Financial data analytics with machine learning, optimization and statistics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken
John Wiley & Sons, Incorporated
2025
|
| Ausgabe: | 1st edition |
| Schriftenreihe: | Wiley Finance Series
|
| Schlagworte: | |
| Beschreibung: | xxvi, 784 Seiten |
| ISBN: | 9781119863373 |
Internformat
MARC
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| 100 | 1 | |a Chen, Sam |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Financial data analytics with machine learning, optimization and statistics |c Sam Chen (Hang Seng University of Hong Kong), Ka Chun Cheung (University of Hong Kong), Phillip Yam (Chinese University of Hong Kong), with programme codes by Kaiser Fan |
| 250 | |a 1st edition | ||
| 264 | 1 | |a Hoboken |b John Wiley & Sons, Incorporated |c 2025 | |
| 300 | |a xxvi, 784 Seiten | ||
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Foreword -- Preface -- Acknowledgements -- Introduction -- Development of Financial Data Analytics -- Organization of the Book -- References -- Part I Data Cleansing and Analytical Models -- Chapter 1 Mathematical and Statistical Preliminaries -- 1.1 Random Vector -- 1.2 Matrix Theory -- 1.3 Vectors and Matrix Norms -- 1.4 Common Probability Distributions -- 1.5 Introductory Bayesian Statistics -- References -- Chapter 2 Introduction to Python and R -- 2.1 What is Python? -- 2.2 What is R? -- 2.3 Package Management in Python and R -- 2.4 Basic Operations in Python and R -- 2.5 One‐way ANOVA and Tukey's HSD for Stock Market Indices -- References -- Chapter 3 Statistical Diagnostics of Financial Data -- 3.1 Normality Assumption for Relative Stock Price Changes -- 3.2 Student's t-distribution for Stock Price Changes -- 3.3 Testing for Multivariate Normality -- 3.4 Sample Correlation Matrix -- 3.5 Empirical Properties of Stock Prices -- 3.A Appendix -- References -- Chapter 4 Financial Forensics -- 4.1 Benford's Law -- 4.2 Scaling Invariance and Benford's Law -- 4.3 Benford's Law in Business Reports -- 4.4 Benford's Law in Growth Figures -- 4.5 Zipf's Law -- 4.6 Zipf's Law and COVID‐19 Figures -- 4.A Appendix -- References -- Chapter 5 Numerical Finance -- 5.1 Fundamentals of Simulation -- 5.2 Variance Reduction Technique -- 5.3 A Review of Financial Calculus and Derivative Pricing -- *5.4 Greeks and their Approximations -- References -- Chapter 6 Approximation for Model Inference -- 6.1 EM Algorithm -- 6.2 MM Algorithm -- *6.3 A Short Course on the Theory of Markov Chains -- *6.4 Markov Chain Monte Carlo -- *6.A Appendix -- References -- Chapter 7 Time‐Varying Volatility Matrix and Kelly Fraction -- 7.1 Fluctuation of Volatilities -- 7.2 Exponentially Weighted Moving Average | |
| 505 | 8 | |a 7.3 ARIMA Time Series Model -- 7.4 ARCH and GARCH Models -- *7.5 Kelly Fraction -- 7.6 Calendar Effects -- *7.A Appendix -- References -- Chapter 8 Risk Measures, Extreme Values, and Copulae -- 8.1 Value‐at‐Risk and Expected Shortfall -- 8.2 Basel Accords and Risk Measures -- 8.3 Historical Simulation (Bootstrapping) -- 8.4 Statistical Model Building Approach -- 8.5 Use of Extreme Value Theory -- 8.6 Backtesting -- 8.7 Estimates of Expected Shortfall -- 8.8 Dependence Modelling via Copulae -- *8.A Appendix -- References -- Part II Linear Models -- Chapter 9 Principal Component Analysis and Recommender Systems -- 9.1 US Zero‐Coupon Rates -- 9.2 PCA Algorithm -- 9.3 Financial Interpretation of PCs for US Zero‐Coupon Rates -- 9.4 PCA as an Eigenvalue Problem -- 9.5 Factor Models via PCA -- 9.6 Value‐at‐Risk via PCA -- 9.7 Portfolio Immunization -- 9.8 Facial Recognition via PCA -- 9.9 Non‐Life Insurance via PCA -- 9.10 Investment Strategies using PCA -- *9.11 Recommender System -- *9.A Appendix -- References -- Chapter 10 Regression Learning -- 10.1 Simple and Multiple Linear Regression Models and Beyond -- 10.2 Polynomial Regression -- 10.3 Generalized Linear Models -- 10.4 Logistic Regression -- 10.5 Poisson Regression -- 10.6 Model Evaluation and Considerations in Practice -- *10.7 Principal Component Regression -- *10.A Appendix -- References -- Chapter 11 Linear Classifiers -- 11.1 Perceptron -- 11.2 Support Vector Machine -- *11.A Appendix -- References -- Part III Nonlinear Models -- Chapter 12 Bayesian Learning -- 12.1 Simple Credibility Theory -- *12.2 Bayesian Asymptotic Inference -- 12.3 Revisiting Polynomial Regression -- 12.4 Bayesian Classifiers -- 12.5 Comonotone‐Independence Bayes Classifier (CIBer) -- 12.A Appendix -- References -- Chapter 13 Classification and Regression Trees, and Random Forests -- 13.1 Classification (Decision) Trees | |
| 505 | 8 | |a *13.2 Concepts of Entropies -- 13.3 Information Gain -- 13.4 Other Impurity Measures for Information -- 13.5 Splitting Against Continuous Attributes -- 13.6 Overfitting in Classification Tree -- 13.7 Classification Trees in Python and R -- 13.8 Regression Trees -- 13.9 Random Forest -- 13.A Appendix -- References -- Chapter 14 Cluster Analysis -- 14.1 K‐means Clustering -- 14.2 K‐Nearest Neighbour -- *14.3 Kernel Regression -- *14.A Appendix -- References -- Chapter 15 Applications of Deep Learning in Finance -- 15.1 Human Brains and Artificial Neurons -- 15.2 Feedforward Network -- 15.3 ANN with Linear Outputs -- 15.4 ANN with Logistic Outputs -- 15.5 Adaptive Learning Rate -- 15.6 Training Neural Networks via Backpropagation -- 15.7 Multilayer Perceptron -- 15.8 Universal Approximation Theorem -- 15.9 Long Short‐Term Memory (LSTM) -- References -- Postlude -- Index -- EULA. | |
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Datensatz im Suchindex
| _version_ | 1827141163545526272 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Chen, Sam |
| author_GND | (DE-588)142380695 |
| author_facet | Chen, Sam |
| author_role | aut |
| author_sort | Chen, Sam |
| author_variant | s c sc |
| building | Verbundindex |
| bvnumber | BV050112116 |
| classification_rvk | QP 890 |
| contents | Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Foreword -- Preface -- Acknowledgements -- Introduction -- Development of Financial Data Analytics -- Organization of the Book -- References -- Part I Data Cleansing and Analytical Models -- Chapter 1 Mathematical and Statistical Preliminaries -- 1.1 Random Vector -- 1.2 Matrix Theory -- 1.3 Vectors and Matrix Norms -- 1.4 Common Probability Distributions -- 1.5 Introductory Bayesian Statistics -- References -- Chapter 2 Introduction to Python and R -- 2.1 What is Python? -- 2.2 What is R? -- 2.3 Package Management in Python and R -- 2.4 Basic Operations in Python and R -- 2.5 One‐way ANOVA and Tukey's HSD for Stock Market Indices -- References -- Chapter 3 Statistical Diagnostics of Financial Data -- 3.1 Normality Assumption for Relative Stock Price Changes -- 3.2 Student's t-distribution for Stock Price Changes -- 3.3 Testing for Multivariate Normality -- 3.4 Sample Correlation Matrix -- 3.5 Empirical Properties of Stock Prices -- 3.A Appendix -- References -- Chapter 4 Financial Forensics -- 4.1 Benford's Law -- 4.2 Scaling Invariance and Benford's Law -- 4.3 Benford's Law in Business Reports -- 4.4 Benford's Law in Growth Figures -- 4.5 Zipf's Law -- 4.6 Zipf's Law and COVID‐19 Figures -- 4.A Appendix -- References -- Chapter 5 Numerical Finance -- 5.1 Fundamentals of Simulation -- 5.2 Variance Reduction Technique -- 5.3 A Review of Financial Calculus and Derivative Pricing -- *5.4 Greeks and their Approximations -- References -- Chapter 6 Approximation for Model Inference -- 6.1 EM Algorithm -- 6.2 MM Algorithm -- *6.3 A Short Course on the Theory of Markov Chains -- *6.4 Markov Chain Monte Carlo -- *6.A Appendix -- References -- Chapter 7 Time‐Varying Volatility Matrix and Kelly Fraction -- 7.1 Fluctuation of Volatilities -- 7.2 Exponentially Weighted Moving Average 7.3 ARIMA Time Series Model -- 7.4 ARCH and GARCH Models -- *7.5 Kelly Fraction -- 7.6 Calendar Effects -- *7.A Appendix -- References -- Chapter 8 Risk Measures, Extreme Values, and Copulae -- 8.1 Value‐at‐Risk and Expected Shortfall -- 8.2 Basel Accords and Risk Measures -- 8.3 Historical Simulation (Bootstrapping) -- 8.4 Statistical Model Building Approach -- 8.5 Use of Extreme Value Theory -- 8.6 Backtesting -- 8.7 Estimates of Expected Shortfall -- 8.8 Dependence Modelling via Copulae -- *8.A Appendix -- References -- Part II Linear Models -- Chapter 9 Principal Component Analysis and Recommender Systems -- 9.1 US Zero‐Coupon Rates -- 9.2 PCA Algorithm -- 9.3 Financial Interpretation of PCs for US Zero‐Coupon Rates -- 9.4 PCA as an Eigenvalue Problem -- 9.5 Factor Models via PCA -- 9.6 Value‐at‐Risk via PCA -- 9.7 Portfolio Immunization -- 9.8 Facial Recognition via PCA -- 9.9 Non‐Life Insurance via PCA -- 9.10 Investment Strategies using PCA -- *9.11 Recommender System -- *9.A Appendix -- References -- Chapter 10 Regression Learning -- 10.1 Simple and Multiple Linear Regression Models and Beyond -- 10.2 Polynomial Regression -- 10.3 Generalized Linear Models -- 10.4 Logistic Regression -- 10.5 Poisson Regression -- 10.6 Model Evaluation and Considerations in Practice -- *10.7 Principal Component Regression -- *10.A Appendix -- References -- Chapter 11 Linear Classifiers -- 11.1 Perceptron -- 11.2 Support Vector Machine -- *11.A Appendix -- References -- Part III Nonlinear Models -- Chapter 12 Bayesian Learning -- 12.1 Simple Credibility Theory -- *12.2 Bayesian Asymptotic Inference -- 12.3 Revisiting Polynomial Regression -- 12.4 Bayesian Classifiers -- 12.5 Comonotone‐Independence Bayes Classifier (CIBer) -- 12.A Appendix -- References -- Chapter 13 Classification and Regression Trees, and Random Forests -- 13.1 Classification (Decision) Trees *13.2 Concepts of Entropies -- 13.3 Information Gain -- 13.4 Other Impurity Measures for Information -- 13.5 Splitting Against Continuous Attributes -- 13.6 Overfitting in Classification Tree -- 13.7 Classification Trees in Python and R -- 13.8 Regression Trees -- 13.9 Random Forest -- 13.A Appendix -- References -- Chapter 14 Cluster Analysis -- 14.1 K‐means Clustering -- 14.2 K‐Nearest Neighbour -- *14.3 Kernel Regression -- *14.A Appendix -- References -- Chapter 15 Applications of Deep Learning in Finance -- 15.1 Human Brains and Artificial Neurons -- 15.2 Feedforward Network -- 15.3 ANN with Linear Outputs -- 15.4 ANN with Logistic Outputs -- 15.5 Adaptive Learning Rate -- 15.6 Training Neural Networks via Backpropagation -- 15.7 Multilayer Perceptron -- 15.8 Universal Approximation Theorem -- 15.9 Long Short‐Term Memory (LSTM) -- References -- Postlude -- Index -- EULA. |
| ctrlnum | (OCoLC)1472955075 (DE-599)BVBBV050112116 |
| dewey-full | 332 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332 |
| dewey-search | 332 |
| dewey-sort | 3332 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| edition | 1st edition |
| format | Book |
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| id | DE-604.BV050112116 |
| illustrated | Not Illustrated |
| indexdate | 2025-03-20T19:06:40Z |
| institution | BVB |
| isbn | 9781119863373 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035449095 |
| oclc_num | 1472955075 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | xxvi, 784 Seiten |
| publishDate | 2025 |
| publishDateSearch | 2025 |
| publishDateSort | 2025 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| series2 | Wiley Finance Series |
| spelling | Chen, Sam Verfasser aut Financial data analytics with machine learning, optimization and statistics Sam Chen (Hang Seng University of Hong Kong), Ka Chun Cheung (University of Hong Kong), Phillip Yam (Chinese University of Hong Kong), with programme codes by Kaiser Fan 1st edition Hoboken John Wiley & Sons, Incorporated 2025 xxvi, 784 Seiten txt rdacontent n rdamedia nc rdacarrier Wiley Finance Series Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Foreword -- Preface -- Acknowledgements -- Introduction -- Development of Financial Data Analytics -- Organization of the Book -- References -- Part I Data Cleansing and Analytical Models -- Chapter 1 Mathematical and Statistical Preliminaries -- 1.1 Random Vector -- 1.2 Matrix Theory -- 1.3 Vectors and Matrix Norms -- 1.4 Common Probability Distributions -- 1.5 Introductory Bayesian Statistics -- References -- Chapter 2 Introduction to Python and R -- 2.1 What is Python? -- 2.2 What is R? -- 2.3 Package Management in Python and R -- 2.4 Basic Operations in Python and R -- 2.5 One‐way ANOVA and Tukey's HSD for Stock Market Indices -- References -- Chapter 3 Statistical Diagnostics of Financial Data -- 3.1 Normality Assumption for Relative Stock Price Changes -- 3.2 Student's t-distribution for Stock Price Changes -- 3.3 Testing for Multivariate Normality -- 3.4 Sample Correlation Matrix -- 3.5 Empirical Properties of Stock Prices -- 3.A Appendix -- References -- Chapter 4 Financial Forensics -- 4.1 Benford's Law -- 4.2 Scaling Invariance and Benford's Law -- 4.3 Benford's Law in Business Reports -- 4.4 Benford's Law in Growth Figures -- 4.5 Zipf's Law -- 4.6 Zipf's Law and COVID‐19 Figures -- 4.A Appendix -- References -- Chapter 5 Numerical Finance -- 5.1 Fundamentals of Simulation -- 5.2 Variance Reduction Technique -- 5.3 A Review of Financial Calculus and Derivative Pricing -- *5.4 Greeks and their Approximations -- References -- Chapter 6 Approximation for Model Inference -- 6.1 EM Algorithm -- 6.2 MM Algorithm -- *6.3 A Short Course on the Theory of Markov Chains -- *6.4 Markov Chain Monte Carlo -- *6.A Appendix -- References -- Chapter 7 Time‐Varying Volatility Matrix and Kelly Fraction -- 7.1 Fluctuation of Volatilities -- 7.2 Exponentially Weighted Moving Average 7.3 ARIMA Time Series Model -- 7.4 ARCH and GARCH Models -- *7.5 Kelly Fraction -- 7.6 Calendar Effects -- *7.A Appendix -- References -- Chapter 8 Risk Measures, Extreme Values, and Copulae -- 8.1 Value‐at‐Risk and Expected Shortfall -- 8.2 Basel Accords and Risk Measures -- 8.3 Historical Simulation (Bootstrapping) -- 8.4 Statistical Model Building Approach -- 8.5 Use of Extreme Value Theory -- 8.6 Backtesting -- 8.7 Estimates of Expected Shortfall -- 8.8 Dependence Modelling via Copulae -- *8.A Appendix -- References -- Part II Linear Models -- Chapter 9 Principal Component Analysis and Recommender Systems -- 9.1 US Zero‐Coupon Rates -- 9.2 PCA Algorithm -- 9.3 Financial Interpretation of PCs for US Zero‐Coupon Rates -- 9.4 PCA as an Eigenvalue Problem -- 9.5 Factor Models via PCA -- 9.6 Value‐at‐Risk via PCA -- 9.7 Portfolio Immunization -- 9.8 Facial Recognition via PCA -- 9.9 Non‐Life Insurance via PCA -- 9.10 Investment Strategies using PCA -- *9.11 Recommender System -- *9.A Appendix -- References -- Chapter 10 Regression Learning -- 10.1 Simple and Multiple Linear Regression Models and Beyond -- 10.2 Polynomial Regression -- 10.3 Generalized Linear Models -- 10.4 Logistic Regression -- 10.5 Poisson Regression -- 10.6 Model Evaluation and Considerations in Practice -- *10.7 Principal Component Regression -- *10.A Appendix -- References -- Chapter 11 Linear Classifiers -- 11.1 Perceptron -- 11.2 Support Vector Machine -- *11.A Appendix -- References -- Part III Nonlinear Models -- Chapter 12 Bayesian Learning -- 12.1 Simple Credibility Theory -- *12.2 Bayesian Asymptotic Inference -- 12.3 Revisiting Polynomial Regression -- 12.4 Bayesian Classifiers -- 12.5 Comonotone‐Independence Bayes Classifier (CIBer) -- 12.A Appendix -- References -- Chapter 13 Classification and Regression Trees, and Random Forests -- 13.1 Classification (Decision) Trees *13.2 Concepts of Entropies -- 13.3 Information Gain -- 13.4 Other Impurity Measures for Information -- 13.5 Splitting Against Continuous Attributes -- 13.6 Overfitting in Classification Tree -- 13.7 Classification Trees in Python and R -- 13.8 Regression Trees -- 13.9 Random Forest -- 13.A Appendix -- References -- Chapter 14 Cluster Analysis -- 14.1 K‐means Clustering -- 14.2 K‐Nearest Neighbour -- *14.3 Kernel Regression -- *14.A Appendix -- References -- Chapter 15 Applications of Deep Learning in Finance -- 15.1 Human Brains and Artificial Neurons -- 15.2 Feedforward Network -- 15.3 ANN with Linear Outputs -- 15.4 ANN with Logistic Outputs -- 15.5 Adaptive Learning Rate -- 15.6 Training Neural Networks via Backpropagation -- 15.7 Multilayer Perceptron -- 15.8 Universal Approximation Theorem -- 15.9 Long Short‐Term Memory (LSTM) -- References -- Postlude -- Index -- EULA. Finance-Data processing Datenanalyse (DE-588)4123037-1 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Finanzierung (DE-588)4017182-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Finanzierung (DE-588)4017182-6 s Datenverarbeitung (DE-588)4011152-0 s Datenanalyse (DE-588)4123037-1 s Maschinelles Lernen (DE-588)4193754-5 s Statistik (DE-588)4056995-0 s DE-604 Cheung, Ka Chun Sonstige (DE-588)142380695 oth Yam, Phillip Sonstige oth Erscheint auch als Online-Ausgabe, EPUB 978-1-119-86339-7 Erscheint auch als Online-Ausgabe, PDF 978-1-119-86338-0 Erscheint auch als Online-Ausgabe 978-1-119-86340-3 |
| spellingShingle | Chen, Sam Financial data analytics with machine learning, optimization and statistics Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Foreword -- Preface -- Acknowledgements -- Introduction -- Development of Financial Data Analytics -- Organization of the Book -- References -- Part I Data Cleansing and Analytical Models -- Chapter 1 Mathematical and Statistical Preliminaries -- 1.1 Random Vector -- 1.2 Matrix Theory -- 1.3 Vectors and Matrix Norms -- 1.4 Common Probability Distributions -- 1.5 Introductory Bayesian Statistics -- References -- Chapter 2 Introduction to Python and R -- 2.1 What is Python? -- 2.2 What is R? -- 2.3 Package Management in Python and R -- 2.4 Basic Operations in Python and R -- 2.5 One‐way ANOVA and Tukey's HSD for Stock Market Indices -- References -- Chapter 3 Statistical Diagnostics of Financial Data -- 3.1 Normality Assumption for Relative Stock Price Changes -- 3.2 Student's t-distribution for Stock Price Changes -- 3.3 Testing for Multivariate Normality -- 3.4 Sample Correlation Matrix -- 3.5 Empirical Properties of Stock Prices -- 3.A Appendix -- References -- Chapter 4 Financial Forensics -- 4.1 Benford's Law -- 4.2 Scaling Invariance and Benford's Law -- 4.3 Benford's Law in Business Reports -- 4.4 Benford's Law in Growth Figures -- 4.5 Zipf's Law -- 4.6 Zipf's Law and COVID‐19 Figures -- 4.A Appendix -- References -- Chapter 5 Numerical Finance -- 5.1 Fundamentals of Simulation -- 5.2 Variance Reduction Technique -- 5.3 A Review of Financial Calculus and Derivative Pricing -- *5.4 Greeks and their Approximations -- References -- Chapter 6 Approximation for Model Inference -- 6.1 EM Algorithm -- 6.2 MM Algorithm -- *6.3 A Short Course on the Theory of Markov Chains -- *6.4 Markov Chain Monte Carlo -- *6.A Appendix -- References -- Chapter 7 Time‐Varying Volatility Matrix and Kelly Fraction -- 7.1 Fluctuation of Volatilities -- 7.2 Exponentially Weighted Moving Average 7.3 ARIMA Time Series Model -- 7.4 ARCH and GARCH Models -- *7.5 Kelly Fraction -- 7.6 Calendar Effects -- *7.A Appendix -- References -- Chapter 8 Risk Measures, Extreme Values, and Copulae -- 8.1 Value‐at‐Risk and Expected Shortfall -- 8.2 Basel Accords and Risk Measures -- 8.3 Historical Simulation (Bootstrapping) -- 8.4 Statistical Model Building Approach -- 8.5 Use of Extreme Value Theory -- 8.6 Backtesting -- 8.7 Estimates of Expected Shortfall -- 8.8 Dependence Modelling via Copulae -- *8.A Appendix -- References -- Part II Linear Models -- Chapter 9 Principal Component Analysis and Recommender Systems -- 9.1 US Zero‐Coupon Rates -- 9.2 PCA Algorithm -- 9.3 Financial Interpretation of PCs for US Zero‐Coupon Rates -- 9.4 PCA as an Eigenvalue Problem -- 9.5 Factor Models via PCA -- 9.6 Value‐at‐Risk via PCA -- 9.7 Portfolio Immunization -- 9.8 Facial Recognition via PCA -- 9.9 Non‐Life Insurance via PCA -- 9.10 Investment Strategies using PCA -- *9.11 Recommender System -- *9.A Appendix -- References -- Chapter 10 Regression Learning -- 10.1 Simple and Multiple Linear Regression Models and Beyond -- 10.2 Polynomial Regression -- 10.3 Generalized Linear Models -- 10.4 Logistic Regression -- 10.5 Poisson Regression -- 10.6 Model Evaluation and Considerations in Practice -- *10.7 Principal Component Regression -- *10.A Appendix -- References -- Chapter 11 Linear Classifiers -- 11.1 Perceptron -- 11.2 Support Vector Machine -- *11.A Appendix -- References -- Part III Nonlinear Models -- Chapter 12 Bayesian Learning -- 12.1 Simple Credibility Theory -- *12.2 Bayesian Asymptotic Inference -- 12.3 Revisiting Polynomial Regression -- 12.4 Bayesian Classifiers -- 12.5 Comonotone‐Independence Bayes Classifier (CIBer) -- 12.A Appendix -- References -- Chapter 13 Classification and Regression Trees, and Random Forests -- 13.1 Classification (Decision) Trees *13.2 Concepts of Entropies -- 13.3 Information Gain -- 13.4 Other Impurity Measures for Information -- 13.5 Splitting Against Continuous Attributes -- 13.6 Overfitting in Classification Tree -- 13.7 Classification Trees in Python and R -- 13.8 Regression Trees -- 13.9 Random Forest -- 13.A Appendix -- References -- Chapter 14 Cluster Analysis -- 14.1 K‐means Clustering -- 14.2 K‐Nearest Neighbour -- *14.3 Kernel Regression -- *14.A Appendix -- References -- Chapter 15 Applications of Deep Learning in Finance -- 15.1 Human Brains and Artificial Neurons -- 15.2 Feedforward Network -- 15.3 ANN with Linear Outputs -- 15.4 ANN with Logistic Outputs -- 15.5 Adaptive Learning Rate -- 15.6 Training Neural Networks via Backpropagation -- 15.7 Multilayer Perceptron -- 15.8 Universal Approximation Theorem -- 15.9 Long Short‐Term Memory (LSTM) -- References -- Postlude -- Index -- EULA. Finance-Data processing Datenanalyse (DE-588)4123037-1 gnd Datenverarbeitung (DE-588)4011152-0 gnd Finanzierung (DE-588)4017182-6 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4011152-0 (DE-588)4017182-6 (DE-588)4056995-0 (DE-588)4193754-5 |
| title | Financial data analytics with machine learning, optimization and statistics |
| title_auth | Financial data analytics with machine learning, optimization and statistics |
| title_exact_search | Financial data analytics with machine learning, optimization and statistics |
| title_full | Financial data analytics with machine learning, optimization and statistics Sam Chen (Hang Seng University of Hong Kong), Ka Chun Cheung (University of Hong Kong), Phillip Yam (Chinese University of Hong Kong), with programme codes by Kaiser Fan |
| title_fullStr | Financial data analytics with machine learning, optimization and statistics Sam Chen (Hang Seng University of Hong Kong), Ka Chun Cheung (University of Hong Kong), Phillip Yam (Chinese University of Hong Kong), with programme codes by Kaiser Fan |
| title_full_unstemmed | Financial data analytics with machine learning, optimization and statistics Sam Chen (Hang Seng University of Hong Kong), Ka Chun Cheung (University of Hong Kong), Phillip Yam (Chinese University of Hong Kong), with programme codes by Kaiser Fan |
| title_short | Financial data analytics with machine learning, optimization and statistics |
| title_sort | financial data analytics with machine learning optimization and statistics |
| topic | Finance-Data processing Datenanalyse (DE-588)4123037-1 gnd Datenverarbeitung (DE-588)4011152-0 gnd Finanzierung (DE-588)4017182-6 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Finance-Data processing Datenanalyse Datenverarbeitung Finanzierung Statistik Maschinelles Lernen |
| work_keys_str_mv | AT chensam financialdataanalyticswithmachinelearningoptimizationandstatistics AT cheungkachun financialdataanalyticswithmachinelearningoptimizationandstatistics AT yamphillip financialdataanalyticswithmachinelearningoptimizationandstatistics |