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...

Based on validated word lists from the Information Systems literature, we use the MD&A section of annual firm reports to construct a text-based measure of digital innovation. In our sample period from 1996 to 2020, we find that firms with a high level of digital innovation are systematically...

We study whether growth in the capital share (KS) of aggregate income (GDP) can explain equity portfolio returns in international stock markets as proposed by Lettau et al. (2019) for the U.S. market. We find that growth in local capital share has positive explanatory power for equity portfolio...