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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Schittenkopf, C., Dorffner, G.: Risk Neutral Density Extraction from Option Prices: Improved Pricing with Mixture Density Networks (1999)
Tino, P., Schittenkopf, C., Dorffner, G.: Volatility Trading via Temporal Pattern Recognition in Quantized Financial Time Series (2000)
Chan, L., Gao, X.: An Algorithm for Trading and Portfolio Management Using Q-learning and Sharpe Ratio Maximization. In: Proceedings of International Conference on Neural Information Processing, ICONIP 2000, Taejon, Korea, pp. 832–837.
Hellstrom, T., Holmstrom, K.: Predictable Pattern in Stock Returns, Technical Report Series, IMA-TOM (1997–09)
Yao, J.T., Tan, C.L.: Guidelines for Financial Forecasting with Neural Networks (2001)
Engle, R.: Autoregressive Conditional Heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987–1007 (1982)
Bollerslev, T.: Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31, 307–327 (1986)
Nelson, D.: Conditional Heteroskedasticity in asset returns: a new approach. Econometrica 59, 349–370 (1991)
Engle, R.F., Ng, V.K.: Measuring and Testing the Impact of News on Volatility. The Journal of Finance 48(5), 1749–1778 (1993)
Okay, N.: Asymmetric Volatility Dynamics: Evidence From The Istanbul Stock Exchange, Business & Economics for the 21st Century, vol II, B&ESI, pp. 207–216 (1998)
Okay, N.: Evidence For Conditional Heteroskedasticity in Turkish Stock Returns. In: Readings Book of the 1998 ABA National Conference, pp. 241–247 (1998)
Bera, A.K., Higgins, M.L.: Arch Models. Properties, Estimation and testing. Journal of Economic Surveys 7, 305–362 (1993)
Okay, N.: Dynamic predictability of the Istanbul Stock Exchange Market using a Vector Autoregressive Model. Bogazici Journal 12, 31–56
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixture of local experts. Neural Computation 3, 79–87 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-39737-3_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20409-1
Online ISBN: 978-3-540-39737-3
eBook Packages: Springer Book Archive