Advances in financial machine learning
- New Jersey: John Wiley & Sons, 2018
- xxi, 366 p. : ill.; 24 cm.
"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"-- "This book begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are explained using math, supported by code. This makes the book very practical and hands-on. Readers become active users who can test the solutions proposed in their work. Readers will learn how to structure, label, weight, and backtest data. Machine learning is the future, and this book will equip investment professionals with the tools to utilize it moving forward"--
9781119482086
Finance Data processing Mathematical models Machine learning Business Pitfall Time Decay Hyper-parameter Tuning Stationarity Dilemma Memory Dilemma ETF Trick Sampling Meta-Labeling Bootstrap Monte Carlo Experiments Economics Investments Securities Information theory in finance Brute Force Amihuds Lambda Algorithm Symmetric Payouts Kyles Lambda CUSUM Tests Encoding Schemes Long-Run Equilibrium Backtest Statistics Motivation Sisyphus Paradigm Backtesting