1. Machine Learning in Finance and Accounting 2. Decision Trees and Random Forests 3. Improving Longevity Risk Management through Machine Learning 4. Kernel Switching Ridge Regression in Business Intelligence 5. Predicting Stock Return Volatility using Sentiment Analysis of Corporate Annual Reports 6. Random Projection Methods in Economics and Finance 7. The Future of Cloud Computing in Financial Services: A Machine Learning and Artificial Intelligence Perspective 8. Prospects and Challenges of Using Artificial Intelligence in Audit Process 9. Web Usage Analysis: Pillar 3 Information Assessment in Turbulent Times 10. Machine Learning in the Fields of Accounting, Economics and Finance: The Emergence of New Strategies 11. Handling Class Imbalance Data in Business Domain 12. Artificial Intelligence (AI) in Recruiting Talents Recruiters' Intention and Actual Use of AI

Mohammad Zoynul Abedin is an associate professor of Finance at the Hajee Mohammad Danesh Science and Technology University, Bangladesh. Dr. Abedin continuously publishes academic papers in refereed journals. Moreover, Dr. Abedin served as an ad hoc reviewer for many academic journals. His research interest includes data analytics and business intelligence.

M. Kabir Hassan is a professor of Finance at the University of New Orleans, USA. Prof. Hassan has over 350 papers (225 SCOPUS, 108 SSCI, 58 ESCI, 227 ABDC, 161 ABS) published as book chapters and in top refereed academic journals. According to an article published in Journal of Finance, the number of publications would put Prof. Hassan in the top 1% of peers who continue to publish one refereed article per year over a long period of time.

Petr Hajek is currently an associate professor with the Institute of System Engineering and Informatics, University of Pardubice, Czech Republic. He is the author or co-author of four books and more than 60 articles in leading journals. His current research interests include business decision making, soft computing, text mining, and knowledge-based systems.

Mohammed Mohi Uddin is an assistant professor of Accounting at the University of Illinois Springfield, USA. His primary research interests concern accountability, performance management, corporate social responsibility, and accounting data analytics. Dr. Uddin published scholarly articles in reputable academic and practitioners' journals.

This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data.
Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.