This book brings together innovative research at the intersection of data science, machine learning, and finance. Covering a wide spectrum of topics--including explainable AI, financial distress prediction, stock market forecasting, investment strategies, audit analytics, and economic modeling--it showcases both theoretical developments and applied case studies from around the world.
With chapters spanning predictive modeling, sentiment analysis, capital structure, IT governance, and Bayesian approaches to productivity, the book offers a multidisciplinary perspective on how data-driven tools are reshaping modern finance and accounting.
This book presents a timely resource for academics, practitioners, and graduate students seeking to understand and apply data science in financial and accounting contexts.