Abstract
Despite the widespread use of time series models in stock index forecasts, some of these models have encountered problems: (1) the selection of input factors may depend on personal experience or opinion; and (2) most conventional time series models consider only one variable. Furthermore, traditional forecasting models suffer from the following drawbacks: (1) models may rely on restrictive assumptions (such as linear separability or normality) about the variables being analyzed; and (2) it is hard to define and select applicable input factors for artificial neural networks (ANNs) in particular, and the rules generated from ANNs are not easily understood. To address these issues, we propose a multi-factor time series model based on an adaptive network-based fuzzy inference system (ANFIS) for stock index forecasting. In the proposed model, stepwise regression was first applied for the objective selection of technical indicators and then combined with ANFIS to construct the forecasting model. We evaluated the performance of our proposed model against three other models, with transaction data from the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Hong Kong Hang Seng Index (HSI) stock markets from 1998 to 2006 as experimental data sets and the root mean square error (RMSE) as the evaluation criterion. The results show the superiority of the proposed combined model, which outperformed other models in terms of RMSE and profitability, with strategies for increasing long-term uses of stock index forecasts made on the TAIEX and the HSI.
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References
Box GEP, Jenkins GM (1976), Time series analysis: forecasting and control. Holden-Day, San Francisco
Chen YS (2013) Modeling hybrid rough set-based classification procedures to identify hemodialysis adequacy for end-stage renal disease patients. Comput Biol Med 43(10):1590–1605
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, pp 1–6
Roh TH (2007) Forecasting the volatility of stock price index. Expert Syst Appl 33(4):916–922
Chen TL, Cheng CH, Teoh HJ (2008) High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets. Phys A 387(4):876–888
Chen MY, Chen DR, Fan MH, Huang TY (2013) International transmission of stock market movements: an adaptive neuro-fuzzy inference system for analysis of TAIEX forecasting. Neural Comput Appl. doi:10.1007/s00521-013-1461-4
Kankal M, Yüksek Ö (2013) Artificial neural network for estimation of harbor oscillation in a cargo harbor basin. Neural Comput Appl. doi:10.1007/s00521-013-1451-6
Rezaeianzadeh M, Tabari H, Arabi Yazdi A, Isik S, Kalin L (2013) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl. doi:10.1007/s00521-013-1443-6
Yao JT, Tan CL, Poh HL (1999) Neural networks for technical analysis: a study on KLCI. Int J Theoretical Appl Finance 2(2):221–241
Windecker RC (2013) Stochastic artificial neurons and neural networks. In: 2013 international joint conference on neural networks, Dallas, Texas
Oppenheimer HR, Schlarbaum GG (1981) Investing with Ben Graham: an ex ante test of the efficient markets hypothesis. J Financ Quant Anal 16(3):341–360
Tsai CF, Lin YC, Yen DC, Chen YM (2011) Predicting stock returns by classifier ensembles. Appl Soft Comput 11(2):2452–2459
Atsalakis G, Valavanis K (2009) Surveying stock market forecasting techniques – Part II: soft computing methods. Expert Syst Appl 36(3):5932–5941
Gorgulho A, Neves RF, Horta N (2011) Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert Syst Appl 38(11):14072– 14085
Pring MJ (1991) Technical analysis. McGraw-Hill, New York
Allen F, Karalainen R (1999) Using genetic algorithms to find technical trading rules. J Financ Econ 51:245–271
William L, Russell P, James MR (2002) Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decis Support Syst 32:361–377
Chang PC, Liao TW, Lin JJ, Fan CY (2011) A dynamic threshold decision system for stock trading signal detection. Appl Soft Comput 11(5):3998–4010
Su CH, Cheng CH, Tsai WL (2013) Fuzzy time series model based on fitting function for forecasting TAIEX index. Intel J Hybri Infor Technol 6:111–121
Park JI, Lee DJ, Song CK, Chun MG (2010) TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization. Expert Syst Appl 37(2): 959–967
Tanaka YM, Tokuoka S (2007) Adaptive use of technical indicators for the prediction of intra-day stock prices. Physica A 383(1):125–133
Murphy JJ (1986) Technical analysis of the futures market: a comprehensive guide to trading methods and applications. New York Institute of Finance (NYIF), New York, pp 2–4
Clarence N, Tan W (1999) A hybrid financial trading system incorporating chaos theory, statistical and artificial intelligence/soft computing methods. In: Queensland Finance Conference, School of Information Technology, Bond University, Queensland
Adhikari R (2015) A mutual association based nonlinear ensemble mechanism for time series forecasting. Appl Intell 43(2):233–250
Ediger V, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35(3):1701–1708
Bas E, Egrioglu E, Aladag CH, Yolcu U (2015) Fuzzy-time-series network used to forecast linear and nonlinear time series. Appl Intell 43(2):343–355
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd ed. Upper Saddle River, 536 New Jersey
Ravi A, Kurniawan H, Thai PNK, Ravi Kumar P (2008) Soft computing system for bank performance prediction. Appl Soft Comput 8:305–315
Li PX, Tan ZX, Yan LL, Deng KH (2011) Time series prediction of mining subsidence based on a SVM. Min Sci Technol 21(4):557–562
Chen SM (1996) Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst 81(3):311–319
Jha GK, Sinha K (2014) Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India. Neural Comput Appl 24(3–4):563– 571
Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intel Fuzzy Syst 2(3):267–278
Nazemi A, Abbasi B, Omidi F (2015) Solving portfolio selection models with uncertain returns using an artificial neural network scheme. Appl Intell 42(4):609–621
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23 (3):665–685
Cheng CH, Wei LY, Chen YS (2009) Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting. Neurocomputing 72(16–18):3462–3468
Chang JR, Wei LY, Cheng CH (2011) A hybrid ANFIS model based on AR and volatility for TAIEX forecasting. Appl Soft Comput 11(1):1388–1395
Khalaj G, Khalaj MJ (2014) Application of ANFIS for modeling of layer thickness of chromium carbonitride coating. Neural Comput Appl 24(3–4):685–694
Ocak H, Ertunc HM (2013) Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems. Neural Comput Appl 23(6):1583–1589
Deneme IO (2013) Estimation of modal damping ratio of impact-damped flexible beams using ANFIS. Neural Comput Appl 23(6):1669–1676
Uçar T, Karahoca A, Karahoca D (2013) Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets. Neural Comput Appl 23(2):471–483
Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. In: Proceeding of the IFAC symposium on fuzzy information, knowledge representation and decision analysis, pp 55–60
Kattan MW, Cooper RB (2000) A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions. Omega - Int J Manage S 28:501–512
Ali S, Abbadeni N, Batouche M (2012) Multidisciplinary computational intelligence techniques: applications in business, engineering, and medicine. IGI Global Publishing, Pennsylvania
Mahjoobi J, Shahidi AI, Kazeminezhad MH (2008) Hindcasting of wave parameters using different soft computing methods. Appl Ocean Res 30(1):28–36
Vairappan C Tamura H Gao S Tan Z (2009) Batch type local search-based Adaptive Neuro-Fuzzy Inference System (ANFIS) with self-feedbacks for time-series prediction. Neurocomputing 72:1870–1877
Chen SM, Chen CD (2011) TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst 19(1):1–11
Yu HK (2005) Weighted fuzzy time-series models for TAIEX forecasting. Phys A 349(3–4):609–624
Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80–83. http://sci2s.ugr.es/keel/pdf/algorithm/articulo/wilcoxon1945.pdf
Weale PR, Amin HL (2003) Bursting the dot.com ‘Bubble’: a case study in investor behavior. Technol Anal Strateg Manage 15(1):117–136
Acknowledgments
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract Nos. NSC 102-2410-H-146-003 & MOST 103-2221-E-146-003-MY2. In particular, the author cordially thanks the Editor-in-Chief, associate editor, and anonymous referees for their useful comments and suggestions, which led to significant improvement in the presentation and quality of this study.
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Chen, YS., Cheng, CH., Chiu, CL. et al. A study of ANFIS-based multi-factor time series models for forecasting stock index. Appl Intell 45, 277–292 (2016). https://doi.org/10.1007/s10489-016-0760-8
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DOI: https://doi.org/10.1007/s10489-016-0760-8