ISSN: 2222-6990
Open access
Accurate stock price forecasting can be immensely helpful for investors, because more accurate predictions will bring more profit for investors. Stock market participants usually make their short-term investment decisions according to recent stock knowledge such as the analytical report before market opened, yesterday stock index volatility of other countries, and the price fluctuations in recent days. However, there are three major drawbacks in the conventional time series model: (1) the majority statistical methods rely upon assumptions for the variables; (2) most models do not consider the volatility of different country stock market; and (3) most forecasting models do not provide the predicting rule of stock price index. Based on these reasons above, this paper proposes a new model, using multi-stock volatility causality (Dow Jones (Dow Jones Industrial Average), NASDAQ (the largest US electronic stock market)) joining to fusion ANFIS models, to forecast Taiwan stock index. Furthermore, one-period and two-period adaptive expectation model are applied to the proposed model for enhancing the forecasting performance. To evaluate the forecasting performances, the proposed model is compared with two different models, Chen’s model and Huarng’s model. The experimental results indicate that the proposed model is superior to the listing methods in terms of RMSE (root mean squared error).
Altan, A., & Karasu, S. (2019) The effect of kernel values in support vector machine to forecasting performance of financial time series, J. Cogn. Syst. 4 (1) 17–21
Bezdek, J. C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. NY: Plenum Press.
Bollerslev, T. (1986) Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics. 31 307-327.
Box, G., & Jenkins, G. (1976) Time series analysis: Forecasting and control, San Francisco: Holden-Day.
Chen, S. M. (1996) Forecasting enrollments based on fuzzy time-series, Fuzzy Sets Syst. 81 311-319.
Chen, S. M., & Chung, N. Y. (2006) Forecasting Enrollments Using High-Order Fuzzy Time Series and Genetic Algorithms. International of Intelligent Systems. 21 485-501.
Chiu, S. L. (1994) Fuzzy model identification based on cluster estimation. J. of Intelligent and Fuzzy Systems. 2 267-278.
Dickinson, D. G. (2000) Stock market integration and macroeconomic fundamentals: An empirical analysis, 1980-95, Applied Financial Economics, 10(3) 261-276.
Devpura, N., Narayan, P. K., & Sharma, S. S. (2018) Is stock return predictability time-varying? J. Int. Final. Mark. Inst. Money 52 152–172.
Engle, R. F. (1982) Autoregressive conditional heteroscedasticity with estimator of the variance of United Kingdom inflation. Econometrica. 50(4) 987-1008.
Huarng, K. H., & Yu, T. H. K. (2006) The application of neural networks to forecast fuzzy time series, Physica A. 336 481-491.
Huarng, K. H., Yu, T. H. K., & Hsu, Y. W. (2007) A Multivariate Heuristic Model for Fuzzy, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 37(4) 836-846.
Jang, J. S. (1993) ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
Kim, K., & Han, I. (2000) Genetic algorithms approach to feature discretization in artificial neural networks for prediction of stock index. Expert System with Applications. 19 125-132.
Karasu, S., Altan, A., Bekiros, S., & Ahmad, W. (2020), A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series, Energy 212 118750.
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990) Stock market prediction system with modular neural network. In Proceedings of the international joint conference on neural networks, San Diego, California, 1-6.
Kmenta, J. (1986) Elements of Econometrics. MacMillan.
Nguyen, T. H., Shirai, K., & Velcin, J. (2015) Sentiment analysis on social media for stock movement prediction, Expert Syst. Appl. 42 (24) 9603–9611.
Nikolopoulos, C., & Fellrath, P. (1994) A hybrid expert system for investment advising. Expert Systems. 11(4) 245-250.
Roh, T. H. (2007) Forecasting the volatility of stock price index. Expert Systems with Applications. 33 916-922.
Schumaker, R. P., & Chen, H. (2009) A quantitative stock prediction system based on financial news, Inf. Process. Manage. 45 (5) 571–583.
Takagi, T., & Sugeno, M. (1983) Derivation of fuzzy control rules from human operator’s control actions, in Proc. IFAC Symp. Fuzzy Inform., Knowledge Representation and Decision Analysis. 55-60.
Timmermann, A. (2008) Elusive return predictability, Int. J. Forecast. 24 (1) 1–18.
In-Text Citation: (Wei & Tsai, 2023)
To Cite this Article: Wei, L.-Y., & Tsai, C.-H. (2023). Fusion ANFIS and Adaptive Expectation Model based on Multi-stock Volatility Causality for TAIEX Forecasting. International Journal of Academic Research in Business & Social Sciences, 13(1), 1610 – 1622.
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