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Forecasting financial indicators by generalized behavioral learning method

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Abstract

Forecasting financial indicators (indexes/prices) is a complex and a quite difficult issue because they depend on many factors such as political events, financial ratios, and economic variables. Also, the psychological facts or decision-making styles of investors or experts are other major reasons for this difficulty. In this study, a generalized behavioral learning method (GBLM) was employed to forecast financial indicators, which are the indexes/prices of 34 different financial indicators (24 stock indexes, 2 forexes, 3 financial futures, and 5 commodities). The achieved results were compared with the reported results in the literature and the obtained results by artificial neural network, which is widely used and suggested for forecasting financial indicators. These results showed that GBLM can be successfully employed in short-term forecasting financial indicators by detecting hidden market behavior (pattern) from their previous values. Also, the results showed that GBLM has the ability to track the fluctuation and the main trend.

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References

  • Alexander SS (1961) Price movements in speculative markets: trends or random walks. Ind Manag Rev 2:7–26

    Google Scholar 

  • Atsalakis GS, Valavanis KP (2009) Surveying stock market forecasting techniques—part II: soft computing methods. Expert Syst Appl 36:5932–5941. doi:10.1016/j.eswa.2008.07.006

    Article  Google Scholar 

  • Atsalakis GS, Valavanis KP (2009) Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst Appl 36:10696–10707. doi:10.1016/j.eswa.2009.02.043

    Article  Google Scholar 

  • Bouton ME, Moody EW (2004) Memory processes in classical conditioning. Neurosci Biobehav Rev 28:663–674. doi:10.1016/j.neubiorev.2004.09.001

    Article  Google Scholar 

  • Boyacioglu MA, Avci D (2010) An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Syst Appl 37:7908–7912. doi:10.1016/j.eswa.2010.04.045

    Article  Google Scholar 

  • Cheng C-H, Chen T-L, Wei L-Y (2010) A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf Sci 180:1610–1629. doi:10.1016/j.ins.2010.01.014

    Article  Google Scholar 

  • Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. neural information processing systems, vol 9. MIT Press, Cambridge

    Google Scholar 

  • Ertuğrul OF, Kaya Y (2014) A detailed analysis on extreme learning machine and novel approaches based on ELM. Am J Comput Sci Eng 1(5):43–50

    Google Scholar 

  • Ertuğrul ÖF, Tağluk ME (2016) A novel machine learning method based on generalized behavioral learning theory. Neural Comput Appl. doi:10.1007/s00521-016-2314-8

    Article  Google Scholar 

  • Ertuğrul ÖF, Tağluk ME (2017a) Forecasting local mean sea level by generalized behavioral learning method. Arab J Sci Eng 11:769–776. doi:10.1007/s13369-017-2468-4

    Article  Google Scholar 

  • Ertuǧrul ÖF, Tagluk ME (2017b) A fast feature selection approach based on extreme learning machine and coefficient of variation. Turk J Electr Eng Comput Sci 25:3409–3420. doi:10.3906/elk-1606-122

    Article  Google Scholar 

  • Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Financ 25:383–416. doi:10.2307/2325486

    Article  Google Scholar 

  • Fama EF (1965) The behavior of stock-market prices. J Bus. doi:10.1086/294743

    Article  Google Scholar 

  • Fernandez JRM, Vidal JDLCB (2016) Improved shape parameter estimation in K clutter with neural networks and deep learning. Int J Interact Multimed Artif Intell 3(7):96–103. doi:10.9781/ijimai.2016.3714

    Article  Google Scholar 

  • Granger CW (1992) Forecasting stock market prices: lessons for forecasters. Int J Forecast 8(1):3–13. doi:10.1016/0169-2070(92)90003-R

    Article  MathSciNet  Google Scholar 

  • Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Expert Syst Appl 38:10389–10397. doi:10.1016/j.eswa.2011.02.068

    Article  Google Scholar 

  • Hadavandi E, Shavandi H, Ghanbari A (2010) Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowl-Based Syst 23:800–808. doi:10.1016/j.knosys.2010.05.004

    Article  Google Scholar 

  • Hebb DO (1961) Distinctive features of learning in the higher animal. In: Delafresnaye JF (ed) Brain mechanisms and learning. Oxford University Press, London

    Google Scholar 

  • Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feed forward neural networks. Neural Netw 2:985–990

    Google Scholar 

  • Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501. doi:10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  • 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. Expert Syst Appl 38:5311–5319. doi:10.1016/j.eswa.2010.10.027

    Article  Google Scholar 

  • Kumar A, Kumar A, Singh SK, Kala R (2016) Human activity recognition in real-times environments using skeleton joints. Int J Interact Multimed Artif Intell 3(7):61–69. doi:10.9781/ijimai.2016.379

    Article  Google Scholar 

  • Liu F, Wang J (2012) Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomputing 83:12–21. doi:10.1016/j.neucom.2011.09.033

    Article  Google Scholar 

  • McNelis PD (2005) Neural networks in financial: gaining predictive edge in the market. Elsevier Inc, New York, p 5, 23

    Google Scholar 

  • Nagelkerke NJ (1992) Maximum likelihood estimation of functional relationships. Springer, New York, pp 11–61. doi:10.1007/978-1-4612-2858-5

    Book  MATH  Google Scholar 

  • Preethi G, Santhi B (2012) Stock market forecasting techniques: a survey. J Theor Appl Inform Technol 46(1):24–30

  • Qin Z, Kar S, Zheng H (2016) Uncertain portfolio adjusting model using semiabsolute deviation. Soft Comput 20(2):717–725

    Article  Google Scholar 

  • Schunk DH (2012) Learning theories an educational perspective, 6th edn. Pearson, London

    Google Scholar 

  • Tsai C-F, Hsiao Y-C (2010) Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches. Decis Support Syst 50:258–269. doi:10.1016/j.dss.2010.08.028

    Article  Google Scholar 

  • Wang J-J, Wang J-Z, Zhang Z-G, Guo S-P (2012) Stock index forecasting based on a hybrid model. Omega 40:758–766. doi:10.1016/j.omega.2011.07.008

    Article  Google Scholar 

  • Zhang P (2016) An interval mean-average absolute deviation model for multiperiod portfolio selection with risk control and cardinality constraints. Soft Comput 20(3):1203–1212

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Electrical and Electronic Engineering, Batman University, 72060, Batman, Turkey

    Ömer Faruk Ertuğrul

  2. Electrical and Electronic Engineering, Inonu University, 44060, Malatya, Turkey

    Mehmet Emin Tağluk

Authors
  1. Ömer Faruk Ertuğrul
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  2. Mehmet Emin Tağluk
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Correspondence to Ömer Faruk Ertuğrul.

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Author Ömer Faruk Ertugrul declares that he has no conflict of interest. Author Mehmet Emin Tağluk declares that he has no conflict of interest.

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

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Communicated by V. Loia.

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Ertuğrul, Ö.F., Tağluk, M.E. Forecasting financial indicators by generalized behavioral learning method. Soft Comput 22, 8259–8272 (2018). https://doi.org/10.1007/s00500-017-2768-3

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