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 …

This paper evaluates the predictive performance of machine learning methods in forecasting
European stock returns. Compared to a linear benchmark model, interactions and nonlinear …

In this paper, we investigated the feasibility of pricing European options with time-
sequencing data processing method and deep learning models, based on two European …

Short-term alpha signals are generally dismissed in traditional asset pricing models,
primarily due to market friction concerns. However, this paper demonstrates that investors …

Abstract Machine learning (ML) methods are attracting considerable attention among
academics in the field of finance. However, it is commonly perceived that ML has not …

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and
AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using …

The emerging literature suggests that machine learning (ML) is beneficial in many asset
pricing applications because of its ability to detect and exploit nonlinearities and interaction …

Machine Learning (ML) is being used in multiple disciplines due to its powerful capability to
infer relationships within data. In particular, Software Engineering (SE) is one of those …

Expectations about the economy and financial markets are often cast as figments of
imaginaries of the future. While the sociology of finance have predominantly dealt with …

Abstract Machine learning (ML) models for predicting stock returns are typically trained on
one-month forward returns. While these models show impressive full-sample gross alphas …