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 .
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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
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DOI: https://doi.org/10.1007/11550907_146
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