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《投资学原理 Investments》课程教学资源（阅读资料）Economic Forces and the Stock Market

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Nai-Fu Chen University of Chicage Richard roll Los angeles Stephen A. Ross Yale University Economic Forces and the stock market I. Introduction This paper tests Asset prices are commonly believed to react sen- acroeconomic varI- sitively to economic news. Daily experience ables are risks that are seems to support the view that individual asset ded in the stock variety of market. Financial unanticipated events and that some events have the followggests that a more pervasive effect on asset prices than do economic anab.. others. Consistent with the ability of investors to should systematically diversify, modern financial theory has focused ffect stock market on pervasive, or"systematic, influences as the returns likely source of investment risk. The general conclusion of theory is that an additional compo interest rates, expected nd unexpected infla nent of long- run return is required and obtained tion, industrial produc henever a particular asset is influenced by sys tion and the between high-and low- can be earned by(needlessly) bearing diversifi- ade bonds. We find hat these sources of ble risk k are priced. Furthermore The authors are grateful to their respective universities to the Center for Research in Security Prices, to the National neither the market Science Foundation for research support, and to Ceajer Chan rtfolio nor aggregate onsumption are priced Cornell, Eugene Fama, Pierre Hillion, Richard Sweeney, and eparately. We also Arthur Warga were most helpful, as were the comments of find that oil price risk articipants in workshops at Claremont Graduate School is not separately re- Stanford University, the University of Tor the univer. warded in the stock sity of Ca market rst revision was written = For example, the APT (Ross 1976) and the models of Merton(1973)and Cox, Ingersoll, and Ross(1985)are consis- tent with this view (Journal of Business, 1986, vol. 59, no. 3) c 1986 by The University of Chicago. All rights reserved 0021-93988659030001s01.50 83 ontent downloaded by the authorized user from 192.168. 82.218 on Tue, 4 Dee 2012 03: 43 28 AM

Nai-Fu Chen University of Chicago Richard Roll University of California, Los Angeles Stephen A. Ross Yale University Economic Forces and the Stock Market* I. Introduction Asset prices are commonly believed to react sensitively to economic news. Daily experience seems to support the view that individual asset prices are influenced by a wide variety of unanticipated events and that some events have a more pervasive effect on asset prices than do others. Consistent with the ability of investors to diversify, modern financial theory has focused on pervasive, or "systematic," influences as the likely source of investment risk.' The general conclusion of theory is that an additional component of long-run return is required and obtained whenever a particular asset is influenced by systematic economic news and that no extra reward can be earned by (needlessly) bearing diversifiable risk. This paper tests whether innovations in macroeconomic variables are risks that are rewarded in the stock market. Financial theory suggests that the following macroeconomic variables should systematically affect stock market returns: the spread between long and short interest rates, expected and unexpected inflation, industrial production, and the spread between high- and lowgrade bonds. We find that these sources of rsk are significantly priced. Furthermore, neither the market portfolio nor aggregate consumption are priced separately. We also find that oil price risk is not separately rewarded in the stock market. * The authors are grateful to their respective universities, to the Center for Research in Security Prices, to the National Science Foundation for research support, and to Ceajer Chan for computational assistance. The comments of Bradford Cornell, Eugene Fama, Pierre Hillion, Richard Sweeney, and Arthur Warga were most helpful, as were the comments of participants in workshops at Claremont Graduate School, Stanford University, the University of Toronto, the University of California, Irvine, the University of Alberta, the University of Chicago, and unknown referees. The University of British Columbia provided a stimulating research environment where part of the first revision was written during August 1984. 1. For example, the APT (Ross 1976) and the models of Merton (1973) and Cox, Ingersoll, and Ross (1985) are consistent with this view. (Journal of Business, 1986, vol. 59, no. 3) ? 1986 by The University of Chicago. All rights reserved. 0021-9398/8615903-0001$01.50 383 This content downloaded by the authorized user from 192.168.82.218 on Tue, 4 Dec 2012 03:43:28 AM All use subject to JSTOR Terms and Conditions

Journal of Business The theory has been silent, however, about which events are likely to influence all assets. a rather embarrassing gap exists between the of systen our complete ignorance of their identity. The comovements of asset prices suggest the presence of underlying exogenous influences, but we have not yet determined which economic variables, if any, are respon Our paper is an exploration of this identification terrain. In Section Il, we employ a simple theoretical guide to help choose likely candi- dates for pervasive state variables In Section Ill we introduce the data and explain the techniques used to measure unanticipated movements in the proposed state variables. Section IV investigates whether expo- sure to systematic state variables explains expected returns. As specific alternatives to the pricing influence of the state variables identified by our simple theoretical model, Section IV considers the value-and the equally weighted market indices, an index of real con sumption, and an index of oil prices. Each of these is found to be unimportant for pricing when compared with the identified economic state variables. Section V briefly summarizes our findings and suggests some directions for future research Il. Theory No satisfactory theory would argue that the relation between financial markets and the macroeconomy is entirely in one direction. However stock prices are usually considered as responding to external forces (even though they may have a feedback on the other variables) It is apparent that all economic variables are endogenous in some ultimate sense. Only natural forces, such as supernovas, earthquakes, and the ike, are truly exogenous to the world economy, but to base an asset- pricing model on these systematic physical factors is well beyond our current abilities. Our present goal is merely to model equity returns as functions of macro variables and nonequity asset returns. Hence this paper will take the stock market as endogenous, relative to other mar- By the diversification argument that is implicit in capital market theory, only general economic state variables will influence the pricing of large stock market aggregates. Any systematic variables that affect the economys pricing operator or that influence dividends would also nfluence stock market returns. Additionally, any variables that are necessary to complete the description of the state of nature will also be part of the description of the systematic risk factors. An example of such a variable would be one that has no direct influence on current cash fows but that does describe the changing investment opportunity ontent downloaded by the authorized user from 192.168. 82.218 on Tue, 4 Dee 2012 03: 43 28 AM

384 Journal of Business The theory has been silent, however, about which events are likely to influence all assets. A rather embarrassing gap exists between the theoretically exclusive importance of systematic "state variables" and our complete ignorance of their identity. The comovements of asset prices suggest the presence of underlying exogenous influences, but we have not yet determined which economic variables, if any, are responsible. Our paper is an exploration of this identification terrain. In Section II, we employ a simple theoretical guide to help choose likely candidates for pervasive state variables. In Section III we introduce the data and explain the techniques used to measure unanticipated movements in the proposed state variables. Section IV investigates whether exposure to systematic state variables explains expected returns. As specific alternatives to the pricing influence of the state variables identified by our simple theoretical model, Section IV considers the value- and the equally weighted market indices, an index of real consumption, and an index of oil prices. Each of these is found to be unimportant for pricing when compared with the identified economic state variables. Section V briefly summarizes our findings and suggests some directions for future research. II. Theory No satisfactory theory would argue that the relation between financial markets and the macroeconomy is entirely in one direction. However, stock prices are usually considered as responding to external forces (even though they may have a feedback on the other variables). It is apparent that all economic variables are endogenous in some ultimate sense. Only natural forces, such as supernovas, earthquakes, and the like, are truly exogenous to the world economy, but to base an assetpricing model on these systematic physical factors is well beyond our current abilities. Our present goal is merely to model equity returns as functions of macro variables and nonequity asset returns. Hence this paper will take the stock market as endogenous, relative to other markets. By the diversification argument that is implicit in capital market theory, only general economic state variables will influence the pricing of large stock market aggregates. Any systematic variables that affect the economy's pricing operator or that influence dividends would also influence stock market returns. Additionally, any variables that are necessary to complete the description of the state of nature will also be part of the description of the systematic risk factors. An example of such a variable would be one that has no direct influence on current cash flows but that does describe the changing investment opportunity set. This content downloaded by the authorized user from 192.168.82.218 on Tue, 4 Dec 2012 03:43:28 AM All use subject to JSTOR Terms and Conditions

Stock prices can be written as expected discounted dividends: E(c) (1) where c is the dividend stream and k is the discount rate. This implies that actual returns in any period are given by dp [(c) dk It follows(trivially) that the systematic forces that infuence returns are those that change discount factors, k, and expected cash flows, E(c) The discount rate is an average of rates over time, and it changes with both the level of rates and the term-structure spreads across dif- ferent maturities. Unanticipated changes in the riskless interest rate will therefore influence pricing, and, through their infuence on the time value of future cash flows, they will infuence returns. The discount rate also depends on the risk premium; hence, unanticipated changes in the premium will influence returns. On the demand side, changes in the indirect marginal utility of real wealth, perhaps as measured by real consumption changes, will influence pricing, and such effects should also show up as unanticipated changes in risk premia Expected cash flows change because of both real and nominal forces. Changes in the expected rate of inflation would influence nomi nal expected cash flows as well as the nominal rate of interest. To the xtent that pricing is done in real terms, unanticipated price-level changes will have a systematic effect, and to the extent that relative prices change along with general inflation, there can also be a change in asset valuation associated with changes in the average inflation rate Finally, changes in the expected level of real production would affect the current real value of cash flows. Insofar as the risk-premium mea- sure does not capture industrial production uncertainty, innovations in the rate of productive activity should have an infiuence on stock re turns through their impact on cash flows Ill. Constructing the Economic Factors Having proposed a set of relevant variables, we must now specify thei measurement and obtain time series of unanticipated movements. We ould proceed by identifying and estimating a vector autoregressive model in an attempt to use its residuals as the unanticipated innova- 2. Since we are only concerned with intuition, we are ignoring the second-order terms from the stochastic calculus in deriving eq. (2). Also notice that the expectation is taken with respect to the martingale pricing measure(see Cox et al. 1985)and not with respect to the ordinary probability distribution ontent downloaded by the authorized user from 192.168. 82.218 on Tue, 4 Dee 2012 03: 43 28 AM

Economic Forces and the Stock Market 385 Stock prices can be written as expected discounted dividends: - E(c) where c is the dividend stream and k is the discount rate. This implies that actual returns in any period are given by dp + c d[E(c)] A + c (2) p p E(c) k P It follows (trivially) that the systematic forces that influence returns are those that change discount factors, k, and expected cash flows, E(c).2 The discount rate is an average of rates over time, and it changes with both the level of rates and the term-structure spreads across different maturities. Unanticipated changes in the riskless interest rate will therefore influence pricing, and, through their influence on the time value of future cash flows, they will influence returns. The discount rate also depends on the risk premium; hence, unanticipated changes in the premium will influence returns. On the demand side, changes in the indirect marginal utility of real wealth, perhaps as measured by real consumption changes, will influence pricing, and such effects should also show up as unanticipated changes in risk premia. Expected cash flows change because of both real and nominal forces. Changes in the expected rate of inflation would influence nominal expected cash flows as well as the nominal rate of interest. To the extent that pricing is done in real terms, unanticipated price-level changes will have a systematic effect, and to the extent that relative prices change along with general inflation, there can also be a change in asset valuation associated with changes in the average inflation rate. Finally, changes in the expected level of real production would affect the current real value of cash flows. Insofar as the risk-premium measure does not capture industrial production uncertainty, innovations in the rate of productive activity should have an influence on stock returns through their impact on cash flows. III. Constructing the Economic Factors Having proposed a set of relevant variables, we must now specify their measurement and obtain time series of unanticipated movements. We could proceed by identifying and estimating a vector autoregressive model in an attempt to use its residuals as the unanticipated innova- 2. Since we are only concerned with intuition, we are ignoring the second-order terms from the stochastic calculus in deriving eq. (2). Also notice that the expectation is taken with respect to the martingale pricing measure (see Cox et al. 1985) and not with respect to the ordinary probability distribution. This content downloaded by the authorized user from 192.168.82.218 on Tue, 4 Dec 2012 03:43:28 AM All use subject to JSTOR Terms and Conditions

Journal of Business tions in the economic factors. It is, however, more interesting and (perhaps) robust out of sample to employ theory to find single equa tions that can be estimated directly. In particular, since monthly rates of return are nearly serially uncorrelated they can be employed as innovations without alteration. The general impact of a failure ade quately to filter out the expected movement in an independent variable is to introduce an errors-in-variables problem. This has to be traded off against the error introduced by misspecification of the estimated equa- tion for determining the expected movement A somewhat subtler version of the same problem arises with proce- dures such as vector autoregression Any such statistically based time series approach will find lagged stock market returns having a signifi- cant predictive content for macroeconomic variables. In the analysis of pricing, then, we will indirectly be using lagged stock market variables to explain the expected returns on portfolios of stocks. Whatever econometric advantages such an approach might offer, it is antithetical to the spirit of this investigation, which is to explore the pricing in- fluence of exogenous macroeconomic variables. For this reason much as for any other, we have chosen to follow the simpler route in constructing the time series we use Throughout this paper we adopt the convention that time subscripts apply to the end of the time period. The standard period is 1 month Thus, E( t-1)denotes the expectation operator at the end of month t-1 conditional on the information set available at the end of month t 1, and X(o denotes the value of variable X in month t, or the growth that prevailed from the end of t- l to the end of I. A. Industrial Production The basic series is the growth rate in U.S. industrial production. It was obtained from the Survey of Current Business. If IP(o) denotes the rate of industrial production in month t, then the monthly growth rate is MP(t)= loge IP(r)-loge IP(t-1) and the yearly growth rate is YP(1)= loge IP(t)-loge IP(I-12) (see table 1 for a summary of variables Because IP(t)actually is the flow of industrial production during month t, MP(t)measures the change in industrial production lagged by at least a partial month. To make this variable contemporaneous with other series, subsequent statistical work will lead it by 1 month. Except for an annual seasonal, it is noisy enough to be treated as an in novation autocorrelations in their returns arising from the nontrading effect ontent downloaded by the authorized user from 192.168. 82.218 on Tue, 4 Dee 2012 03: 43 28 AM

386 Journal of Business tions in the economic factors. It is, however, more interesting and (perhaps) robust out of sample to employ theory to find single equations that can be estimated directly. In particular, since monthly rates of return are nearly serially uncorrelated, they can be employed as innovations without alteration. The general impact of a failure adequately to filter out the expected movement in an independent variable is to introduce an errors-in-variables problem. This has to be traded off against the error introduced by misspecification of the estimated equation for determining the expected movement. A somewhat subtler version of the same problem arises with procedures such as vector autoregression. Any such statistically based timeseries approach will find lagged stock market returns having a significant predictive content for macroeconomic variables. In the analysis of pricing, then, we will indirectly be using lagged stock market variables to explain the expected returns on portfolios of stocks. Whatever econometric advantages such an approach might offer, it is antithetical to the spirit of this investigation, which is to explore the pricing influence of exogenous macroeconomic variables. For this reason, as much as for any other, we have chosen to follow the simpler route in constructing the time series we use.3 Throughout this paper we adopt the convention that time subscripts apply to the end of the time period. The standard period is 1 month. Thus, E( It - 1) denotes the expectation operator at the end of month t - 1 conditional on the information set available at the end of month t - 1, and X(t) denotes the value of variable X in month t, or the growth that prevailed from the end of t - 1 to the end of t. A. Industrial Production The basic series is the growth rate in U.S. industrial production. It was obtained from the Survey of Current Business. If IP(t) denotes the rate of industrial production in month t, then the monthly growth rate is MPMt) = loge IPMt) - loge IP(t - 1), (3) and the yearly growth rate is YP(t) = loge IP(t) - loge IP(t - 12) (4) (see table 1 for a summary of variables). Because IP(t) actually is the flow of industrial production during month t, MP(t) measures the change in industrial production lagged by at least a partial month. To make this variable contemporaneous with other series, subsequent statistical work will lead it by 1 month. Except for an annual seasonal, it is noisy enough to be treated as an innovation. 3. In addition, the pricing tests reported below used portfolios that have induced autocorrelations in their returns arising from the nontrading effect. This content downloaded by the authorized user from 192.168.82.218 on Tue, 4 Dec 2012 03:43:28 AM All use subject to JSTOR Terms and Conditions

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