Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis

  • José Miguel Hernández-Lobato
  • Alberto Suárez
Conference paper

DOI: 10.1007/11840930_72

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)
Cite this paper as:
Hernández-Lobato J.M., Suárez A. (2006) Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis. In: Kollias S., Stafylopatis A., Duch W., Oja E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg

Abstract

We compare the performance of competitive and collaborative strategies for mixtures of autoregressive experts with normal innovations for conditional risk analysis in financial time series. The prediction of the mixture of collaborating experts is an average of the outputs of the experts. If a competitive strategy is used the prediction is generated by a single expert. The expert that becomes activated is selected either deterministically (hard competition) or at random, with a certain probability (soft competition). The different strategies are compared in a sliding window experiment for the time series of log-returns of the Spanish stock index IBEX 35, which is preprocessed to account for the heteroskedasticity of the series. Experiments indicate that the best performance for risk analysis is obtained by mixtures with soft competition, where the experts have a probability of activation given by the output of a gating network of softmax units.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • José Miguel Hernández-Lobato
    • 1
  • Alberto Suárez
    • 1
  1. 1.Computer Science Department, Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

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