State‐Level Business Cycles and Local Return Predictability

This study examines whether local stock returns vary with local business cycles in a predictable manner. We find that U.S. state portfolios earn higher future returns when state‐level unemployment rates are higher and housing collateral ratios are lower. During the 1978 to 2009 period, geography‐based trading strategies earn annualized risk‐adjusted returns of 5%. This abnormal performance reflects time‐varying systematic risks and local‐trading induced mispricing. Consistent with the mispricing explanation, the evidence of predictability is stronger among firms with low visibility and high local ownership. Nonlocal domestic and foreign investors arbitrage away the predictable patterns in local returns in 1 year.     

Stock Return Predictability: A Bayesian Model Selection Perspective

Attempts to characterize stock return predictability have resultedin little consensus on the important conditioning variables,giving rise to model uncertainty and data snooping fears. Weintroduce a new methodology that explicitly incorporates modeluncertainty by comparing all possible models simultaneouslyand in which the priors are calibrated to reflect economicallymeaningful information. Our approach minimizes data snoopinggiven the information set and the priors. We compare the priorviews of a skeptic and a confident investor. The data implyposterior probabilities that are in general more supportiveof stock return predictability than the priors for both typesof investors.     

Income Hedging,Dynamic Style Preferences,and Return Predictability

We propose a theoretical measure of income hedging demand and show that it affects asset prices. We focus on the value factor and first demonstrate that our demand estimates are correlated with the actual demands of retail and mutual fund investors. We then show that the aggregate high‐minus‐low (HML) demand predicts HML returns. Exploiting the state‐level variation in income risk, we demonstrate that state‐level hedging demands predict state‐level HML returns. A long‐short portfolio that exploits this hedging‐induced predictability earns an annualized risk‐adjusted return of 6%.     

A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability

We present a simulation-based method for solving discrete-timeportfolio choice problems involving non-standard preferences,a large number of assets with arbitrary return distribution,and, most importantly, a large number of state variables withpotentially path-dependent or non-stationary dynamics. The methodis flexible enough to accommodate intermediate consumption,portfolio constraints, parameter and model uncertainty, andlearning. We first establish the properties of the method forthe portfolio choice between a stock index and cash when thestock returns are either iid or predictable by the dividendyield. We then explore the problem of an investor who takesinto account the predictability of returns but is uncertainabout the parameters of the data generating process. The investorchooses the portfolio anticipating that future data realizationswill contain useful information to learn about the true parametervalues.     

REIT Short Sales and Return Predictability

We examine REIT short sale transactions and show REITs are shorted less frequently than non-REITs. Results also show short sellers are less able to predict negative future returns for REITs, relative to non-REITs, which is consistent with increased pricing efficiency for REITs and suggests REIT assets are more transparent. In a broader context, these results suggest differences in transparency across asset types influence the effectiveness of short selling. Results showing REIT short sellers are contrarian imply traders target REITs that are performing well instead of underperforming REITs, suggesting restrictions on REIT short sales should be re-evaluated.     

Assessing the Economic Significance of Return Predictability: A Research Note

The predictability of stock returns is often assessed using classical statistical significance from predictive regressions. Statistical inference, however, can belie the economic importance with which investors regard various predictors. This paper examines the influence that predictors have on an investor's optimal portfolio allocations. The results show that return predictability is sufficient to induce horizon effects in optimal allocations. After incorporating estimation risk, however, little evidence of predictability remains. We also assess the relative importance of three predictor variables. While dividend yield is the most important predictor, optimal allocations are also sensitive to the term spread and the relative bill rate.     

Stock Return Predictability and The Role of Monetary Policy

This article examines whether shifts in the stance of monetary policy can account for the observed predictability in excess stock returns. Using long-horizon regressions and short-horizon vector autoregressions, the article concludes that monetary policy variables are significant predictors of future returns, although they cannot fully account for observed stock return predictability. I undertake variance decompositions to investigate how monetary policy affects the individual components of excess returns (risk-free discount rates, risk premia, or cash flows).     

Return Predictability of Turkish Stocks: An Empirical Investigation

Employing the portfolio method and cross-sectional regressions, this paper provides a comprehensive analysis of stock return predictability in Turkey from January 1997 to July 2011. In the risk-related predictors, we found predictive power for beta, total volatility, and idiosyncratic volatility. The "cheapness" variable, book-to-market ratio, is the most important return predictor for the stocks traded on the Istanbul Stock Exchange (now part of the Borsa Istanbul). Grouping the stocks as small and large according to the median value of the market capitalization of the stocks adds important insights to the analysis. Our results show the set of large stocks on the Istanbul Stock Exchange to be the least predictable set of stocks.     

Disappearing Dividends: Implications for the Dividend–Price Ratio and Return Predictability

The conventional dividend–price ratio is highly persistent, and the literature reports mixed evidence on its role in predicting stock returns. We argue that the decreasing number of firms with a traditional dividend‐payout policy is responsible for these results, and develop a model in which the long‐run relationship between the dividends and stock price is time varying. An adjusted dividend–price ratio that accounts for the time‐varying long‐run relationship is considerably less persistent. Furthermore, the predictive regression model that employs the adjusted dividend–price ratio as a regressor outperforms the random‐walk model. These results are robust with respect to the firm size.     

Explaining the Magnitude of Liquidity Premia: The Roles of Return Predictability,Wealth Shocks,and State‐Dependent Transaction Costs

Constantinides (1986) documents how the impact of transaction costs on per‐annum liquidity premia in the standard dynamic allocation problem with i.i.d. returns is an order of magnitude smaller than the cost rate itself. Recent papers form portfolios sorted on liquidity measures and find spreads in expected per‐annum return that are the same order of magnitude as the transaction cost spread. When we allow returns to be predictable and introduce wealth shocks calibrated to labor income, transaction costs are able to produce per‐annum liquidity premia that are the same order of magnitude as the transaction cost spread.     

Term Premium Determinants, Return Enhancement and Interest Rate Predictability

This paper investigates whether simple term premium estimation techniques provide potential for return enhancement and interest rate predictability. Using short-term US government securities, during 1959—93, it is demonstrated that utilization of such knowledge allows investors to enhance returns on fixed income portfolios, provided that other than money market alternatives can be considered as potential repositories of funds. In addition, such knowledge yielded short-term interest rate predictions that were weakly superior to other methodologies, including the naive no-change forecast, except during the volatile early 1980s.     

Investor Sentiment and Return Predictability of the Option to Stock Volume Ratio

We study the effect of investor sentiment on the relation between the option to stock volume ratio (O/S) and future stock returns. Relative option volume has return predictability under short sale constraints. For this reason, we expect and find a stronger O/S‐return relation during high sentiment periods than during low sentiment periods. We find that Baker and Wurgler's Investor Sentiment Index affects the O/S‐return relation after controlling for consumer sentiment indices and economic environment factors. While prior studies have used consumer sentiment indices as alternative measures of investor sentiment, our results suggest these effects are distinct.     

Time-Varying Variance Risk Premium and the Predictability of Chinese Stock Market Return

A number of studies have shown that the variance risk premium (VRP), defined as the difference between risk-neutral and physical expected variances, has strong predictive power for the excess stock market return, and this predictability peaks at 3- to 6-month prediction horizons. However, little research presents empirical evidences for Chinese stock market due to the absence of option market. Under general equilibrium asset pricing framework, this article estimates time-varying VRP using the Chinese stock market data. We find that the estimated VRP predicts the excess Chinese stock market return, and this forecasting power is stronger at 4- and 5-month horizons, which is consistent with the findings of existing literature.     

Time‐Varying Risk–Return Trade‐off in the Stock Market

We uncover a strong comovement of the stock market risk–return trade‐off with the consumption–wealth ratio (CAY). The finding reflects time‐varying investment opportunities rather than countercyclical aggregate relative risk aversion. Specifically, the partial risk–return trade‐off is positive and constant when we control for CAY as a proxy for investment opportunities. Moreover, conditional market variance scaled by CAY is negatively priced in the cross‐section of stock returns. Our results are consistent with a limited stock market participation model, in which shareholders require an illiquidity premium that increases with CAY, in addition to the risk premium that is proportional to conditional market variance.     

Economic and Accounting Rates of Return A Statistical Model

This paper develops a number of statistical procedures than can be used to determine whether a firm's ex post accounting rate of return (ARR) is likely to provide guidance as to the economic return the firm will earn over its remaining life. The paper argues that if a corporation's future process cash flows are generated by some form of stochastic process, then the probability density function induced by this can be used to assess whether the ARR provides a reasonable reflection of the economic return the corporation is likely to earn over its remaining life. Five large listed companies are used as case studies to illustrate the model's application.     

Long‐Term Return Reversals: Overreaction or Taxes?

Long‐term reversals in U.S. stock returns are better explained as the rational reactions of investors to locked‐in capital gains than an irrational overreaction to news. Predictors of returns based on the overreaction hypothesis have no power, while those that measure locked‐in capital gains do, completely subsuming past returns measures that are traditionally used to predict long‐term returns. In data from Hong Kong, where investment income is not taxed, reversals are nonexistent, and returns are not forecastable either by traditional measures or by measures based on the capital gains lock‐in hypothesis that successfully predict U.S. returns.     

The Three‐Factor Model: A Practitioner's Guide

The three‐factor model (3FM) has slowly but steadily become a popular alternative to the CAPM for measuring risk from the perspective of both corporate finance and portfolio management. The evidence clearly shows a negative relationship between market capitalization and returns, and a positive relationship between the book‐to‐market ratio and returns. Under the assumption that size and value are risk factors, the 3FM incorporates a market risk premium, a size premium, and a value premium into a model that aims to assess risk in a more comprehensive way, and ultimately to provide a more reliable estimation of required return. The required return produced by the 3FM has corporate finance applications (such as cost of capital estimation, project evaluation, and firm valuation) as well as portfolio management applications (such as performance evaluation). This article discusses the foundations and intuition behind the 3FM, as well as its application to the estimation of the cost of equity and excess returns.     

A Model of Dynamic Takeover Behavior

Several observed features of takeover contests appear to be inconsistent with value-maximizing behavior on the part of the agents involved. For instance, managers occasionally resist takeover bids, presumably in order to facilitate competition among bidders. However, counterbids do not always materialize, suggesting that management resistance was not in the best interests of the firm's shareholders. On the other hand, a successful takeover is sometimes accompanied by a decrease in the value of the acquirer's shares. In addition, valuable combinations are occasionally not consummated. We present a simple illustration of sequential takeover bidding in which all managers act in the best interests of their respective shareholders. Within the context of this model, we provide an explanation of the type of behavior described above.     

The N-Stage Discount Model and Required Return: A Comment

A number of financial economists have observed that estimates of the market discount rate have a downward bias when dividend timing is ignored. They have done so in academic and utility industry journals as well as in testimony. Most conclude or imply that such a downward bias carries over to the calculation of a regulated utility's required rate of return. This paper demonstrates that in fact the conventional cost of equity calculation, ignoring quarterly compounding and even without adjustment for fractional periods, serves very well as a measure of required return.