Detail práce

On the Utilization of Machine Learning in Asset Return Prediction on Limited Datasets

Autor: Mgr. Lukáš Petrásek,
Rok: 2019 - letní
Vedoucí: doc. PhDr. Jozef Baruník Ph.D.
Konzultant:
Typ práce: Diplomová
Finance, finanční trhy a bankovnictví
Jazyk: Anglicky
Stránky: 74
Ocenění:
Odkaz:
Abstrakt: In this thesis, we conduct a comparative analysis of how various modern machine learning techniques perform when employed to asset return prediction
on a relatively small sample. We consider a broad selection of machine learning methods, including e.g. elastic nets, random forests or recently highly
popularized neural networks. We find that these methods fail to outperform
a simple linear model containing only 5 factors and estimated via ordinary
least squares. Our conclusion is that applications of machine learning in finance should be conducted carefully, because the techniques may not actually
be as powerful as one might think when they are applied under unfavorable
circumstances.

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance