Application of machine learning in quantitative investment strategies on global stock markets

Jan Grudniewicz and Robert Ślepaczuk
Additional contact information
Jan Grudniewicz: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2021-23, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: The thesis undertakes the subject of machine learning based quantitative investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500 and selected CEE indices in the period between 2002-01-01 to 2020-10-30. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Quality of estimation was evaluated on independent subsets and with the use of sensitivity analysis. The research results indicated that quantitative strategies generate better risk adjusted returns than passive strategies and that for the analysed indices predominantly Bayesian Generalized Linear Model and Naïve Bayes were the best performing models. More comprehensive rank approach based on the results for all analysed models and indices allowed to select Bayesian Generalized Linear Model as the model which on average generated the best results.

Keywords: quantitative investment strategies; machine learning; neural networks; regression trees; random forests; support vector machine; technical analysis; equity stock indices; developed and emerging markets; information ratio (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2021
New Economics Papers: this item is included in nep-big and nep-cmp
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.wne.uw.edu.pl/index.php/download_file/6844/ First version, 2021 (application/pdf)

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:war:wpaper:2021-23

Access Statistics for this paper

More papers in Working Papers from Faculty of Economic Sciences, University of Warsaw Contact information at EDIRC.
Bibliographic data for series maintained by Marcin Bąba ().