Read more
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.
List of contents
On Explainable Data Science.- Why Shapley Value and Its Generalizations Are Effective in Economics and Finance, Machine Learning, and Systems Engineering.- Why Skew-Normal Distributions and How They Are Related to ReLU Activation Function in Deep Learning.- A Study of Machine Learning Models for Financial Distress Prediction.- SPredict Stock Market Prediction with Social Media Sentiment Analysis and Machine Learning.- Post-IPO Performance Prediction: A Comparative Study of Logistic Regression and Machine Learning Techniques for Thai IPO Firms.- Enhancing Corporate Bankruptcy Prediction with Machine Learning and Textual Analysis.- AI-Enhanced Investing Sentiment Analysis, Strategy Design, and Automation.- Explainable AI in Finance: Enhancing Transparency and Interpretability of AI Models in Financial Decision-Making.- On Shared Directors and Liquidation Evidence from UK SMEs.- Optimizing Portfolio and Asset Allocation Strategies.- The Impact of Capital Structure on the Performance of Non-financial Enterprises in Vietnam.- Analyze Financial Data Using Benford s Law Evidence From Vietnam Before and During the COVID-19 Pandemic.- IT Governance and Perceived Usefulness of CAATs An Empirical Study.- Micro-level Determinants of Household Financial Portfolio Choices in Rural Vietnam.- The Impact of Information and Communication Technology on Economic Growth and Productivity Paradox in Southeast Asia, Analyzed with Bayesian Methods.- Big Data Analytics for Financial Decisions of Companies a Systematic Literature Review.
Summary
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.
