Studies show the inconclusive results regarding the relation between corporate social and environmental responsibility (CSR and CER) and expected returns. We argue that the reason for these mixed results is that the sustainability premium (i.e., the return difference of high-intensity minus...

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...

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...

We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month-anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8-2.0%, and over 80% of the models...

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...

We examine the value of analyst recommendations across 45 countries and 3.8 million firm-month observations from 1994 to 2019. Recommendation-based portfolio strategies lead to highly significant (insignificant) abnormal returns in international markets (in the U.S.). In line with...