The Essentials of Machine Learning in Finance and Accounting
- Available for pre-order. Item will ship after June 21, 2021
The book introduces the topic of machine learning in finance and accounting and illustrates how the computational tools and techniques can be used in real-world contexts. These computational tools and techniques are particularly useful in areas such as financial risk management, corporate bankruptcy prediction, stock price prediction, fraud and error detection, and portfolio management. This book explains how large amounts of data can be captured and processed to help improve forecasting accuracy and managing business risk. It also offers practical and managerial implications. The book will be a helpful guide for research students, scholars, and practitioners who want to learn about the uses of machine learning and its applications in finance and accounting.
Table of Contents
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. Use of 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 Sci. & Tec. Univ., Bangladesh. Dr. Abedin continuously publishes academic paper 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.
Mohammad 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. 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 according to an article published in Journal of Finance.
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 coauthor 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, PhD, 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 will serve as a valuable source for the digital transformation of the financial industry."
Dr. Zamir Iqbal, VP Finance and Chief Financial Officer (CFO), Islamic Development Bank (IsDB)
"An essential resource for financial accounting managers and students of financial management."
Professor Mehmet Huseyin Bilgin, Istanbul Medeniyet University, Turkey
"A comprehensive coverage of emerging intelligent technologies in finance."
Professor Ohaness Paskelian, University of Houston-Downtown, USA