The use of support vector machine and Naive Bayes algorithms and its combination with risk measure and fuzzy theory in the selection of stock portfolio

Subject Areas : Financial Engineering

Danial Mohammadi ¹ , Emran Mohammadi ² , Naeim Shokri ^{3 *} , Nima Heidari ⁴

1 - Department of Financial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
2 - Department of Financial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
3 - Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
4 - Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

Received: 2023-10-21 Accepted : 2023-12-22 Published : 2023-12-30

Keywords: Machine Learning, Stock Portfolio, Conditional Value at Risk, Tehran security exchange,

Abstract :

Purpose: The purpose of the current research is to create an optimal portfolio using machine learning algorithms and fuzzy theory, which has a better return than the average return of the market (total index of the stock exchange).Research Methodology:In this article, the stocks of the selected companies are classified in the first stage using the two introduced algorithms. In the next step, stocks that entered the positive class are predicted for the next trading day with the help of random forest algorithm. For each company, three predictions are made, which are the inputs of fuzzy method optimization. Optimization is done with the aim of minimizing the risk with risk measures of value at risk and value at conditional risk. Shares information is five years old (daily) and its time period is from the beginning of 2017 to the end of 2021.Findings: In the end, each of the algorithms and the risk measure used were measured and compared with the actual market return. Based on the obtained results, the CVAR risk measure has a better capability and result than the VAR risk measure, and the support vector machine algorithm has also achieved a better performance in choosing the investment portfolio.Originality/ value: This research is optimized in the form of a capital sample by integrating machine learning methods and risk measures. Adding VaR and CVaR risk metrics enhances the decision-making process regarding risk reduction. Forecasting with the help of random forest and using an approach based on fuzzy theory for risk and value analysis gives the research an innovative perspective in portfolio formation. The findings provide investors and researchers with valuable insights in their search for better investment strategies.

References:

Beale, R., & Jackson, T. (2007). Introduction to Neural Networks. (M. Alborzi, Trans.). Tehran: Sharif University of Technology, Scientific Publications Institute. [In Persian]

Behera, J., Pasayat, A. K., Behera, H., & Kumar, P. (2023). Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets. Engineering Applications of Artificial Intelligence, 120, 105843.‏

Bi, J., & Zhang, T. (2004). Support vector classification with input data uncertainty. Advances in neural information processing systems, 17.‏

Bishop, C. M., & Tipping, M. (2013). Variational relevance vector machines. arXiv preprint arXiv:1301.3838.‏

Bleuler, S., Brack, M., Thiele, L., & Zitzler, E. (2001). Multiobjective genetic programming: Reducing bloat using SPEA2. In Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 536-543). IEEE.‏

Chowdhury, R., Mahdy, M. R. C., Alam, T. N., Al Quaderi, G. D., & Rahman, M. A. (2020). Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model. Physica A: Statistical Mechanics and its Applications, 555, 124444.‏

Ebadati, O. M., Jafari, M. A., & Davoodifar, N. (2022). Forecasting Stocks in the Financial Market by Using GA-SVM Hybrid Algorithm. Advances in Finance and Investment, 2(5), 1-22. [In Persian]

Farahabadi, M., Eyvazloo, R., Safari, H. (2022). An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking. Journal of Securities Exchange, 15(59), 121-156. [In Persian]

Fazlzadeh, A., Ranjpour, R., Tohidi, R. (2012). The Investigation of the Ability of Single-Index Sharp and DEA Models for Choosing Efficient Portfolio in Tehran Stock Exchange. Journal of Securities Exchange, 5(18), 39-59. [In Persian]

Feizi, J., & Foroush Bastani, A. (2012). Investigating Monte Carlo Methods for Approximating Value at Risk (VaR) and Conditional Value at Risk (CVaR). In Proceedings of the Third Conference on Financial Mathematics and Applications. Zanjan University, Faculty of Mathematics, Department of Financial Mathematics. [In Persian]

Goykhman, M., & Teimouri, A. (2018). Machine learning in sentiment reconstruction of the simulated stock market. Physica A: Statistical Mechanics and its Applications, 492, 1729-1740.‏

Haratizadeh, S., & Rezaee, F. (2023). A novel machine learning approach for portfolio optimization. Journal of Decisions and Operations Research, 8(2), 527-539. [In Persian]

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.‏

Huang, C. F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807-818.‏

Lai, K. K., Yu, L., Wang, S., & Zhou, C. (2006). A double-stage genetic optimization algorithm for portfolio selection. In International Conference on Neural Information Processing (pp. 928-937). Berlin, Heidelberg: Springer Berlin Heidelberg.‏

Lin, C. M., & Gen, M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied mathematical sciences, 1(5), 201-210.‏

Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.‏

Paiva, F. D., Cardoso, R. T. N., Hanaoka, G. P., & Duarte, W. M. (2019). Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications, 115, 635–655.

Paula, A., & Vitor Venceslau Curtis. (2023). A Model-Based Approach Machine Learning to Scalable Portfolio Selection. International Journal on Cybernetics & Informatics, 12(3), 23–40.

Qiu, H., Han, F., Liu, H., & Caffo, B. (2015). Robust portfolio optimization. Advances in Neural Information Processing Systems, 28.

Raei, R., & Beigi, H.A. (2010). Portfolio optimization using particle swarm optimization method. Financial Research Journal, 12(29), 21-40. [In Persian]

Rai, R., & Pouyanfar, A. (2017). Advanced Investment Management. Institute for Research and Development in the Humanities (SAMT) Publications. [In Persian]

Ravi, K., & Ravi, V. (2017). A novel automatic satire and irony detection using ensembled feature selection and data mining. Knowledge-based systems, 120, 15-33.‏

Reddy, E. U., & Nagarjuna, N. (2023). Applying Machine Learning for Portfolio Switching Decisions. Lecture Notes in Computer Science, 399–406.

Schlottmann, F., & Seese, D. (2001). A hybrid genetic-quantitative method for risk-return optimisation of credit portfolios. In Proceedings of the Conference of Quantitative Methods in Finance, University of Technology, Sydney, Australia (Vol. 55).‏

Sinaei, H., Zamani, S. (2014). Decision-Making for Stock Portfolio Selection: A Comparison of Genetic Algorithms and Bee Algorithm. Executive Management Research, 6(11), 83-102. [In Persian]

Sultan Panah, H., Fatemi, A., Nazaripour, M., & Bigoli, B. (2013). Comparing Stock Selection Models for Portfolio Formation in Terms of Expected Return Estimation, Actual Future Returns, and Their Risks in the Tehran Stock Exchange. In Proceedings of the Sixth Conference on the Development of the Financial System in Iran. Islamic Azad University, Sanandaj Branch; University of Kurdistan. [In Persian]

Taghizadeh, K., Mullah Alizadeh Zavardehi, S., Salehi, A. K., & Mahmoudi Rad, A. (2022). Evaluation of the optimal portfolio portfolio using market criteria using multi-criteria decision criteria under conditions of uncertanty in the Iranian capital market. Advances in Finance and Investment, 3(6), 101-128. [In Persian]

Wang, S., Xia, Y., Wang, S., & Xia, Y. (2002). Mean-variance-skewness model for portfolio selection with transaction costs. Portfolio Selection and Asset Pricing, 129-144.‏

Wang, W., Li, W., Zhang, N., & Liu, K. (2020). Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems with Applications, 143, 113042.‏

Yamai, Y., & Yoshiba, T. (2002). Comparative analyses of expected shortfall and value-at-risk (2): expected utility maximization and tail risk. Monetary and economic studies, 20(2), 95-115.‏

‌Zare Mehrjerdi, Y., Shahmohammadi, M., & Emami Maibodi, L. (2013). A Hybrid Intelligent Algorithm for Portfolio Selection using Fuzzy Mean-Variance-Skewness. International Journal of Industrial Engineering and Production Management, 23(4), 447-458. [In Persian]

Zhong, X., & Enke, D. (2017). A comprehensive cluster and classification mining procedure for daily stock market return forecasting. Neurocomputing, 267, 152-168.‏

Zolfaghari, M., & Gholami, S. (2021). A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction. Expert Systems with Applications, 182, 115149.‏