How to Discount Cashflows with Time-Varying Expected Returns

While many studies document that the market risk premium is predictable and that betas are not constant, the dividend discount model ignores time‐varying risk premiums and betas. We develop a model to consistently value cashflows with changing risk‐free rates, predictable risk premiums, and conditional betas in the context of a conditional CAPM. Practical valuation is accomplished with an analytic term structure of discount rates, with different discount rates applied to expected cashflows at different horizons. Using constant discount rates can produce large misvaluations, which, in portfolio data, are mostly driven at short horizons by market risk premiums and at long horizons by time variation in risk‐free rates and factor loadings.     

Liquidity and Expected Returns: Lessons from Emerging Markets

Given the cross-sectional and temporal variation in their liquidity,emerging equity markets provide an ideal setting to examinethe impact of liquidity on expected returns. Our main liquiditymeasure is a transformation of the proportion of zero dailyfirm returns, averaged over the month. We find that it significantlypredicts future returns, whereas alternative measures such asturnover do not. Consistent with liquidity being a priced factor,unexpected liquidity shocks are positively correlated with contemporaneousreturn shocks and negatively correlated with shocks to the dividendyield. We consider a simple asset-pricing model with liquidityand the market portfolio as risk factors and transaction coststhat are proportional to liquidity. The model differentiatesbetween integrated and segmented countries and time periods.Our results suggest that local market liquidity is an importantdriver of expected returns in emerging markets, and that theliberalization process has not fully eliminated its impact.     

Are the Latent Variables in Time-Varying Expected Returns Compensation for Consumption Risk?

Multibeta asset pricing models are examined using proxies for economic state variables in a framework which exploits time-varying expected returns to estimate conditional betas. Examples include multiple consumption-beta models and models where asset returns proxy for the state variables. When the state variables are not specified, the tests indicate two or three time-varying expected risk premiums in the sample of quarterly asset returns. Conditional betas relative to consumption generate less striking evidence against the model than betas relative to asset returns, but both the consumption and the market variables fail to proxy for the state variables.     

Spatial Correlation in Expected Returns in Commercial Real Estate Markets and the Role of Core Markets

This study is based on survey data on investor expectations for 40 metropolitan statistical areas (MSAs) in the US over the period from 2003:Q2 to 2014:Q2. The paper has two main objectives. These are firstly to identify whether expected rates of return across different commercial real estate markets are positively spatially correlated and secondly to analyze the role of core markets like New York, Washington, DC, Los Angeles, San Francisco, and Chicago in the information-spreading process. All the tests conducted are conditional on the maintained hypothesis that expected returns across different markets are spatially uncorrelated. To carry out these tests, we regress a measure of excess return in each of the 40 markets on (contemporaneous) measures of expected returns in other markets, controlling for the interaction between (neighboring) markets. We find very large spatial interaction coefficients across different markets. Our basic estimates yield a spatial correlation coefficient of near one and statistically significant at standard levels. Chow tests allow rejection of the hypothesis that core markets like New York, Washington, DC, Los Angeles, San Francisco, and Chicago are no different than markets like Cleveland, Columbus, Orlando, Pittsburgh, Salt Lake City, St. Louis, and Tampa in explaining the covariances of the rates of return among different commercial real estate markets. Tests of structural change also imply that core markets currently play a more prominent role that in the past in the transfer of information from one market to another.     

Fundamental Economic Variables,Expected Returns,and Bond Fund Performance

In this article, we develop relative pricing (APT) models that are successful in explaining expected returns in the bond market. We utilize indexes as well as unanticipated changes in economic variables as factors driving security returns. An innovation in this article is the measurement of the economic factors as changes in forecasts. The return indexes are the most important variables in explaining the time series of returns. However, the addition of the economic variables leads to a large improvement in the explanation of the cross-section of expected returns. We utilize our relative pricing models to examine the performance of bond funds.     

Modeling Time-Varying Volatility and Expected Returns: Evidence from the GCC and MENA Regions

The aim of this study is to investigate empirically the underlying nexus of stock market returns and volatility in the Gulf Cooperation Council (GCC) countries and Middle East and North Africa (MENA) region by using the GARCH-M model. We find that volatility is time-varying in all countries, which indicates substantial variation in the degree of risk across time. However, we do not find empirical support that this time-varying volatility significantly explains expected returns, except in the case of Kuwait, United Arab Emirates, and the MENA region portfolio. Our findings show that stock return volatility is negatively correlated with stock returns in these three markets under the assumption of investor risk aversion. This lends some support to the hypothesis of a volatility-driven negative relationship in the literature. The policy implications of our results are discussed.     

Expected Option Returns

This paper examines expected option returns in the context of mainstream asset-pricing theory. Under mild assumptions, expected call returns exceed those of the underlying security and increase with the strike price. Likewise, expected put returns are below the risk-free rate and increase with the strike price. S&P index option returns consistently exhibit these characteristics. Under stronger assumptions, expected option returns vary linearly with option betas. However, zero-beta, at-the-money straddle positions produce average losses of approximately three percent per week. This suggests that some additional factor, such as systematic stochastic volatility, is priced in option returns.     

Expected Idiosyncratic Volatility Measures and Expected Returns

We find that idiosyncratic volatility forecasts using information available to traders at the time of the forecast are not related to expected returns. The positive relation documented in a number of other papers only exists when forward‐looking information is incorporated into the volatility estimate. That positive relation is driven by the realized idiosyncratic volatility component that cannot be forecasted by investors. Our findings are robust to several different empirical tests, volatility forecasting models and time periods.     

Earnings and Expected Returns

The aggregate dividend payout ratio forecasts excess returns on both stocks and corporate bonds in postwar U.S. data. High dividends forecast high returns. High earnings forecast low returns. The correlation of earnings with business conditions gives them predictive power for returns; they contain information about future returns that is not captured by other variables. Dividends and earnings contribute substantial explanatory power at short horizons. For forecasting long-horizon returns, however, only (scaled) stock prices matter. Forecasts of low long-horizon stock returns in the mid-1990s are caused not by earnings or dividends, but by high stock prices.     

Covariance Stationarity of International Equity Markets Returns: Recent Evidence

Intertemporal stationarity tests of the variance-covariance matrix of monthly returns on seven international equity indices are conducted over the most recent period. Pairwise covariances are then decomposed into their component statistics for further examination of the source(s) of stationarity or nonstationarity. Historical analysis reveals that pairwise covariances were invariably highly nonstationary over forecast intervals that varied in length between one month and five years. Reliance on historical covariances to estimate future covariances over a hold-out sample produced suboptimal results in comparison to an alternative naive forecasting model. These findings were robust in that they were invariant to whether nominal or real returns were used. Evidence on the intertemporal stationarity of the vector of mean returns is also provided.     

Volatility Reversion and Correlation Structure of Returns in Major International Stock Markets

This paper investigates the stochastic behavior of weekly stock market returns in the U.S., Japan, and the U.K. during the period 1984 to 1994. The analysis is carried out using an augmented version of Bollerslev's [7] multivariate GARCH model with structural dummies to test for differences in the mean, volatility, and covariance structure of returns during the pre- and post-October 1987 crash periods. In addition, the paper explores the issue of the volatility reversion and time-varying behavior of correlation structure of returns in these markets. Mean-spillovers exist from the U.S. and Japan to the U.K. The magnitude of these spillovers is, however, low. Volatility spillovers exist from the U.S. and, to a lesser extent, from Japan to the U.K. Mean returns in all three markets and volatility in Japan and the U.K. are the same during the two periods, while volatility in the U.S. is lower during the post-crash period. With the exception of the correlation of returns between Japan and the U.K., which has doubled since the October 1987 crash, the remaining correlations are statistically similar during the two periods. Simulations performed indicate that volatility is reverting in the sense that, when it departs from its long-run equilibrium level, it tends to revert back to that level.     

Consumption Fluctuations and Expected Returns

This paper introduces a novel consumption-based variable, cyclical consumption, and examines its predictive properties for stock returns. Future expected stock returns are high (low) when aggregate consumption falls (rises) relative to its trend and marginal utility from current consumption is high (low). We show that the empirical evidence ties consumption decisions of agents to time variation in returns in a manner consistent with asset pricing models based on external habit formation. The predictive power of cyclical consumption is not confined to bad times and subsumes the predictability of many popular forecasting variables.     

线上销售与未来股票收益

本文通过分析相关上市公司在电商平台的线上销售数据,发现线上销售增长可以预测未来股票收益。根据线上销售增长率构建投资组合可以获得月均1.27%的超额收益,经三因子、五因子模型调整后收益率分别为1.40%和1.35%,并且该超额收益在较长时间内不会逆转。横截面回归结果显示,线上销售增长与未来股票收益显著正相关,并在控制其他市场异象因子后仍然显著。此外,本文还发现线上销售数据的预测能力主要集中在投资者关注有限、线上销售占比高以及套利成本高的公司,其投资价值来源于对公司未来基本面信息的预测能力。进一步研究表明,同时利用线上销售指标和营业收入指标进行投资可以获得更高的超额收益。在考虑业绩预告和业绩快报对线上销售指标预测能力的潜在影响后,结果依然稳健。     

Expected Returns and Habit Persistence

Using a consumption-based asset pricing model with infinite-horizonnonlinear habit formation, Campbell and Cochrane (1999) showthat low consumption in surplus of habit should forecast highexpected returns. This article argues that the finite-horizonlinear habit model also implies an inverse relation betweenexpected returns and surplus consumption. This article alsopresents empirical evidence, which indicates that expected returnson stocks and bonds vary with surplus consumption implied bythe habit models. The volatility of returns and the reward tovolatility are also related to surplus consumption. However,less than 30% of the predictable variation of expected returns,using standard lagged information variables, is attributed tosurplus consumption.     

Information Uncertainty and Expected Returns

This study examines the role of information uncertainty (IU) in predicting cross-sectional stock returns. We define IU in terms of “value ambiguity,” or the precision with which firm value can be estimated by knowledgeable investors at reasonable cost. Using several different proxies for IU, we show that (1) on average, high-IU firms earn lower future returns (the “mean” effect), and (2) price and earnings momentum effects are much stronger among high-IU firms (the “interaction” effect). These findings are consistent with analytical models in which high IU exacerbates investor overconfidence and limits rational arbitrage.This revised version was published online in August 2005 with a corrected cover date.     

Stock Returns,Expected Returns,and Real Activity

Measuring the total return variation explained by shocks to expected cash flows, time-varying expected returns, and shocks to expected returns is one way to judge the rationality of stock prices. Variables that proxy for expected returns and expected-return shocks capture 30% of the variance of annual NYSE value-weighted returns. Growth rates of production, used to proxy for shocks to expected cash flows, explain 43% of the return variance. Whether the combined explanatory power of the variables—about 58% of the variance of annual returns—is good or bad news about market efficiency is left for the reader to judge.     

Time-Varying Betas of German Stock Returns

The market model assumes stock returns to be a linear function of the market return. However, there is considerable evidence that the beta stability assumption commonly used when estimating the market model is invalid. In this paper we account for beta instability in German stock returns by allowing the coefficients to vary over time in estimation. For time-varying beta estimation we rely on the Flexible Least Squares approach, the Random Walk Model and Moving Window Least Squares. Due to our results time-varying estimation fits the data considerably better than time-invariant estimation and, hence, increases the efficiency of beta based risk measurement.Acknowledgements: The authors thank Stefan Mittnik, Christian Pigorsch, an anonymous referee and the editor for constructive comments.     

U.S. Monetary Policy and International Bond Markets

This paper analyzes how U.S. monetary policy affects the pricing of dollar‐denominated sovereign debt. We document that yields on dollar‐denominated sovereign bonds are highly responsive to U.S. monetary policy surprises—during both the conventional and unconventional policy regimes—and that the passthrough of unconventional policy to foreign bond yields is, on balance, comparable to that of conventional policy. In addition, a conventional U.S. monetary easing (tightening) leads to a significant narrowing (widening) of credit spreads on sovereign bonds issued by countries with a speculative‐grade credit rating but has no effect on the corresponding weighted average of bilateral exchange rates for a basket of currencies from the same set of risky countries; this indicates that an unanticipated tightening of U.S. monetary policy widens credit spreads on risky sovereign debt directly through the financial channel, as opposed to indirectly through the exchange rate channel. During the unconventional policy regime, yields on both investment‐ and speculative‐grade sovereign bonds move one‐to‐one with policy‐induced fluctuations in yields on comparable U.S. Treasuries. We also examine whether the response of sovereign credit spreads to US monetary policy differs between policy easings and tightenings and find no evidence of such asymmetry.     

Modeling Corporate Bond Returns

We propose a conditional factor model for corporate bond returns with five factors and time-varying factor loadings. We have three main empirical findings. First, our factor model excels in describing the risks and returns of corporate bonds, improving over previously proposed models in the literature by a large margin. Second, our model recommends a systematic bond investment portfolio whose high out-of-sample Sharpe ratio suggests that the credit risk premium is notably larger than previously estimated. Third, we find closer integration between debt and equity markets than found in prior literature.     

The Cross-Section of Expected Stock Returns

Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market β, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in β that is unrelated to size, the relation between market β and average return is flat, even when β is the only explanatory variable.