Skip to main content
SpringerLink
Log in

Development and Evaluation of Decision-Making Model for Stock Markets

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

The paper introduces an intelligent decision-making model which is based on the application of artificial neural networks (ANN) and swarm intelligence technologies. The proposed model is used to generate one-step forward investment decisions for stock markets. The ANN are used to make the analysis of daily stock returns and to calculate one day forward decision for purchase of the stocks. Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the “the best” ANN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different forms of decision-making model: different number of ANN, ANN inputs, sliding windows, and commission fees. The paper introduces the decision-making model, its evaluation results and discusses its application possibilities.

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.

Similar content being viewed by others

References

  • Bartholdson, K. and Mauboussin, J.M. (2002), Thoughts on organizing for investing success, Credit Suisse First Boston Equity Research.

  • Blandis, E. and Simutis, R. (2002), Using principal component analysis and neural network for forecasting of stock market index, Bizinesa augstskola Turiba SIA, Zinatne, Riga, pp. 31–35.

  • Carlisle, A. and Dozier, G. (2000), Adapting particle swarm optimization to dynamic environments, In: Proceedings of ICAI Conference on Artificial Intelligence, Las Vegas, USA. pp. 199–204.

  • Din, A. (2002), Optimization and Forecasting with Financial Time Series, Note from Seminar at CERN.

  • Eberhart, R.C. and Yuhui Shi, (1998), Comparison between genetic algorithms and particle swarm optimization, Evolutionary Programming 611–616.

  • Franks N.R. (1989). Army of ants: a collective intelligence. American Scientist 77(2): 138–145

    Google Scholar 

  • Gudise, V.G. and Venayagamoorthy, G.K. (2003), Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In: Proceedings of the 2003 IEEE on Swarm Intelligence Symposium, SIS ’03, 24–26 April, 110–117.

  • Hellstrom, T. (2001), Optimizing the sharpe ration for a rank based trading system, Lecture Notes in Artificial Intelligence, LNA 2258 Springer-Verlag, New York, pp. 142–155.

  • Hellstrom T. and Holmstrom K. (2000). The relevance of trends for predictions of stock returns. International Journal of Intelligent Systems in Accounting, Finance & Management 9(1): 23–34

    Article  Google Scholar 

  • Interactive Brokers (2004), http://www.interactivebrokers.com.

  • Kaastra I. and Milton B. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3): 215–236

    Article  Google Scholar 

  • Lu W.Z., Fan H.Y. and Lo S.M. (2003). Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong. Neurocomputing 51: 387–400

    Article  Google Scholar 

  • Lowe D., Webb A.R. (1991), Time Series Prediction by Adaptive Networks: A Dynamical Systems Perspective, IEEE computer society press.

  • Mirmirani S. and Li C.H. (2004). Gold price, neural networks and genetic algorithms. Computational Economics 23(2): 193–200

    Article  Google Scholar 

  • Nenortaite, J. and Simutis, R. (2004), Workshop on computational methods in finance and insurance, Stocks’ Trading System Based on the Particle Swarm Optimization Algorithm, Springer-Verlag, New York, vol. 10, pp. 843–850.

  • Pavlidis, N.G., Tasoulis, D. and Vrahatis, M.N. (2003), Financial forecasting through unsupervised clustering and evolutionary trained neural networks, In: Proceedings of the 2003 Congress on Evolutionary Computation, Canberra Australia.

  • Po-Chang, K. and Ping-Chen, L. (2004), A Hybrid swarm intelligence based mechanism for earning forecast. In: Proceedings of the 2nd International Conference on Information Technology for Application (ICITA 2004), pp. 193–198.

  • Rejas, L.M.P., Ponce, E.R.R. and Silva, J.E.B. (2001), The wiener gauss stochastic process: an application to the food index at the madrid stock exchange, In: Proceeeings of 2001 The Journal of Global Business Perspedtives, International Business Association (IBA).

  • Simutis R. (2003). Stock trading systems based on stock’s price ranks (in Lithuanian). Ekonomika 62: 157–164

    Google Scholar 

  • White, H. (1998), Economic prediction using neural networks: The case of IBM daily stock returns, IEEE International Conference on Neural Networks, IEEE Press, New York, pp. 451–458.

Download references

Author information

Authors and Affiliations

  1. Faculty of Humanities, Vilnius University Kaunas, Muitines 8, 44280, Kaunas, Lithuania

    Jovita Nenortaite & Rimvydas Simutis

Authors
  1. Jovita Nenortaite
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rimvydas Simutis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jovita Nenortaite.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nenortaite, J., Simutis, R. Development and Evaluation of Decision-Making Model for Stock Markets. J Glob Optim 36, 1–19 (2006). https://doi.org/10.1007/s10898-005-5371-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-005-5371-6

Keywords

Search

Navigation