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Financial Data Analytics with Machine Learning, Optimization and Statistics
Series: Wiley Finance
Brief Description
An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals, and working data analysts delivers an expertly balanced combination...
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Description
An essential introduction to data analytics and Machine Learning techniques in the business sector
In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals, and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry, with an emphasis on mathematical understanding and statistical principles, and connects them with common and practical financial problems.
Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves.
This book can help readers become well-equipped with the following skills:
- To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions
- To apply effective data dimension reduction tools to enhance supervised learning
- To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose
The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam.
Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.
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Book Details
INFORMATION
ISBN: 9781119863373
Publisher: John Wiley & Sons Inc
Format: Hardback
Date Published: 24 October 2024
Country: United States
Imprint: John Wiley & Sons Inc
Audience: Professional and scholarly
DIMENSIONS
Spine width: 61.0mm
Width: 180.0mm
Height: 246.0mm
Weight: 1179g
Pages: 816
About the Author
YONGZHAO CHEN (SAM) [BSC(ACTUARSC) & PHD (HKU)] is currently an Assistant Professor at the Department of Mathematics, Statistics and Insurance, The Hang Seng University of Hong Kong. His research interests include actuarial science, especially credibility theory, and data analytics.
KA CHUN CHEUNG [BSC(ACTUARSC) & PHD (HKU), ASA (SOA)] was the Director of the Actuarial Science Programme, and is currently Head and full Professor at the Department of Statistics and Actuarial Science in School of Computing and Data Science, The University of Hong Kong. His current research interests include various topics in actuarial science, including optimal reinsurance, stochastic orders, dependence structures, and extreme value theory.
PHILLIP YAM [BSC(ACTUARSC) & MPHIL (HKU), MAST (CANTAB), DPHIL (OXON)] is currently Director of QFRM programme, and a full Professor at the Department of Statistics of The Chinese University of Hong Kong, also Assistant Dean (Education) of CUHK Faculty of Science, and a Visiting Professor in Columbia University and UTD Business School. He has more than 100 top journal articles in actuarial science, applied mathematics, data analytics, engineering, financial mathematics, operations management, and statistics. His research project CIBer won a Silver Medal in the 48th International Exhibition of Inventions Geneva in 2023.
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