Dashan Huang

Robust portfolios: contributions from operations research and finance

In this paper we provide a survey of recent contributions to robust portfolio strategies from operations research and finance to the theory of portfolio selection. Our survey covers results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estimation methods and Bayesian robust approaches.

Investor sentiment aligned: : A powerful predictor of stock returns

We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices have both in and out of sample, and the predictability becomes both statistically and economically significant. In addition, it outperforms well-recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum. The driving force of the predictive power appears to stem from investors biased beliefs about future cash flows.

Upper Bounds on Return Predictability

Can the degree of predictability found in the data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R-squares of predictive regressions. Using data on the market and component portfolios, we find that the empirical R-squares are significantly greater than the theoretical upper bounds. Our results suggest that the most promising direction for future research should aim to identify new state variables that are highly correlated with stock returns, instead of seeking more elaborate stochastic discount factors.

Models for Portfolio Revision with Transaction Costs in the Mean–Variance Framework

The contribution of the mean–variance framework by Markowitz more than a half century ago cannot be understated. Since that time, the framework has been extended in several ways. One extension has been to consider the practical application of the framework when an existing portfolio of securities must be revised by changing the composition of the portfolio rather than the initial deployment of cash to construct a portfolio. Since then there have been several proposals for modeling portfolio revision with transaction costs within the mean–variance framework. This chapter summarizes these proposals and presents one recently formulated model and some empirical evidence. More specifically, we consider the portfolio revision problem with transaction costs that are paid at the end of the planning horizon, and present some analytical solutions for some special cases in the mean–variance framework. Moveover, a simple empirical experiment with actual market data shows that the impact of the transaction costs is significant, confirming the findings of Chen et al. (1971) that transaction costs should be integrated into the portfolio revision optimization problem, and that lower revision frequency may reduce the magnitude of the impact.

Forecasting Stock Returns in Good and Bad Times: The Role of Market States

This paper proposes a state-dependent predictive regression model and finds that a market momentum predictor predicts the excess market return negatively in good times and positively in bad times. The out-of-sample R-square is 2.06% in NBER expansions and 1.84% in NBER recessions. There are similar predictability patterns in the cross-section of U.S. stocks and in the international markets. Our study shows the importance of market states in predicting stock returns, and finds that the concentration of return predictability in bad times, a well documented fact in the literature, is largely due to the assumption of a one-state predictive regression model.

Volume and Return: The Role of Mispricing

We find that expected return is related to trading volume positively among underpriced stocks but negatively among overpriced stocks. As such, trading volume amplifies mispricing. Our results are robust to alternative mispricing and trading volume measures, alternative portfolio formation methods, and controlling for variables that are known to have amplification effects on mispricing. By attributing trading volume to investor disagreement, we show that our results are consistent with the recent theoretical model of Atmaz and Basak (2018) in that investor disagreement predicts stock returns conditional on expectation bias.

Time-Series Momentum: Is It There?

Time series momentum (TSM) refers to the predictability of the past 12-month return on the next one-month return and is the focus of several recent influential studies. This paper shows that asset-by-asset time series regressions reveal little evidence of TSM, both in- and out-of-sample. While the t-statistic in a pooled regression appears large, it is not statistically reliable as it is less than the critical values of parametric and nonparametric bootstraps. From an investment perspective, the TSM strategy is profitable, but its performance is virtually the same as that of a similar strategy that is based on historical sample mean and does not require predictability. Overall, the evidence on TSM is weak, particularly for the large cross section of assets.

Cost Behavior and Stock Returns

This paper shows that investors fail to fully incorporate cost behavior information into valuation. Firms with higher growth in operating costs generate substantially lower future stock returns. A long-short spread portfolio earns an average return of about 12% per year after controlling for extant risk factors and firm characteristics. Mean-variance spanning tests show that an investor can benefit from investing in this spread portfolio in addition to well-known factors. Firms with high cost growth also suffer from deterioration in future operating performance. The negative cost growth-return relation is much stronger around earnings announcement days, among firms with lower investor attention, higher idiosyncratic volatility, and higher transaction costs, suggesting that investor underreaction and limits to arbitrage mainly drive the effect.