Stock Price Prediction with Combination of XGBoost and Binary Gray Wolf Optimizer
Subject Areas : Financial EconomicsHadi Esmaeili 1 , Shahab جهانگیری 2 * , Ali Rezazadeh 3
1 - Department of Economics- faculty of economics and management-urmia university
2 - استادیار، گروه اقتصاد، دانشگاه ارومیه
3 - Department of Economics, Urmia University, Urmia, Iran
Keywords: Stock Price Prediction, XGBoost, Binary Gray Wolf Optimizer,
Abstract :
Aim: Quantitative investing powered by machine learning has opened up new opportunities to generate more insights from financial data, leading to the development of ideas to enhance the performance of investments and portfolio management in stock markets. Method: In this research, the proposed model called CBGWO-XGBoost is proposed to predict the closing price of the future stock of Tehran Stock Exchange companies in the form of two sets of developed features. Findings: In general, the developed feature set 2 performed better than the developed feature set 1 without the feature engineering process. Also, by applying the feature engineering process in the developed feature set 2, the algorithms CBGWO, BGWO2 and BGWO1 obtained the best performance, respectively. Conclusion: This study showed that the successful performance of stock price forecasting does not depend on an exclusive forecasting method. Instead, it is shown that a good prediction performance depends largely on feature engineering processes. Aim: Quantitative investing powered by machine learning has opened up new opportunities to generate more insights from financial data, leading to the development of ideas to enhance the performance of investments and portfolio management in stock markets. Method: In this research, the proposed model called CBGWO-XGBoost is proposed to predict the closing price of the future stock of Tehran Stock Exchange companies in the form of two sets of developed features. Conclusion: This study showed that the successful performance of stock price forecasting does not depend on an exclusive forecasting method. Instead, it is shown that a good prediction performance depends largely on feature
باباجانی، جعفر؛ تقوا، بولو؛ قاسم، و عبدالهی. (1398). « پیش‌بینی قیمت سهام در بورس تهران با استفاده از شبکه عصبی بازگشتی بهینه شده با الگوریتم کلونی زنبور عسل مصنوعی». راهبرد مدیریت مالی، 7(2)، 195-228. #
باقری مزرعه، دانشور و معدنچی زاج. (1401). «توسعه یک رویکرد جدید یادگیری جمعی برای انتخاب پورتفوی سهام با استفاده از ماشین بردار پشتیبان چند کلاسه و الگوریتم ژنتیک». مهندسی مالی و مدیریت اوراق بهادار، 13(50)، 282-305.#
حسینی، سیدعلی؛ پورمانی، جهانشاد و آزیتا. (1399).« ارائه مدلی برای انتخاب سبد بهینه سهام با استفاده از الگوریتم هوش جمعی سالپ و شبکه‌های عصبی پرسپترون چندلایه». مهندسی مالی و مدیریت اوراق بهادار، 44(11)، 479-503.#
حیدری، مهدی؛ امیری، حمیدرضا. (1401). «بررسی قدرت مدل‌های مبتنی بر هوش مصنوعی در پیش‌بینی روند قیمت سهام بورس اوراق بهادار تهران» تحقیقات مالی.#
عابدینی،سحر.ابونوری،اسمعیل.کشاورز حداد،غلامرضا.(1402). بهینه‌سازی سبد سهام صنایع مختلف در بورس اوراق بهادار تهران با استفاده از گارچ متعامد.فصلنامه اقتصاد مالی.دانشکده اقتصاد دانشگاه تهران مرکزی.66(18):1-26#
روحانی،مریم.هوشمند،محمود.احمدی شامهری،محمدطاهر.(1402). تحلیل آثار شوک‌های سیاست پولی بر قیمت سهام در اقتصاد ایران؛ کاربردی ترکیبی از روش میانگین گیری بیزین و رهیافت خودرگرسیون برداری تعمیم یافته با پارامترهای متغیر در طول زمان. فصلنامه اقتصاد مالی.دانشکده اقتصاد دانشگاه تهران مرکزی.66(18):51-68#
فلاح مقدم،رضا.بابایی کفاکی،سامان.(1402). بررسی تاثیر ارزش معاملات خرد سهام بر شاخص کل بورس ایران. فصلنامه اقتصاد مالی.دانشکده اقتصاد دانشگاه تهران مرکزی.66(18): 409-434#
Albahli, S., Nazir, T., Mehmood, A., Irtaza, A., Alkhalifah, A., & Albattah, W. (2022). AEI-DNET: a novel densenet model with an autoencoder for the stock market predictions using stock technical indicators. Electronics, 11(4), 611.#
Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567.#
Chaudhari, K., & Thakkar, A. (2023). Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction. Expert Systems with Applications, 119527.#
Chen, W., Zhang, H., Mehlawat, M. K., & Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, and 106943.#
Chung, H., & Shin, K. S. (2018). Genetic algorithm-optimized long short-term memory network for stock market prediction. Sustainability, 10(10), 3765.#
Chung, H., & Shin, K. S. (2020). Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction. Neural Computing and Applications, 32, 7897-7914.#
Devan, P., & Khare, N. (2020). An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Computing and Applications, 32, 12499-12514.#
Dezhkam, A., & Manzuri, M. T. (2023). Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huangtransform. Engineering Applications of Artificial Intelligence, 118, 105626.#
Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016). Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172, 371-381#.
Huang, Q., Xia, T., Sun, H., Yamada, M., & Chang, Y. (2020, April). Unsupervised nonlinear feature selection from high-dimensional signed networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 4182-4189).#
Jiang, Z., Ji, R., & Chang, K. C. (2020). A machine learning integrated portfolio rebalance framework with risk-aversion adjustment. Journal of Risk and Financial Management, 13(7), 155.#
Kainen, P. C. (1997). Utilizing geometric anomalies of high dimension: When complexity makes computation easier.#
Kumar, M., & Thenmozhi, M. (2014). Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. International Journal of Banking, Accounting and Finance, 5(3), 284-308.#
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: a literature review. Expert Systems with Applications, 116659.#
Li B, Sun Y, Aw G, Teo KL (2019) uncertain portfolio optimization problem under a minimax risk measure. Appl Math Model 76:274–281#
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature selection: A data perspective. ACM computing surveys (CSUR), 50(6), 1-45.#
Luo, S., Zhang, S., & Cong, H. (2021, June). Research on Consumer Purchasing Prediction Based on XGBoost Algorithm. In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 1173-1176). IEEE.#
Ma, Y., Han, R., & Wang, W. (2020). Prediction-based portfolio optimization models using deep neural networks. IEEE Access, 8, 115393-115405.#
Miao, J., & Niu, L. (2016). A survey on feature selection. Procedia computer science, 91, 919-926#.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.#
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., & Salwana, E. (2020). Deep learning for stock market prediction. Entropy, 22(8), 840.#
Naik, M. J., & Albuquerque, A. L. (2022). Hybrid optimization search-based ensemble model for portfolio optimization and return prediction in business investment. Progress in Artificial Intelligence, 11(4), 315-331.#
Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W., & Wills, G. (2020). A survey on machine learning for stock price prediction: Algorithms and techniques.#
Park, H. J., Kim, Y., & Kim, H. Y. (2022). Stock market forecasting using a multi-task approach integrating long short-term memory and the random forest framework. Applied Soft Computing, 114, and 108106.#
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, 11(5), e0155133.#
Ramamohan, V., Goyal, N., Parth, P., Mahlawat, S., & Prabhakar, U. (2020). A framework for utilizing stock trend prediction outputs in stock selection and portfolio optimization. Available at SSRN 3673874.#
Ramírez‐Gallego, S., Lastra, I., Martínez‐Rego, D., Bolón‐Canedo, V., Benítez, J. M., Herrera, F., & Alonso‐Betanzos, A. (2017). Fast‐mRMR: Fast minimum redundancy maximum relevance algorithm for high‐dimensional big data. International Journal of Intelligent Systems, 32(2), 134-152.#
Saetia, K., & Yokrattanasak, J. (2023). Stock Movement Prediction Using Machine Learning Based on Technical Indicators and Google Trend Searches in Thailand. International Journal of Financial Studies, 11(1), 5.#
Sharma, M., & Shekhawat, H. S. (2022). Portfolio optimization and return prediction by integrating modified deep belief network and recurrent neural network. Knowledge-Based Systems, 109024.#
Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. International Journal of Financial Studies, 11(3), 94.#
Srijiranon, K., Lertratanakham, Y., & Tanantong, T. (2022). A hybrid Framework Using PCA, EMD and LSTM methods for stock market price prediction with sentiment analysis. Applied Sciences, 12(21), 10823.#
Thakkar, A., & Chaudhari, K. (2021-a). A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization. Archives of Computational Methods in Engineering, 28(4), 2133-2164.#
Thakkar, A., & Chaudhari, K. (2021-b). A comprehensive survey on deep neural networks for stock market: the need, challenges, and future directions. Expert Systems with Applications, 177, 114800.#
Thakkar, A., & Chaudhari, K. (2021-c). Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions. Information Fusion, 65, 95-107#.
Too, J., & Abdullah, A. R. (2021). Opposition based competitive grey wolf optimizer for EMG feature selection. Evolutionary Intelligence, 14(4), (pp. 1691-1705).#
Toochaei, M. R., & Moeini, F. (2023). Evaluating the performance of ensemble classifiers in stock returns prediction using effective features. Expert Systems with Applications, 213, 119186.#
Vachhani, H., Obiadat, M. S., Thakkar, A., Shah, V., Sojitra, R., Bhatia, J., & Tanwar, S. (2020). Machine learning based stock market analysis: A short survey. In Innovative Data Communication Technologies and Application: ICIDCA 2019 (pp. 12-26). Springer International Publishing.#
VO, N. N., He, X., Liu, S., & Xu, G. (2019). Deep learning for decision making and the optimization of socially responsible investments and portfolio. Decision Support Systems, 124, 113097.#
Wang, Q., Xu, W., & Zheng, H. (2018). Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles. Neurocomputing, 299, 51-61.#
Xu, Y., Chhim, L., Zheng, B., & Nojima, Y. (2020). Stacked deep learning structure with bidirectional long-short term memory for stock market prediction. In Neural Computing for Advanced Applications: First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings 1 (pp. 447-460). Springer Singapore.#
Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 856-863)#.
Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716.#
Zhang, C., Liang, S., Lyu, F., & Fang, L. (2020). Stock-index tracking optimization using auto-encoders. Frontiers in Physics, 8, 388.#
Zhao, Y., & Yang, G. (2023). Deep Learning-based Integrated Framework for stock price movement prediction. Applied Soft Computing, 133, 109921#