We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

Interested in fundamental analysis and inspired by Bartram and Grinblatt (2018 & 2021), we apply linear regression (LR) and tree-based machine learning (ML) methods to estimate monthly peer-implied fair values of European stocks from 21 accounting variables. Comparing LR and ML models, we...

Financial data is characterized by a low signal-to-noise ratio making it difficult to identify robust functional forms that map the characteristics of financial securities to expected returns (Lettau and Pelger, 2020). In this paper, we modify the standard prediction problem in empirical asset...

An AI analyst we build to digest corporate financial information, qualitative disclosure, and macroeconomic indicators are able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analysts. In the contest of “man vs...

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the...

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the...

This paper presents an empirical approach that combines competing paradigms of mod-eling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which...