Big Data Asset Pricing 5 : Machine Learning in Asset Pricing

These lecture notes are part of a course on Big Data Asset Pricing. The topics covered include an overview of machine learning, supervised and unsupervised learning, and general ML methods such as bias-variance trade-off, hyper-parameter tuning, train-validation-test methods, and feature importance. Further, the notes describe specific methods such as penalized regressions, regression trees, random forests, boosting, and neural networks. Lastly, the notes discuss applications to asset pricing.For more information, see https://www.lhpedersen.com/big-data-asset-pricing