Financial Data Analytics with Machine Learning, Optimization and Statistics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Newark
John Wiley & Sons, Incorporated
2024
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Wiley Finance Series
|
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (813 Seiten) |
| ISBN: | 9781119863397 |
Internformat
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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. | |
| 650 | 4 | |a Finance-Data processing | |
| 700 | 1 | |a Cheung, Ka Chun |e Sonstige |4 oth | |
| 700 | 1 | |a Yam, Phillip |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Chen, Sam |t Financial Data Analytics with Machine Learning, Optimization and Statistics |d Newark : John Wiley & Sons, Incorporated,c2024 |z 9781119863373 |
| 912 | |a ZDB-30-PQE | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035439787 | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31733449 |l DE-2070s |p ZDB-30-PQE |q HWR_PDA_PQE |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1818806145893007360 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Chen, Sam |
| author_facet | Chen, Sam |
| author_role | aut |
| author_sort | Chen, Sam |
| author_variant | s c sc |
| building | Verbundindex |
| bvnumber | BV050102625 |
| collection | ZDB-30-PQE |
| 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 | (ZDB-30-PQE)EBC31733449 (ZDB-30-PAD)EBC31733449 (ZDB-89-EBL)EBL31733449 (OCoLC)1463767640 (DE-599)BVBBV050102625 |
| 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 ed |
| format | Electronic eBook |
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| id | DE-604.BV050102625 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T19:05:07Z |
| institution | BVB |
| isbn | 9781119863397 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035439787 |
| oclc_num | 1463767640 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (813 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| 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 1st ed Newark John Wiley & Sons, Incorporated 2024 ©2025 1 Online-Ressource (813 Seiten) txt rdacontent c rdamedia cr rdacarrier Wiley Finance Series Description based on publisher supplied metadata and other sources 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 Cheung, Ka Chun Sonstige oth Yam, Phillip Sonstige oth Erscheint auch als Druck-Ausgabe Chen, Sam Financial Data Analytics with Machine Learning, Optimization and Statistics Newark : John Wiley & Sons, Incorporated,c2024 9781119863373 |
| 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 |
| 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 |
| title_fullStr | Financial Data Analytics with Machine Learning, Optimization and Statistics |
| title_full_unstemmed | Financial Data Analytics with Machine Learning, Optimization and Statistics |
| 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 |
| topic_facet | Finance-Data processing |
| work_keys_str_mv | AT chensam financialdataanalyticswithmachinelearningoptimizationandstatistics AT cheungkachun financialdataanalyticswithmachinelearningoptimizationandstatistics AT yamphillip financialdataanalyticswithmachinelearningoptimizationandstatistics |