<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, the use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest algorithm was selected as an ideal representative of the nonlinear algorithms based on decision trees. Various brokering and investment firms and individual investors need comprehensive and insight information such as the drivers of stock price movements and relationships existing between the various factors of the stock market so that they can invest efficiently through better understanding. Our work focuses on determining the factors that drive the future price movements of Stoxx Europe 600, DAX, and WIG20 by using the importance of input variables in the Random Forest classifier. The main determinants were derived from a large dataset containing macroeconomic and market data, which were collected everyday through various ways.</p></abstract>

In this paper, the use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest algorithm was selected as an ideal representative of the nonlinear algorithms based on decision trees. Various brokering and investment firms and individual investors need comprehensive and insight information such as the drivers of stock price movements and relationships existing between the various factors of the stock market so that they can invest efficiently through better understanding. Our work focuses on determining the factors that drive the future price movements of Stoxx Europe 600, DAX, and WIG20 by using the importance of input variables in the Random Forest classifier. The main determinants were derived from a large dataset containing macroeconomic and market data, which were collected everyday through various ways.

Identification of nonlinear determinants of stock indices derived by Random Forest algorithm

International Journal of Management and Economics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, the use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest...

Identification of nonlinear determinants of stock indices derived by Random Forest algorithm

Article Category: Empirical Paper

Data publikacji: 10 wrz 2020

Zakres stron: 209 - 217

Otrzymano: 28 sty 2020

Przyjęty: 30 cze 2020

DOI: https://doi.org/10.2478/ijme-2020-0017

Słowa kluczowe
determinants, Random Forest, stock index, machine learning

© 2020 Grzegorz Tratkowski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Identification of nonlinear determinants of stock indices derived by Random Forest algorithm

Article Category: Empirical Paper

Data publikacji: 10 wrz 2020

Zakres stron: 209 - 217

Otrzymano: 28 sty 2020

Przyjęty: 30 cze 2020

DOI: https://doi.org/10.2478/ijme-2020-0017

Słowa kluczowedeterminants, Random Forest, stock index, machine learning

© 2020 Grzegorz Tratkowski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
determinants, Random Forest, stock index, machine learning