Part 1. Introduction 1. Notations and data 2. Introduction 3. Factor investing and asset pricing anomalies 4. Data preprocessing Part 2. Common supervised algorithms 5. Penalized regressions and sparse hedging for minimum variance portfolios 6. Tree-based methods 7. Neural networks 8. Support vector machines 9. Bayesian methods Part 3. From predictions to portfolios 10. Validating and tuning 11. Ensemble models 12. Portfolio backtesting Part 4. Further important topics 13. Interpretability 14. Two key concepts: causality and non-stationarity 15. Unsupervised learning 16. Reinforcement learning Part 5. Appendix 17. Data description 18. Solutions to exercises
Guillaume Coqueret is associate professor of finance and data science at EMLYON Business School. His recent research revolves around applications of machine learning tools in financial economics.
Tony Guida is co-head of Systematic Macro at RAM Active Investments. He is the editor and co-author of Big Data and Machine Learning in Quantitative Investment.
"Machine learning is considered promising for investment management
applications, yet the associated low signal to noise ratio presents
a high bar for improving on the incumbent quant asset management
tooling. The book of Coqueret and Guida is a treat for those who do
not want to lose sight of the machine learning forest for the
trees. Whether you are an academic scholar or a finance
practitioner, you will learn just what you need to rigorously
investigate machine learning techniques for factor investing
applications, along with plenty of useful code snippets."
-Harald Lohre, Executive Director of Research at Robeco and
Honorary Researcher at Lancaster University Management
School"Written by two experts on quantitative finance, this book
covers everything from basic materials to advanced techniques in
the field of quantitative investment strategies: data processing,
alpha signal generation, portfolio optimization, backtesting and
performance evaluation. Concrete examples related to asset
management problems illustrate each machine learning technique,
such as neural network, lasso regression, autoencoder or
reinforcement learning. With more than 20 coding exercises and
solutions provided in Python, this publication is a must for both
students, academics and professionals who are looking for an
up-to-date technical exposition on quantitative asset management
from basic smart beta portfolios to enhanced alpha strategies
including factor investing."
-Thierry Roncalli, Head of Quantitative Portfolio Strategy at
Amundi Institute, Amundi Asset Management
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