Soft Computing

, Volume 22, Issue 4, pp 1295–1312 | Cite as

A novel technical analysis-based method for stock market forecasting

  • Yuh-Jen Chen
  • Yuh-Min Chen
  • Shiang-Ting Tsao
  • Shu-Fan Hsieh
Methodologies and Application


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.


Stock market forecasting Technical analysis Support vector machine Particle swarm optimization 



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.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.


  1. 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 487–499Google Scholar
  2. 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):18–33Google Scholar
  3. Behera S, Sahoo S, Pati BB (2015) A review on optimization algorithms and application to wind energy integration to grid. Renew Sustain Energy Rev 48:214–227CrossRefGoogle Scholar
  4. 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):6889–6898CrossRefGoogle Scholar
  5. Chavarnakul T, Enke D (2008) Intelligent technical analysis based equivolume charting for stock trading using neural networks. Exp Syst Appl 34(2):1004–1017CrossRefGoogle Scholar
  6. Darvas N (2001) How I made $2,000,000 in the stock market. Lyle Stuart, New YorkGoogle Scholar
  7. Diler AI (2003) Predicting direction of ISE National-100 index with back propagation trained neural network. J Istanb Stock Exch 7(25–26):65–81Google Scholar
  8. 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):175–186CrossRefGoogle Scholar
  9. 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):14072–14085Google Scholar
  10. 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):1140–1149CrossRefGoogle Scholar
  11. Directorate-General of Budget, Accounting and Statistics, Executive Yuan, R.O.C. (Taiwan)
  12. Taiwan Stock Exchange, R.O.C (Taiwan)
  13. Huang CL, Tsai CY (2009) A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Exp Syst Appl 36(2):1529–1539CrossRefGoogle Scholar
  14. Hung JC (2015) Robust Kalman filter based on a fuzzy GARCH model to forecast volatility using particle swarm optimization. Soft Comput 19(10):2861–2869CrossRefGoogle Scholar
  15. 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):5311–5319CrossRefGoogle Scholar
  16. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report, Computer Engineering Department, Erciyes University, TurkeyGoogle Scholar
  17. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks 4:1942–1948CrossRefGoogle Scholar
  18. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetCrossRefzbMATHGoogle Scholar
  19. Kirkpatrick CD, Dahlguist JR (2010) Technical analysis: the complete resource for financial market technicians. Vice President, Tim Moore, Upper Saddle RiverGoogle Scholar
  20. Krolzig HM, Toro J (2004) Multiperiod forecasting in stock markets: a paradox solved. Decis Support Syst 37(4):531–542CrossRefGoogle Scholar
  21. 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):3761–3773CrossRefGoogle Scholar
  22. 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 398:36–45CrossRefGoogle Scholar
  23. Liang TP (2006) Decision support systems and business intelligence. BestWize, TaipeiGoogle Scholar
  24. Liu LX, Zhuang YQ, Liu XY (2011) Tax forecasting theory and model based on SVM optimized by PSO. Exp Syst Appl 38(1):116–120CrossRefGoogle Scholar
  25. Lu CJ, Lee TS, Chiu CC (2009) Financial time series forecasting using independent component analysis and support vector regression. Decis Support Syst 47(2):115–125CrossRefGoogle Scholar
  26. 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):125–133CrossRefGoogle Scholar
  27. Storn R, Price KV (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetCrossRefzbMATHGoogle Scholar
  28. Tsai CF, Hsiao YC (2010) Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches. Decis Support Syst 50(1):258–269CrossRefGoogle Scholar
  29. 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):5311–5319CrossRefGoogle Scholar
  30. 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 336–345Google Scholar
  31. Zhiqiang G, Huaiqing W, Quan L (2013) Financial time series forecasting using LPP and SVM optimized by PSO. Soft Comput 17(5):805–818CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yuh-Jen Chen
    • 1
  • Yuh-Min Chen
    • 2
  • Shiang-Ting Tsao
    • 2
  • Shu-Fan Hsieh
    • 3
  1. 1.Department of Accounting and Information SystemsNational Kaohsiung First University of Science and TechnologyKaohsiungTaiwan, ROC
  2. 2.Institute of Manufacturing Information and SystemsNational Cheng Kung UniversityTainanTaiwan, ROC
  3. 3.Department of Money and BankingNational Kaohsiung First University of Science and TechnologyKaohsiungTaiwan, ROC

Personalised recommendations