Abstract
This paper investigates the use of artificial neural networks (ANN) in risk estimation of asset returns. Istanbul Stock Exchange (ISE) index (XU100) is studied with a mixture of experts ANN architecture using daily data over a 12-year period. Results are compared to feed-forward neural networks, multilayer perceptron (MLP) and radial basis function (RBF) networks and recurrent neural networks (RNN). They are also compared to widely accepted Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) volatility model. These results suggest that mixture of experts (MoE) have the strength to capture the volatility in index return series and prepares a valuable basis for financial decision making.
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© 2003 Springer-Verlag Berlin Heidelberg
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Yumlu, M.S., Gurgen, F.S., Okay, N. (2003). Financial Time Series Prediction Using Mixture of Experts. In: Yazıcı, A., Şener, C. (eds) Computer and Information Sciences - ISCIS 2003. ISCIS 2003. Lecture Notes in Computer Science, vol 2869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39737-3_69
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DOI: https://doi.org/10.1007/978-3-540-39737-3_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20409-1
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