2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 6 , Issue 1 , PP: 23-35, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Mingzhong Liu 1 * , N Metawa 2
Doi: https://doi.org/10.54216/AJBOR.060102
Stock market direction prediction becomes an essential task in the business sector. The inherent volatile behavior of stock markets worldwide makes the prediction process difficult. The improvement in the prediction accuracy of the stock market direction prediction helps to avoid the risks involved in the investment process. In this aspect, this study designs a swallow swarm optimization (SSO) with a fuzzy support vector machine (FSVM) model for stock market direction prediction. The proposed SSO-FSVM model encompasses preprocessing, feature extraction, FSVM, and SSO based parameter tuning. The usage of the SSO algorithm to fine-tune the parameters involved in the FSVM model helps to significantly improve the overall predictive performance. To validate the improved performance of the SSO-FSVM model, a wide range of experiments were carried out using two benchmark datasets. The experimental outcomes reported the betterment of the SSO-FSVM model over the recent approaches in terms of several evaluation metrics.
Machine learning, Stock market, Prediction model, Fuzzy SVM, Classification, Feature extraction.
[1] Y. Yuniningsih, S. Widodo, and M. B. N. Wajdi, “An analysis of decision making in the stock investment,” Economic Times: Journal of Economic and Islamic Law, vol. 8, no. 2, pp. 122–128, 2017.
[2] L. Kengatharan and N. Kengatharan, “The influence of behavioral factors in making investment decisions and performance: study on investors of colombo stock exchange, Sri Lanka,” Asian Journal of Finance & Accounting, vol. 6, no. 1, p. 1, 2014.
[3] R. Batra and S. M. Daudpota, “Integrating stocktwits with sentiment analysis for better prediction of stock price movement,” in Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1–5, IEEE, Sukkur, Pakistan, March 2018.
[4] S. C. Nayak, B. B. Misra, and H. S. Behera, “ACFLN: artificial chemical functional link network for prediction of stock market index,” Evolving Systems, vol. 10, no. 4, pp. 567–592, 2019.
[5] S. Rehman, I. U. Chhapra, M. Kashif, and R. Rehan, “Are stock prices a random walk? an empirical evidence of asian stock markets,” ETIKONOMI, vol. 17, no. 2, pp. 237–252, 2018.
[6] S. Carta, A. Ferreira, A. S. Podda, R. D. Reforgiato, and A. Sanna, “Multi-DQN: an ensemble of deep Q-learning agents for stock market forecasting,” Expert Systems with Applications, vol. 164, Article ID 113820, 2021.
[7] R. Efendi, N. Arbaiy, and M. M. Deris, “A new procedure in stock market forecasting based on fuzzy random auto-regression time series model,” Information Sciences, vol. 441, pp. 113–132, 2018.
[8] S. P. Chatzis, V. Siakoulis, A. Petropoulos, E. Stavroulakis, and N. Vlachogiannakis, “Forecasting stock market crisis events using deep and statistical machine learning techniques,” Expert Systems with Applications, vol. 112, pp. 353–371, 2018.
[9] R. Xiong, E. P. Nichols, and Y. Shen, “Deep learning stock volatility with google domestic trends,” 2015.
[10] Yan, Y. and Yang, D., 2021. A stock trend forecast algorithm based on deep neural networks. Scientific Programming, 2021.
[11] Patil, P., Wu, C.S.M., Potika, K. and Orang, M., 2020, January. Stock market prediction using ensemble of graph theory, machine learning and deep learning models. In Proceedings of the 3rd International Conference on Software Engineering and Information Management (pp. 85-92).
[12] Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H. and Chouhan, L., 2018, December. Stock market prediction using machine learning. In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 574-576). IEEE.
[13] Chiong, R., Fan, Z., Hu, Z., Adam, M.T., Lutz, B. and Neumann, D., 2018, July. A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 278-279).
[14] Mankar, T., Hotchandani, T., Madhwani, M., Chidrawar, A. and Lifna, C.S., 2018, January. Stock market prediction based on social sentiments using machine learning. In 2018 International Conference on Smart City and Emerging Technology (ICSCET) (pp. 1-3). IEEE.
[15] Moghar, A. and Hamiche, M., 2020. Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, pp.1168-1173.
[16] Pathak, A. and Shetty, N.P., 2019. Indian stock market prediction using machine learning and sentiment analysis. In Computational Intelligence in Data Mining (pp. 595-603). Springer, Singapore.
[17] Ampomah, E.K., Nyame, G., Qin, Z., Addo, P.C., Gyamfi, E.O. and Gyan, M., 2021. Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm. Informatica, 45(2).
[18] Kumar, S. and Acharya, S., 2020. Application of machine learning algorithms in stock market prediction: a comparative analysis. In Handbook of Research on Smart Technology Models for Business and Industry (pp. 153-180). IGI Global.
[19] Madeeh, O.D. and Abdullah, H.S., 2021, February. An efficient prediction model based on machine learning techniques for prediction of the stock market. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012008). IOP Publishing.
[20] Jeyakarthic, M. and Punitha, S., 2020. An effective stock market direction prediction model using water wave optimization with multi-kernel extreme learning machine. IIOAB J, 11, pp.103-109.
[21] Zhou, M., Zhao, Q. and Chen, Y., 2019. Endpoint prediction of BOF by flame spectrum and furnace mouth image based on fuzzy support vector machine. Optik, 178, pp.575-581.
[22] Neshat, M., Sepidnam, G. and Sargolzaei, M., 2013. Swallow swarm optimization algorithm: a new method to optimization. Neural Computing and Applications, 23(2), pp.429-454.
[23] Basak, S., Kar, S., Saha, S., Khaidem, L. and Dey, S.R., 2019. Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, pp.552-567.
