Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309713 
Year of Publication: 
2020
Citation: 
[Journal:] International Journal of Management and Economics [ISSN:] 2543-5361 [Volume:] 56 [Issue:] 3 [Year:] 2020 [Pages:] 209-217
Publisher: 
Sciendo, Warsaw
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.
Subjects: 
determinants
Random Forest
stock index
machine learning
JEL: 
C45
C5
G11
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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