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Machine Learning for Asset Pricing

In: Econometrics with Machine Learning

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  • Jantje Sönksen

    (Eberhard Karls University)

Abstract

This chapter reviews the growing literature that describes machine learning applications in the field of asset pricing. In doing so, it focuses on the additional benefits that machine learning – in addition to, or in combination with, standard econometric approaches – can bring to the table. This issue is of particular importance because in recent years, improved data availability and increased computational facilities have had huge effects on finance literature. For example, machine learning techniques inform analyses of conditional factor models; they have been applied to identify the stochastic discount factor and purposefully to test and evaluate existing asset pricing models. Beyond those pertinent applications, machine learning techniques also lend themselves to prediction problems in the domain of empirical asset pricing.

Suggested Citation

  • Jantje Sönksen, 2022. "Machine Learning for Asset Pricing," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 337-366, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-15149-1_10
    DOI: 10.1007/978-3-031-15149-1_10
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