000			03037 a2200253 4500
003			OSt
005			20250626105353.0
008			250626b |||||||| |||| 00| 0 eng d
020			_a9781119863373
082			_a330.0285 _bC42f
100			_aChen, Sam
245			_aFinancial data analytics _bwith machine learning, optimization and statistics _cSam Chen, Ka Chun Cheung and Phillip Yam
260			_bJohn Wiley _c2025 _aHoboken
300			_axxvi, 784p
440			_aWiley finance series
520			_aAn 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.
650			_aAccounting -- Data processing
650			_aFinancial statements
650			_aData analytics in finance and insurence
650			_aData analytics in insurance
700			_aCheung, Ka Chun
700			_aYam, Phillip
942			_cBK
999			_c567537 _d567537