Financial Turbulence, Parameter Uncertainty and Aggregate Stock Returns
FMA Europe 2017, SFA 2015, WFC 2015
Abstract: In this paper I show that financial turbulence (FT) is as strong a predictor for the equity premium as is the short interest index (SII), which is the best performing predictor up to date (Rapach et al. 2016). However, FT has the advantage of not needing external (fundamental) information for its calculation, as it depends solely on the distribution of the cross-section of stock returns. Additionally, FT is amongst the strongest predictors of future volatility both in-sample as well as out-of-sample. Combining these two findings, I show that a mean-variance investor relying on predictions from FT outperforms all other predictor variables in terms of Sharpe ratios and achieves annualized certainty equivalent returns of 7.78%. I also show that financial turbulence proxies for parameter uncertainty in the market that robust (ambiguity averse) portfolio optimizers avoid by shifting funds out of the market, thereby causing falling prices and negative returns in the short term.
Higher Moments Matter! Cross-Sectional (higher) Moments and the Predictability of Stock Returns
(with Lars Kaiser)
SGF 2017 // (SSRN)
Abstract: 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 out-of-sample, we highlight the additional role of cross-sectional skewness and cross-sectional kurtosis. Applying a principal component approach, we show that cross-sectional higher moments add to the predictive quality of cross-sectional volatility by stabilizing the predictive performance and yielding a positive trend in in-sample and out-of-sample predictive quality since the burst of the dot-com bubble. In particular, we observe cross-sectional skewness to span the predictive quality of cross-sectional volatility over short-forecasting horizons, whereas cross-sectional kurtosis significantly contributes to long-horizon forecasting of 12 months and above. Results are both statistically and economically significant.