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Autor
Tratkowski Grzegorz (Wrocław University of Economics, Poland)
Tytuł
Identification of nonlinear determinants of stock indices derived by Random Forest algorithm
Źródło
International Journal of Management and Economics, 2020, vol. 56, nr 3, s. 209-217, wykr., bibliogr. 21 poz.
Zeszyty Naukowe / Szkoła Główna Handlowa. Kolegium Gospodarki Światowej
Słowa kluczowe
Indeks giełdowy, Uczenie maszynowe, Algorytmy
Stock market indexes, Machine learning, Algorithms
Uwagi
Klasyfikacja JEL : C45, C5, G11
summ.
Abstrakt
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.(original abstract)
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Biblioteka Szkoły Głównej Handlowej w Warszawie
Pełny tekst
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Bibliografia
Pokaż
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  11. Garefalakis, A., Dimitras, A., Spinthiropoulos, K.G., Koemtzopoulos, D. (2013), Determinant factors of Hong Kong stock market, International Journal of Finance and Economics, Forthcoming.
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  17. Ho, T.K. (1998), The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 832-844.
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  19. Jeon, J.H. (2020), Macro and non-macro determinants of Korean tourism stock performance: a quantile regression approach, The Journal of Asian Finance, Economics, and Business, Vol. 7, No. 3, pp. 149-156.
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Cytowane przez
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ISSN
2299-9701
Język
eng
URI / DOI
https://doi.org/10.2478/ijme-2020-0017
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