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
Acharya, V. V., & Pedersen, L. H. (2005). Asset pricing with liquidity risk. Journal of Financial Economics, 77(2), 375-410. https://doi.org/10.1016/j.jfineco.2004.06.007
DOI: https://doi.org/10.1016/j.jfineco.2004.06.007
Google Scholar
Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, (pp. 106-112). IEEE. https://doi.org/10.1109/UKSim.2014.67
DOI: https://doi.org/10.1109/UKSim.2014.67
Google Scholar
Briola, A., Turiel, J., Marcaccioli, R., Cauderan, A., & Aste, T. (2021). Deep reinforcement learning for active high frequency trading. arXiv. https://doi.org/10.48550/arXiv.2101.07107
Google Scholar
Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, 3(2), 185–205. https://doi.org/10.1142/s0219720005001004
DOI: https://doi.org/10.1142/S0219720005001004
Google Scholar
Srivastava, D., & Bhambhu, L. (2010). Data classification using support vector machine. Journal of Theoretical and Applied Information Technology, 12(1), 1-7. Retrieved from http://www.jatit.org/volumes/research-papers/Vol12No1/1Vol12No1.pdf
Google Scholar
Fama, E. F., & Laffer, A. B. (1971). Information and capital markets. Journal of Business, 44(3), 289-298. http://dx.doi.org/10.1086/295379
DOI: https://doi.org/10.1086/295379
Google Scholar
Fama, E. F. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575-1617. https://doi.org/10.1111/j.1540-6261.1991.tb04636.x
DOI: https://doi.org/10.1111/j.1540-6261.1991.tb04636.x
Google Scholar
Fama, E. F., & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46. https://doi.org/10.1257/0895330042162430
DOI: https://doi.org/10.1257/0895330042162430
Google Scholar
Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 70(3), 393-408. http://www.jstor.org/stable/1805228
Google Scholar
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of Finance and Data Science, 4(3), 183-201. https://doi.org/10.1016/j.jfds.2018.04.003
DOI: https://doi.org/10.1016/j.jfds.2018.04.003
Google Scholar
Ph.-D. B. I. J., & Levy, K. N. (1989). The complexity of the stock market. The Journal of Portfolio Management, 16(1), 19-27. https://ssrn.com/abstract=2447013
DOI: https://doi.org/10.3905/jpm.1989.409244
Google Scholar
Jensen, M. C. (1968). The performance of mutual funds in the period 1945-1964. The Journal of Finance, 23(2), 389-416. https://doi.org/10.1111/j.1540-6261.1968.tb00815.x
DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00815.x
Google Scholar
Ji, X., Wang, J., & Yan, Z. (2021). A stock price prediction method based on deep learning technology. International Journal of Crowd Science, 5(1), 55-72. https://doi.org/10.1108/IJCS-05- 2020-0012
DOI: https://doi.org/10.1108/IJCS-05-2020-0012
Google Scholar
Kohda, S., & Yoshida, K. (2022). Characteristics and forecast of high-frequency trading. Transactions of the Japanese Society for Artificial Intelligence, 37(5), 1-9. https://doi.org/10.1527/tjsai.37-5_B-M44
DOI: https://doi.org/10.1527/tjsai.37-5_B-M44
Google Scholar
Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319. https://doi.org/10.1016/S0925-2312(03)00372-2
DOI: https://doi.org/10.1016/S0925-2312(03)00372-2
Google Scholar
Lai, S., Wang, M., Zhao, S., & Arce, G. R. (2023). Predicting high-frequency stock movement with differential transformer neural network. Electronics, 12(13), 2943. https://doi.org/10.3390/electronics12132943
DOI: https://doi.org/10.3390/electronics12132943
Google Scholar
Lintner, J. (1969). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets: A reply. The Review of Economics and Statistics, 51(2), 222–224. https://doi.org/10.2307/1926735
DOI: https://doi.org/10.2307/1926735
Google Scholar
Lu, W., Li, J., Wang, J., & Oin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing & Applications 33, 4741–4753. https://doi.org/10.1007/s00521-020-05532-z
DOI: https://doi.org/10.1007/s00521-020-05532-z
Google Scholar
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
DOI: https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Google Scholar
Merton, R. C. (1973). Theory of rational option pricing. Bell Journal of Economics and Management Science, 4(1), 141-183. https://doi.org/10.2307/3003143
DOI: https://doi.org/10.2307/3003143
Google Scholar
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783. https://doi.org/10.2307/1910098
DOI: https://doi.org/10.2307/1910098
Google Scholar
Sapankevych, N. I., & Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2), 24-38. https://doi.org/10.1109/MCI.2009.932254
DOI: https://doi.org/10.1109/MCI.2009.932254
Google Scholar
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
DOI: https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Google Scholar
Treynor, J. L. (1965). How to rate management of investment funds. Harvard Business Review, 43, 63-75. https://doi.org/10.1002/9781119196679.ch10
DOI: https://doi.org/10.1002/9781119196679.ch10
Google Scholar
Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. arXiv. https://doi.org/10.48550/arXiv.1206.2944
Google Scholar
Vapnik, V., & Cortes, C. (1995). Support-vector networks. Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018
DOI: https://doi.org/10.1007/BF00994018
Google Scholar
Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine https://doi.org/10.1007/s11408-022-00421-y
DOI: https://doi.org/10.1016/j.procs.2020.03.326
Google Scholar
Yu, P., Yan, X. (2020). Stock price prediction based on deep neural networks. Neural Computing and Applications, 32, 1609-1628. https://doi.org/10.1007/s00521-019-04212-x
DOI: https://doi.org/10.1007/s00521-019-04212-x
Google Scholar
Zhang, Z., Khushi, M. (2020, July). Ga-mssr: Genetic algorithm maximizing sharpe and sterling ratio method for RoboTrading. 2020 International Joint Conference on Neural Networks (IJCNN)(pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN48605.2020.9206647
DOI: https://doi.org/10.1109/IJCNN48605.2020.9206647
Google Scholar
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
Statistics
Abstract views: 552PDF downloads: 249
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Karolina FERYSIUK, Karolina M. WÓJCIAK, THE SPECTROPHOTOMETRIC ANALYSIS OF ANTIOXIDANT PROPERTIES OF SELECTED HERBS IN VISION-PRO™ UV-VIS , Applied Computer Science: Vol. 15 No. 4 (2019)
- Sri INDRA MAIYANTI, Anita DESIANI, Syafrina LAMIN, P PUSPITAHATI, Muhammad ARHAMI, Nuni GOFAR, Destika CAHYANA, ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION , Applied Computer Science: Vol. 19 No. 3 (2023)
- Andrzej ŁUKASZEWICZ, Jerzy JÓZWIK, Kamil CYBUL, IMPACT OF FRICTION COEFFICIENT VARIATION ON TEMPERATURE FIELD IN ROTARY FRICTION WELDING OF METALS – FEM STUDY , Applied Computer Science: Vol. 19 No. 3 (2023)
- Wojciech DANILCZUK, THE USE OF SIMULATION ENVIRONMENT FOR SOLVING THE ASSEMBLY LINE BALANCING PROBLEM , Applied Computer Science: Vol. 14 No. 1 (2018)
- Rafał KWOKA, Janusz KOZAK, Michał MAJKA, TESTS OF HTS 2G SUPERCONDUCTING TAPES USING THE LABVIEW ENVIRONMENT , Applied Computer Science: Vol. 14 No. 1 (2018)
- Edyta ŁUKASIK, Wiktor FLIS, EFFICIENCY COMPARISON OF NETWORKS IN HANDWRITTEN LATIN CHARACTERS RECOGNITION WITH DIACRITICS , Applied Computer Science: Vol. 19 No. 4 (2023)
- Damian KRASKA, Tomasz TRZEPIECIŃSKI, FINITE ELEMENT BASED PREDICTION OF DEFORMATION IN SHEET METAL FORMING PROCESS , Applied Computer Science: Vol. 14 No. 3 (2018)
- Ziadeddine MAKHLOUF, Abdallah MERAOUMIA , Laimeche LAKHDAR, Mohamed Yassine HAOUAM , ENHANCING MEDICAL DATA SECURITY IN E-HEALTH SYSTEMS USING BIOMETRIC-BASED WATERMARKING , Applied Computer Science: Vol. 20 No. 1 (2024)
- Krzysztof Michalczyk, Mariusz Warzecha, Robert Baran, A NEW METHOD FOR GENERATING VIRTUAL MODELS OF NONLINEAR HELICAL SPRINGS BASED ON A RIGOROUS MATHEMATICAL MODEL , Applied Computer Science: Vol. 19 No. 2 (2023)
- Przemysław KRAKOWSKI, Robert KARPIŃSKI, Marcin MACIEJEWSKI, APPLICATIONS OF MODERN IMAGING TECHNOLOGY IN ORTHOPAEDIC TRAUMA SURGERY , Applied Computer Science: Vol. 14 No. 3 (2018)
<< < 13 14 15 16 17 18 19 > >>
You may also start an advanced similarity search for this article.
