The impact of the cognitive biases of overconfidence, underconfidence and anchoring on the distribution of errors of forecasting models is analyzed using an analytical framework based on a flexible two-piece generalized distribution. The total forecasting bias, measured by the expected value of...

We investigate whether the distributional characteristics of corporate bonds predict the cross-sectional differences in future bond returns. The results indicate a significantly positive (negative) link between volatility (skewness) and expected returns, whereas kurtosis does not make a robust...

We investigate the cross-sectional return predictability of delta-hedged equity options using machine learning and big data. Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample...