Portfolio Returns,Market Volatility,and Seasonality

This paper examines three important issues related to the relationship between stock returns and volatility. First, are Duffee's (1995) findings of the relationship between individual stock returns and volatility valid at the portfolio level? Second, is there a seasonality of the market return volatility? Lastly, do size portfolio returns react symmetrically to the market volatility during business cycles? We find that the market volatility exhibits strong autocorrelation and small size portfolio returns exhibit seasonality. However, this phenomenon is not present in large size portfolios. For the entire sample period of 1962–1995, the highest average monthly volatility occurred in October, followed by November, and then January. Examining the two sub-sample periods, we find that the average market volatility increases by 15.4% in the second sample period of 1980–1995 compared to the first sample period of 1962–1979. During the contraction period, the average market volatility is 60.9% higher than that during the expansion period. Using a binary regression model, we find that size portfolio returns react asymmetrically with the market volatility during business cycles. This paper documents a strongly negative contemporaneous relationship between the size portfolio returns and the market volatility that is consistent with the previous findings at the aggregate level, but is inconsistent with the findings at the individual firm level. In contrast with the previous findings, however, we find an ambiguous relationship between the percentage change in the market volatility and the contemporaneous stock portfolio returns. This ambiguity is attributed to strongly negative contemporaneous and one-month ahead relationships between the market volatility and portfolio returns.

     

The Predictability of Short-HorizonStock Returns

This examines the predictability of short-horizonstock returns in the UK. We show that the subsequentreturn reversal of previous extreme performers isunlikely to be caused by either lead-lag effects orinventory imbalances, the most likely explanationbeing market overreaction. A market or trading basedexplanation is reinforced by the finding that thesereturn reversals are asymmetric, being lesssignificant after bad news. Further, we find that thelower transacting stocks exhibit the stronger returnreversals, in direct contrast to both the existing USevidence and the implication that liquidity effectscan explain the return reversals.     

The Predictability of Short-Horizon Stock Returns

This examines the predictability of short-horizon stock returnsin the UK. We show that the subsequent return reversal of previousextreme performers is unlikely to be caused by either lead-lageffects or inventory imbalances, the most likely explanationbeing market overreaction. A market or trading based explanationis reinforced by the finding that these return reversals areasymmetric, being less significant after bad news. Further,we find that the lower transacting stocks exhibit the strongerreturn reversals, in direct contrast to both the existing USevidence and the implication that liquidity effects can explainthe return reversals. JEL Classification: G10, G11, G12     

The Risk and Predictability of International Equity Returns

We investigate predictability in national equity market returns,and its relation to global economic risks. We show how to consistentlyestimate the fraction of the predictable variation that is capturedby an asset pricing model for the expected returns. We use amodel in which conditional betas of the national equity marketsdepend on local information variables, while global risk premiadepend on global variables. We examine single- and multiple-betamodels, using monthly data for 1970 to 1989. The models capturemuch of the predictability for many countries. Most of thisis related to time variation in the global risk premia.     

Active Portfolio Management,Implied Expected Returns,and Analyst Optimism

This paper investigates whether implied expected returns based on the approach of CLAUS/THOMAS (2001) can be implemented in active portfolio management. This approach uses analysts' forecasts to derive return expectations by equating the present value of expected cash-flows to the current market price. It is found that active investment strategies which maximize implied expected returns significantly outperform a passive index investment. A significant part of this outperformance can be explained by the difference between the implied expected return and the return expectation justified by the CAPM. The empirical results suggest that a substantial part of this difference can be attributed to an optimism bias in analysts' forecasts.     

Asymmetric Information and the Predictability of Real Estate Returns

This article examines the relation between systematic price changes and the heterogeneity of investors information sets in real estate asset markets. The empirical implications rely on a theoretical economy in which information asymmetry alters the dynamic relation between returns and trading volume. We employ a filter-rule methodology to determine predictability in returns and augment the return-based conditioning set with trading volume. The additional conditioning information is necessary since the model is underspecified when predictability is based on returns alone. Our results provide new insight into the coexistence of informational and noninformational exchange in the speculative markets for real estate assets. Specifically, we find that the predictability of real estate returns is generally more indicative of portfolio rebalancing effects than an adverse-selection problem. These results are unique in addressing the time-variation in information asymmetry.     

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.     

Predictability of Stock Returns: Robustness and Economic Significance

This article examines the robustness of the evidence on predictability of U.S. stock returns, and addresses the issue of whether this predictability could have been historically exploited by investors to earn profits in excess of a buy-and-hold strategy in the market index. We find that the predictive power of various economic factors over stock returns changes through time and tends to vary with the volatility of returns. The degree to which stock returns were predictable seemed quite low during the relatively calm markets in the 1960s, but increased to a level where, net of transaction costs, it could have been exploited by investors in the volatile markets of the 1970s.     

International Evidence on the Predictability of Stock Returns

This article examines the predictability of stock returns using international stock market data from eighteen countries. The study finds that the ability of dividend yields to predict stock returns increases as the return horizon lengthens from one month to forty-eight months. These results add to earlier ones, based on U.S. data only, showing that predictability grows with the return horizon. The study also explores why the observed pattern of predictability arises and provides evidence supporting the reasons suggested by Fama and French.     

Portfolio Inefficiency and the Cross-section of Expected Returns

The Capital Asset Pricing Model implies that (i) the market portfolio is efficient and (ii) expected returns are linearly related to betas. Many do not view these implications as separate, since either implies the other, but we demonstrate that either can hold nearly perfectly while the other fails grossly. If the index portfolio is inefficient, then the coefficients and $R^2$ from an ordinary least squares regression of expected returns on betas can equal essentially any values and bear no relation to the index portfolio's mean-variance location. That location does determine the outcome of a mean-beta regression fitted by generalized least squares.     

Optimal Investment, Growth Options, and Security Returns

As a consequence of optimal investment choices, a firm's assets and growth options change in predictable ways. Using a dynamic model, we show that this imparts predictability to changes in a firm's systematic risk, and its expected return. Simulations show that the model simultaneously reproduces: (i) the time-series relation between the book-to-market ratio and asset returns; (ii) the cross-sectional relation between book-to-market, market value, and return; (iii) contrarian effects at short horizons; (iv) momentum effects at longer horizons; and (v) the inverse relation between interest rates and the market risk premium.     

Housing Price Dynamics in Time and Space: Predictability,Liquidity and Investor Returns

It is widely accepted that aggregate housing prices are predictable, but that excess returns to investors are precluded by the transactions costs of buying and selling property. We examine this issue using a unique data set—all private condominium transactions in Singapore during an eleven-year period. We model directly the price discovery process for individual dwellings. Our empirical results clearly reject a random walk in prices, supporting mean reversion in housing prices and diffusion of innovations over space. We find that, when house prices and aggregate returns are computed from models that erroneously assume a random walk and spatial independence, they are strongly autocorrelated. However, when they are calculated from the appropriate model, predictability in prices and in investment returns is completely absent. We show that this is due to the illiquid nature of housing transactions. We also conduct extensive simulations, over different time horizons and with different investment rules, testing whether better information on housing price dynamics leads to superior investment performance.     

Emerging Market Exposures and the Predictability of Hedge Fund Returns

We examine emerging market and global macro hedge funds and find a significant positive relation between hedge funds’ future returns and their exposure to both emerging market equities and emerging market currencies. We present evidence that the strong predictive power of emerging market betas is related to the superior market‐timing ability of these fund managers. Results are robust after controlling for commonly used hedge fund factors, the emerging market equity index, lagged fund returns, liquidity risk, and fund characteristics. Our results suggest that hedge funds can earn positive excess returns by timing their exposure to emerging market securities.     

A Note on the Predictability of UK Stock Returns

This note presents evidence on the predictability of UK stock returns using a database of companies in the FTSE-Allshare Index newly constructed towards the beginning of 1998. The tests used are autocorrelations at various lags and variance ratios for several aggregations of base observations. The evidence is consistent with that published for US stock returns, namely that daily stock returns contain a strong element of predictability.     

Sequential Sales,Learning, and Cascades

When IPO shares are sold sequentially, later potential investors can learn from the purchasing decisions of earlier investors. This can lead rapidly to “cascades” in which subsequent investors optimally ignore their private information and imitate earlier investors. Although rationing in this situation gives rise to a winner's curse, it is irrelevant. The model predicts that: (1) Offerings succeed or fail rapidly. (2) Demand can be so elastic that even risk-neutral issuers underprice to completely avoid failure. (3) Issuers with good inside information can price their shares so high that they sometimes fail. (4) An underwriter may want to reduce the communication among investors by spreading the selling effort over a more segmented market.     

The Nature and Persistence of Initial Public Offering Aftermarket Returns Predictability

This paper examines the predictability of monthly aftermarket returns of initial public offerings during the first six years of trading. Predictability is tested under the null hypothesis of random walk using a Markov chain analysis. The evidence shows that excess returns of IPOs (adjusted for the return on the equally weighted NASDAQ index) demonstrate non-random walk behavior through the first five years of trading and random walk behavior in the sixth year. This is accompanied by predictability of monthly excess returns conditioned on the two previous months' excess returns. A trading strategy is offered to capitalize on the predictability patterns. Implementing the trading strategy is not possible due to institutional barriers, providing additional explanation for why IPOs do not reach their intrinsic values for extended periods of time.     

Recovery of Real Estate Returns for Portfolio Allocation

Appraisal-based return indexes may not approximate the true real estate return distributions because of understated return volatility. Recovery of returns from reported, appraisal-based returns may be possible by evoking models to correct for appraisal-based smoothing of the second moment. Because recovery intentionally alters the volatility of the reported return distribution and the correlations among assets in the portfolio, the weights to real estate are likely affected. Our examination of the portfolio implications of altering the return distribution indicates that weights may be quite sensitive to the effects of recovery across a reasonable range of correlation regimes. A comparative analysis of several recovery models reveals that all models achieve the objective of inflating the volatility of reported returns. However, the models also change the mean of the return distribution, which either counteracts or magnifies the effect of the volatility change on allocations. These findings bring into question the applicability of recovery models in their current form.     

Predictability in Consumption Growth and Equity Returns: A Bayesian Investigation

We use a Bayesian method to estimate a consumption-based asset pricing model featuring long-run risks. Although the model is generally consistent with consumption and dividend growth moments in annual data, the conditional mean of consumption growth (a latent process) is not persistent enough to satisfy the restriction that the price-dividend ratio be an affine function of the latent process. The model also requires relatively high intertemporal elasticity of substitution to match the low volatility of the risk-free return. These two restrictions lead to the equity volatility puzzle. The model accounts for only 50% of the total variation in asset returns.     

On the Predictability of Japanese Stock Returns Using Dividend Yield

The aim of this paper is to provide a critical and comprehensive reexamination of empirical evidence on the ability of the dividend yield to predict Japanese stock returns. Our empirical results suggest that in general, the predictability is weak. However, (1) if the bubble economy period (1986–1998), during which dividend yields were persistently lower than the historical average, is excluded from the sample, and (2) if positive autocorrelation in monthly aggregate returns is taken into account, there is some evidence that the log dividend yield is indeed useful in forecasting future stock returns. More specifically, the log dividend yield contributes to predicting monthly stock returns in the sample after 1990 and when lagged stock returns are included simultaneously.     

Dividend Dynamics,Learning, and Expected Stock Index Returns

We present a latent variable model of dividends that predicts, out‐of‐sample, 39.5% to 41.3% of the variation in annual dividend growth rates between 1975 and 2016. Further, when learning about dividend dynamics is incorporated into a long‐run risks model, the model predicts, out‐of‐sample, 25.3% to 27.1% of the variation in annual stock index returns over the same time horizon, with learning contributing approximately half of the predictability in returns. These findings support the view that investors' aversion to long‐run risks and their learning about these risks are important in determining stock index prices and expected returns.