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Optimizing Financial Portfolios from the Perspective of Mining Temporal Structures of Stock Returns

  • Kai-Chun Chiu
  • Lei Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2734)

Abstract

In the literature, return-based approaches which directly used security prices or returns to control portfolio weights were often used. Inspired by the arbitrage pricing theory (APT), some other efforts concentrate on indirect modelling using hidden factors. In this paper, we investigate how the gaussian temporal factor analysis (TFA) technique can be used for portfolio optimization. Since TFA is based on the classical APT model and has the benefit of removing rotation indeterminacy via temporal modelling, using TFA for portfolio management allows portfolio weights to be indirectly controlled by several hidden factors.

Keywords

Stock Return Portfolio Optimization Independent Component Analysis Portfolio Management Short Selling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Sharpe, W.F.: Mutual fund performance. Journal of Business 39 (1966) 119–138CrossRefGoogle Scholar
  2. 2.
    Moody, J., Wu, L., Liao, Y., Saffell, M.: Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting 17 (1998) 441–470CrossRefGoogle Scholar
  3. 3.
    Xu, L., Cheung, Y.M.: Adaptive supervised learning decision networks for traders and portfolios. Journal of Computational Intelligence in Finance 5 (1997) 11–15Google Scholar
  4. 4.
    Hung, K.K., Cheung, C.C., Xu, L.: New sharpe-ratio-related methods for portfolio selection. Proc. of Computational Intelligence for Financial Engineering (CIFEr 2000) (2000) 34–37Google Scholar
  5. 5.
    Back, A.D., Weigend, A.S.: A first application of independent component analysis to extracting structure from stock returns. International Journal of Neural Systems 8 (1997) 473–484CrossRefGoogle Scholar
  6. 6.
    Yip, F., Xu, L.: An application of independent component analysis in the arbitrage pricing theory. Proceedings of the International Joint Conference on Neural Networks (IJCNN’2000) 5 (2000) 279–284Google Scholar
  7. 7.
    Jöreskog, K.G.: A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34 (1969) 183–202CrossRefGoogle Scholar
  8. 8.
    Xu, L.: Temporal byy learning for state space approach, hidden markov model and blind source separation. IEEE Trans. on Signal Processing 48 (2000) 2132–2144zbMATHCrossRefGoogle Scholar
  9. 9.
    Ross, S.: The arbitrage theory of capital asset pricing. Journal of Economic Theory 13 (1976) 341–360CrossRefMathSciNetGoogle Scholar
  10. 10.
    Roll, R., Ross, S.: An empirical investigation of the arbitrage pricing theory. Journal of Finance 35 (1980) 1073–1103CrossRefGoogle Scholar
  11. 11.
    Roll, R., Ross, S.: The arbitrage pricing theory approach to strategic portfolio planning. Financial Analysts Journal 40 (1984) 14–26CrossRefGoogle Scholar
  12. 12.
    Xu, L.: Byy harmony learning, independent state space and generalized apt financial analyses. IEEE Transactions on Neural Networks 12 (2001) 822–849CrossRefGoogle Scholar
  13. 13.
    Xu, L.: Rbf nets, mixture experts, and bayesian ying-yang learning. Neurocomputing 19 (1998) 223–257zbMATHCrossRefGoogle Scholar
  14. 14.
    Chiu, K.C., Xu, L.: Financial apt-based gaussian tfa learning for adaptive portfolio management. In: Artificial Neural Networks-ICANN’2002, LNCS 2415. (2002) 1019–1024Google Scholar
  15. 15.
    Chan, L., Karceski, J., Lakonishok, J.: On portfolio optimization: Forecasting covariances and choosing the risk model. The Review of Financial Studies 12 (1999) 937–974CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kai-Chun Chiu
    • 1
  • Lei Xu
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatin, N.T., Hong KongP.R. China

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