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.     

The Inventory-Sales Ratio and Homebuilder Return Predictability

The firm’s inventory-sales ratio prices exposure to the housing cycle with a predictable sign. The buyer of a new home holds a pre-construction contract at a guaranteed price with the right to cancel at any date up to delivery. The demand for contracts rises with falling user costs while lot supply is inelastic, leading to land bidding in booms. During busts sales decline and land bidding largely disappears. Delivery is from inventory at a cost of carry below that of construction. The firm’s inventory-sales ratio leads and is negatively correlated with its subsequent returns. For U.S. homebuilders over 1975 to 2009, a 1% increase in the inventory-sales ratio lowers next-quarter returns by five basis points.     

Stock Return Predictability and Asset Pricing Models

This article develops an asset allocation framework that incorporatesprior beliefs about the extent of stock return predictabilityexplained by asset pricing models. We find that when prior beliefsallow even minor deviations from pricing model implications,the resulting asset allocations depart considerably from andsubstantially outperform allocations dictated by either theunderlying models or the sample evidence on return predictability.Under a wide range of beliefs about model pricing abilities,asset allocations based on conditional models outperform theirunconditional counterparts that exclude return predictability.     

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).     

Stock Return Predictability: Is it There?

We examine the predictive power of the dividend yields for forecastingexcess returns, cash flows, and interest rates. Dividend yieldspredict excess returns only at short horizons together withthe short rate and do not have any long-horizon predictive power.At short horizons, the short rate strongly negatively predictsreturns. These results are robust in international data andare not due to lack of power. A present value model that matchesthe data shows that discount rate and short rate movements playa large role in explaining the variation in dividend yields.Finally, we find that earnings yields significantly predictfuture cash flows. (JEL C12, C51, C52, E49, F30, G12)     

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.     

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.     

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%.     

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.     

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.     

Short‐Horizon Return Predictability in International Equity Markets

This study measures the degree of short‐horizon return predictability of 50 international equity markets and examines how its variation is related to the indicators of equity market development. Two multiple‐horizon variance ratio tests are employed to measure the degree of return predictability. We find evidence that return predictability is negatively correlated with publicly available indicators of equity market development. Our cross‐sectional regression analysis shows that the per capita gross domestic product, market turnover, investor protection, and absence of short‐selling restrictions are correlated with cross‐market variations in return predictability.     

A Dynamic Model of Characteristic‐Based Return Predictability

We present a dynamic model that links characteristic‐based return predictability to systematic factors that determine the evolution of firm fundamentals. In the model, an economy‐wide disruption process reallocates profits from existing businesses to new projects and thus generates a source of systematic risk for portfolios of firms sorted on value, profitability, and asset growth. If investors are overconfident about their ability to evaluate the disruption climate, these characteristic‐sorted portfolios exhibit persistent mispricing. The model generates predictions about the conditional predictability of characteristic‐sorted portfolio returns and illustrates how return persistence increases the likelihood of observing characteristic‐based anomalies.     

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.     

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.     

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.     

Dispersion of Beliefs, Asset Prices, and Noisy Aggregation of Information

This paper discusses the determinants of the dispersion of beliefs of informed investors and the effects of this dispersion on the equilibrium level of asset prices in a noisy rational expectations model. It is shown that lower asset prices are usually associated with an increase in the dispersion of beliefs. However, when the noise level of private signals is high, or when asset prices are abnormally high, increased dispersion of beliefs will be associated with higher asset prices. This paper shows that the dispersion of beliefs is an increasing function of the standard deviations of an asset's payoff and the noise in the asset's supply, while it may be a decreasing or an increasing function of the standard deviation of errors in private signals.     

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.     

卖空限制与收益可预测性——A股融资融券制度的证据

基于A股市场融资和融券余额的巨大差距,本文拓展了Hong et al.(2016)的理论模型,在融券端和融资端分别找到了影响股票收益率的变量:融券比率(融券余额/流通市值)和融资回补天数(融资比率/日均换手率)。进一步,本文利用组合价差法和Fama-MacBeth横截面回归法,实证检验了A股市场中融券比率与融资回补天数解释和预测股票收益率的能力。实证结果表明,在存在融券限制条件下,融券比率相比融券回补天数(融券比率/日均换手率)能更好地代表套利者对股票价格高估程度的看法,根据融券比率构建的等权重多空组合能带来月均1.58%的显著收益;而由于融资约束相对较少,融资回补天数相比融资比率(融资余额/流通市值)能更好地代表套利者对股票价格低估程度的看法,根据融资回补天数构建的等权重多空组合能带来月均1.28%的显著收益。实证结果与本文存在融券数量限制下的理论模型相符,且该收益率不能被多因子模型和常规股票特征所解释。     

卖空限制与收益可预测性——A股融资融券制度的证据

基于A股市场融资和融券余额的巨大差距,本文拓展了Hong et al.(2016)的理论模型,在融券端和融资端分别找到了影响股票收益率的变量:融券比率(融券余额/流通市值)和融资回补天数(融资比率/日均换手率)。进一步,本文利用组合价差法和Fama-MacBeth横截面回归法,实证检验了A股市场中融券比率与融资回补天数解释和预测股票收益率的能力。实证结果表明,在存在融券限制条件下,融券比率相比融券回补天数(融券比率/日均换手率)能更好地代表套利者对股票价格高估程度的看法,根据融券比率构建的等权重多空组合能带来月均1.58%的显著收益;而由于融资约束相对较少,融资回补天数相比融资比率(融资余额/流通市值)能更好地代表套利者对股票价格低估程度的看法,根据融资回补天数构建的等权重多空组合能带来月均1.28%的显著收益。实证结果与本文存在融券数量限制下的理论模型相符,且该收益率不能被多因子模型和常规股票特征所解释。     

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.