The Essentials of Machine Learning in Finance and Accounting (eBook, ePUB)

Redaktion: Abedin, Mohammad Zoynul; Uddin, Mohammed Mohi; Hajek, Petr; Hassan, M. Kabir

• Format: ePub

Produktbeschreibung

This book introduces machine learning in finance and illustrates how to integrate computational tools with numerical finance with real world applications.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 258
• Erscheinungstermin: 20. Juni 2021
• Englisch
• ISBN-13: 9781000394122
• Artikelnr.: 61762816

Autorenporträt

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.

Inhaltsangabe

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