In order to learn a task through behavior cloning, a dataset consisting of state-action pairs is needed. However, this kind of data is often not available in sufficient quantity or quality. Consequently, several publications have addressed the issue of extracting actions from a sequence of...

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox-Ingersoll-Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for...

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fuctuate. The random walk hypothesis and efcient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes...

In the feld of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction efects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting diferent measures of stock...