Financial data analytics: (Record no. 8196)
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| 000 -LEADER | |
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| fixed length control field | 08342nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250119122444.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250119b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119863373 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 332.0285 |
| Item number | CHE |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Chen |
| 245 ## - TITLE STATEMENT | |
| Title | Financial data analytics: |
| Remainder of title | with machine learning, optimization and statistics |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Wiley |
| Place of publication, distribution, etc. | Hoboken |
| Date of publication, distribution, etc. | 2025 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxvi, 784 p. |
| 365 ## - TRADE PRICE | |
| Price type code | USD |
| Price amount | 75.00 |
| 490 ## - SERIES STATEMENT | |
| Series statement | Wiley Finance |
| 500 ## - GENERAL NOTE | |
| General note | Table of content:<br/>About the Authors xvii<br/><br/>Foreword xix<br/><br/>Preface xxi<br/><br/>Acknowledgements xxv<br/><br/>Introduction 1<br/><br/>Development of Financial Data Analytics 1<br/><br/>Organization of the Book 5<br/><br/>References 7<br/><br/>Part One Data Cleansing and Analytical Models<br/><br/>Chapter 1 Mathematical and Statistical Preliminaries 11<br/><br/>1.1 Random Vector 12<br/><br/>1.2 Matrix Theory 16<br/><br/>1.3 Vectors and Matrix Norms 23<br/><br/>1.4 Common Probability Distributions 24<br/><br/>1.5 Introductory Bayesian Statistics 30<br/><br/>References 40<br/><br/>Chapter 2 Introduction to Python and R 41<br/><br/>2.1 What is Python? 41<br/><br/>2.2 What is R? 42<br/><br/>2.3 Package Management in Python and R 42<br/><br/>2.4 Basic Operations in Python and R 44<br/><br/>2.5 One-Way ANOVA and Tukey’s HSD for Stock Market Indices 49<br/><br/>References 64<br/><br/>Chapter 3 Statistical Diagnostics of Financial Data 67<br/><br/>3.1 Normality Assumption for Relative Stock Price Changes 67<br/><br/>3.2 Student’s tν-distribution for Stock Price Changes 76<br/><br/>3.3 Testing for Multivariate Normality 81<br/><br/>3.4 Sample Correlation Matrix 84<br/><br/>3.5 Empirical Properties of Stock Prices 86<br/><br/>3.A Appendix 93<br/><br/>References 97<br/><br/>Chapter 4 Financial Forensics 99<br/><br/>4.1 Benford’s Law 99<br/><br/>4.2 Scaling Invariance and Benford’s Law 101<br/><br/>4.3 Benford’s Law in Business Reports 104<br/><br/>4.4 Benford’s Law in Growth Figures 117<br/><br/>4.5 Zipf’s Law 125<br/><br/>4.6 Zipf’s Law and COVID-19 Figures 127<br/><br/>4.A Appendix 132<br/><br/>References 136<br/><br/>Chapter 5 Numerical Finance 139<br/><br/>5.1 Fundamentals of Simulation 139<br/><br/>5.2 Variance Reduction Technique 146<br/><br/>5.3 A Review of Financial Calculus and Derivative Pricing 158<br/><br/>*5.4 Greeks and their Approximations 179<br/><br/>References 199<br/><br/>Chapter 6 Approximation for Model Inference 201<br/><br/>6.1 EM Algorithm 201<br/><br/>6.2 mm Algorithm 216<br/><br/>*6.3 A Short Course on the Theory of Markov Chains 222<br/><br/>*6.4 Markov Chain Monte Carlo 236<br/><br/>*6.A Appendix 261<br/><br/>References 268<br/><br/>Chapter 7 Time-Varying Volatility Matrix and Kelly Fraction 271<br/><br/>7.1 Fluctuation of Volatilities 271<br/><br/>7.2 Exponentially Weighted Moving Average 275<br/><br/>7.3 ARIMA Time Series Model 277<br/><br/>7.4 ARCH and GARCH Models 291<br/><br/>*7.5 Kelly Fraction 317<br/><br/>7.6 Calendar Effects 330<br/><br/>*7.A Appendix 335<br/><br/>References 343<br/><br/>Chapter 8 Risk Measures, Extreme Values, and Copulae 345<br/><br/>8.1 Value-at-Risk and Expected Shortfall 345<br/><br/>8.2 Basel Accords and Risk Measures 348<br/><br/>8.3 Historical Simulation (Bootstrapping) 350<br/><br/>8.4 Statistical Model Building Approach 354<br/><br/>8.5 Use of Extreme Value Theory 356<br/><br/>8.6 Backtesting 359<br/><br/>8.7 Estimates of Expected Shortfall 364<br/><br/>8.8 Dependence Modelling via Copulae 369<br/><br/>*8.A Appendix 402<br/><br/>References 404<br/><br/>Part Two Linear Models<br/><br/>Chapter 9 Principal Component Analysis and Recommender Systems 409<br/><br/>9.1 US Zero-Coupon Rates 409<br/><br/>9.2 PCA Algorithm 411<br/><br/>9.3 Financial Interpretation of PCs for US Zero-Coupon Rates 417<br/><br/>9.4 PCA as an Eigenvalue Problem 421<br/><br/>9.5 Factor Models via PCA 422<br/><br/>9.6 Value-at-Risk via PCA 424<br/><br/>9.7 Portfolio Immunization 427<br/><br/>9.8 Facial Recognition via PCA 430<br/><br/>9.9 Non-Life Insurance via PCA 439<br/><br/>9.10 Investment Strategies using PCA 442<br/><br/>*9.11 Recommender System 447<br/><br/>*9.A Appendix 456<br/><br/>References 465<br/><br/>Chapter 10 Regression Learning 467<br/><br/>10.1 Simple and Multiple Linear Regression Models and Beyond 467<br/><br/>10.2 Polynomial Regression 473<br/><br/>10.3 Generalized Linear Models 478<br/><br/>10.4 Logistic Regression 484<br/><br/>10.5 Poisson Regression 497<br/><br/>10.6 Model Evaluation and Considerations in Practice 501<br/><br/>*10.7 Principal Component Regression 510<br/><br/>*10.A Appendix 518<br/><br/>References 522<br/><br/>Chapter 11 Linear Classifiers 525<br/><br/>11.1 Perceptron 526<br/><br/>11.2 Support Vector Machine 533<br/><br/>*11.A Appendix 545<br/><br/>References 567<br/><br/>Part Three Nonlinear Models<br/><br/>Chapter 12 Bayesian Learning 571<br/><br/>12.1 Simple Credibility Theory 571<br/><br/>*12.2 Bayesian Asymptotic Inference 573<br/><br/>12.3 Revisiting Polynomial Regression 575<br/><br/>12.4 Bayesian Classifiers 578<br/><br/>12.5 Comonotone-Independence Bayes Classifier (CIBer) 580<br/><br/>12.A Appendix 609<br/><br/>References 612<br/><br/>Chapter 13 Classification and Regression Trees, and Random Forests 613<br/><br/>13.1 Classification (Decision) Trees 613<br/><br/>*13.2 Concepts of Entropies 615<br/><br/>13.3 Information Gain 623<br/><br/>13.4 Other Impurity Measures for Information 626<br/><br/>13.5 Splitting Against Continuous Attributes 629<br/><br/>13.6 Overfitting in Classification Tree 630<br/><br/>13.7 Classification Trees in Python and R 633<br/><br/>13.8 Regression Trees 641<br/><br/>13.9 Random Forest 649<br/><br/>13.A Appendix 654<br/><br/>References 659<br/><br/>Chapter 14 Cluster Analysis 661<br/><br/>14.1 K-Means Clustering 661<br/><br/>14.2 K-Nearest Neighbour 694<br/><br/>*14.3 Kernel Regression 703<br/><br/>*14.A Appendix 714<br/><br/>References 725<br/><br/>Chapter 15 Applications of Deep Learning in Finance 727<br/><br/>15.1 Human Brains and Artificial Neurons 727<br/><br/>15.2 Feedforward Network 729<br/><br/>15.3 ANN with Linear Outputs 730<br/><br/>15.4 ANN with Logistic Outputs 737<br/><br/>15.5 Adaptive Learning Rate 740<br/><br/>15.6 Training Neural Networks via Backpropagation 742<br/><br/>15.7 Multilayer Perceptron 746<br/><br/>15.8 Universal Approximation Theorem 752<br/><br/>15.9 Long Short-Term Memory (LSTM) 754<br/><br/>References 764<br/><br/>Postlude 767<br/><br/>Index 769<br/><br/>[https://www.wiley.com/en-au/Financial+Data+Analytics+with+Machine+Learning%2C+Optimization+and+Statistics-p-9781119863373#tableofcontents-section] |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | An essential introduction to data analytics and Machine Learning techniques in the business sector<br/><br/>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.<br/><br/>This book can help readers become well-equipped with the following skills:<br/><br/>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<br/>To apply effective data dimension reduction tools to enhance supervised learning<br/>To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose<br/>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.<br/><br/>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.<br/><br/>(https://www.wiley.com/en-au/Financial+Data+Analytics+with+Machine+Learning%2C+Optimization+and+Statistics-p-9781119863373) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Finance--Data processing |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Cheung, Ka Chun |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Phillip, Yam |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Dewey Decimal Classification |
No items available.