Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection.
Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection.
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.
Inhaltsangabe
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
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
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