Machine Learning and the Optimal Choice of Asset Pricing Model

Aleksadner Bielinski, Daniel Broby

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This chapter evaluates the traditional methods for price prediction and examines, what we believe, are the most promising machine learning techniques for that task. Asset price forecasting is one of the fundamental problems in the financial field. Traditional forecasting methods include Capital Asset Pricing Theory (CAPM) or Factor Models to estimate stocks’ excess returns. More recently, an increasing number of researchers and financial practitioners began to explore the role of machine learning in asset pricing. We show how these methods have been already applied in practice and discuss their results. We also explore the potential use of neural networks in asset pricing as we believe that their capacity to process large amounts of data together with the ability to accurately capture non-linear relationships among the variables makes them a great tool for price prediction.
Original languageEnglish
Title of host publicationArtificial Intelligence for Capital Markets
PublisherTaylor & Francis
Publication statusAccepted/In press - 27 Jun 2022


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