Financial time series prediction using mixture of experts
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), cilt.2869, ss.553-560, 2003 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 2869
- Basım Tarihi: 2003
- Doi Numarası: 10.1007/978-3-540-39737-3_69
- Dergi Adı: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Derginin Tarandığı İndeksler: Scopus
- Sayfa Sayıları: ss.553-560
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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. © Springer-Verlag Berlin Heidelberg 2003.