EVALUATION OF SUPPORT VECTOR MACHINE BASED STOCK PRICE PREDICTION
Tilla IZSÁK
izsak.tilla@student.ujs.skJ. Selye University, Faculty of Economics and Informatics, Department of Economics, (Slovakia)
https://orcid.org/0009-0002-4275-5279
László MARÁK
J. Selye University, Faculty of Economics and Informatics, Department of Informatics (Slovakia)
https://orcid.org/0000-0002-2280-8014
Mihály ORMOS
J. Selye University, Faculty of Economics and Informatics, Department of Economics (Slovakia)
https://orcid.org/0000-0002-3224-7636
Abstract
In recent years with the advent of computational power, Machine Learning has become a popular approach in financial forecasting, particularly for stock price analysis. In this paper, the authors develop a non-recurrent active trading algorithm based on stock price prediction, using Support Vector Machines on high frequency data, and compare its risk adjusted performance to the returns of a statistical portfolio predicted by the Capital Asset Pricing Model. The authors selected the three highest volume securities from a pool of 100 initially selected stock dataset to investigate the algorithmic trading strategy. The abnormal return estimates are significant and positive, and the systematic risk is lower than unity in all cases, suggesting lower risk compared to the market. Moreover, the estimated beta values for all stocks were close to zero, indicating a market independent process. The correlation analysis revealed weak correlations among the processes, supporting the potential for risk reduction and volatility mitigation through portfolio diversification. The authors tested an equally weighted portfolio of the selected three assets and demonstrated a remarkable return of 1348% during the evaluation period from July 1st, 2020, to January 1st, 2023. The results suggest that the weak form of market efficiency can be questioned, as the algorithmic trading strategy, employing a Support Vector Machine binary classification model, has consistently generated statistically significant and substantial abnormal returns using historical market data.
Keywords:
Stock Trading Algorithm, Machine Learning, SVM, Performance AnalysisReferences
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Authors
Tilla IZSÁKizsak.tilla@student.ujs.sk
J. Selye University, Faculty of Economics and Informatics, Department of Economics, Slovakia
https://orcid.org/0009-0002-4275-5279
Authors
László MARÁKJ. Selye University, Faculty of Economics and Informatics, Department of Informatics Slovakia
https://orcid.org/0000-0002-2280-8014
Authors
Mihály ORMOSJ. Selye University, Faculty of Economics and Informatics, Department of Economics Slovakia
https://orcid.org/0000-0002-3224-7636
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