Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

We provide the first systematic evidence on the link between long‐short anomaly portfolio
returns—a cornerstone of the cross‐sectional literature—and the time‐series predictability of …

This paper shows that investments based on deep learning signals extract profitability from
difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular …

This paper compares various machine learning models to predict the cross-section of
emerging market stock returns. We document that allowing for non-linearities and …

A series of recent papers introduce the concept of Forecast Reconciliation, a process by
which independently generated forecasts of a collection of linearly related time series are …

Artificial intelligence (AI) has grown in presence in asset management and has
revolutionized the sector in many ways. It has improved portfolio management, trading, and …

Stock trend forecasting, which forecasts stock prices' future trends, plays an essential role in
investment. The stocks in a market can share information so that their stock prices are highly …

We examine the efficacy of machine learning in a central task of fundamental analysis:
forecasting corporate earnings. We find that machine learning models not only generate …

This systematic literature review summarizes the extant research in the Behavioral Finance
(BeFi) and digital asset spaces to understand better the interactions of behavioral effects on …

Explanatory variables in a predictive regression typically exhibit low signal strength and
various degrees of persistence. Variable selection in such a context is of great importance …