Stock Market Price Forecasting Using Metaheuristic Search Algorithms: A Comparative Analysis
DOI:
https://doi.org/10.47839/ijc.23.4.3772Keywords:
Stock market forecasting, metaheuristic search algorithms, computational intelligence, crow search algorithm, particle swarm optimizer, gray wolf optimizer, dandelion optimizerAbstract
Stock market forecasting is an essential factor in the daily operations of many companies and individuals. However, the complex and nonlinear nature of the stock market and the unpredictable variations in factors affecting stock prices present significant challenges in accurate forecasting. To address this, we employ four model-based metaheuristic search algorithms (MHs), namely the Crow Search Algorithm (CSA), Particle Swarm Optimizer (PSO), Gray Wolf Optimizer (GWO), and Dandelion Optimizer (DO), to estimate the parameters of stock market prices models. The data utilized in our experiments are extracted from the widely recognized stock index of Standard & Poor’s 500 (S&P 500), that serves as a representative benchmark for the United States stock market. Our findings demonstrate that the CSA outperforms other MHs by providing the best combination of parameters for modeling stock market prices. The optimized parameters for the CSA model yielded Variance-Account-For (VAF) values of 97.846% in the training set and 93.483% in the testing set. This suggests that CSA offers promising capabilities for enhancing the accuracy and effectiveness of stock market forecasting models.
References
A. Sheta, “Software effort estimation and stock market prediction using takagi-sugeno fuzzy models,” 2006 IEEE International Conference on Fuzzy Systems, pp. 171–178, 2006. https://doi.org/10.1109/FUZZY.2006.1681711.
A. Sheta, S. E. M. Ahmed, and H. Faris, “A comparison between regression, artificial neural networks and support vector machines for predicting stock market index,” International Journal of Advanced Research in Artificial Intelligence, vol. 4, no. 7, 2015. https://doi.org/10.14569/IJARAI.2015.040710.
Edu-dwar. Top 10 stock exchanges in the world 2023. Accessed: May 25, 2023. [Online]. Available: https://www.edudwar.com/top-10-stock-exchanges-in-the-world//
Y. Finance. World indices. Accessed: May 25, 2023. [Online]. Available: https://finance.yahoo.com/world-indices/
G. Kumar, S. Jain, and U. P. Singh, “Stock market forecasting using computational intelligence: A survey,” Archives of Computational Methods in Engineering, vol. 28, pp. 1069–1101, 2021. https://doi.org/10.1007/s11831-020-09413-5.
K.-Y. Woo, C. Mai, M. McAleer, and W.-K. Wong, “Review on efficiency and anomalies in stock markets,” Economies, vol. 8, no. 1, 2020. https://doi.org/10.3390/economies8010020.
T. Phuoc, P. T. K. Anh, and P. H. Tam, “Applying machine learning algorithms to predict the stock price trend in the stock market – the case of vietnam,” Humanit Soc Sci Commun, vol. 11, p. 393, 2024. https://doi.org/10.1057/s41599-024-02807-x.
T. A. Jilani and S. M. A. Burney, “A refined fuzzy time series model for stock market forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 387, no. 12, pp. 2857–2862, 2008. https://doi.org/10.1016/j.physa.2008.01.099.
G. S. Atsalakis and K. P. Valavanis, “Surveying stock market forecasting techniques-part i: Conventional methods,” Journal of Computational Optimization in Economics and Finance, vol. 2, no. 1, pp. 45–92, 2010.
A. A. Ariyo, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the arima model,” in 2014 UKSim-AMSS 16th international conference on computer modelling and simulation. IEEE, 2014, pp. 106–112. https://doi.org/10.1109/UKSim.2014.67.
D. Faranda, F. M. E. Pons, E. Giachino, S. Vaienti, and B. Dubrulle, “Early warnings indicators of financial crises via auto regressive moving average models,” Communications in Nonlinear Science and Numerical Simulation, vol. 29, no. 1-3, pp. 233–239, 2015. https://doi.org/10.1016/j.cnsns.2015.05.002.
B. Billah, M. L. King, R. D. Snyder, and A. B. Koehler, “Exponential smoothing model selection for forecasting,” International journal of forecasting, vol. 22, no. 2, pp. 239–247, 2006. https://doi.org/10.1016/j.ijforecast.2005.08.002.
F. Shahrokhi Sardu and F. Beighzadeh Abbasi, “Evaluating and comparing the performance of metaheuristic algorithms in predicting the stock price,” International Journal of Business Management, vol. 2, no. 2, pp. 109–117, 2017. [Online]. Available: http://www.sciarena.com
D. K. Tripathi, S. Chadha, and A. Tripathi, “Metaheuristic enabled intelligent model for stock market prediction via integrating volatility spillover: India and its asian and european counterparts,” Data & Knowledge Engineering, vol. 144, p. 102127, 2023. https://doi.org/10.1016/j.datak.2022.102127.
M. Shahvaroughi Farahani and S. Razavi Hajiagha, “Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models,” Soft Computing, vol. 25, pp. 8483–8513, 2021. https://doi.org/10.1007/s00500-021-05775-5.
M. Khairalla, “Meta-heuristic search optimization and its application to time series forecasting model,” Intelligent Systems with Applications, vol. 16, p. 200142, 2022. https://doi.org/10.1016/j.iswa.2022.200142.
A. Sheta, H. Faris, and M. Alkasassbeh, “A genetic programming model for S&P500 stock market prediction,” International Journal of Control and Automation, vol. 6, pp. 303–314, 2013. https://doi.org/10.14257/ijca.2013.6.6.29.
A. Sheta, S. E. M. Ahmed, and H. Faris, “Evolving stock market prediction models using multi-gene symbolic regression genetic programming,” Artificial Intelligence and Machine Learning (AIML) Journal, vol. 1, no. 12, pp. 1–10, 2015.
R. Chopra and G. D. Sharma, “Application of artificial intelligence in stock market forecasting: A critique, review, and research agenda,” Journal of Risk and Financial Management, vol. 14, no. 11, 2021. https://doi.org/10.3390/jrfm14110526.
Z. Wang, G. Shi, M. Shang, and Y. Zhang, “The stock market model with delayed information impact from a socioeconomic view,” Entropy, vol. 23, no. 7, 2021. https://doi.org/10.3390/e23070893.
A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Computers Structures, vol. 169, pp. 1–12, 2016. https://doi.org/10.1016/j.compstruc.2016.03.001.
S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in engineering software, vol. 69, pp. 46–61, 2014. https://doi.org/10.1016/j.advengsoft.2013.12.007.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, 1995. https://doi.org/10.1109/ICNN.1995.488968.
S. Zhao, T. Zhang, S. Ma, and M. Chen, “Dandelion optimizer: A natureinspired metaheuristic algorithm for engineering applications,” Engineering Applications of Artificial Intelligence, vol. 114, p. 105075, 2022. https://doi.org/10.1016/j.engappai.2022.105075.
T. Thaher, A. Sheta, M. Awad, and M. Aldasht, “Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization,” Expert Systems with Applications, vol. 238, p. 121712, 2024. https://doi.org/10.1016/j.eswa.2023.121712.
“Yahoo finance,” https://finance.yahoo.com/quote/%5EGSPC/history?period1=1367366400&period2=1682899200&interval=1wk&filter=history&frequency=1wk&includeAdjustedClose=true, accessed: May 5, 2023.
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