We propose a novel method to forecast corporate earnings, which combines the accuracy of analysts' forecasts with the unbiasedness of a cross-sectional model. We build on recent insights from the earnings forecasts literature to improve analysts' forecasts in two ways: reducing their...

I estimate a theory-based behavioral momentum using analysts' predictable underreaction (APU) as a proxy for newswatchers underreaction. The results show that APU strongly predicts analysts' errors and, more importantly, stock returns. A long-short strategy based on APU generates a...

We examine the out-of-sample performance of 240 stock market anomalies enhanced by 49 machine learning algorithms and over 260 individually trained models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant...

This paper analyzes the determinants of returns generated by European private equity funds. It starts from the presumption that this asset class is characterized by illiquidity, stickiness and segmentation. As a consequence, Gompers and Lerner (2000) have shown that venture deal valuations are...

This paper presents a cash flow based analysis of the return and risk characteristics of European Private Equity Funds. For that purpose a comprehensive data set has been provided by Thomson Venture Economics. We document the typical time pattern of cash flows for European private equity funds....