Accurately predicting stock market movements remains a critical challenge in finance, driven by the increasing role of algorithmic trading and the centrality of financial markets in economic sustainability. This study examines the incorporation of artificial intelligence (AI) and machine...

Purpose - This study aims to find suitable replacements for hypothesis testing and variable-importance measures. Design/methodology/approach - This study explores under-used predictive methods. Findings - The study's hypothesis testing can and should be replaced by predictive methods. It is the...

Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic word-of-mouth (eWOM). With the objective of mining actionable insights from eWOM, this study conducted opinion mining for homestays located...

Occupational accidents are a significant concern, resulting in human suffering, economic crises, and social issues. Despite ongoing efforts to comprehend their causes and predict their occurrences, the use of machine learning models in this domain remains limited. This study aims to address this...

This paper proposes Simultaneous Trainings of Identical Neural Networks (STNN) that aims to predict when sufficient data is not available for training neural networks (NN). While predictive applications of neural networks are growing, a common assumption in the NN algorithms is to have a...

This study investigates the effects of key economic factors on the median list price and median selling price in the U.S. housing market. Key economic factors such as interest rates, unemployment rates, inflation rates, real gross domestic product, money supply, mortgage rate, Standard & Poor's...