k=1

15

ρs F iscalY ears,t + μi + i,t ,(8)hwhere z dumli,j,t , z dumm
i,j,t , and z dumi,j,t denote dummy variables that take the valueof unity if ﬁrm i belongs to the low, medium, and high categories of the ﬁrm-speciﬁccharacteristic j, respectively. A ﬁrm is deﬁned as the low category if its value of the ﬁrmspeciﬁc variable is in the bottom 33% (or in the bottom 10%) of the distribution, the highcategory if it is in the top 33% (or in the top 10%), and the medium category otherwise.
Table 7 provides empirical results for Eq.(8), the upper panel reporting the results
for the 33%–67% categorization and the lower panel for the 10%–90% categorization.
The results in this table are qualitatively similar to those reported in Table 5: negative
reactions of stock returns for high-category ﬁrms are larger, in absolute values, than thosefor low-category ﬁrms in regard to capital intensity, ﬁnancial leverage, interest burden,and short-term debt, while the reverse is true for openness, working capital and cash
ﬂow. Such diﬀerences between β2h and β2l are statistically signiﬁcant for all ﬁrm-speciﬁcvariables.
Asymmetric heterogeneous responses are found, except for ﬁnancial leverage in the
33%–67% categorization and working capital in the 10%–90% categorization. That is, adiﬀerence between β2h and β2m and the one between β2m and β2l are not statistically equal
for most of the ﬁrm-speciﬁc variables. For example, for the case of capital intensity in the
33%–67% categorization, a move from the middle category to the high implies about a
2% increase in the stock price reaction, whereas a shift to the low category leads to only
a 1% decrease. Those results in Table 7 suggests that, in a future study, we may need toestimate non-linear models rather than linear models to investigate heterogeneity in moredetail.
Finally, with regard to short-term debt, monetary policy has signiﬁcantly weaker eﬀects
on low-category ﬁrms than on medium- and high-category ﬁrms, whereas it has similareﬀects on the latter two categories. This feature of the asymmetric response for short-term
debt may cause the marginal signiﬁcant estimate on the interaction term in Table 5.

5

Monetary Policy Eﬀects in Expansion and Recession

We have assumed that monetary policy eﬀects are stable over the full sample period. However, there might be structural changes in the magnitude of policy eﬀects depending on
16economic conditions. In particular, as discussed in Garcia and Schaller (2002) and Peersman and Smets (2005), it is possible that monetary policy has diﬀerent eﬀects dependingon whether the economy is in expansion or recession. In fact, Garcia and Schaller (2002)
and Peersman and Smets (2005) provided evidence that monetary policy aﬀected output
more strongly in recessions than in expansions. There are at least two explanations for theevidence. The ﬁrst explanation, which is related to the conventional interest rate channel,
is that the short-run aggregate supply curve is convex reﬂecting a capacity constraint ofﬁrms. With the convexity of the aggregate supply curve, a shift of the aggregate demand
curve caused by a monetary policy shock generates a larger change of output when output
is low (i.e., the economy is in recession) than when it is high (i.e., the economy is in expansion). The second explanation, which is connected with the balance sheet channel, is thatﬁnancial constraints are more likely to bind in recession than in expansion, since ﬁrms’
net worth is typically low in recession. When ﬁnancial constraint binds, ﬂuctuations inﬁrms’ net worth lead to ﬂuctuations in external ﬁnancial premium and thus ﬂuctuations
in real activity. Therefore, monetary policy that aﬀects the ﬁrms’ net worth would havegreater eﬀects in recession than in expansion.
In order to examine whether monetary policy has diﬀerent eﬀects depending on thephases of the business cycle, in this section we split our sample into three subsamples.
The ﬁrst subsample is a boom period ranging from the beginning of our sample to thecollapse of the asset price bubble (August 1989 – January 1991). The second subsample isa mild recession period in which Japanese economy has improved a little from the economic
downturn in the 1990s (October 1993 – April 1997 and January 1999 – October 2000). The
third subsample is a recession period mainly spanning the 1990s, but excluding the second
subsample (February 1991 – September 1993, May 1997 – December 1998, and November
2000 – March 2001). Our identiﬁcation of peaks and troughs of the Japanese business cycle
follows the deﬁnition given by the Cabinet Oﬃce in Japan. However, as noted above, we
distinguish economic expansions in the late 1980s from those in the 1990s: The former
is deﬁned in this paper as a boom reﬂecting a strong economy under the environment ofelevated asset prices. On the other hand, the latter is called a mild recession since those
expansions were not strong enough to recover the economy from a longstanding recession
beginning in the early 1990s. The number of policy events (i.e., the time dimension of our17panel data) is 12 for the boom, 23 for the mild recession, and 20 for the recession.
We estimate for each ﬁrm-speciﬁc characteristic j the following regression equation:
Δsi,t = α + β1 Δrte +
p=b,mr,r+8


β2p bc dumpt Δrts +τk M acroN ewsk,t +

k=1

1998



p=b,mr,r

β3p bc dumpt zi,j,t Δrts

ρs F iscalY ears,t + μi + i,t ,(9)

s=1989r
where bc dumbt , bc dummr
t , and bc dumt denote business cycle dummies that take thevalue of unity if the economy is in boom, mild recession, and recession, respectively. β2b ,β2mr , and β2r measure average responses of stock returns to monetary policy surprises foreach stage of bussines cycle. β3b , β3mr , and β3r indicate the degree of heterogeneity in policy
eﬀects for each stage of business cycle.
Table 8 reports estimation results for Eq.(9). Three main results are worth noting.
First, the average impacts of monetary policy in the mild recession and recession are
signiﬁcantly larger than in the boom (that is, the estimates of β2mr and β2r are greater, inabsolute value, than β2b for all speciﬁcations). Second, on the contrary, the heterogeneouseﬀects of monetary policy are more remarkable in the boom (that is, the estimate of β3b
is greater, in absolute value, than β3mr or β3r for all speciﬁcations). Third, there is no
statistically signiﬁcant diﬀerence in the average eﬀects if comparing the mild recession
with the recession. For the degree of heterogeneity, although the estimated values of β3mr
and β3r appear to be somewhat diﬀerent, there is also no statistically signiﬁcant diﬀerence
between the two estimates, except for openness and short-term debt. To sum up thesethree results above, monetary policy had larger eﬀects, on average, in the post-1991 period(mild recession and recession periods) than in the pre-1991 period (boom period), while
the degree of heterogeneity in monetary policy eﬀects became smaller since the early 1990s.
The evidence of low heterogeneity since the early 1990s seems to be odd, because bothinterest rate and balance sheet channels predict greater eﬀects in recessions as describedabove. However, note that the average response, β2 , captures not only the eﬀects associates with the interest rate channel, but also the eﬀects stemming from the balance sheet
channel. Thus, we should see the total eﬀect (the sum of the average eﬀect and the heterogeneous one), if we would like to determine whether monetary policy has stronger eﬀects

18

in recessions or booms. Table 9 gives the ranges of the total responses of stock returns to
a 1% surprise increase in the call-rate target, calculated from the estimates in Table 8. If
β3p (p = b, mr, or r) in Table 8 is negative (positive), the left ﬁgure in the corresponding
bracket in Table 9 indicates a total response of the ﬁrm lying on the 90 percentile (the 10
percentile) of the ﬁrm-speciﬁc-variable distribution, and the right ﬁgure the 10 percentile(the 90 percentile). That is, each range in the table provides a set of the total responses
for ﬁrms that are in the middle 80% of the ﬁrm-speciﬁc variable.
Table 9 shows that there is no overlapping portion in the ranges of total eﬀects between
boom and recession, or between boom and mild recession. Namely, decreases in the degreeof heterogeneity since the early 1990s are trivial relative to the increases in average eﬀects.
It is likely that many ﬁrms were ﬁnancially constrained in the post-1991 period. Thus, it
is not surprising that a large part of monetary policy eﬀects associated with the balance
sheet channel were reﬂected as the average eﬀect rather than the heterogeneous one in the
post-1991 period. Similarly, for the policy eﬀects associated with the interest rate channel,
the average eﬀect might dominate the heterogeneous eﬀect in the period.
Miyao (2000), Fujiwara (2006), and Inoue and Okimoto (2008) provided evidence that
there were signiﬁcant eﬀects of the call-rate shocks on macroeconomic variables, such as
output and price, during the period from 1975 to the mid 1990s, but that such eﬀects
have diminished since the mid-1990s. Combining their results with ours, monetary policy
during the 1990s had signiﬁcant eﬀects at least on equity markets, but it was not large
enough to stimulate real economic activity. In other words, investors expected greater
eﬀects of monetary policy in the 1990s, but in fact its policy turned out to be ineﬀective instimulating Japanese economy. The ineﬀectiveness for macroeconomy since the mid-1990s
may be due to a zero interest rate bound or a weak ﬁnancial system. Further study would
be needed to investigate why Japanese monetary policy had signiﬁcant eﬀects on equity
markets, but no or small eﬀects on macroeconomy in the lost decade.

6

Conclusion

This paper has investigated the eﬀects of Japanese monetary policy on equity markets byusing ﬁrm-level data. Our goal was to examine whether there existed the heterogeneous

19

response of stock returns among ﬁrms, and if any, what ﬁrm-speciﬁc characteristics could
account for such heterogeneity. To this end, we tried to divide stock price responses
into the average and heterogeneous ones, and link the heterogeneous responses to ﬁrmspeciﬁc characteristics associated with the theory of monetary transmission mechanism.
In order to avoid the endogenous problem, this paper adopted the event-study approachwith the expected and surprise components of policy-rate changes, which were constructedby Honda and Kuroki (2006).
Several results are obtained and summarized as follows. First, a 1% cut in the callrate target increases stock returns by about 3% on average, which is very close to theestimated value by Honda and Kuroki (2006). Second, there are signiﬁcant diﬀerences inmonetary policy eﬀects across industries. Third, the heterogeneous responses of individual
stock returns were accounted for by the ﬁrm-speciﬁc characteristics associated with thetheory of monetary transmission mechanism. In particular, monetary policy eﬀects are
greater for ﬁrms with high capital intensity, low openness, high leverage, high interest
payment burden, and low working capital. This suggests that heterogeneity of monetary
policy eﬀects in Japan is attributable to both interest rate and balance sheet channels.
Fourth, the heterogeneous response of individual stock returns is asymmetric for mostof the ﬁrm-speciﬁc characteristics. This result implies that we may need to estimate
non-linear models in a future study to investigate heterogeneity in more detail. Finally,monetary policy eﬀects on equity markets are greater in the recession period of the 1990s
than the boom period of the late 1980s. The evidence is consistent with the predictions
on the basis of the interest rate channel as well as the balance sheet channel. Moreover,the larger eﬀect in the 1990s is not reﬂected in the heterogeneous response of stock returns
but in the average response.
Ogawa (2000), Hosono and Watanabe (2002), Suzuki (2004) and others found that thebalance sheet channel of monetary policy worked in the real side of Japanese economy(e.g., ﬁrms’ ﬁxed investments or output). Our empirical results partially support theirevidence from the viewpoint of equity market. However, it is impossible in our study
to determine which channel, balance sheet channel or interest rate channel, is dominantduring the 1990s in Japan. In the future, we would like to separate more rigorously thepolicy eﬀect through the balance sheet channel from the one through the interest rate
20

channel. Honda and Kuroki (2006) (and we) showed that monetary policy had signiﬁcanteﬀects on Japanese stock markets, even though a large part of their (and our) sample
period comprises the low interest rate era in the 1990s. Furthermore, it is found from
our analysis that the eﬀect on equity markets is largely attributed to the monetary policy
during the recession of the 1990s. Combining evidence shown by Miyao (2000), Fujiwara(2006), and Inoue and Okimoto (2008), it follows that BOJ’s monetary policy in the 1990s
had impacts at least on equity markets, but that it was not large enough to stimulate real
economic activity. Exploring reasons of the phenomenon will be also a topic of our future
research.

References
Bernanke, Ben S. and Mark Gertler (1999) “Monetary Policy and Asset Price Volatility”,
Federal Reserve Bank of Kansas City, Economic Review, Vol. 84, No. 4, pp. 17–51.
Bernanke, Ben S. and Kenneth N. Kuttner (2005) “What Explains the Stock Market’s
Reaction to Federal Reserve Policy?”, Journal of Finance, Vol. 60, No. 3, pp. 1221–
1257.
Cook, Timothy and Thomas Hahn (1989) “The Eﬀect of Changes in the Federal Funds
Rate Target on Market Interest Rates in the 1970s”, Journal of Monetary Economics,
Vol. 24, No. 3, pp. 331–351.
Dedola, Luca and Francesco Lippi (2005) “The Monetary Transmission Mechanism: Evidence from the Industries of Five OECD Countries”, European Economic Review, Vol.
49, No. 6, pp. 1543–1569.
Ehrmann, Michael and Marcel Fratzscher (2004) “Taking Stock: Monetary Policy Transmission to Equity Markets”, Journal of Money, Credit, and Banking, Vol. 36, No. 4, pp.
719–737.
Fazzari, Steven M., Glenn R. Hubbard, and Bruce C. Petersen (1988) “Financing Constraints and Corporate Investment”, Brookings Papers on Economic Activity, No. 1, pp.
141–195.

21

Fujiwara, Ippei (2006) “Evaluating Monetary Policy When Nominal Interest Rates Are
Almost Zero”, Journal of the Japanese and International Economies, Vol. 20, No. 3, pp.
434–453.
Garcia, René and Huntley Schaller (2002) “Are the Eﬀects of Monetary Policy Asymmetric?”, Economic Inquiry, Vol. 40, No. 1, pp. 102–119.
Honda, Yuzo and Yoshihiro Kuroki (2006) “Financial and Capital Markets’ Response to
Changes in the Central Bank’s Target Interest Rate: The Case of Japan”, Economic
Journal, Vol. 116, No. 3, pp. 357–386.
Honda, Yuzo, Yoshihiro Kuroki, and Minoru Tachibana (2007) “An Injection of Base
Money at Zero Interest Rates: Empirical Evidence from the Japanese Experience 20012006”, Discussion Papers in Economics and Business 07-08, Osaka University, Graduate
School of Economics and Osaka School of International Public Policy (OSIPP).
Hosono, Kaoru and Tsutomu Watanabe (2002) “Kigyou Baransu Shiito to Kinyu Seisaku(The Transmission of Monetary Policy Through Borrowers’ Balance Sheets)”, Economic
Reviews (Keizai Kenkyu), Vol. 53, No. 2, pp. 117–133. in Japanese.
Inoue, Tomoo and Tatsuyoshi Okimoto (2008) “Were There Structural Breaks in theEﬀects of Japanese Monetary Policy? Re-Evaluating Policy Eﬀects of the Lost Decade”,
Journal of the Japanese and International Economies, Vol. 22, No. 3, pp. 320–342.
Kaplan, Steven N. and Luigi Zingales (1997) “Do Investment-Cash Flow Sensitivities
Provide Useful Measures of Financing Constraints”, Quarterly Journal of Economics,
Vol. 112, No. 1, pp. 169–215.
Kuttner, Kenneth N. (2001) “Monetary Policy Surprises and Interest Rates: Evidence
from the Fed Funds Futures Market”, Journal of Monetary Economics, Vol. 47, No. 3,
pp. 523–544.
Miyao, Ryuzo (2000) “The Role of Monetary Policy in Japan: A Break in the 1990s?”,
Journal of the Japanese and International Economies, Vol. 14, No. 4, pp. 366–384.
(2002) “The Eﬀects of Monetary Policy in Japan”, Journal of Money, Credit, andBanking, Vol. 34, No. 2, pp. 376–392.
22

Ogawa, Kazuo (2000) “Monetary Policy, Credit, and Real Activity: Evidence from the Balance Sheet of Japanese Firms”, Journal of the Japanese and International Economies,
Vol. 14, No. 4, pp. 358–407.
Patelis, Alex D. (1997) “Stock Return Predictability and the Role of Monetary Policy”,
Journal of Finance, Vol. 52, No. 5, pp. 1951–1972.
Peersman, Gert and Frank Smets (2005) “The Industry Eﬀects of Monetary Policy in theEuro Area”, Economic Journal, Vol. 115, pp. 319–342.
Rigobon, Roberto and Brian Sack (2003) “Measuring the Reaction of Monetary Policy to
the Stock Market”, Quarterly Journal of Economics, Vol. 118, No. 2, pp. 639–669.
and(2004) “The Impact of Monetary Policy on Asset Prices”, Journal of

Monetary Economics, Vol. 51, No. 8, pp. 1553–1575.
Suzuki, Tomoya (2004) “Credit Channel of Monetary Policy in Japan: Resolving the
Supply versus Demand Puzzle”, Applied Economics, Vol. 36, No. 21, pp. 2385–2396.
Thorbecke, Willem (1997) “On Stock Market Returns and Monetary Policy”, Journal of
Finance, Vol. 52, No. 2, pp. 635–654.

23

Table 1: Average impact of monetary policy on individual stock returns
VariableEq.(4)
0.504∗∗(0.090)-1.790∗∗(0.136)αβEq.(5)
0.587∗∗(0.090)-0.130(0.224)-3.208∗∗(0.206)β1
β2Dummy forAnnouncement
GDP
TankanUnemployment
Business conditions
IIP
CPI
WPI
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
Obs.
R2

0.971∗∗(0.051)-3.249∗∗(0.116)2.331∗∗(0.095)-6.277∗∗(0.167)6.716∗∗(0.123)3.316∗∗(0.128)3.555∗∗(0.116)
0.396∗∗(0.052)-2.015∗∗(0.138)-1.888∗∗(0.114)-1.713∗∗(0.119)-6.730∗∗(0.144)-0.792∗∗(0.110)-0.160(0.104)-2.252∗∗(0.119)-0.021(0.113)-0.549∗∗(0.188)-0.686∗∗(0.111)
56774
0.194

1.017∗∗(0.052)-3.105∗∗(0.119)2.037∗∗(0.096)-5.506∗∗(0.184)7.170∗∗(0.131)2.904∗∗(0.133)2.941∗∗(0.130)0.258∗∗(0.054)-1.889∗∗(0.139)-1.907∗∗(0.113)-1.282∗∗(0.134)-6.605∗∗(0.144)-0.790∗∗(0.110)-0.078(0.104)-2.152∗∗(0.120)-0.218†(0.116)-0.540∗∗(0.188)-0.861∗∗(0.113)
56774
0.196

Notes: Values in parentheses are heteroskedasticity robust standard errors. ∗∗ , ∗ , † indicates signiﬁcance at the 1, 5, and 10% levels, respectively.
24

Table 2: Industry-speciﬁc eﬀects of monetary policy
Variable
αβ1
β2j :Shipbuilding
Railway & Bus
Power
Gas
Real Estate
MarineMarine Transportation
Construction
Medicine
WarehouseLand Transportation
Pulp & Paper
Steel
RubberChemistry
Oil
MiningOthers Manufacture
Trading
Ceramic
Air Transportation
Food
RetailTextilesTransport Equipment
Nonferrous Metal
MachineCommunications
Automobile
Service
Precision Equipment
Electric
Obs.
R2

0.582∗∗-0.132(0.090)(0.224)-8.935∗∗ (2.457)-8.642∗∗ (0.769)-8.606∗∗ (0.551)-7.762∗∗ (1.567)-7.382∗∗ (1.418)-6.424∗∗ (1.935)-6.300∗∗ (1.072)-5.415∗∗ (0.544)-5.394∗∗ (0.835)-5.358∗∗ (1.534)-5.327∗∗ (1.113)-5.276∗∗ (1.270)-4.803∗∗ (0.952)-4.552∗∗ (1.573)-4.433∗∗ (0.491)-4.414∗∗ (0.964)-4.196∗∗ (1.294)-3.798∗∗ (0.858)-3.720∗∗ (0.674)-3.639∗∗ (1.079)-3.509∗∗ (0.839)-3.410∗∗ (0.594)(1.387)-3.328∗-3.079∗∗ (0.840)-2.413(1.532)-2.220∗∗ (0.745)-1.791∗∗ (0.529)-1.594(2.218)(0.611)-1.361∗(0.772)-1.321†-1.267(1.206)0.684(0.463)
56774
0.199

Notes: Values in parentheses are heteroskedasticity robust standard errors. ∗∗ , ∗ , † indicates signiﬁcance at the 1, 5, and 10% levels, respectively. The parameter estimates of themacroeconomic news and ﬁscal year dummies are suppressed.

25

Table 3: Descriptive statistics of ﬁrm-speciﬁc variables
VariableCap. int.
Open.
Lev.
Int. burden
Short. debt
Work. cap.
Cash ﬂow

Mean
24.430
0.094
2.908
0.019
0.151
0.279
0.046

Std. Dev.
52.174
0.146
5.908
0.029
0.139
0.153
0.041

Min
0.076
0.000
0.032
0.000
0.000
-0.480-1.082

Max
873.7790.997
664.571
1.202
0.851
0.894
1.684

Table 4: Cross-correlations of ﬁrm-speciﬁc variables

Cap. Int.
Open.
Lev.
Int. burden
Short. debt
Work. cap.
Cash ﬂow

Cap.
Int.
1
-0.1220.038
0.417-0.020-0.229
0.011

Open.

Lev.

Int.
burdenShort.
debt

Work.
cap.

Cash
ﬂow

1
0.001
-0.062-0.0500.141-0.025

1
0.158
0.266
-0.134-0.2171
0.179-0.199-0.1201
0.065-0.251

1-0.097

1

Table 5: Firm-speciﬁc eﬀects of monetary policy (Separate Estimation)

α
β1
β2
β3
Obs.
R2

Cap.
int.
0.590∗∗(0.090)-0.127(0.224)-3.257∗∗(0.205)-0.021∗∗(0.003)
56774
0.196

Open.

Lev.

0.586∗∗(0.090)-0.134(0.224)-3.187∗∗(0.205)7.425∗∗(1.103)
56774
0.196

0.583∗∗(0.090)-0.131(0.224)-3.178∗∗(0.204)-0.181∗∗(0.042)
56774
0.196

Int.
burden0.569∗∗(0.090)-0.090(0.224)-3.184∗∗(0.204)-47.397∗∗(11.805)
56774
0.197

Short.
debt.
0.588∗∗(0.090)-0.128(0.224)-3.193∗∗(0.206)-2.690∗(1.268)
56774
0.196

Work.
cap.
0.593∗∗(0.090)-0.127(0.224)-3.258∗∗(0.203)
6.547∗∗(1.100)
56774
0.196

Cash
ﬂow
0.590∗∗(0.090)-0.130(0.224)-3.258∗∗(0.207)14.066†(0.207)
56774
0.196

Notes: Values in parentheses are heteroskedasticity robust standard errors. ∗∗ , ∗ , † indicates signiﬁcance at the 1, 5, and 10% levels, respectively. The parameter estimates of themacroeconomic news and ﬁscal year dummies are suppressed.

26

Table 6: Firm-speciﬁc eﬀects of monetary policy (Joint Estimation)

α
β1
β2
β3
Obs.
R2

Cap.
int.
0.581∗∗(0.090)-0.108(0.225)-3.252∗∗(0.204)-0.009∗∗(0.003)
56774
0.198

Open.

Lev.

Int.
burdenShort.
debt.

Work.
cap.

Cash
ﬂow

6.146∗∗(1.089)-0.104∗(0.045)-27.323∗∗(10.353)

0.186(1.418)6.547∗∗(1.195)10.924(9.412)

Notes: Values in parentheses are heteroskedasticity robust standard errors. ∗∗ , ∗ , † indicates signiﬁcance at the 1, 5, and 10% levels, respectively. The parameter estimates of themacroeconomic news and ﬁscal year dummies are suppressed.

27

Table 7: Asymmetric heterogeneity in monetary policy eﬀects
Cap.
int.
α
β1
β2h
β2m
β2l
Obs.
R2
α
β1
β2h
β2m
β2l
Obs.
R2

0.595∗∗(0.090)-0.125(0.224)-4.949∗∗(0.299)-3.037∗∗(0.305)-2.073∗∗(0.312)56774
0.197
0.591∗∗(0.090)-0.123(0.224)-6.959∗∗(0.428)-3.083∗∗(0.220)-1.408∗∗(0.535)
56774
0.197

Open.

Lev.

Int.
Short.
burdendebt
33%–67% Categorization
0.589∗∗ 0.581∗∗ 0.553∗∗ 0.585∗∗(0.090) (0.090) (0.090) (0.090)-0.137-0.112-0.080-0.127(0.224) (0.224) (0.225) (0.224)-2.303∗∗ -4.814∗∗ -4.666∗∗ -3.595∗∗(0.338) (0.301) (0.297) (0.303)-3.440∗∗ -3.091∗∗ -3.341∗∗ -3.644∗∗(0.292) (0.297) (0.291) (0.310)-3.882∗∗ -1.375∗∗ -1.382∗∗ -2.316∗∗(0.312) (0.306) (0.319) (0.301)
56774
56774
56774
56774
0.196
0.196
0.197
0.196
10%–90% Categorization
0.586∗∗ 0.580∗∗ 0.559∗∗ 0.585∗∗(0.090) (0.090) (0.090) (0.090)-0.134-0.124-0.091-0.130(0.224) (0.224) (0.224) (0.224)-0.324-6.183∗∗ -6.762∗∗ -3.566∗∗(0.544) (0.531) (0.486) (0.544)-3.298∗∗ -3.016∗∗ -3.032∗∗ -3.477∗∗(0.248) (0.221) (0.214) (0.231)-3.881∗∗ -1.040∗-0.327-1.883∗∗(0.312) (0.521) (0.589) (0.392)
56774
56774
56774
56774
0.196
0.197
0.197
0.196

Work.
cap.

Cash
ﬂow

0.592∗∗(0.090)-0.133(0.224)-1.941∗∗(0.312)-3.446∗∗(0.306)-4.366∗∗(0.292)
56774
0.196

0.589∗∗(0.090)-0.128(0.224)-2.324∗∗(0.304)-3.166∗∗(0.296)-4.351∗∗(0.325)
56774
0.196

0.590∗∗(0.090)-0.126(0.224)-1.741∗∗(0.541)-3.231∗∗(0.223)-4.781∗∗(0.519)
56774
0.196

0.589∗∗(0.090)-0.129(0.224)-1.443∗∗(0.520)-3.336∗∗(0.220)-4.478∗∗(0.646)
56774
0.196

Notes: Values in parentheses are heteroskedasticity robust standard errors. ∗∗ , ∗ , † indicates signiﬁcance at the 1, 5, and 10% levels, respectively. The parameter estimates of themacroeconomic news and ﬁscal year dummies are suppressed.

28

Table 8: Monetary policy eﬀects in boom, mild recession, and recession

β2b
β2mr
β2r
β3b
β3mr
β3r
Obs.
R2

Cap.
int.
-1.036∗∗(0.346)-4.388∗∗(0.349)-5.218∗∗(0.338)-0.028∗∗(0.007)-0.017∗∗(0.004)-0.014∗∗(0.003)
56774
0.198

Open.

Lev.

-0.786∗(0.345)-4.438∗∗(0.351)-5.199∗∗(0.337)13.767∗∗(2.449)2.496†(1.438)7.627∗∗(1.833)
56774
0.198

-0.693∗(0.346)-4.438∗∗(0.350)-5.199∗∗(0.337)-0.423∗∗(0.069)-0.091†(0.055)-0.103†(0.056)
56774
0.198

Int.
burden-0.850∗(0.344)-4.489∗∗(0.351)-5.090∗∗(0.338)-85.020∗∗(10.983)-25.163∗(11.042)-38.356∗∗(13.897)
56774
0.198

Short.
debt.
-0.727∗(0.351)-4.428∗∗(0.350)-5.175∗∗(0.340)-8.875∗∗(2.207)-2.402(1.702)4.650∗(2.143)
56774
0.197

Work.
cap.
-1.098∗∗(0.340)-4.422∗∗(0.350)-5.224∗∗(0.336)
11.459∗∗(1.840)1.887(1.525)5.039∗∗(1.829)
56774
0.198

Cash
ﬂow
-1.414∗∗(0.360)-4.472∗∗(0.356)-5.210∗∗(0.341)55.577∗∗(10.323)-4.413(12.224)0.925(9.056)
56774
0.198

Notes: Values in parentheses are heteroskedasticity robust standard errors. ∗∗ , ∗ , † indicates signiﬁcance at the 1, 5, and 10% levels, respectively. The parameter estimatesof intercept, expected change, and the macroeconomic news and ﬁscal year dummies aresuppressed.

Table 9: The ranges of the total responses of stock returns to a 1% monetary tightening

Cap. int.
Open.
Lev.
Int. burden
Short. debt.
Work. cap.
Cash ﬂow

Boom[-1.344, -0.453][-2.082, 1.357][-2.199, 0.238][-2.621, 0.439][-2.499, 0.615][-3.032, 1.406][-2.968, 1.060]

Mild recession[-4.810, -4.085][-4.673, -3.979][-4.699, -4.217][-4.907, -4.064][-4.906, -4.065][-4.803, -4.062][-4.628, -4.295]

Recession[-5.550, -4.945][-5.917, -3.794][-5.493, -4.949][-6.459, -4.405][-5.878, -4.249][-6.241, -4.261][-5.247, -5.177]

Note: Each range provides a set of the total responses (the sum of the average response
and the heterogeneous one) of stock returns to a 1% surprise increase in the call-ratetarget, for ﬁrms that are in the middle 80% of the ﬁrm-speciﬁc variable.

29