Financial Data Analytics with Machine Learning, Optimization and Statistics (Wiley Finance)
Author: Sam Chen (Author), Ka Chun Cheung (Author), Phillip Yam (Author) & 0 more
Publisher finelybook 出版社: Wiley
Edition 版次: 1st
Publication Date 出版日期: 2024-10-21
Language 语言: English
Print Length 页数: 816 pages
ISBN-10: 1119863376
ISBN-13: 9781119863373
Book Description
By finelybook
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.
The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford’s Law, Zipf’s Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly’s formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems.
The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech.
After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction.
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.
From the Inside Flap
Contemporary financial and insurance data analytics is a complex, nuanced, and layered subject. Students and practitioners in the area are often overwhelmed by the mathematical theory underlying it, coming away from the topic confused. A resource that combines a focus on practical solutions―but also elegantly explains the mathematical and statistical foundation―is sorely needed.
In Financial Data Analytics, an interdisciplinary team including an actuarial professional, an applied mathematician and statistician, a working data analyst, and a quant delivers an authoritative and enduring combination of traditional financial statistics, effective machine learning tools, and mathematics.
This book explains contemporary techniques used for data analytics in finance and insurance with a strong emphasis on mathematical understanding and statistical principles. It connects those techniques and principles with common, hands-on financial problems and illustrates their solutions with working Python and R code examples. The book can also be viewed as a research monograph, aiming to introduce to readers cutting-edge results stemming from the authors’ own research findings, in the hope of clearly depicting the research discipline and scope of financial data analytics.
The authors demonstrate how to correctly evaluate financial and insurance data quality and use the distilled knowledge obtained from data to make timely, profitable financial decisions. They explain how to apply data dimension reduction tools to enhance supervised learning and describe how to select suitable data analytics tools for a variety of given datasets and purposes.
Financial Data Analytics includes extensive coverage of the materials tested by several professional examinations, including the Stochastic Risk Modelling (SRM), Predictive Analytics (PA) and Advanced Topics in Predictive Analytics (ATPA) exams offered by the Society of Actuaries, and the Actuarial Statistics exam offered by the Institute and Faculty of Actuaries.
An intuitive and hands-on resource for senior undergraduate and graduate students studying financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and AI mathematics, the book will also earn a place in the hands of practicing quantitative analysts working in investment and commercial banking.
From the Back Cover
PRAISE FOR FINANCIAL DATA ANALYTICS
“Really interesting, and an impressive masterpiece! Financial Data Analytics contains a rich amount of material, with original research findings in almost every chapter; many parts of the book will even be directly helpful for my own teaching in business school. In view of its dedication towards data-driven analytical tools genuinely needed in financial problems, I believe that it is the very book that defines the scope of financial data analytics.” ―Alain Bensoussan, Fellow of AMS, IEEE, and SIAM; President of INRIA (1984-1996); President of CNES (Centre National d’Etudes Spatiales) (1996-2003); Chairman of ESA Council (European Space Agency) (1999-2002); Former Member of Advisory Board, Mathematical Finance; Lars Magnus Ericsson Chair Professor of Management, Naveen Jindal School of Management, University of Texas at Dallas
“Financial Data Analytics is an exceptional book that integrates mathematics, practical examples, and real-life scenarios. With its focus on real datasets and practical programming codes in Python and R, the book offers a comprehensive exploration of various topics. It presents novel research findings and provides valuable insights for researchers, practitioners, and actuarial students. The book strikes a balance between foundational concepts and advanced techniques, making it an invaluable reference for professionals in the field … By redefining the landscape of financial data analytics in FinTech and InsurTech, this book establishes itself as a trusted guide in the industry.” ― Simon Lam, Fellow of SOA, CFA, FRM; President of The Actuarial Society of Hong Kong (2018, 2023); Deputy CEO & General Manager, Munich Re (Hong Kong)
“The book will certainly play an impactful role in the advancement of financial analytics and should be on the bookshelf of every serious student of the topic.” ― Wai Keung Li, Fellow of Am. Stat. Assoc. and Inst. Math. Stat.; Emeritus Professor, The University of Hong Kong; Dean, Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong
“… The dual focus on theory and applications, together with the discussion on recent advancements of the fields, makes the book one of a kind, even field-defining, among books on similar topics, and an ideal resource for anyone interested in understanding and implementing statistical models in this era of big data, as well as for students preparing for professional examinations on data analytics, such as the SRM, PA and ATPA exams of the Society of Actuaries.” ― Ambrose Lo, Fellow of SOA, Chartered Enterprise Risk Analyst; Author of ACTEX Study Manual for SOA Exam SRM, ACTEX Study Manual for SOA Exam PA, and ACTEX Study Manual for SOA Exam ATPA
“… Financial Data Analytics is one comprehensive biblical handbook for academic researchers, financial practitioners, and graduate students for both methodologies and applications. The book also lays a systematic framework for future extension and enrichment for financial data analytics.” ― Nai-pan Tang, Former Chief Risk Officer and Member of Executive Committee, Hang Seng Bank; Former Deputy CEO and Chief Risk Officer, Shanghai Commercial Bank Ltd.; Former Director of the Board, Deputy CEO, Alternative CEO, Chief Risk Officer, and Vice Chairman of Asset Management, China CITIC Bank International; Director, The Hong Kong Institute of Bankers (2019-2021); Professor of Practice, Department of Finance, Chinese University of Hong Kong
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.
