Factors in prominent asset pricing models are positively autocorrelated. We derive a transformation that turns an autocorrelated factor to a ``time-series efficient'' factor. Time-series efficient factors earn significantly higher Sharpe ratios than the original factors and contain all the...

Implied correlation and variance risk premium stand out in predicting market returns. However, while the predictive ability of implied correlation lasts for up to a year, the variance risk premium predicts market returns only for one quarter ahead. Contrary to the accepted view, implied...

It is common to evaluate mutual fund (and in general, security) returns by linear factor models. However, performance measures from these models are misleading if there are some omitted factors that explain cross-sectional variation in returns. We propose to use a latent-factor approach,...

Motivated from investment-based asset pricing, we propose a new factor model that consists of the market factor, a size factor, an investment factor, and a return-on-equity factor. The new model [i] outperforms the Carhart (1997) four-factor model in pricing portfolios formed on earnings...

We propose a conditional model of asset returns in the presence of common factors and downside risk. Specifically, we generalize existing latent factor models in three ways: we show how to estimate the threshold which identifies the 'disappointment' event triggering the bad state of the world; we...

This paper examines the economic implications of new factor models and shows that the Hou, Xue, and Zhang (HXZ, 2015a) four-factor model outperforms the Fama and French (FF5, 2015a) five-factor model for investing in anomalies in- and out-of-sample. The difference in certainty-equivalent returns...

Approximate factor models and their extensions are widely used in economic analysis and forecasting due to their ability to extracting useful information from a large number of relevant variables. In these models, candidate predictors are typically subject to some common components. In this...