Skip to main content
SpringerLink
Log in

Mining stock category association on Tehran stock market

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

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%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://old.tse.ir/en/statsindex.aspx.

References

  • Aghamiri W, Esfahanipour A (2010) Adapted neuro-fuzzy inference system on indirect approach TSK fuzzy rule base for stock market analysis. Expert Syst Appl 37:4742–4748

    Article  Google Scholar 

  • Akgiray V, Booth G (1988) The stable-law model of stock returns. J Bus Econ Stat 6:51–57

    Google Scholar 

  • Ala M (2012) Providing an algorithm to discover association rules in stock price changes (Unpublished master‘s thesis). Payam noor University, Tehran

    Google Scholar 

  • Chang TS (2011) A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Syst Appl 38:14846–14851

    Article  Google Scholar 

  • 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 30:901–923

    Article  MATH  Google Scholar 

  • 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 40:7596–7606

    Article  Google Scholar 

  • Dorr DH, Denton AM (2009) Establishing relationships among patterns in stock market data. Data Knowl Eng 68:318–337

    Article  Google Scholar 

  • Enke D, Thawornwong S (2005) The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl 29:927–940

    Article  Google Scholar 

  • 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 

  • Haeri A (2008) Forcasting Forex market using data mining techniques (Unpublished master‘s thesis). Sanati Sharif University, Tehran

    Google Scholar 

  • Hajihoseyni H (2013) Multi-objectivedecision support systems for identifying financial data relationships by exploring fuzzy association rules (Unpublished master‘s thesis). University of Raja, Qazvin

    Google Scholar 

  • Hajizadeh E, Davari HD, Shahrabi J (2010) Application of data mining techniques in stock markets. J Econ Int Finance 2(7):109–118

    Google Scholar 

  • Han J, Kamber M (2013) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann publisher, San Francisco (chapter7)

    MATH  Google Scholar 

  • Ko PC, Lin PC (2008) Resource allocation neural network in portfolio selection. Expert Syst Appl 35:330–337

    Article  Google Scholar 

  • Kovalerchuk B, Vityaev E (2000) Data mining in finance: advances in relational and hybrid methods. Kluwer academic publisher, New York (chapter1)

    MATH  Google Scholar 

  • 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 118:21–45

    Article  MathSciNet  Google Scholar 

  • 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 221–238

    Chapter  Google Scholar 

  • 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 40:1542–1554

    Article  Google Scholar 

  • Liao SH, Ho H, Lin H (2008) Mining stock category association and cluster on Taiwan stock market. Expert Syst Appl 35:19–29

    Article  Google Scholar 

  • Liao SH, Chu PH, Teng TK (2011) Mining the co-movement in the Taiwan stock funds market. Expert Syst Appl 38:5276–5288

    Article  Google Scholar 

  • Liu B (2007) Web data mining: exploring, hyperlinks, contents and usage data, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  • Lo AW, Mackinlay AC (1990) when are contrarian profits due to stock market overreaction. Rev Financ Stud 3(2):175–205

    Article  Google Scholar 

  • Qodsi MM (2012) Capital market data classification model by multi objective association rules mining (Unpublished master‘s thesis). University of Raja, Qazvin

    Google Scholar 

  • Refenes AN, Zapranis A, Francis G (1994) Stock performance modeling using neural networks: a comparative study with regression models. Neural Netw 7(2):375–388

    Article  Google Scholar 

  • Roh TH (2007) Forecasting the volatility of stock price index. Expert Syst Appl 33:916–922

    Article  Google Scholar 

  • Shahrabi J (2013) Data mining, 1st edn. Soroush Gita publisher, Tehran

    Google Scholar 

  • Shamsuddin AFM, Kim JH (2003) Integration and interdependence of stock and foreign exchange markets: an Australian perspective. J Int Financ Mark Inst Money 13:237–254

    Article  Google Scholar 

  • Voditel PP, Deshpande U (2013) Astock market portfolio recommender system based on association rule mining. Appl Soft Comput 13:1055–1063

    Article  Google Scholar 

  • Wang YH (2009) Nonlinear neural network forecasting model for stock index option price: hybrid GJR-GARCH approach. Expert Syst Appl 36:564–570

    Article  Google Scholar 

  • Willinger W, Taqqu MS, Teverovsky V (1999) Stock market prices and long-range dependence. Finance Stoch 3:1–13

    Article  MATH  Google Scholar 

  • 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 30:653–667

    Google Scholar 

  • Yang J, Cabrera J, Wang T (2010) Nonlinearity, data-snooping, and stock index ETF return predictability. Eur J Oper Res 200:498–507

    Article  MATH  Google Scholar 

  • 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 438:178–187

    Article  MathSciNet  Google Scholar 

  • Zamani Sh, Souri D, Sanaeialam M (2010) A dynamic investigation to indexes spillovers in Tehran stock exchange using a multivariate model. Tahghighat-e-Eghtesadi. 93:29–54

    Google Scholar 

  • 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 34:3043–3064

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. Department of industrial management, Payame Noor University, P. O. Box, 19395-3697, Tehran, Islamic Republic of Iran

    Zahra Hoseyni Masum

Authors
  1. Zahra Hoseyni Masum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zahra Hoseyni Masum.

Ethics declarations

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.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masum, Z.H. Mining stock category association on Tehran stock market. Soft Comput 23, 1165–1177 (2019). https://doi.org/10.1007/s00500-017-2835-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2835-9

Keywords

Search

Navigation