Document Type : Original Article

Authors

1 Department of Finance, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Financial Management, Electronic Branch, Islamic Azad University, Tehran, Iran

Abstract

Forecasting the stock price index volatility is considered a strategic and challenging issue in the stock markets, and it is momentous for traders and investors in the decision-making process. Hence, the presentation of an efficient model for forecasting the stock price index volatility is a crucial and hard task because stock market data and price fluctuations have high volatility and nonlinearity characteristics. To beat this challenge, this paper proposes a new hybrid model by applying artificial intelligence algorithms to forecast the stock price index. It incorporates four phases to provide a dynamic and exact model: (1) Select popular and key technical indicators as input variables (2) Apply Adaptive Neuro-Fuzzy Inference System (ANFIS) for designing a substructure to provide a high-quality and quick solution (3) Use Modified Particle Swarm Optimization (MPSO) to enhance predictive accuracy by simultaneously and adjusting the length of each interval in the discourse universe and the appropriate degree of membership (4) Employ Parallel Genetic Algorithm (PGA) to solve complex issues with computational weight optimization and adjusting the decision vectors employing genetic operators. The stock market data of “Tehran Stock Exchange (TSE)” from 01/01/2011 to 31/12/2021 are utilized to examine the functionality of the proposed model. In comparative assessments, the overall performance of the ANFIS-MPSO-PGA model based on 5 criteria achieved 81.45%, which was significantly better than other methods.

Keywords

llen, F. and R. Karjalainen (1999). "Using genetic algorithms to find technical trading rules." Journal of financial Economics 51(2): 245-271.
Azoff, E. M. (1994). Neural network time series forecasting of financial markets, John Wiley & Sons, Inc.
Bessembinder, H. and K. Chan (1995). "The profitability of technical trading rules in the Asian stock markets." Pacific-Basin Finance Journal 3(2-3): 257-284.
Chung, H. and K.-s. Shin (2018). "Genetic algorithm-optimized long short-term memory network for stock market prediction." Sustainability 10(10): 3765.
Chung, H. and K.-s. Shin (2020). "Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction." Neural Computing and Applications 32(12): 7897-7914.
Davis, L. (1991). "Handbook of genetic algorithms."
Dutta, G., P. Jha, A. K. Laha and N. Mohan (2006). "Artificial neural network models for forecasting stock price index in the Bombay stock exchange." Journal of Emerging Market Finance 5(3): 283-295.
Eberhart, R. and J. Kennedy (1995). A new optimizer using particle swarm theory. MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Ieee.
Goldberg, D. E. (1989). others, Genetic algorithms in search, optimization, and machine learning, vol. 412, Addison-wesley Reading Menlo Park.
Goldberg, D. E. and J. H. Holland (1988). "Genetic algorithms and machine learning." Machine learning 3(2): 95-99.
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press.
Inthachot, M., V. Boonjing and S. Intakosum (2016). "Artificial neural network and genetic algorithm hybrid intelligence for predicting thai stock price index trend." Computational intelligence and neuroscience 2016.
Jang, J.-S. (1993). "ANFIS: adaptive-network-based fuzzy inference system." IEEE transactions on systems, man, and cybernetics 23(3): 665-685.
Jang, J.-S. and C.-T. Sun (1995). "Neuro-fuzzy modeling and control." Proceedings of the IEEE 83(3): 378-406.
Jang, J.-S. R., C.-T. Sun and E. Mizutani (1997). "Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]." IEEE Transactions on automatic control 42(10): 1482-1484.
Kai, F. and X. Wenhua (1997). Training neural network with genetic algorithms for forecasting the stock price index. 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No. 97TH8335), IEEE.
Kennedy, J. and R. Eberhart (1995). "Particle swarm optimization Proceeding IEEE International Conference of Neural Network IV." IEEE Service centre, Piscataway.
Leigh, W., R. Purvis and J. M. Ragusa (2002). "Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support." Decision support systems 32(4): 361-377.
Li, R., T. Han and X. Song (2022). "Stock price index forecasting using a multiscale modelling strategy based on frequency components analysis and intelligent optimization." Applied Soft Computing: 109089.
Lin, Y., Y. Yan, J. Xu, Y. Liao and F. Ma (2021). "Forecasting stock index price using the CEEMDAN-LSTM model." The North American Journal of Economics and Finance 57: 101421.
Lo, A. W., H. Mamaysky and J. Wang (2000). "Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation." The journal of finance 55(4): 1705-1765.
Long, W., Z. Lu and L. Cui (2019). "Deep learning-based feature engineering for stock price movement prediction." Knowledge-Based Systems 164: 163-173.
Majhi, B., M. Rout and V. Baghel (2014). "On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices." Journal of King Saud University-Computer and Information Sciences 26(3): 319-331.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications, Penguin.
Neely, C., P. Weller and R. Dittmar (1997). "Is technical analysis in the foreign exchange market profitable? A genetic programming approach." Journal of financial and Quantitative Analysis 32(4): 405-426.
Nelson, D. M., A. C. Pereira and R. A. de Oliveira (2017). Stock market's price movement prediction with LSTM neural networks. 2017 International Joint Conference on Neural Networks (IJCNN), IEEE.
Pan, Y., Z. Xiao, X. Wang and D. Yang (2017). "A multiple support vector machine approach to stock index forecasting with mixed frequency sampling." Knowledge-Based Systems 122: 90-102.
Patel, J., S. Shah, P. Thakkar and K. Kotecha (2015). "Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques." Expert Systems with Applications 42(1): 259-268.
Pettey, C. B., M. R. Leuze and J. J. Grefenstette (1987). Parallel genetic algorithm. Genetic algorithms and their applications: proceedings of the second International Conference on Genetic Algorithms: July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA, Hillsdale, NJ: L. Erlhaum Associates, 1987.
Pring, M. J. (2002). How to select stocks using technical analysis, McGraw-Hill.
Russell, S. and P. Norvig (2002). "Artificial intelligence: a modern approach."
Sedighi, M., H. Jahangirnia and M. Gharakhani (2019). "A New Efficient Metaheuristic Model for Stock Portfolio Management and its Performance Evaluation by Risk-adjusted Methods." Int. J. Financ. Manag. Account 3: 63-77.
Seo, M., S. Lee and G. Kim (2019). "Forecasting the Volatility of Stock Market Index Using the Hybrid Models with Google Domestic Trends." Fluctuation and Noise Letters 18(01): 1950006.
Shi, Y. and R. Eberhart (1998). A modified particle swarm optimizer. 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), IEEE.
Shi, Y. and R. C. Eberhart (1999). Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), IEEE.
Singh, P. and B. Borah (2014). "Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization." International Journal of Approximate Reasoning 55(3): 812-833.
Sivanandam, S. and S. Deepa (2008). Genetic algorithms. Introduction to genetic algorithms, Springer: 15-37.
Theil, H. (1958). "Economic forecasts and policy."
Theil, H. (1971). "Applied economic forecasting."
Xiong, T., Y. Bao and Z. Hu (2014). "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting." Knowledge-Based Systems 55: 87-100.
Zhang, J., S. Cui, Y. Xu, Q. Li and T. Li (2018). "A novel data-driven stock price trend prediction system." Expert Systems with Applications 97: 60-69.
Zhang, Y., S. Balochian, P. Agarwal, V. Bhatnagar and O. J. Housheya (2016). Artificial intelligence and its applications 2014, Hindawi.