TY  - BOOK
AU  - Chen, Sam 
AU  - Cheung, Ka Chun 
AU  - Yam, Phillip
TI  - Financial data analytics with machine learning, optimization and statistics
SN  - 9781119863373
U1  - 332.0285 
PY  - 2025///
CY  - Hoboken
PB  - John Wiley & Sons, Inc.
KW  - Finance Data processing
KW  - Finances Informatique
KW  - Machine learning
KW  - Economic aspects
N1  - Introduction 
Part One Data Cleansing and Analytical Models
Chapter 1 Mathematical and Statistical Preliminaries
Chapter 2 Introduction to Python and R
Chapter 3 Statistical Diagnostics of Financial Data
Chapter 4 Financial Forensics
Chapter 5 Numerical Finance
Chapter 6 Approximation for Model Inference
Chapter 7 Time-Varying Volatility Matrix and Kelly Fraction
Chapter 8 Risk Measures, Extreme Values, and Copulae
Part Two Linear Models
Chapter 9 Principal Component Analysis and Recommender Systems
Chapter 10 Regression Learning
Chapter 11 Linear Classifiers
Part Three Nonlinear Models
Chapter 12 Bayesian Learning
Chapter 13 Classification and Regression Trees, and Random Forests
Chapter 14 Cluster Analysis
Chapter 15 Applications of Deep Learning in Finance

N2  - 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
ER  -