Abstract
Analysis of financial databases is sensitive to missing values (no reported information, provider errors, outlier filters...). Risk analysis and portfolio asset allocation require cylindrical and complete samples. Moreover, return distributions are characterised by non-normalities due to heteroskedasticity, leverage effects, volatility feedbacks and asymmetric local correlations. This makes completion algorithms very useful for portfolio management applications, specifically if they can deal properly with the empirical stylised facts of asset returns. Kohonen maps constitute powerful nonlinear financial classification tools (see [3], [4] or [6] for instance), following the approach of Cottrell et al. (2003), we use a Kohonen algorithm (see [2]), altogether with the Constrained Randomization Method (see [8]) to deal with mutual fund missing Net Asset Values. The accuracy of rebuilt NAV estimated series is then evaluated according to a comparison between the first moments of the series.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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.
Similar content being viewed by others
References
Agarwal, V., Naïk, N.: Multi-period Performance Persistence Analysis of Hedge Funds. Journal of Financial and Quantitative Analysis 35, 327–342 (2000)
Cottrell, M., Ibbou, S., Letrémy, P.: Traitement des données manquantes au moyen de l’algorithme de Kohonen. In: Proceedings of the tenth ACSEG Conference, in french, 12 pages (2003)
Cottrell, M., de Bodt, E., Grégoire, P.: Financial Application of the Self-Organizing Map. In: Proceedings of EUFIT 1998 (1), Verlag Mainz, pp. 205–209 (1998)
De Bodt, E., Cottrell, M.: Bootstrapping Self-Organizing Maps to Assess the Statistical Significance of Local Proximity. In: European Symposium on Artificial Neural Networks, pp. 245–254 (2000)
Kohonen, T.: Self-Organizing Maps, 362 pages. Springer, Berlin (1995)
Maillet, B., Rousset, P.: Classifying Hedge Funds using Kohonen Map. In: Cottrell-Lesage (ed.) Connectionist Approaches in Economics and Management Sciences. Series in Advances in Computational Management Science, vol. 6, pp. 233–259. Kluwer Academic Publisher, Dordrecht (2003)
Samad, T., Harp, S.: Self Organization with Partial Data. Network 3, 205–212 (1992)
Schreiber, T.: Constrained Randomization of Times Series Data. Physical Review Letter 80(10), 2105–2108 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Merlin, P., Maillet, B. (2005). Completing Hedge Fund Missing Net Asset Values Using Kohonen Maps and Constrained Randomization. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_146
Download citation
DOI: https://doi.org/10.1007/11550907_146
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)