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Essentials of Machine Learning in Finance and Accounting

Inglese · Tascabile

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Descrizione

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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.


Info autore










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.


Riassunto

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

Dettagli sul prodotto

Con la collaborazione di M Kabir Hassan (Editore), M. Kabir Hassan (Editore), Petr Hajek (Editore), Mohammad Zoynul Abedin (Editore), Mohammed Mohi Uddin (Editore), Hassan M. Kabir (Editore), Hajek Petr (Editore)
Autori M. Kabir Hassan, Petr Hajek, Mohammad Zoynul (Hstu Abedin, Mohammad Zoynul (Dalian Maritime Universit Abedin, Mohammad Zoynul Abedin, Mohammed Mohi Uddin
Editore Taylor & Francis Ltd.
 
Contenuto Libro
Forma del prodotto Tascabile
Data pubblicazione 30.06.2021
Categoria Scienze naturali, medicina, informatica, tecnica > Matematica > Teoria delle probabilità, stocastica, statistica m
Scienze sociali, diritto, economia > Economia > Economia aziendale
 
EAN 9780367480813
ISBN 978-0-367-48081-3
Numero di pagine 234
 
Serie Routledge Advanced Texts in Economics and Finance
Categorie machine learning, BUSINESS & ECONOMICS / Economics / General, COMPUTERS / Mathematical & Statistical Software, Business Intelligence, BUSINESS & ECONOMICS / Finance / Financial Engineering, COMPUTERS / Machine Theory, Economic statistics, Applied mathematics, economic forecasting, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / General, Econometrics and economic statistics, svm, Ridge Regression, hr professional, Roc Curve, Frequent Itemsets, supervised machine learning, Cee Country, RP, artificial intelligence audit, Ml Algorithm, UTAUT Model, longevity risk analysis, cloud computing financial services, decision support systems finance, sentiment analysis finance, class imbalance data, SVR, longevity risk, Stock Return Volatility, Partial Dependence Plot, Sgd Algorithm, Positive Definite Kernel, Precision Recall Curve, Random Oversampling, Minority Class Examples, Stochastic Mortality Models, Data Imbalance Problem
 

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