Soft Computing

, Volume 23, Issue 4, pp 1165–1177 | Cite as

Mining stock category association on Tehran stock market

  • Zahra Hoseyni MasumEmail author
Methodologies and Application


Following the recent efforts made to achieve a predictable capital market, this study attempted to explore the interlocking relationships between the stock returns of companies listed on Tehran stock exchange (TSE). For that purpose, data concerning 36 industry classes between 2000 and 2013 were examined through clustering and association rule. Preparation and initial refining of data suggested that only 25 out of 36 industries met the requirement for 13-year membership at TSE. Finally, a total of 249,061 records were evaluated, and the results were presented in the form of several rules and recommendations for investors. The results suggested that there were no two-item rules (rules with one antecedent) within industries. The best rules entailed three and four items with a lift of more than two, confidence more than 81% and support more than 1%.


Association rule Clustering Stock return 



Funding was provided by Payame Noor University (Grant No. 12345678).

Compliance with ethical standards

Conflict of interest

I have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Aghamiri W, Esfahanipour A (2010) Adapted neuro-fuzzy inference system on indirect approach TSK fuzzy rule base for stock market analysis. Expert Syst Appl 37:4742–4748CrossRefGoogle Scholar
  2. Akgiray V, Booth G (1988) The stable-law model of stock returns. J Bus Econ Stat 6:51–57Google Scholar
  3. Ala M (2012) Providing an algorithm to discover association rules in stock price changes (Unpublished master‘s thesis). Payam noor University, TehranGoogle Scholar
  4. Chang TS (2011) A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Syst Appl 38:14846–14851CrossRefGoogle Scholar
  5. Chen AS, Leung MT, Daouk H (2003) Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Comput Oper Res 30:901–923CrossRefzbMATHGoogle Scholar
  6. De Oliveira FA, Nobre CN, Zárate LE (2013) Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index–case study of PETR4, Petrobras, Brazil. Expert Syst Appl 40:7596–7606CrossRefGoogle Scholar
  7. Dorr DH, Denton AM (2009) Establishing relationships among patterns in stock market data. Data Knowl Eng 68:318–337CrossRefGoogle Scholar
  8. Enke D, Thawornwong S (2005) The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl 29:927–940CrossRefGoogle Scholar
  9. Göçken M, Özçalıc M, Boru A, Dosdoğru AT (2015) Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst Appl. doi: 10.1016/j.eswa.2015.09.029 Google Scholar
  10. Haeri A (2008) Forcasting Forex market using data mining techniques (Unpublished master‘s thesis). Sanati Sharif University, TehranGoogle Scholar
  11. Hajihoseyni H (2013) Multi-objectivedecision support systems for identifying financial data relationships by exploring fuzzy association rules (Unpublished master‘s thesis). University of Raja, QazvinGoogle Scholar
  12. Hajizadeh E, Davari HD, Shahrabi J (2010) Application of data mining techniques in stock markets. J Econ Int Finance 2(7):109–118Google Scholar
  13. Han J, Kamber M (2013) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann publisher, San Francisco (chapter7)zbMATHGoogle Scholar
  14. Ko PC, Lin PC (2008) Resource allocation neural network in portfolio selection. Expert Syst Appl 35:330–337CrossRefGoogle Scholar
  15. Kovalerchuk B, Vityaev E (2000) Data mining in finance: advances in relational and hybrid methods. Kluwer academic publisher, New York (chapter1)zbMATHGoogle Scholar
  16. Kuo RJ, Chen CH, Hwang YC (2001) An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets Syst 118:21–45MathSciNetCrossRefGoogle Scholar
  17. Lazo G, Maria M, Vellasco R, Aurélio M, Pacheco C (2002) Portfolio selection and management using a hybrid intelligent and statistical system. In: Chen SH (ed) Genetic algorithms and genetic programming in computational finance. Kluwer academic publisher, Netherlands, pp 221–238CrossRefGoogle Scholar
  18. Liao SH, Chou SY (2013) Data mining investigation of co-movements on the Taiwan and China stock markets for future investment portfolio. Expert Syst Appl 40:1542–1554CrossRefGoogle Scholar
  19. Liao SH, Ho H, Lin H (2008) Mining stock category association and cluster on Taiwan stock market. Expert Syst Appl 35:19–29CrossRefGoogle Scholar
  20. Liao SH, Chu PH, Teng TK (2011) Mining the co-movement in the Taiwan stock funds market. Expert Syst Appl 38:5276–5288CrossRefGoogle Scholar
  21. Liu B (2007) Web data mining: exploring, hyperlinks, contents and usage data, 2nd edn. Springer, BerlinzbMATHGoogle Scholar
  22. Lo AW, Mackinlay AC (1990) when are contrarian profits due to stock market overreaction. Rev Financ Stud 3(2):175–205CrossRefGoogle Scholar
  23. Qodsi MM (2012) Capital market data classification model by multi objective association rules mining (Unpublished master‘s thesis). University of Raja, QazvinGoogle Scholar
  24. Refenes AN, Zapranis A, Francis G (1994) Stock performance modeling using neural networks: a comparative study with regression models. Neural Netw 7(2):375–388CrossRefGoogle Scholar
  25. Roh TH (2007) Forecasting the volatility of stock price index. Expert Syst Appl 33:916–922CrossRefGoogle Scholar
  26. Shahrabi J (2013) Data mining, 1st edn. Soroush Gita publisher, TehranGoogle Scholar
  27. Shamsuddin AFM, Kim JH (2003) Integration and interdependence of stock and foreign exchange markets: an Australian perspective. J Int Financ Mark Inst Money 13:237–254CrossRefGoogle Scholar
  28. Voditel PP, Deshpande U (2013) Astock market portfolio recommender system based on association rule mining. Appl Soft Comput 13:1055–1063CrossRefGoogle Scholar
  29. Wang YH (2009) Nonlinear neural network forecasting model for stock index option price: hybrid GJR-GARCH approach. Expert Syst Appl 36:564–570CrossRefGoogle Scholar
  30. Willinger W, Taqqu MS, Teverovsky V (1999) Stock market prices and long-range dependence. Finance Stoch 3:1–13CrossRefzbMATHGoogle Scholar
  31. Wu KP, Wu YP, Lee HM (2014) Stock trend prediction by using \(K\)-means and apriori algorithm for sequential chart pattern mining. J Inf Sci Eng 30:653–667Google Scholar
  32. Yang J, Cabrera J, Wang T (2010) Nonlinearity, data-snooping, and stock index ETF return predictability. Eur J Oper Res 200:498–507CrossRefzbMATHGoogle Scholar
  33. Zahedi J, Rounaghi MM (2015) Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran stock exchange. Phys A 438:178–187MathSciNetCrossRefGoogle Scholar
  34. Zamani Sh, Souri D, Sanaeialam M (2010) A dynamic investigation to indexes spillovers in Tehran stock exchange using a multivariate model. Tahghighat-e-Eghtesadi. 93:29–54Google Scholar
  35. Zhu X, Wang H, Xu L, Li H (2008) Predicting stock index increments by neural networks: the role of trading volume under different horizons. Expert Syst Appl 34:3043–3064CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of industrial managementPayame Noor UniversityTehranIslamic Republic of Iran

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