In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

We examine whether the distribution of trades along the set of strike prices of option contracts on the same stock contains information about underlying price discovery. We show that option traders' demand for delta exposure drives the volume-weighted average strike-spot price ratio (VWKS). In...

This paper examines the cross-sectional properties of stock return forecasts based on Fama-MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999-December 2018 period. Our estimation approach is strictly out-of-sample, mimicking an investor who...

We investigate the informational content of credit default swap (CDS) spreads for future volatility of (firm) assets and equity. In the cross-section, CDS spreads are significantly more informative about future asset than equity volatility. The informational content of historical and option...

Finance literature highlights various reasons for stock performance subsequent to earnings announcements. However, other moving parts in these scenarios must also be simultaneously specified. While both revenue and earnings surprises are important for determining stock performance,...

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market’s expected excess return is positively related to the market’s conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to...

We generalize the Ferreira and Santa-Clara (2011) sum-of-the-parts method for forecasting stock market returns. Rather than summing the parts of stock returns, we suggest summing some of the frequency-decomposed parts. The proposed method signi cantly improves upon the original sum-of-the-parts...

This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although...

Economic theory identifies two potential sources of return predictability: time variation in expected returns (beta-predictability) or market inefficiencies (alpha-predictability). For the latter, Samuelson argued that macro-returns exhibit more inefficiencies than micro-returns, as individual...

We systematically re-examine the efficacy of trend-based technical indicators in predicting cryptocurrency market returns at daily, weekly, and monthly horizons. It shows that the price-based signals are more effective than the volume-based signals in the short horizon (daily and weekly), while...