Chapter

Artificial Neural Networks – ICANN 2006

Volume 4132 of the series Lecture Notes in Computer Science pp 691-700

Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis

  • José Miguel Hernández-LobatoAffiliated withCarnegie Mellon UniversityComputer Science Department, Escuela Politécnica Superior, Universidad Autónoma de Madrid
  • , Alberto SuárezAffiliated withCarnegie Mellon UniversityComputer Science Department, Escuela Politécnica Superior, Universidad Autónoma de Madrid

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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.