We examine predictive machine learning studies from 50 top business and economic journals published between 2010 and 2023. We investigate their transparency regarding the predictive performance of machine learning models compared to less complex traditional statistical models that require fewer...

This systematic review investigates sentiment analysis of news headlines in the banking sector, a field susceptible to public sentiment, as demonstrated by phenomena like bank runs leading to rapid deposit withdrawals. We trace the evolution of analytic methods from traditional machine learning...

This study examines the importance of game elements of the first set in Wimbledon matches in deciding the match outcome. We propose a LASSO-induced logistic regression model based on first set data to identify the variables that impact the match outcome. Our findings indicate that winning...

We examine predictive machine learning studies from 50 top business and economic journals published between 2010 and 2023. We investigate their transparency regarding the predictive performance of machine learning models compared to less complex traditional statistical models that require fewer...

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting...