WORKING PAPER SERIES TAKING STOCK: MONETARY POLICY TRANSMISSION TO EQUITY MARKETS NO. 354 / MAY by Michael Ehrmann and Marcel Fratzscher

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1 WORKING PAPER SERIES NO. 354 / MAY 2004 TAKING STOCK: MONETARY POLICY TRANSMISSION TO EQUITY MARKETS by Mchael Ehrmann and Marcel Fratzscher

2 WORKING PAPER SERIES NO. 354 / MAY 2004 TAKING STOCK: MONETARY POLICY TRANSMISSION TO EQUITY MARKETS 1 by Mchael Ehrmann 2 and Marcel Fratzscher 3 In 2004 all publcatons wll carry a motf taken from the 100 banknote. Ths paper can be downloaded wthout charge from or from the Socal Scence Research Network electronc lbrary at 1 We would lke to thank Ester Faa, Govann Favara, Leonardo Gambacorta,Anl Kashyap, Francsco Maeso-Fernandez, Paul Mzen, Bran Sack, Anna Sanz de Galdeano, Plutarchos Sakellars, Frank Smets, Phlp Vermeulen, Ken West, as well as the anonymous referees and semnar partcpants at the Tobn Symposum at the Chcago Fed, and at the for comments and dscussons, and Reuters for provdng some of the data seres.ths paper presents the authors personal opnons and does not necessarly reflect the vews of the European Central Bank. 2 European Central Bank, Kaserstrasse 29, D Frankfurt/Man, Germany; phone: or 6871, fax: emal: 3 European Central Bank: emal:

3 European Central Bank, 2004 Address Kaserstrasse Frankfurt am Man, Germany Postal address Postfach Frankfurt am Man, Germany Telephone Internet Fax Telex ecb d All rghts reserved. Reproducton for educatonal and noncommercal purposes s permtted provded that the source s acknowledged. The vews expressed n ths paper do not necessarly reflect those of the European Central Bank. The statement of purpose for the Workng Paper Seres s avalable from the webste, ISSN (prnt) ISSN (onlne)

4 CONTENTS Abstract 4 Non-techncal summary 5 1. Introducton 7 2. Monetary polcy and equty markets: conceptual ssues and data Overall stock market reacton to monetary polcy Industry effects, the credt channel and Tobn s q Industry-specfc effects Frm-specfc effects Propensty score matchng Algorthm of propensty score matchng Emprcal results Conclusons 30 References 31 Tables and fgures 34 European Central Bank workng paper seres 44 3

5 Abstract Ths paper analyses the effects of US monetary polcy on stock markets. We fnd that, on average, a tghtenng of 50 bass ponts reduces returns by about 3%. Moreover, returns react more strongly when no change had been expected, when there s a drectonal change n the monetary polcy stance and durng perods of hgh market uncertanty. We show that ndvdual stocks react n a hghly heterogeneous fashon and relate ths heterogenety to fnancal constrants and Tobn's q. Frst, we show that there are strong ndustry-specfc effects of US monetary polcy. Second, we fnd that for the ndvdual stocks comprsng the S&P500 those wth low cashflows, small sze, poor credt ratngs, low debt to captal ratos, hgh prce-earnngs ratos or hgh Tobn's q are affected sgnfcantly more. The use of propensty score matchng allows us to dstngush between frmand ndustry-specfc effects, and confrms that both play an mportant role. JEL classfcaton: G14, E44, E52. Keywords: monetary polcy; stock market; credt channel; Tobn s q; fnancal constrants; S&P500; propensty score matchng. 4

6 Non-techncal summary The relatonshp between monetary polcy and equty prces s stll not well understood n the lterature. As the frst step of our analyss, the paper asks whether and how monetary polcy affects equty markets by lookng at the returns of the S&P500 ndex on days of monetary polcy decsons of the Federal Reserve snce the change of ts dsclosure practces n 1994 and untl We fnd strong and hghly sgnfcant effects of US monetary polcy shocks on equty returns: an unexpected tghtenng of 50 bass ponts s estmated to decrease US equty returns by about 3% on the day of the monetary polcy announcement. Moreover, we fnd strong asymmetres n these effects: equty returns react more strongly to monetary polcy shocks (1) when changes by the FOMC are unexpected, (2) when there s a drectonal change n the monetary polcy stance of the Fed, and (3) durng perods of hgh equty market volatlty. In the lterature on the credt channel of monetary polcy transmsson, most of the work has focused on the role of varous nformaton asymmetres: frms for whch less nformaton s publcly avalable may fnd t more dffcult to access credt when credt condtons become tghter. If a credt channel s at work for frms that are quoted on stock markets, one would expect that ther stock prces respond to monetary polcy n a heterogeneous fashon, wth the prces of frms that are subject to relatvely larger nformatonal asymmetres reactng more strongly. The reason s that ther expected future earnngs are affected more due to constrants on the supply of ther goods. Alternatvely, prces mght react more strongly f the demand for frms' products dffers across sectors. Ths paper analyses both effects, and ams to dstngush ther respectve contrbutons to the overall stock market response. In a frst step, we present evdence that the ndvdual frms ncluded n the S&P500 ndex react n a remarkably heterogeneous fashon to US monetary polcy shocks. Second, we nvestgate whether we can dentfy ndustry-specfc effects of monetary polcy. It s found that cyclcal sectors, such as technology, communcatons and cyclcal consumer goods, react two to three tmes stronger to monetary polcy than less cyclcal sectors. As a thrd step, we test whether monetary polcy has a stronger effect on the equty returns of frms that are fnancally constraned and/or have good nvestment opportuntes. We fnd strong emprcal support for ths hypothess usng varous proxes for fnancal constrants, wth large dfferences n the effects of monetary polcy across frms. We show that frms wth low cashflows, small sze, poor credt ratngs, low debt to captal ratos, 5

7 hgh prce-earnngs ratos or a hgh Tobn's q are affected sgnfcantly more by US monetary polcy. For nstance, monetary polcy affects frms wth poor cashflows almost twce as much as frms wth hgh cashflows. Fnally, after presentng evdence for the presence of ndustry- and frm-specfc effects, we am to dsentangle the two. Snce the frm-specfc varables are hghly correlated wth the ndustry afflaton, we use a novel emprcal methodology n ths lterature, based on propensty score matchng, to properly dstngush between the ndustry-specfc and frmspecfc factors. The results suggest that t s n partcular the ndustry-specfc effects that explan a large share of the dfferent reactons of frms to US monetary polcy shocks. 6

8 1. Introducton One central argument of James Tobn s semnal 1969 Journal of Money, Credt and Bankng paper was that fnancal polces can play a crucal role n alterng what later became known as Tobn s q, the market value of a frm s assets relatve to ther replacement costs. Tobn emphaszed that n partcular monetary polcy can change ths rato. Ths 1969 JMCB paper together wth another of hs contrbutons (Tobn 1978) became a key element n the formulaton and understandng of the stock market channel of monetary polcy transmsson. Tobn s argument n ths work was that a tghtenng of monetary polcy, whch may result from an ncrease n nflaton, lowers the present value of future earnng flows and hence depresses equty markets. The second part of Tobn s argument, namely the relatonshp between monetary polcy and equty prces, s stll not very well understood. On the one hand, t has proven dffcult to properly dentfy monetary polcy, snce monetary polcy may be endogenous n that central banks mght react to developments n stock markets. Consderable progress has recently been made n ths respect. Rgobon and Sack (2002, 2003) develop a methodology that explots the heteroskedastcty present n fnancal markets to dentfy monetary polcy shocks, whle Kuttner (2001) and Bernanke and Kuttner (2003) derve monetary polcy shocks through measures of market expectatons obtaned from federal funds futures contracts. In ths paper, we wll employ a methodology smlar to Bernanke and Kuttner (2003), by dentfyng monetary polcy shocks through market expectatons obtaned from surveys of market partcpants. As the frst step of our analyss, the paper asks whether and how monetary polcy affects equty markets by lookng at the returns of the S&P500 ndex on days of monetary polcy decsons of the Federal Reserve snce the change of ts dsclosure practces n 1994 and untl We fnd strong and hghly sgnfcant effects of US monetary polcy shocks on equty returns: an unexpected tghtenng of 50 bass ponts s estmated to decrease US equty returns by about 3% on the day of the monetary polcy announcement. Moreover, we fnd strong asymmetres n these effects: equty returns react more strongly to monetary polcy shocks (1) when changes by the FOMC are unexpected, (2) when there s a drectonal change n the monetary polcy stance of the Fed, and (3) durng perods of hgh equty market volatlty. 7

9 Despte the recent progress n understandng the overall stock market response to monetary polcy, more research s needed to understand why ndvdual stocks react so dfferently to monetary polcy shocks, and what the drvng force s behnd ths reacton. The recent paper by Bernanke and Kuttner (2003) shows that very lttle of the market s reacton can be attrbuted to the effect of monetary polcy on the real rate of nterest. Rather, the response of stock prces s drven by the mpact on expected future excess returns and to some extent on expected future dvdends. In ths paper, we go a step further by analyzng whch factors of these expectatons are mportant for understandng the large heterogenety n the reacton of ndvdual stocks to monetary polcy. In the lterature on the credt channel of monetary polcy transmsson, Bernanke and Blnder (1992) and Kashyap, Sten and Wlcox (1993) show that a tghtenng of monetary polcy has a partcularly strong mpact on frms that are hghly bank-dependent borrowers as banks reduce ther overall supply of credt. Bernanke and Gertler (1989) and Kyotak and Moore (1997) argue that worsenng credt market condtons affect frms also by weakenng ther balance sheets as the present value of collateral falls wth rsng nterest rates, and that ths effect can be stronger for some frms than for others. Both arguments are based on nformaton asymmetres: frms for whch less nformaton s publcly avalable may fnd t more dffcult to access bank loans when credt condtons become tghter as banks tend to reduce credt lnes frst to those customers about whom they have the least nformaton (Gertler and Hubbard 1988, Gertler and Glchrst 1994). For nstance, Thorbecke (1997) and Perez-Quros and Tmmermann (2000) show that the response of stock returns to monetary polcy s larger for small frms. If a credt channel s at work for frms that are quoted on stock markets, one would expect that ther stock prces respond to monetary polcy n a heterogeneous fashon, wth the prces of frms that are subject to relatvely larger nformatonal asymmetres reactng more strongly. The reason s that ther expected future earnngs are affected more, snce these frms wll fnd t harder to access funds followng a monetary tghtenng, whch should lead to a constrant of the supply of ther goods. Another dfferentaton of the response of stock prces to monetary polcy s lkely to be related to the response of the demand for frms' products. Frms that produce goods for whch demand s hghly cyclcal or nterest-senstve should see ther expected future earnngs be affected relatvely more followng a monetary polcy move. These effects are not based on the credt channel; rather, they arse through the nterest-rate channel. Therefore, one would expect that the dfferentaton of responses to monetary polcy s not 8

10 only dependent on the frm-specfc characterstcs, but also on those of the ndustry to whch the frm s afflated. Ths paper analyses both effects, and ams to dstngush ther respectve contrbutons to the overall stock market response. In a frst step, we present evdence that the ndvdual frms ncluded n the S&P500 ndex react n a remarkably, hghly heterogeneous fashon to US monetary polcy shocks. Second, we nvestgate whether we can dentfy ndustryspecfc effects of monetary polcy. It s found that cyclcal sectors, such as technology, communcatons and cyclcal consumer goods, react two to three tmes stronger to monetary polcy than less cyclcal sectors. As a thrd step, we test whether monetary polcy has a stronger effect on the equty returns of frms that are fnancally constraned and/or have good nvestment opportuntes. We use a measure of Tobn s q as a proxy of nvestment opportuntes, whch s an mportant corollary of the analyzed fnancal constrant varables. We fnd strong emprcal support for ths hypothess usng varous proxes for fnancal constrants and nvestment opportuntes, wth large dfferences n the effects of monetary polcy across frms. We show that frms wth low cashflows, poor credt ratngs, low debt to captal ratos, hgh prce-earnngs ratos or a low Tobn's q are affected sgnfcantly more by US monetary polcy. For nstance, monetary polcy affects frms wth poor cashflows or low debt almost twce as much as frms wth hgh cashflows or hgh debt. Fnally, after presentng evdence for the presence of ndustry- and frm-specfc effects, we am to dsentangle the two. Snce the frm-specfc varables are hghly correlated wth the ndustry afflaton, we use a novel emprcal methodology n ths lterature, based on propensty score matchng, to properly dstngush between the ndustry-specfc and frmspecfc factors. The results suggest that t s n partcular the ndustry-specfc effects that explan a large share of the dfferent reactons of frms to US monetary polcy shocks. The paper proceeds as follows. Secton 2 presents the data employed n ths study and dscusses some conceptual ssues mportant for the emprcal analyss. Our emprcal results for the overall S&P500 ndex are reported n Secton 3. Secton 4 tests the role of the nterest rate channel, credt channel and of Tobn s q for the response of equty markets to US monetary polcy. Secton 5 ntroduces the propensty score matchng methodology and reports the respectve results. Secton 6 concludes. 9

11 2. Monetary polcy and equty markets: conceptual ssues and data An mportant ssue that arses when measurng the effect of monetary polcy on equty markets s the correct dentfcaton of monetary polcy. Many papers n ths lterature (e.g., Lamont, Polk and Saa-Requejo 2001, Perez-Quros and Tmmermann 2000) use changes n market nterest rates or offcal rates as ther measures of monetary polcy. The problem wth these measures, however, s that changes n nterest rates can concde wth changes n busness cycle condtons and other relevant economc varables. It s therefore not clear whether the effect attrbuted to monetary polcy n those papers reflects other factors. A number of studes have therefore followed the example of Chrstano, Echenbaum and Evans (1994) and extract monetary polcy shocks as the orthogonalzed nnovatons from VAR models. Thorbecke (1997) employs ths methodology and fnds that for the perod the response of US stock returns to monetary polcy shocks, based on federal fund rates, dffers sgnfcantly across ndustres and that small frms returns react much more strongly than those of large frms. Patels (1997) also employs a related methodology and arrves at very smlar results, but also shows that the overall explanatory power of monetary polcy for stock returns s rather low. Conover, Jensen and Johnson (1999) look at 16 ndustralzed countres and fnd that equty markets n several of these markets react both to the local as well as to the US monetary envronment,.e. to changes n monetary polcy. A central shortcomng of ths methodology s, however, that t s subject to an endogenety bas,.e. monetary polcy shocks that are extracted from structural VAR models or from changes n nterest rates usng monthly or quarterly frequences are unlkely to be purely exogenous. Rgobon and Sack (2002, 2003) have shown convncngly that monetary polcy reacts to stock market developments n a way that consstently takes the mpact of stock market movements on aggregate demand nto account. The essence of Rgobon and Sack s argument s that causalty between nterest rates and equty prces runs n both drectons. They show that not accountng for ths endogenety may ntroduce a sgnfcant bas n emprcal estmatons of the reacton of equty returns to monetary polcy. To dentfy monetary polcy shocks more accurately, several papers have conducted event studes based on hgher frequency observatons, mostly daly data, analyzng how equty markets react to monetary polcy. A semnal paper employng such an event-study methodology s that of Cook and Hahn (1989), who test whether changes to the federal funds rate affected asset prces durng the perod Thorbecke (1997) uses the 10

12 same methodology but extends the data also to the early Greenspan perod and fnds that the US equty ndex ndeed reacted sgnfcantly to changes n the federal funds rate on days when such changes took place. Other event studes lookng at the lnk between monetary polcy and equty returns are those of Bomfm (2001), Durham (2002), Jensen and Johnson (1995) and Lobo (2000). For nstance, Lobo (2000) fnds for the perod that tghtenngs n the federal funds and/or dscount rate had a stronger effect on equty markets than monetary polcy easngs. Bomfm (2001) shows that volatlty of equty markets tends to be relatvely lower on days before and hgher on days after monetary polcy decsons. One shortcomng of the exstng event-study lterature about monetary polcy and equty markets s that monetary polcy changes are smply measured as changes of polcy rates on days of FOMC meetngs. Kuttner (2001) has shown that on the day of announcements, markets react mostly not to the announcements per se, but to ther unexpected component that s not already prced nto the market. Ths argument s consstent wth the effcent market hypothess that asset prces should reflect all nformaton avalable at any pont n tme. The emprcal methodology we use n ths paper falls nto the category of event studes. For the perod from February 1994 to February e. snce the Fed dscloses decsons concernng the fed funds rate target - we analyze the effect of the surprse component of monetary polcy decsons on equty returns on the days of ther announcement. Ths surprse s measured as the dfference between the announcement of the FOMC decson and the market expectaton. The expectatons data for monetary polcy decsons orgnates from a Reuters poll among market partcpants, conducted on Frdays before each FOMC meetng. We use the mean of the survey as our expectatons measure although usng the medan yelds smlar econometrc results. Employng standard technques n the lterature (e.g. Gravelle and Moessner, 2001), we test for unbasedness and effcency of the survey data. Tables A1 and A2 show the results for the respectve tests for the forecasts of monetary polcy announcements. We fnd that the survey expectatons are of good qualty as they prove to be unbased and effcent. As shown prevously (Ehrmann and Fratzscher 2002, 2003) and also tested here n ths paper, the survey-based measures perform very smlar to expectatons data based on federal funds futures, as employed by Kuttner (2001) and Bernanke and Kuttner (2003). For our measure of stock market returns, we use the returns of the S&P500 ndex, and of the 500 ndvdual stocks theren as n early 2003, wth Bloomberg as the source. Ths 11

13 allows us to cover a broad spectrum of ndustres and frms, and thus to get at the ssue of ndustry- and frm-specfc effects of monetary polcy. We calculate the daly returns as the log-dfference of the daly closng quotes. Obvously, there are varous ssues n the measurement of both the monetary polcy surprses and the daly stock returns that mert dscussng. Snce the Reuters surveys are conducted on Frdays pror to the FOMC meetngs, they cannot capture any change n market expectatons that occurs n between. However, we are comforted by the fact that results are robust to the use of market expectatons derved from the Fed funds futures market, where ths ssue does not arse. Regardng the measure of stock returns, the choce of a daly frequency ams at strkng a balance between dentfablty of exogenous monetary polcy surprses and estmaton of sustaned stock market effects. At lower frequences, as we have argued above, t s dffcult to dsentangle the response of monetary polcy to stock markets and thus to dentfy monetary polcy surprses. Hgher frequency data, as used e.g. by Andersen et al. (2003) for exchange rates, on the other hand, mght capture overshootng effects that quckly dsappear. We therefore assume that effects found on a daly bass are lkely to reveal the longer-run mpact n a more relable fashon. Our sample covers 79 meetngs of the FOMC, from February 4 th, 1994 to January 29 th, The begnnng of the sample concdes wth a change n FOMC practces: snce 1994, the FOMC announces the fed fund target rate n openness, whereas before, the market needed to nfer the target rate from the Fed s behavor. We delete the unscheduled meetng of September 17 th, 2001, where the FOMC decded to cut nterest rates by 50 bass ponts n response to the events of September 11 th, for the unusual crcumstances of ths nterest rate decson. Not all stocks are observed for the full sample perod; on average, we observe stocks for 71 of the FOMC meetng days. 3. Overall stock market reacton to monetary polcy As a frst pass at the stock market effects of monetary polcy, we test whether and how the S&P500 ndex responds to surprses. The econometrc model used s formulated as follows: r (1) t s t t 12

14 where r t denotes the stock market return on day t, and s t the monetary polcy surprse. 1 Table 1 around here The frst row of table 1 shows that a monetary tghtenng of 100 bass ponts lowers stock market returns by 5.5%, sgnfcant at the 1%-level. However, ths s an average effect over tme, and t can be expected to vary consderably, e.g. dependng on the crcumstances n whch monetary polcy s actng or the type of acton of monetary polcy. In order to test for such tme-varyng monetary polcy effects, we wll splt the sample of surprses nto two subsets s, t 1 and s 2, t accordng to varous crtera, enter both n the regresson separately, and test for the statstcal dfference of the two parameters ( 1 and 2 ) wth an F-test. 2 The model therefore changes to r t 1 s1, t 2s2, t t (2) Snce the monetary polcy surprse covers cases where nterest rates were changed, but markets expected a dfferent magntude (or no change), as well as cases where nterest rates were left unchanged, but markets had expected a move, we frst test whether these cases lead to dfferent stock market reactons. The coeffcents are reported n the second row of table 1. It turns out that stock returns are affected sgnfcantly only n the former case,.e. when there s a change n monetary polcy rates. In these cases, the response of stock markets s slghtly hgher than average: returns fall by about 6% n a response to an unexpected change by 100 bass ponts. Condtonal on there beng a change n polcy rates, the magntude of the stock market response s even stronger n certan crcumstances. If the market had not expected any change n polcy rates (.e., the survey expectatons were equal to zero), returns are estmated to change by 9%. An even stronger effect s found f the polcy move ntates a drectonal change,.e. n case of the frst tghtenng after a perod of easngs, or vce versa. A smlar effect has been found for t-bll rates (Demralp and Jorda, 2002). Snce the Fed usually changes nterest rates several tmes n the same drecton before t reverses ts stance, a frst tghtenng after a seres of easngs (or vce versa) contans valuable 1 As expected, lagged values of the stock market return proved to be nsgnfcant, and were therefore not ncluded. The estmated parameter for the ntercept s generally nsgnfcant. All regressons n ths secton are performed wth heteroskedastcty-robust standard errors. The estmates are performed for the sample of FOMC meetng days only. 2 For a set of related tests see Bernanke and Kuttner (2003). Ther results are smlar to our fndngs. 13

15 nformaton about the future course of monetary polcy. Ths s reflected n the response of both equty and t-bll markets. The dfference s hghly sgnfcant. The effect of monetary polcy s also stronger n an envronment of ncreased market uncertanty. We have tested for ths by splttng the surprses nto subsets, dependng whether market volatlty over the last month has been low (below the 10 th, 50 th or 80 th percentle of the overall dstrbuton of volatlty over the full sample), or hgh (above the varous thresholds). If market volatlty s hgh, we estmate stronger responses, as shown n rows 5 to 7 of table 1. As a matter of fact, the response s only sgnfcant f volatlty s hgh. Monetary polcy sgnals are therefore more nfluental when market uncertanty s hgh. Fnally, we have tested whether postve surprses lead to dfferent responses than negatve surprses. A postve surprse mples that monetary polcy has tghtened more or loosened less than expected, or has not moved whereas the market expected a loosenng. It turns out that a negatve surprse (.e. a loosenng relatve to market expectatons) has larger effects. The last row of table 1 reports the results of a model where we ntroduce both the surprse component and the expected component of a monetary polcy announcement. Ths allows us to test for the qualty of the expectatons measure. We would expect no reacton of the stock market to the expected component of a monetary polcy decson on the day of the announcement, snce markets should already have prced the nformaton. Ths s ndeed what we fnd. Tables A3 to A4 n the appendx contan robustness checks for these results. Table A3 defnes the market expectaton by the medan of the Reuters survey, as opposed to the mean as we have defned t here. Table A4 uses monetary polcy surprses as calculated by Kuttner (2001), whch are derved from federal funds futures markets,.e. are market-based rather than survey-based as the measure used n ths paper. All results are qualtatvely, and generally quanttatvely extremely robust. In summary, the analyss of the reacton of the S&P500 shows a strong effect of monetary polcy on equty returns. Moreover, there are large asymmetres n the reacton of equty returns dependng on the nature and type of the monetary polcy news. We next analyze the whether the effect of monetary polcy vares across frms. 14

16 4. Industry effects, the credt channel and Tobn s q We now turn to the queston of whch frms are affected partcularly strongly by monetary polcy. Our sample of frms comprses the 500 ndvdual stocks that currently consttute the S&P500. As a startng pont, we use the emprcal model of equaton (1) and regress each frm s return seres ndvdually on our monetary polcy surprses. We fnd a glarng and large heterogenety n the response across the 500 stocks n the S&P500 ndex. Fgure 1 shows the dstrbuton of the estmated parameters. They range from to +0.15, wth a mean of 0.06 and a medan of The dstrbuton s strongly skewed towards the left. Overall, these results show that the stock market response to monetary polcy s hghly asymmetrc. Understandng and explanng ths asymmetry and heterogenety s the focus of the remander of the paper. Fgure 1 around here As to the emprcal methodology, to carry out the analyss n a panel framework of 500 stocks, we wll turn to panel regressons of the form r, t 1st 2st x, t x, t, t (3) where x, t denotes some frm-specfc characterstc, whch can be ether tme-varyng (e.g. ts sze or ts cash flow to ncome rato), or fxed over tme (e.g. ts ndustry afflaton). If ths varable vares wth the stock prce (e.g. the prce-earnngs rato), we enter t wth one lag to avod problems wth endogenety of the regressors. Contrary to most of the lterature on stock market effects, we decded not to run estmates on a stock by stock bass, and then explan the coeffcents n a cross-sectonal regresson, although the tme-seres dmenson of our sample would have allowed us to do so. Rather, we decded to pool the data for two reasons. Frst, many of our frm-specfc characterstcs are tme-varyng. In a cross-sectonal regresson, we could not account for changes n these characterstcs over tme. Second, poolng allows us to take nto account a potental crosssectonal correlaton of resduals, whch we consder a realstc assumpton for stock market data: a hgh resdual n one stock s lkely to be accompaned by hgh resduals n other stocks. To account for ths dependence across observatons, we estmate equaton (3) va OLS usng panel-corrected standard errors (PCSE). Ths estmator corrects for heteroskedastcty and assumes that resduals are contemporaneously correlated across panels, and estmates the covarance of the OLS coeffcents as 15

17 ˆ 1 1 V ( X ' X ) X ' X ( X ' X ) (4) where Ω s the covarance matrx of the resduals: mxm I T xt where I s an dentty matrx and Σ the m by m panel-by-panel covarance matrx of the resduals, formulated as ' j j (5) T j where ε and ε j are the resduals for panels and j from equaton (3) and T j s the number of resduals between the panels that can be matched by tme perod. Ths varance estmator corrects for the dependence across observatons. Neglectng such correlaton wll lead to decreased estmates of the varance and to a serous overestmaton of the sgnfcance of parameters. As a matter of fact, ths effect turns out to be mportant. The results are extremely robust to other changes n the model specfcaton, no matter whether we allow for fxed effects or not, or run the model over all tradng days and use feasble GLS to allow for the presence of AR(1) autocorrelaton wthn panels. We expermented usng a lag of stock returns; however, t never turned out sgnfcant, confrmng the valdty of the effcent market hypothess n ths context. Smlarly, usng further lags of the monetary polcy surprse does not add any explanatory value the effects are prced nto the market wthn one day. We checked for robustness wth respect to pure tme effects by calculatng the mean of all stock returns on a daly bass, and by subtractng ths daly mean from each stock, agan day by day. Ths does control for pure tme effects n the same way a full set of tme dummes would do. All results are robust to ths treatment. Fnally, we conducted several other robustness checks. Most mportantly, excludng large outlers of monetary polcy surprses yelds qualtatvely smlar results for the estmates. Moreover, we repeated the analyss usng monetary polcy surprses as calculated by Kuttner (2001) and used n Bernanke and Kuttner (2003), whch are derved from federal 16

18 funds futures markets,.e. are market-based rather than survey-based as the measure used n ths paper. All results are qualtatvely, and generally quanttatvely extremely robust Industry-specfc effects The effect of monetary polcy on stock market returns s lkely to dffer across ndustres for varous reasons. The nterest-senstvty of the demand for products dffers. Furthermore, f monetary polcy affects exchange rates, tradable goods ndustres are lkely to be affected more strongly. Fnally, changes n the cost of captal nduced by monetary polcy are more mportant for captal-ntensve ndustres. All these factors mply that expected future earnngs are affected n a heterogeneous fashon across ndustres, whch should be reflected n the responsveness of stock returns. We would therefore expect frms n cyclcal ndustres, captal-ntensve ndustres, and ndustres that are relatvely open to trade to be affected more strongly. There s only relatvely lttle evdence of the cross-sectonal dmenson of monetary polcy effects n the lterature to date. Exceptons are Dedola and Lpp (2000) and Peersman and Smets (2002), who analyze the effect of dentfed VAR shocks on sectoral producton ndces for fve OECD countres and seven countres of the euro area, respectvely. Ganley and Salmon (1997) and Hayo and Uhlenbruck (2000) smlarly analyze ndustry effects n the UK and Germany. In a smlar fashon to the tests employed n ths paper, Angelon and Ehrmann (2003) analyze cross-sectonal responses of stock market returns to monetary polcy n the euro area. For the US, to our knowledge only Bernanke and Kuttner (2003) perform a smlar analyss. Overall, the fndngs of ths lterature support the hypotheses expressed above. Tables 2 and 3 around here Tables 2 and 3 report results for a breakdown of 9 sectors and 60 ndustry groups, sorted by the magntude of monetary polcy effects. The left-hand columns report results of the panel verson of equaton (1), where we repeatedly run regressons wth stocks of one sector only. In order to get an assessment of the dfferences across sectors, we also report results from model (3), where all stocks enter, regardless of ther ndustry afflaton. We run ths model repeatedly, each tme redefnng the ndustry dummy x to capture stocks wth dfferent 3 See tables A3 and A4 n the appendx. 17

19 ndustry afflatons. 4 The second panels of tables 2 and 3 report the correspondng results for 2. In that sense, the set of results shown n the second panels controls for market movements, and ams to estmate how senstve stock returns of a gven ndustry are to monetary polcy relatve to the market return. In other words, we are nterested n understandng whether sector afflaton can help to explan what s commonly known as n the captal asset prcng model, the covarablty of a stock wth the returns of the ndex on the occason of a monetary polcy surprse. Stock returns of frms n the technology, communcaton and cyclcal consumer goods ndustres are more responsve than the average stock, whereas non-cyclcal consumer goods, energy and utltes are ndustres that respond below average, where the dfferences are always estmated at a 1% sgnfcance level. Industres whose reacton to monetary polcy shocks s around the average are the basc materals, ndustral and fnancal sectors. Overall, ths supports the hypothess that cyclcal and captal-ntensve ndustres are affected most. Lookng at the fner dsaggregaton presented n table 3, ths mpresson gans further support. Hghly non-cyclcal sectors lke food, agrculture or beverages respond less, whereas frms n semconductors, nternet, telecommuncatons, computers and software, to name a few, react more strongly than the average. Lke n fgure 1, the effects vary consderably also n magntude: whereas stock returns n the semconductor ndustry drop by more than 20%, there are even ndustres that show a postve response, such as the beverages sector. 4.2 Frm-specfc effects The lterature on the credt channel of monetary polcy mples that the effect of monetary polcy on frms tends to be asymmetrc. In partcular, frms that are fnancally constraned are lkely to be affected more strongly by changes n nterest rates than frms that are less constraned. Consstent wth and buldng on Fama and French s (1995) evdence that small frms equty returns are dstnct from those of larger frms, Perez-Quros and Tmmermann (2000) use the sze of frms as a proxy for credt constrants. Analyzng monthly equty returns of sze-sorted equty portfolos durng the perod , they ndeed fnd that smaller frms returns are much more affected by monetary polcy tghtenng and durng recessons than those of larger frms. Usng the sze of frms as a proxy for the degree of 4 Snce there s no cross-sectonal varaton n s t, estmatng model (1) ncludng ndvdual stocks should yeld dentcal results as estmatng these models usng returns of unweghted ndustry ndces. Indeed, estmatng the models usng such ndustry ndces produces dentcal pont estmates and standard errors. 18

20 credt constrants has been wdespread. For nstance, Gertler and Hubbard (1988) and Gertler and Glchrst (1994) show that small frms are more dependent on bank loans. Nevertheless, a number of papers pont out that sze s only an mperfect proxy for the degree of credt constrants and attempt to fnd other, more drect measures. Lamont, Polk and Saa-Requejo (2001), buldng on work by Kaplan and Zngales (1997), use a qualtatve measure for fnancal constrants from nformaton n frms annual reports n fulfllment of SEC requrements and regulatons. 5 Lamont, Polk, and Saa-Requejo (2001) fnd for the perod that fnancally constraned frms exhbt a sgnfcant degree of comovements n terms of stock returns, and that ths common factor cannot be attrbuted to the sze of frms or other characterstcs such as ndustry-specfc effects. The mportant fndng of ther paper for our purpose s that they do not detect evdence that fnancally constraned frms react more strongly to changes n monetary polcy or to busness cycle condtons than less constraned ones. To analyze the role of the credt channel, we borrow from the lterature deas of several proxes for the degree of fnancal constrants of frms. Followng Kaplan and Zngales (1997), we defne the term fnancal constrant to mply a wedge between nternal and external fnancng of a frm s nvestment. Frms wth stronger fnancal constrants are those that fnd t relatvely more dffcult to rase funds to fnance nvestment. Frst, we look at the sze of frms, usng the number of employees as well as the market value of frms as our sze varables. Second, we follow the example of Lamont, Polk and Saa-Requejo (2001) and Kaplan and Zngales (1997) and use several more drect measures of fnancal constrants: the cash flow to ncome rato and the rato of debt to total captal. The underlyng ratonale for ncludng these two measures s that a frm can fnance nvestment ether by rasng funds nternally - by usng exstng cashflows generated - or externally - va bank loans or captal markets. In theory, our prors are that frms wth large cashflows should be more mmune to changes n nterest rates as they can rely more on nternal fnancng of nvestment. One may expect that frms wth a lower rato of debt to captal are affected more by monetary polcy because they are more bank-dependent and bank-dependent borrowers are ht more strongly 5 More precsely, Kaplan and Zngales (1997) test whether varous captal and book ratos the cash flow to captal rato, Tobn s q (proxed by frms market to book ratos), and the three ratos of debt, of dvdends and of cash holdngs to total captal are systematcally related to ther qualtatve measure of fnancal constrants. They do fnd a sgnfcant relatonshp for most of these varables, though the fndng for q s ambguous, as dscussed above n Secton 1. The man focus on the paper by Kaplan and Zngales (1997) s on the lnk between nvestment and fnancal constrants, and does not analyse equty markets. 19

21 by a change n the supply of credt (Bernanke and Blnder 1992, Kashyap, Sten and Wlcox 1993). Moreover, we nclude the prce-earnngs rato n our analyss. Fnally, we employ Moody s nvestment ratng and Moody s bank loan ratng as two measures of fnancal constrants. We would expect that frms wth a better ratng should fnd t easer to obtan fnancng of ther nvestments and therefore should be less affected by changes n monetary polcy. Tables 4-5 around here Table 4 provdes some summary statstcs of the varous measures of fnancal constrants after correctng for outlers, to llustrate the pure cross-sectonal dsperson of the varables. Table 5 present the correlatons of the varous measures of fnancal constrants and Tobn's q. The key pont from ths table s that most of the varables have a low degree of correlaton. 6 Exceptons are the two sze measures - number of employees and market sze - as well as the correlatons of the two sze measures wth the debt to total captal rato. Table 6 around here Table 6 shows the emprcal fndngs for the varous measures of fnancal constrants. As a general prncple underlyng the analyss, frms have been dvded nto three groups accordng to ther poston n the cross-sectonal dstrbuton of each varable, whch has been calculated on a daly bass. 7 The left-hand-sde columns use the bottom thrd of the dstrbuton of each respectve varable for a frm to have a low measure, the mddle thrd (.e. between 33% and 67%) to have a medum level, and the top thrd to have a hgh level of the varable. The rght-hand-sde columns of Table 6 make a smlar categorzaton, but usng nstead the 10% and 90% levels as cut-offs. All results have undergone robustness checks, such as excludng outlers from the estmaton. The results proved, however, hghly robust to such changes. Frst, the results provde evdence that the sze of frms s an mportant factor for the determnaton of the monetary polcy transmsson n equty markets. Small frms, based 6 The low correlatons between dfferent measures of fnancal constrants mply that a frm that s relatvely constraned accordng to one measure need not be constraned accordng to another measure, a possble outcome snce fnancal constrants can take dfferent forms and degrees. 7 Some of the frm characterstcs are evolvng consderably through tme. Accordngly, t s mportant to categorse frms on a daly bass n order to dsentangle the effects of monetary polcy on a gven day on the dstrbuton of frms from the asymmetres of monetary polcy over tme. 20

22 ether on the number of employees or the market value of frms, are estmated to react more to monetary polcy shocks than medum-szed and large frms. Ths s very much n lne wth the fndng n the lterature, as dscussed above, that small frms tend to be more affected by such shocks. Nevertheless, t s nterestng that we can confrm ths result also for a set of frms that are overall qute large. Second, the results show that frms wth low cashflows are affected sgnfcantly stronger by US monetary polcy shocks. For the 10%-90% categorzaton, stock returns of frms wth low cashflows respond almost twce as much to monetary polcy (.e. 8.8% n response to a 100 bp shock) as compared to frms wth hgh cashflows (.e. 4.7% to the same shock). Thrd, frms that have a good Moody s nvestment ratng and frms that have a good Moody s bank loan ratng 8 are more mmune to monetary polcy shocks than those wth a poor ratng. Frms wth a poor nvestment ratng or wth a low bank loan ratng react nearly twce as much to monetary polcy ( 6.5% or 6.1%, respectvely) than frms wth hgh ratngs ( 3.8% or 3.9%, respectvely). Fourth, the effects for the debt to captal rato are found to be non-lnear: frms wth ether hgh or low values of these ratos respond more to monetary polcy than frms that have ntermedate levels. Overall, the largest effect of monetary polcy s recorded for frms wth a low level of debt, whereas frms wth hgh levels of debt react smlar to the average frm. 9 Ths fndng s nterestng because t may come somewhat unexpected. Indeed, ths fndng conveys a very nterestng message. We nterpret t as ndcatng that frms that have a hgh level of debt are not more constraned fnancally than others. On the contrary, the results suggest that frms hold low levels of debt because they are currently fnancally constraned and thus may fnd t relatvely more dffcult to borrow more. A smlar result has been found, e.g., n Peersman and Smets (2002) and Dedola and Lpp (2000). Ffth, frms wth a hgh prce earnngs rato are affected more strongly by monetary polcy, ndcatng that the re-assessment of ther earnngs expectatons s partcularly senstve to changes n nterest rates. Fnally, economc theory s ambguous about the relatonshp between monetary polcy, equty markets and Tobn s q, as a proxy of nvestment opportuntes and an mportant corollary of the analyzed fnancal constrant proxes. On the one hand, a hgh q ndcates 8 Of course, both measures are hghly correlated as frms wth good nvestment ratngs also tend to have good bank loan ratngs. 21

23 that ample nvestment opportuntes are present for a frm, whch may mply, ceters parbus, that ths frm has hgher fnancal constrants by requrng more external funds to fnance ths nvestment. The hgher degree of constrants may therefore also mply a hgher senstvty of ths frm to monetary polcy shocks. On the other hand, a frm wth a relatvely hgh value of ts assets (a larger q) may fnd t easer and may receve more favorable condtons to rase external funds to fnance nvestment. Ths n turn would mply that frms wth a large q have lower fnancal constrants and hence they may be less senstve to monetary polcy shocks. Followng Kaplan and Zngales (1997) and Lamont, Polk and Saa-Requejo (2001), we use frms market to book ratos as proxes for Tobn s q. It s clearly dffcult f not mpossble to measure q accurately, 10 but usng the market to book rato s farly common n the lterature and should provde a reasonably close approxmaton. Table 6 reveals that the strongest response of equty returns to monetary polcy shocks s experenced by frms wth a hgh q. Ths dfference s szeable, but sgnfcant only for the 33%-67% categorzaton. Overall, the results show that much of the asymmetrc response of frms to monetary polcy shocks, as shown n Fgure 1, s explaned by dfferences across frms n ther degree to whch they are fnancally constraned and to whch they have dfferent nvestment opportuntes, as proxed by Tobn's q. 5. Propensty Score Matchng A potental shortcomng of the results presented n the prevous secton s that the ndustry afflaton of a frm s hghly correlated wth ts fnancal characterstcs, as documented n table 7. As argued n secton 1, frm-specfc effects that are related to fnancal constrants affect a frm s future earnngs stream because they change the supply of goods by ths frm. On the other hand, once these effects have been accounted for, all remanng dfferentaton of stock responses to monetary polcy should sgnal the effect of changes n nterest rates on the demand of the goods of a frm, and ths way on ts expected future earnngs stream. We have argued that t s especally the ndustry-specfc effects that are lkely to be 9 Ths effect s found to be statstcally sgnfcant also when comparng the effects of low levels versus hgh levels of debt, the test for whch s not shown n Table See Erckson and Whted (2000) for a detaled analyss of the potental mportance of measurement errors n Tobn's q. The focus of the paper by Erckson and Whted s, however, prmarly on the relatonshp between Tobn's q and nvestment. 22

24 demand-drven. However, n order to dsentangle these effects, we need to take any correlaton between ndustry afflaton and the fnancal characterstcs nto account. Gven the large number of fnancal varables and sectors we need to nclude, t s not feasble to smultaneously estmate all factors n one model. 11 Table 7 around here To solve for ths problem, we employ Propensty Score Matchng methods that have long been used n bology and other felds (Rosenbaum and Rubn 1983) but have only recently been utlzed n economcs, mostly n labor economcs. 12 The usual termnology s that of "treatment effects", the term comng from expermental economcs where some ndvduals undergo a certan experment or treatment (T=1) whereas others reman n a control group (T=0). The treatment effect 2 over the entre populaton s defned as T 1 T 0 T 1 Er T 1 Er T 1 2 E 1,2 T (6) In other words, the treatment effect s defned as the dfference n the expected outcome of the varable of nterest (r ) of an ndvdual, f ths one ndvdual could be observed once n the treatment group and once n the control group. In our case, ths mples that the pure ndustry-specfc demand effect of monetary polcy 2 s the dfference of the response of one frm's equty returns f t could be observed once as beng n sector T=1 and also once as beng n sector T=0. Of course t s not possble to measure the treatment effect drectly snce an ndvdual can only be observed as beng ether n the treatment group or n the control group -.e. only n one ndustral sector - but never both. The usual way of obtanng the treatment effect s by estmatng the expected treatment effect e 2 as T 1 T 0 r T 1 Er T 0 e 2 E T 1 j (7) 11 Accountng for the hgher degree of collnearty wthn sectors for the fnancal constrant varables would requre to nclude n the econometrc model nteracton terms for each of the 9 sectors wth each of the 8 fnancal constrant varables n each of the 3 categorsatons (low, medum, hgh) shown n Table 6. Hence the model would have to nclude at least 9x8x3 = 216 nteracton terms, plus addtonal nteracton terms to account for collnearty across some of the fnancal constrant varables shown n Table 5. 23

25 and assumng that T 0 T 0 r T 1 Er T 0 e E T T 1 j (8) whch mples that what s usually approxmated as the treatment effect s the dfference n the expected reacton of the varable of nterest between one ndvdual, who s n the treatment group, and another ndvdual j, who s n the control group. Rubn (1977) shows that a necessary and suffcent condton for the treatment effect 2 to be dentfed s that the outcome r for an ndvdual be ndependent from what group t actually belongs to condtonal on a vector of covarates X, T 1 T 0 r,, r, T X, (9) t t.e. the outcome r dffers across ndvduals only as far as they have dfferent ndvdual characterstcs X. Ths condton s generally referred to as the "condtonal ndependence assumpton" (or CIA) because t entals the assumpton that all relevant dfferences between the two outcomes r are fully captured by the observable and ncluded covarates X. 13 Clearly, e 2 s a based estmator of the true treatment effect 2 f ths assumpton s volated,.e. ndvduals n dfferent groups dffer along a relevant vector of covarates X and thus are not comparable. A common way of solvng for ths bas va randomsaton,.e. by pckng ndvduals for the control group randomly from a large populaton of other ndvduals wth the same covarates X. In a non-expermental settng, as s the case for our analyss, randomsaton s not an opton. The alternatve s the propensty score matchng method, whose basc dea s that precse causal nference can be made f two observatons are dentcal n all aspects that jontly affect treatment status and the varable of nterest but dffer n ther treatment status. In our case, ths means that we can determne the pure demand effect of monetary polcy 2 f we can compare the reacton of frms that are dentcal n X wth the excepton that they belong to dfferent sectors T. Ths means that we could estmate 2 by analyzng only those frms from our sample that are dentcal n ther relevant ndvdual characterstcs X -.e. fnancal constrants and Tobn's q - but belong to dfferent sectors. 12 Two nfluental papers n the feld of labour economcs are by Heckman, Ichmura and Todd (1997), and by Deheja and Wahba (2002), who use the method to assess the performance of job tranng programs n the Unted States. 24

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