Advances in financial machine learning

TABLE OF CONTENTS

1 Financial Machine Learning as a Distinct Subject

PART 1 DATA ANALYSIS

2 Financial Data Structures

3 Labeling

4 Sample Weights

5 Fractionally Differentiated Features

PART 2 MODELLING

6 Ensemble Methods

7 Cross-Validation in Finance

8 Feature Importance

9 Hyper-Parameter Tuning with Cross-Validation

PART 3 BACKTESTING

10 Bet Sizing

11 The Dangers of Backtesting

12 Backtesting through Cross-Validation 161

13 Backtesting on Synthetic Data

14 Backtest Statistics

15 Understanding Strategy Risk

16 Machine Learning Asset Allocation

PART 4 USEFUL FINANCIAL FEATURES

17 Structural Breaks

18 Entropy Features

19 Microstructural Features 281

PART 5 HIGH-PERFORMANCE COMPUTING RECIPES

20 Multiprocessing and Vectorization

21 Brute Force and Quantum Computers

22 High-Performance Computational Intelligence and Forecasting Technologies

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

https://www.wiley.com/en-aw/Advances+in+Financial+Machine+Learning-p-9781119482086