This study proposes a framework for fraud detection in the auto insurance industry by using
predictive models. The feature selection is performed utilizing a publicly available car …

This article aims to contribute to the academic knowledge in the field of scientific production
regarding decision support tools for investments in the capital market, specifically focusing …

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 key aspect of asset investment and risk management is the study of forecasting stock
prices. We investigate the machine learning stock price prediction in a new hybrid neural …

Deep learning technology is rapidly adopted in financial market settings. Using a large data
set from the Chinese stock market, we propose a return-risk trade-off strategy via a new …

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 …

This study introduces an augmented Long-Short Term Memory (LSTM) neural network
architecture, integrating Symbolic Genetic Programming (SGP), with the objective of …

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 …

This study examines the profitability of using machine learning algorithms to select a subset
of stocks over the S&P 500 using factors as features. We use tree-based algorithms …

Purpose We propose a new SCORE model, inspired by Piotroski's (2000) well-known F-
SCORE. But here we examine past, present, and future earnings forecasts in this binary …