Abstract
Owing to the dynamic changes of the stock market and numerous influences on stock prices, assessing stock prices has become increasingly difficult. Furthermore, when dealing with information on stocks, people tend to amplify the importance of available and self-correlative information, a habit that runs contrary to objective and reasonable investment decision-making. Therefore, how to use effective stock information to assist investors in making stock investment decisions is a major topic in stock investment. This study develops a novel technical analysis method for stock market forecasting to effectively promote forecasting accuracy, which can help investors to increase their decision support quality and profitability. Specifically, this study involves the following tasks: (1) design a technical analysis-based stock market forecasting process, (2) develop techniques related to technical analysis-based stock market forecasting, and (3) demonstrate and evaluate the developed technical analysis-based method for stock market forecasting. In developing techniques associated with the technical analysis-based stock market forecasting method, the techniques involve trend-based stock classification, adaptive stock market indicator selection, and stock market trading signal forecasting.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Agrawal R, Srikant R (1994) Fast algorithms for mining generalized association rules. Proceedings of the 20th international conference on very large database (VLDB94). Santiago, Chile, pp 487–499
Altay E, Satman MH (2005) Stock market forecasting: artificial neural networks and linear regression comparison in an emerging market. J Financ Manag Anal 18(2):18–33
Behera S, Sahoo S, Pati BB (2015) A review on optimization algorithms and application to wind energy integration to grid. Renew Sustain Energy Rev 48:214–227
Chang PC, Liu CH, Lin JL, Fan CY, Ng CSP (2009) A neural network with a case based dynamic window for stock trading prediction. Exp Syst Appl 36(3):6889–6898
Chavarnakul T, Enke D (2008) Intelligent technical analysis based equivolume charting for stock trading using neural networks. Exp Syst Appl 34(2):1004–1017
Darvas N (2001) How I made $2,000,000 in the stock market. Lyle Stuart, New York
Diler AI (2003) Predicting direction of ISE National-100 index with back propagation trained neural network. J Istanb Stock Exch 7(25–26):65–81
Goodwin P, Önkal-Atay D, Thomson ME, Pollock AC, Macaulay A (2004) Feedback-labelling synergies in judgmental stock price forecasting. Decis Support Syst 37(1):175–186
Gorgulho A, Neves R, Horta N (2011) Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Exp Syst Appl 38(11):14072–14085
Ha YM, Sanghyun P, Kim SW, Won JI, Yoon JH (2009) A stock recommendation system exploiting rule discovery in stock databases. Inf Softw Technol 51(7):1140–1149
http://www.dgbas.gov.tw/mp.asp?mp=1. Directorate-General of Budget, Accounting and Statistics, Executive Yuan, R.O.C. (Taiwan)
http://www.twse.com.tw/ch/index.php. Taiwan Stock Exchange, R.O.C (Taiwan)
Huang CL, Tsai CY (2009) A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Exp Syst Appl 36(2):1529–1539
Hung JC (2015) Robust Kalman filter based on a fuzzy GARCH model to forecast volatility using particle swarm optimization. Soft Comput 19(10):2861–2869
Kara Y, Boyacioglu MA, Baykan ÖK (2011) Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Exp Syst Appl 38(5):5311–5319
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report, Computer Engineering Department, Erciyes University, Turkey
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks 4:1942–1948
Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Kirkpatrick CD, Dahlguist JR (2010) Technical analysis: the complete resource for financial market technicians. Vice President, Tim Moore, Upper Saddle River
Krolzig HM, Toro J (2004) Multiperiod forecasting in stock markets: a paradox solved. Decis Support Syst 37(4):531–542
Lai RK, Fan CY, Huang WH, Chang PC (2009) Evolving and clustering fuzzy decision tree for financial time series data forecasting. Exp Syst Appl 36(2):3761–3773
Li Z, Smith KH, Mumford KA, Wang Y, Stevens GW (2015) Regression of NRTL parameters from ternary liquid-liquid equilibria using particle swarm optimization and discussions. Fluid Phase Equilib 398:36–45
Liang TP (2006) Decision support systems and business intelligence. BestWize, Taipei
Liu LX, Zhuang YQ, Liu XY (2011) Tax forecasting theory and model based on SVM optimized by PSO. Exp Syst Appl 38(1):116–120
Lu CJ, Lee TS, Chiu CC (2009) Financial time series forecasting using independent component analysis and support vector regression. Decis Support Syst 47(2):115–125
Mieko TY, Seiji T (2007) Adaptive use of technical indicators for the prediction of intra-day stock prices. Phys A: Stat Mech Appl 383(1):125–133
Storn R, Price KV (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359
Tsai CF, Hsiao YC (2010) Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches. Decis Support Syst 50(1):258–269
Yakup K, Melek AB, Ömer KB (2011) Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Exp Syst Appl 38(5):5311–5319
Yu L, Wang S, Lai KK (2005) Mining stock market tendency using GA-based support vector machines. In: Deng X, Ye Y (eds) Lecture notes in computer science, vol 3828. Springer, Heidelberg, pp 336–345
Zhiqiang G, Huaiqing W, Quan L (2013) Financial time series forecasting using LPP and SVM optimized by PSO. Soft Comput 17(5):805–818
Acknowledgments
The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC100-2410-H-327-003-MY2. Additionally, we deeply appreciate Yu-Ting Luo for her editorial assistance and the editor and reviewers for their constructive comments and suggestions on the paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Chen, YJ., Chen, YM., Tsao, ST. et al. A novel technical analysis-based method for stock market forecasting. Soft Comput 22, 1295–1312 (2018). https://doi.org/10.1007/s00500-016-2417-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-016-2417-2