Asset Return Predictability Research Articles

Behavioral Finance - Asset Prices Predictability, Equity Premium Puzzle, Volatility Puzzle: The Rational Finance Approach

In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as “anomalies” the theory of rational finance cannot explain: (i) Predictability of asset returns; (ii) The Equity Premium; (iii) The Volatility Puzzle. We offer resolutions of those objections within the rational finance. We do not claim that those are the only possible explanations of the “anomalies”, but offer statistical models within the rational theory of finance which can be used without relying on behavioral finance assumptions when searching for explanations of those “anomalies”.

A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange

Skewness and kurtosis, the third and fourth order moments, are statistics to summarize the shape of a distribution function. Recent studies show that investors would take these higher-order moments into consideration to make a profitable investment decision. Unfortunately, due to the difficulties in solving the multi-objective problem with higher-order moments, the literature on portfolio selection problem with higher-order moments is few. This paper proposes a new hybrid approach to solve the portfolio selection problem with skewness and kurtosis, which includes not only the multi-objective optimization but also the data-driven asset selection and return prediction, where the techniques of two-stage clustering, radial basis function neural network and genetic algorithm are employed. With the historical data from Shanghai stock exchange, we find that the out-of-sample performance of our model with higher-order moments is significantly better than that of traditional mean-variance model and verify the robustness of our hybrid algorithm.

Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based

Abstract Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Optimal investment and consumption with return predictability and execution costs

We provide a closed-form solution to an optimal investment and consumption problem for a constant absolute risk aversion (CARA) agent, who faces execution costs when trading correlated risky assets with return predictability. The optimal investment strategy indicates that the agent should trade gradually toward a dynamic aim portfolio, which is an adjusted Merton portfolio with modifications to account for the persistence of the return-predicting signals and the execution costs. The optimal consumption strategy is quadratic in the return-predicting signals and linear in the agent's wealth. Our numerical studies show that the execution costs diminish the importance of asset return predictability on the agent's optimal investment strategy, thereby confirming the conjecture raised by Liu (2004). In addition, the presence of the intermediate consumption leads to a more aggressive aim portfolio than the case without consumption.

Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based

Research in finance and macroeconomics has routinely used multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested, and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Simulation experiments demonstrate its remarkable finite-sample performance, comparing with various other widely used tests. We re-examine the predictive ability of various popular predictors for aggregate equity premium.

A bootstrap test for predictability of asset returns

A bootstrap test is proposed for predictability of asset returns. The bootstrap is conducted with the likelihood ratio test in a restricted VAR form. The test shows no size distortion in small samples with desirable power properties. A wild bootstrap version, valid for financial returns showing unknown forms of conditional heteroskedasticty, is also proposed. As an application, predictive powers of dividend-price ratio and interest rate for U.S stock returns are evaluated.

Is Human Capital the Sixth Factor? Evidence from US Data

Problem/Relevance: Measuring the risk of an asset and the economic forces driving the price of the risk is a challengingtask that preoccupied the asset pricing literature for decades. However, there exists no consensus on the integrated asset pricing framework among the financial economists in the contemporaneous asset pricing literature. Thus, we consider and study this research problem that has greater relevance in pricing the risks of an asset. In this backdrop, we develop an integrated equilibrium asset pricing model in an intertemporal (ICAPM) framework. Research Objective/Questions: Broadly we have two research objectives. First, we examine the joint dynamics of the human capital component and common factors in approximating the variation in asset return predictability. Second, we test whether the human capital component is the unaccounted and the sixth pricing factor of FF five-factor asset pricing model. Additionally, we assess the economic and statistical significance of the equilibrium six-factor asset pricing model. Methodology: The human capital component, market portfolio, size, value, profitability, and investment are the pricing factors of the equilibrium six-factor asset pricing model. We use Fama-French (FF) portfolios of 2  3, 5  5, 10  10 sorts, 2  4  4 sorts, and the Industry portfolios to examine the equilibrium six-factor asset pricing model. The Generalized method of moments (GMM) estimation is used to estimate the parameters of variant asset pricing models and Gibbons-Ross-Shanken test is employed to evaluate the performance of the variant asset pricing frameworks. Major Findings: Our approaches led to three conclusions. First, the GMM estimation result infers that the human capital component of the six-factor asset pricing model significantly priced the variation in excess return on FF portfolios of variant sorts and the Industry portfolios. Further, the sensitivity to human capital component priced separately in the presence of the market portfolios and the common factors. Second, the six-factor asset pricing model outperforms the CAPM, FF three-factor model, and FF five-factor model, which indicates that the human capital component is a significant pricing factor in asset return predictability. Third, we argue that the human capital component is the unaccounted asset pricing factor and equally the sixth-factor of the FF five-factor asset pricing model. The additional robustness test result confirms that the parameter estimation of the six-factor asset pricing model is robust to the alternative definitions of the human capital component. Implications: The empirical results and findings equally pose the more significant effects for the decision-making process of the rational investor, institutional managers, portfolio managers, and fund managers in formulating the better investment strategies, which can help in diversifying the aggregate risks.

Gone With the Vol: A Decline in Asset Return Predictability During the Great Moderation

We document a significant shift in the comovement of asset returns and macroeconomic volatility during the Great Moderation. Strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982 was followed by a significant predictability decline during the Great Moderation (1982-2008). These findings are robust to alternative empirical specifications and out-of-sample tests. In a calibrated time-varying-volatility equilibrium model, the predictability decline is consistent with changes in monetary policy and shock dynamics. A stronger policy response to inflation and lower cost-push shock variance reduce the sensitivity of macroeconomic variables and asset returns to a persistent volatility factor, explaining the lower predictability. The results contribute to examine macroeconomic volatility as a driver of expected asset returns, and identify sources of the Great Moderation using asset price dynamics.

Optimal Investment and Consumption with Return Predictability and Execution Costs

We provide a closed-form solution to an optimal investment and consumption problem for a constant absolute risk aversion (CARA) agent, who faces execution costs when trading correlated risky assets with return predictability. The optimal investment strategy indicates that the agent should trade gradually toward a dynamic aim portfolio, which is an adjusted Merton portfolio with modifications to account for the persistence of the return predicting signals and the execution costs. The optimal consumption strategy is quadratic in the return predicting signals and linear in the agent’s wealth. Our numerical studies show that the execution costs diminish the importance of asset return predictability on the agent’s optimal investment strategy, thereby confirming the conjecture raised by Liu (2004). In addition, the presence of the intermediate consumption leads to a more aggressive aim portfolio than the case without consumption.

Timing the market: the economic value of price extremes

By decomposing asset returns into potential maximum gain (PMG) and potential maximum loss (PML) with price extremes, this study empirically investigated the relationships between PMG and PML. We found significant asymmetry between PMG and PML. PML significantly contributed to forecasting PMG but not vice versa. We further explored the power of this asymmetry for predicting asset returns and found it could significantly improve asset return predictability in both in-sample and out-of-sample forecasting. Investors who incorporate this asymmetry into their investment decisions can get substantial utility gains. This asymmetry remains significant even when controlling for macroeconomic variables, technical indicators, market sentiment, and skewness. Moreover, this asymmetry was found to be quite general across different countries.

A perspective on recent methods on testing predictability of asset returns

This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictors are highly persistent and their innovations are contemporarily correlated with dependent variable, the ordinary least squares estimator has a finite-sample bias, and its limiting distribution relies on some unknown nuisance parameter, which is not consistently estimable. Without correcting these issues, conventional test statistics are subject to a serious size distortion and generate a misleading conclusion in testing predictability of asset returns in real applications. In the past two decades, sequential studies have contributed to this subject and proposed various kinds of solutions, including, but not limit to, the bias-correction procedures, the linear projection approach, the IVX filtering idea, the variable addition approaches, the weighted empirical likelihood method, and the double-weight robust approach. Particularly, to catch up with the fast-growing literature in the recent decade, we offer a selective overview of these methods. Finally, some future research topics, such as the econometric theory for predictive regressions with structural changes, and nonparametric predictive models, and predictive models under a more general data setting, are also discussed.

Two Birds, One Stone: Joint Timing of Returns and Capital Gains Taxes

In asset return predictability, realized returns and future expected returns tend to move in opposite directions. This generates a tension between tax-timing and market-timing incentives. In this study, a portfolio choice problem in the presence of both return predictability and capital gains tax is examined. We characterize various features of the optimal trading strategy, and demonstrate that the optimal strategy helps mitigate the tension between market- and tax-timing. The calibrated model suggests that return predictability can significantly increase both the utility loss due to capital gains tax and the value of deferring capital gains realization. Overall, our results suggest that the nature of the asset return process can have important implications for the welfare effects of capital gains tax.

Robust Inference of Long-Horizon Predictability

Examination over multiple horizons has been a routine in testing asset return predictability in finance and macroeconomics. In a simple predictive regression model, we find that the popular scaled test for multiple-horizon predictability has zero null rejection rate if the forecast horizon increases at a faster rate than the inverse of proximity of the predictor autoregressive root to the unity. Correspondingly, the scaled test has zero power for long horizons, e.g. if the horizon increases faster than n^(1/2), where n is the sample size, when the predictor is stationary. The t-test based on an implication of the short-run model, together with Bonferroni correction we suggest, is shown to have controlled size agnostic of persistence of the predictor, and is uniformly more powerful than the robust scaled test. Simulation experiments support the asymptotic results and show substantial power gain of the implied test over various other widely-used tests. In the empirical application, we re-examine predictive ability of the short interest and the dividend-price-ratio for aggregate equity premium.

A new tight and general bound on return predictability

We propose a novel upper bound on the predictability of asset returns. This bound is tighter than the bound proposed by Ross (2005) because it takes into account not only the volatility of the pricing kernel but also the correlation between the pricing kernel and trading strategies that exploit predictability. It is also at least as tight as the bound proposed by Huang and Zhou (2017). We apply our bound to study the predictability of returns on currencies of emerging and developed economies from 1994 to 2016. We find evidence of return predictability in excess of the bound, especially for emerging markets currencies. This implies either market inefficiency or, alternatively, that investors either can become very risk-averse or price currencies using a model radically different from the CAPM. In contrast, the evidence of excess-predictability is much weaker under the wider bound proposed by Ross (2005).

A New Tight and General Bound on Return Predictability

We propose a novel upper bound on the predictability of asset returns. This bound is tighter than the bound proposed by Ross (2005) because it takes into account not only the volatility of the pricing kernel but also the correlation between the pricing kernel and trading strategies that exploit predictability. It is also at least as tight as the bound proposed by Huang and Zhou (2017). We apply our bound to study the predictability of returns on currencies of emerging and developed economies from 1994 to 2016. We find evidence of return predictability in excess of the bound, especially for emerging markets currencies. This implies either market inefficiency or, alternatively, that investors either can become very risk-averse or price currencies using a model radically different from the CAPM. In contrast, the evidence of excess-predictability is much weaker under the wider bound proposed by Ross (2005).

Two Birds, One Stone: Joint Timing of Returns and Capital Gains Taxes

In asset return predictability, realized returns and future expected returns tend to move in opposite directions. This generates a tension between tax- and market-timing incentives. In this study, a portfolio choice problem in the presence of both return predictability and capital gains tax is examined. We characterize various features of the optimal trading strategy, and demonstrate that the optimal strategy helps mitigate the tension between market- and tax-timing. The calibrated model suggests that return predictability can significantly increase both the utility loss due to capital gains tax and the value of deferring capital gains realization. Overall, our results suggest that the nature of the asset return process can have important implications for the welfare effects of capital gains tax. This paper was accepted by Karl Diether, finance.

Stock return predictability in emerging markets: Does the choice of predictors and models matter across countries?

This study aims to examine return predictability in 24 emerging markets disaggregated in different regions. We propose four specifications, including a benchmark model. Then, an augmented model appropriate for each country, including a large set of potential factors, is evaluated. Furthermore, a dynamic multifactor model is investigated for all countries. Finally, we relax the symmetric hypothesis in asset return predictability based on a non-parametric non-linear approach: the projection pursuit regression model. Our study reveals three main findings. First, we reject all previous findings supporting a standard model of asset return predictability that is valuable for all countries, as we show that each country has specific domestic factors (both macroeconomic and financial) useful to predict future returns. Second, our empirical framework shows that asset return predictability might be robustly modelled based on non-linear specification based on the projection pursuit regression model. Our findings’ explanatory power of out-of-sample estimations is economically relevant. Our results are useful for investors and policy-makers for portfolio diversification and regulation policies.

Asymptotic Inference for Performance Fees and the Predictability of Asset Returns

In this article, we provide analytical, simulation, and empirical evidence on a test of equal economic value from competing predictive models of asset returns. We define economic value using the concept of a performance fee—the amount an investor would be willing to pay to have access to an alternative predictive model used to make investment decisions. We establish that this fee can be asymptotically normal under modest assumptions. Monte Carlo evidence shows that our test can be accurately sized in reasonably large samples. We apply the proposed test to predictions of the U.S. equity premium.

The Decline in Asset Return Predictability and Macroeconomic Volatility

We document strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982, and a significant decline in this predictability during the Great Moderation. These findings are robust to alternative empirical specifications and out-of-sample tests. We explore the predictability decline using a model that incorporates monetary policy and shocks with time-varying volatility. The decline is consistent with changes in both policy and shock dynamics. While an increase in the response to inflation in the interest-rate policy rule decreases volatility, more persistent and less volatile shocks explain the lower predictability.

Stock Return Prediction with Fully Flexible Models and Coefficients

We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions, and we should account for this. Our approach has superior out-of-sample predictive performance compared to the historical mean, from a statistical and economic perspective. We also find that our model statistically dominates its nested models, including models in which parameters evolve at a constant rate. By decomposing sources of prediction uncertainty into five parts, we find that our fully flexible approach more precisely identifies time-variation in coefficients and in forecasting models, leading to mitigation of estimation risk and forecasting improvements. Finally, we relate predictability to the business cycle.