Machine Learning for Asset Pricing

Jantje Sönksen ()
Additional contact information
Jantje Sönksen: Eberhard Karls University

Chapter Chapter 10 in Econometrics with Machine Learning, 2022, pp 337-366 from Springer

Abstract: 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.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:adschp:978-3-031-15149-1_10

Ordering information: This item can be ordered from
http://www.springer.com/9783031151491

DOI: 10.1007/978-3-031-15149-1_10

Access Statistics for this chapter

More chapters in Advanced Studies in Theoretical and Applied Econometrics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().