The most relevant practical impediment to an application of the Markowitz portfolio selectionapproach is the problem of estimating return moments, in particular return expectations. We analyzethe consequences of using return estimates implied by analysts’ dividend forecasts under the...

We develop new liquidity measures for bond markets. Existing measures suffer from the combination of two effects. First, transaction costs in OTC markets strongly depend on trade size. Second, many bonds trade only scarcely with strongly differing trading volumes. Therefore, changes in average...

This paper unifies macro-finance and multifactor asset pricing theories to show that, in sample and out of sample: (i) Larger cross-sectional book-to-market medians and spreads - price of risk proxies - predict larger market (in sample), size, value, and investment premiums; (ii) the investment...

We find a negative relationship between the individual stocks' semivariance premia, defined as the difference between the risk-neutral and physical expected downside semivariances, and future stock returns. The high-minus-low hedge portfolio earns the excess return of -64 (-46) basis points per...

We zero in on the expected returns of long-short portfolios based on 120 stock market anomalies by accounting for (1) effective bid-ask spreads, (2) post-publication effects, and (3) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly's...

This paper empirically describes how the risk premiums of size portfolios vary with macro-economic fluctuations in the price of risk at the portfolio formation dates, thereby explaining the lack of robustness involving the unconditional size premium: Only portfolios formed in "bad" states - with...

High (low) quality stocks generate anomalously high (low) returns above and beyond expected returns based on betas, market sizes, valuations, and momentum. We provide a comprehensive overview of commonly used quality definitions and test their predictive power for stock returns. We show that...

This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high...

This paper studies how the stock market perceives and prices cyber risk. To estimate the ex-ante likelihood that a firm will experience a cyber attack, we apply cross-validated logistic LASSO regressions to a set of firm and industry characteristics along with an estimate of a firm's...

We develop a four-factor model intended to capture size, value, and credit rating transition patterns in excess returns for a panel of predominantly mid- and large-cap entities. Using credit transition matrices and rating histories from 48 US issuers, we provide evidence to support a...