The Uncertainty of Machine Learning Predictions in Asset Pricing

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Event details

Date 29.11.2024
Hour 11:4513:00
Speaker Andreas Neuhierl - Washington Uni. in St. Louis
Location
UniL Campus, Room Extra 126
Category Conferences - Seminars
Event Language English

We develop novel methodology to construct forecast confidence intervals (FCI) for machine learning predictions in asset pricing. We show FCIs for machine learning predictions obtained from sophisticated ML methods, such as neural networks, can be accurately approximated by simpler nonparametric methods such as B-splines. We prove that these FCIs provide correct coverage probabilities. In addition, we also establish the validity of a version of the wild bootstrap. We illustrate the practical use of the obtained confidence intervals in the context of a portfolio selection application for an uncertainty averse investor.