Review of Machine Learning for Asset Managers

– Machine Learning for Asset Managers is a short book that explores how applying the right data analysis techniques can solve challenging asset management problems that cannot be addressed using classical statistical analysis.
– The book focuses on problem-solving techniques and provides Python code to help quantitative analysts implement these solutions.
– The author argues that machine learning techniques can significantly benefit asset managers by enhancing theory and improving data clarity.
– The book covers seven complex problems in asset management where ML techniques can add value, including covariance matrices, distance matrices, financial labeling, statistical significance, portfolio construction, and tests for overfitting.
– Machine Learning for Asset Managers effectively demonstrates the power of ML techniques in solving difficult asset management problems, but it may not be suitable as an introduction for general asset managers.