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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sharpe, W.F.: Mutual fund performance. Journal of Business 39 (1966) 119–138
Moody, J., Wu, L., Liao, Y., Saffell, M.: Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting 17 (1998) 441–470
Xu, L., Cheung, Y.M.: Adaptive supervised learning decision networks for traders and portfolios. Journal of Computational Intelligence in Finance 5 (1997) 11–15
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) 34–37
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) 473–484
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) 279–284
Jöreskog, K.G.: A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34 (1969) 183–202
Xu, L.: Temporal byy learning for state space approach, hidden markov model and blind source separation. IEEE Trans. on Signal Processing 48 (2000) 2132–2144
Ross, S.: The arbitrage theory of capital asset pricing. Journal of Economic Theory 13 (1976) 341–360
Roll, R., Ross, S.: An empirical investigation of the arbitrage pricing theory. Journal of Finance 35 (1980) 1073–1103
Roll, R., Ross, S.: The arbitrage pricing theory approach to strategic portfolio planning. Financial Analysts Journal 40 (1984) 14–26
Xu, L.: Byy harmony learning, independent state space and generalized apt financial analyses. IEEE Transactions on Neural Networks 12 (2001) 822–849
Xu, L.: Rbf nets, mixture experts, and bayesian ying-yang learning. Neurocomputing 19 (1998) 223–257
Chiu, K.C., Xu, L.: Financial apt-based gaussian tfa learning for adaptive portfolio management. In: Artificial Neural Networks-ICANN’2002, LNCS 2415. (2002) 1019–1024
Chan, L., Karceski, J., Lakonishok, J.: On portfolio optimization: Forecasting covariances and choosing the risk model. The Review of Financial Studies 12 (1999) 937–974
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chiu, KC., Xu, L. (2003). Optimizing Financial Portfolios from the Perspective of Mining Temporal Structures of Stock Returns. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_23
Download citation
DOI: https://doi.org/10.1007/3-540-45065-3_23
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40504-7
Online ISBN: 978-3-540-45065-8
eBook Packages: Springer Book Archive