Y. Chen, K.C. Cheung, S.C.P. Yam (2024). Financial Data Analytics: with Machine Learning, Optimization and Statistics.
Wiley Finance Series; John Wiley & Sons.

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

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

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

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

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

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

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

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

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

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

Chapter 11 Linear Classifiers
11.1 Perceptron
11.2 Support Vector Machine
11.A Appendix

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

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

Chapter 14 Cluster Analysis
14.1 K-Means Clustering
14.2 K-Nearest Neighbour
14.3 Kernel Regression
14.A Appendix

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)