{"product_id":"financial-data-analytics-with-machine-learning-optimization-and-statistics-isbn-9781119863373","title":"Financial Data Analytics with Machine Learning, Optimization and Statistics","description":"\u003cp\u003e\u003cb\u003eAn essential introduction to data analytics and Machine Learning techniques in the business sector\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eFinancial Data Analytics with Machine Learning, Optimization and Statistics\u003c\/i\u003e, 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.\u003c\/p\u003e \u003cp\u003eThis book can help readers become well-equipped with the following skills:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eTo 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\u003c\/li\u003e \u003cli\u003eTo apply effective data dimension reduction tools to enhance supervised learning\u003c\/li\u003e \u003cli\u003eTo describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eBesides 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, \u003ci\u003eFinancial Data Analytics with Machine Learning, Optimization and Statistics\u003c\/i\u003e also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.\u003c\/p\u003e \u003cp\u003eAbout the Authors xvii\u003c\/p\u003e \u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAcknowledgements xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDevelopment of Financial Data Analytics 1\u003c\/p\u003e \u003cp\u003eOrganization of the Book 5\u003c\/p\u003e \u003cp\u003eReferences 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Data Cleansing and Analytical Models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Mathematical and Statistical Preliminaries 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Random Vector 12\u003c\/p\u003e \u003cp\u003e1.2 Matrix Theory 16\u003c\/p\u003e \u003cp\u003e1.3 Vectors and Matrix Norms 23\u003c\/p\u003e \u003cp\u003e1.4 Common Probability Distributions 24\u003c\/p\u003e \u003cp\u003e1.5 Introductory Bayesian Statistics 30\u003c\/p\u003e \u003cp\u003eReferences 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Introduction to Python and R 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 What is Python? 41\u003c\/p\u003e \u003cp\u003e2.2 What is R? 42\u003c\/p\u003e \u003cp\u003e2.3 Package Management in Python and R 42\u003c\/p\u003e \u003cp\u003e2.4 Basic Operations in Python and R 44\u003c\/p\u003e \u003cp\u003e2.5 One-Way ANOVA and Tukey’s HSD for Stock Market Indices 49\u003c\/p\u003e \u003cp\u003eReferences 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Statistical Diagnostics of Financial Data 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Normality Assumption for Relative Stock Price Changes 67\u003c\/p\u003e \u003cp\u003e3.2 Student’s t\u003csub\u003eν\u003c\/sub\u003e-distribution for Stock Price Changes 76\u003c\/p\u003e \u003cp\u003e3.3 Testing for Multivariate Normality 81\u003c\/p\u003e \u003cp\u003e3.4 Sample Correlation Matrix 84\u003c\/p\u003e \u003cp\u003e3.5 Empirical Properties of Stock Prices 86\u003c\/p\u003e \u003cp\u003e3.A Appendix 93\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Financial Forensics 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Benford’s Law 99\u003c\/p\u003e \u003cp\u003e4.2 Scaling Invariance and Benford’s Law 101\u003c\/p\u003e \u003cp\u003e4.3 Benford’s Law in Business Reports 104\u003c\/p\u003e \u003cp\u003e4.4 Benford’s Law in Growth Figures 117\u003c\/p\u003e \u003cp\u003e4.5 Zipf’s Law 125\u003c\/p\u003e \u003cp\u003e4.6 Zipf’s Law and COVID-19 Figures 127\u003c\/p\u003e \u003cp\u003e4.A Appendix 132\u003c\/p\u003e \u003cp\u003eReferences 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Numerical Finance 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Fundamentals of Simulation 139\u003c\/p\u003e \u003cp\u003e5.2 Variance Reduction Technique 146\u003c\/p\u003e \u003cp\u003e5.3 A Review of Financial Calculus and Derivative Pricing 158\u003c\/p\u003e \u003cp\u003e*5.4 Greeks and their Approximations 179\u003c\/p\u003e \u003cp\u003eReferences 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Approximation for Model Inference 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 EM Algorithm 201\u003c\/p\u003e \u003cp\u003e6.2 mm Algorithm 216\u003c\/p\u003e \u003cp\u003e*6.3 A Short Course on the Theory of Markov Chains 222\u003c\/p\u003e \u003cp\u003e*6.4 Markov Chain Monte Carlo 236\u003c\/p\u003e \u003cp\u003e*6.A Appendix 261\u003c\/p\u003e \u003cp\u003eReferences 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Time-Varying Volatility Matrix and Kelly Fraction 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Fluctuation of Volatilities 271\u003c\/p\u003e \u003cp\u003e7.2 Exponentially Weighted Moving Average 275\u003c\/p\u003e \u003cp\u003e7.3 ARIMA Time Series Model 277\u003c\/p\u003e \u003cp\u003e7.4 ARCH and GARCH Models 291\u003c\/p\u003e \u003cp\u003e*7.5 Kelly Fraction 317\u003c\/p\u003e \u003cp\u003e7.6 Calendar Effects 330\u003c\/p\u003e \u003cp\u003e*7.A Appendix 335\u003c\/p\u003e \u003cp\u003eReferences 343\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Risk Measures, Extreme Values, and Copulae 345\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Value-at-Risk and Expected Shortfall 345\u003c\/p\u003e \u003cp\u003e8.2 Basel Accords and Risk Measures 348\u003c\/p\u003e \u003cp\u003e8.3 Historical Simulation (Bootstrapping) 350\u003c\/p\u003e \u003cp\u003e8.4 Statistical Model Building Approach 354\u003c\/p\u003e \u003cp\u003e8.5 Use of Extreme Value Theory 356\u003c\/p\u003e \u003cp\u003e8.6 Backtesting 359\u003c\/p\u003e \u003cp\u003e8.7 Estimates of Expected Shortfall 364\u003c\/p\u003e \u003cp\u003e8.8 Dependence Modelling via Copulae 369\u003c\/p\u003e \u003cp\u003e*8.A Appendix 402\u003c\/p\u003e \u003cp\u003eReferences 404\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Linear Models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Principal Component Analysis and Recommender Systems 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 US Zero-Coupon Rates 409\u003c\/p\u003e \u003cp\u003e9.2 PCA Algorithm 411\u003c\/p\u003e \u003cp\u003e9.3 Financial Interpretation of PCs for US Zero-Coupon Rates 417\u003c\/p\u003e \u003cp\u003e9.4 PCA as an Eigenvalue Problem 421\u003c\/p\u003e \u003cp\u003e9.5 Factor Models via PCA 422\u003c\/p\u003e \u003cp\u003e9.6 Value-at-Risk via PCA 424\u003c\/p\u003e \u003cp\u003e9.7 Portfolio Immunization 427\u003c\/p\u003e \u003cp\u003e9.8 Facial Recognition via PCA 430\u003c\/p\u003e \u003cp\u003e9.9 Non-Life Insurance via PCA 439\u003c\/p\u003e \u003cp\u003e9.10 Investment Strategies using PCA 442\u003c\/p\u003e \u003cp\u003e*9.11 Recommender System 447\u003c\/p\u003e \u003cp\u003e*9.A Appendix 456\u003c\/p\u003e \u003cp\u003eReferences 465\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Regression Learning 467\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Simple and Multiple Linear Regression Models and Beyond 467\u003c\/p\u003e \u003cp\u003e10.2 Polynomial Regression 473\u003c\/p\u003e \u003cp\u003e10.3 Generalized Linear Models 478\u003c\/p\u003e \u003cp\u003e10.4 Logistic Regression 484\u003c\/p\u003e \u003cp\u003e10.5 Poisson Regression 497\u003c\/p\u003e \u003cp\u003e10.6 Model Evaluation and Considerations in Practice 501\u003c\/p\u003e \u003cp\u003e*10.7 Principal Component Regression 510\u003c\/p\u003e \u003cp\u003e*10.A Appendix 518\u003c\/p\u003e \u003cp\u003eReferences 522\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Linear Classifiers 525\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Perceptron 526\u003c\/p\u003e \u003cp\u003e11.2 Support Vector Machine 533\u003c\/p\u003e \u003cp\u003e*11.A Appendix 545\u003c\/p\u003e \u003cp\u003eReferences 567\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Nonlinear Models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Bayesian Learning 571\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Simple Credibility Theory 571\u003c\/p\u003e \u003cp\u003e*12.2 Bayesian Asymptotic Inference 573\u003c\/p\u003e \u003cp\u003e12.3 Revisiting Polynomial Regression 575\u003c\/p\u003e \u003cp\u003e12.4 Bayesian Classifiers 578\u003c\/p\u003e \u003cp\u003e12.5 Comonotone-Independence Bayes Classifier (CIBer) 580\u003c\/p\u003e \u003cp\u003e12.A Appendix 609\u003c\/p\u003e \u003cp\u003eReferences 612\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Classification and Regression Trees, and Random Forests 613\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Classification (Decision) Trees 613\u003c\/p\u003e \u003cp\u003e*13.2 Concepts of Entropies 615\u003c\/p\u003e \u003cp\u003e13.3 Information Gain 623\u003c\/p\u003e \u003cp\u003e13.4 Other Impurity Measures for Information 626\u003c\/p\u003e \u003cp\u003e13.5 Splitting Against Continuous Attributes 629\u003c\/p\u003e \u003cp\u003e13.6 Overfitting in Classification Tree 630\u003c\/p\u003e \u003cp\u003e13.7 Classification Trees in Python and R 633\u003c\/p\u003e \u003cp\u003e13.8 Regression Trees 641\u003c\/p\u003e \u003cp\u003e13.9 Random Forest 649\u003c\/p\u003e \u003cp\u003e13.A Appendix 654\u003c\/p\u003e \u003cp\u003eReferences 659\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Cluster Analysis 661\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 K-Means Clustering 661\u003c\/p\u003e \u003cp\u003e14.2 K-Nearest Neighbour 694\u003c\/p\u003e \u003cp\u003e*14.3 Kernel Regression 703\u003c\/p\u003e \u003cp\u003e*14.A Appendix 714\u003c\/p\u003e \u003cp\u003eReferences 725\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Applications of Deep Learning in Finance 727\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Human Brains and Artificial Neurons 727\u003c\/p\u003e \u003cp\u003e15.2 Feedforward Network 729\u003c\/p\u003e \u003cp\u003e15.3 ANN with Linear Outputs 730\u003c\/p\u003e \u003cp\u003e15.4 ANN with Logistic Outputs 737\u003c\/p\u003e \u003cp\u003e15.5 Adaptive Learning Rate 740\u003c\/p\u003e \u003cp\u003e15.6 Training Neural Networks via Backpropagation 742\u003c\/p\u003e \u003cp\u003e15.7 Multilayer Perceptron 746\u003c\/p\u003e \u003cp\u003e15.8 Universal Approximation Theorem 752\u003c\/p\u003e \u003cp\u003e15.9 Long Short-Term Memory (LSTM) 754\u003c\/p\u003e \u003cp\u003eReferences 764\u003c\/p\u003e \u003cp\u003ePostlude 767\u003c\/p\u003e \u003cp\u003eIndex 769\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eYONGZHAO CHEN (SAM) [BSC(ACTUARSC) \u0026amp; PHD (HKU)]\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKA CHUN CHEUNG [BSC(ACTUARSC) \u0026amp; PHD (HKU), ASA (SOA)]\u003c\/b\u003e 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.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePHILLIP YAM [BSC(ACTUARSC) \u0026amp; MPHIL (HKU), MAST (CANTAB), DPHIL (OXON)]\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003eContemporary 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. \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eFinancial Data Analytics\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eThis 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 \u003cb\u003eR \u003c\/b\u003ecode 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eFinancial Data Analytics \u003c\/i\u003eincludes extensive coverage of the materials tested by several professional examinations, including the \u003ci\u003eStochastic Risk Modelling\u003c\/i\u003e (SRM), \u003ci\u003ePredictive Analytics\u003c\/i\u003e (PA) and \u003ci\u003eAdvanced Topics in Predictive Analytics\u003c\/i\u003e (ATPA) exams offered by the Society of Actuaries, and the \u003ci\u003eActuarial Statistics\u003c\/i\u003e exam offered by the Institute and Faculty of Actuaries. \u003c\/p\u003e\u003cp\u003eAn 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003csmall\u003ePRAISE FOR\u003c\/small\u003e\u003cbr\u003e FINANCIAL DATA ANALYTICS\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e“Really interesting, and an impressive masterpiece! \u003ci\u003eFinancial Data Analytics\u003c\/i\u003e 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.”\u003cbr\u003e \u003cb\u003e—Alain Bensoussan,\u003c\/b\u003e Fellow of AMS, IEEE, and SIAM; President of \u003ci\u003eINRIA\u003c\/i\u003e (1984-1996); President of \u003ci\u003eCNES (Centre National d'Etudes Spatiales)\u003c\/i\u003e (1996-2003); Chairman of \u003ci\u003eESA Council (European Space Agency)\u003c\/i\u003e (1999-2002); Former Member of Advisory Board, \u003ci\u003eMathematical Finance\u003c\/i\u003e; Lars Magnus Ericsson Chair Professor of Management, \u003ci\u003eNaveen Jindal School of Management, University of Texas at Dallas\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e“\u003ci\u003eFinancial Data Analytics\u003c\/i\u003e 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 \u003cb\u003eR\u003c\/b\u003e, 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.”\u003cbr\u003e \u003cb\u003e— Simon Lam,\u003c\/b\u003e Fellow of SOA, CFA, FRM; President of \u003ci\u003eThe Actuarial Society of Hong Kong\u003c\/i\u003e (2018, 2023); Deputy CEO \u0026amp; General Manager, \u003ci\u003eMunich Re (Hong Kong)\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e“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.”\u003cbr\u003e \u003cb\u003e— Wai Keung Li,\u003c\/b\u003e Fellow of Am. Stat. Assoc. and Inst. Math. Stat.; Emeritus Professor, \u003ci\u003eThe University of Hong Kong;\u003c\/i\u003e Dean, \u003ci\u003eFaculty of Liberal Arts and Social Sciences, The Education University of Hong Kong\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e“... 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.”\u003cbr\u003e \u003cb\u003e— Ambrose Lo,\u003c\/b\u003e Fellow of SOA, Chartered Enterprise Risk Analyst; Author of \u003ci\u003eACTEX Study Manual for SOA Exam SRM, ACTEX Study Manual for SOA Exam PA,\u003c\/i\u003e and \u003ci\u003eACTEX Study Manual for SOA Exam ATPA\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e“... \u003ci\u003eFinancial Data Analytics \u003c\/i\u003eis 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.”\u003cbr\u003e \u003cb\u003e— Nai-pan Tang,\u003c\/b\u003e Former Chief Risk Officer and Member of Executive Committee, \u003ci\u003eHang Seng Bank\u003c\/i\u003e; Former Deputy CEO and Chief Risk Officer, \u003ci\u003eShanghai Commercial Bank Ltd.\u003c\/i\u003e; Former Director of the Board, Deputy CEO, Alternative CEO, Chief Risk Officer, and Vice Chairman of Asset Management, \u003ci\u003eChina CITIC Bank International\u003c\/i\u003e; Director, \u003ci\u003eThe Hong Kong Institute of Bankers \u003c\/i\u003e(2019-2021); Professor of Practice, \u003ci\u003eDepartment of Finance, Chinese University of Hong Kong\u003c\/i\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989209497829,"sku":"NP9781119863373","price":75.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119863373.jpg?v=1761783216","url":"https:\/\/k12savings.com\/es\/products\/financial-data-analytics-with-machine-learning-optimization-and-statistics-isbn-9781119863373","provider":"K12savings","version":"1.0","type":"link"}