Yufeng Han, Ke Yang and Guofu Zhou
Journal of Financial and Quantitative Analysis, Volume 48 Issue 05, October 2013, pp 1433-1461.

In this paper, we document that an application of a moving average timing strategy of technical analysis to portfolios sorted by volatility generates investment timing portfolios that substantially outperform the buy-and-hold strategy. For high-volatility portfolios, the abnormal returns, relative to the capital asset pricing model (CAPM) and the Fama-French 3-factor models, are of great economic significance, and are greater than those from the well-known momentum strategy. Moreover, they cannot be explained by market timing ability, investor sentiment, default, and liquidity risks. Similar results also hold if the portfolios are sorted based on other proxies of information uncertainty.

Yufeng Han
Journal of Banking & Finance, Vol 36, Issue 9, Sept 2012, Pages 2575–2592

We propose a novel Bayesian framework to incorporate uncertainty about the state of the market. Among others, one advantage of the framework is the ability to model a large collection of time-varying parameters simultaneously. When we apply the framework to estimate the cost of equity we find economically significant effects of state uncertainty. A state-independent pricing model overestimates the cost of equity by about 4% per annum for a utility firm and by as much as 3% for industries. We also observe that the expected return, volatility, risk loading, and pricing error all display state-dependent dynamics that coincide with the business cycle. More interestingly, the forecasted market and Fama–French factor risk premiums can predict the future real GDP growth rate even though the model does not use any macroeconomic variables, which suggests that the proposed Bayesian framework captures the state-dependent dynamics well.

Yufeng Han and David Lesmond
Review of Financial Studies Vol. 24, Issue 5 Pp. 1590-1629

We model a microstructure effect on daily security returns, embodied by zero returns and the bid-ask spread, and derive a closed-form solution for the resulting bias in the estimated idiosyncratic volatility. Our empirical tests show that controlling for the bias eliminates the ability of idiosyncratic volatility estimates to predict future returns. We also find a significant reduction in the pricing ability of idiosyncratic volatility after exogenous shocks to liquidity evidenced in the 1997 reduction in the quotes to sixteenths and the 2001 decimalization. Finally, minimizing liquidity’s influence on the estimated idiosyncratic volatility, by orthogonalizing the percentage of zero-return and spread effects on the estimated idiosyncratic volatility, demonstrates that the resulting idiosyncratic volatility estimate has little pricing ability.

Yufeng Han
ANNALS OF ECONOMICS AND FINANCE Vol. 11 Issue 1, Pages: 1–33

Recent studies provide strong statistical evidence challenging the existence of out-of-sample return predictability. The economic significance of return
predictability is also controversial. In this paper, we find significant economic gains for dynamic trading strategies based on return predictability when ap-
propriate portfolio constraints are imposed. We findthat imposingappropriate portfolio constraints is critical for obtaining economic profits, which seems to
explain the contradictory findings about economic significance in the literature. We also compare the performance of several predictive models including
the VAR, the VAR-GARCH, and the (semi)nonparametric models and find that the simple VAR model performs similarly to other more complex models.