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FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Talk

Shawash, J; Selviah, DR; (2010) FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Talk. Presented at: Algorithmic Trading – Future Directions and Opportunities for Research, University College London, London, UK. Green open access

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Abstract

This talk compares Higher Order Neural Networks (HONN) with Neural Networks, and linear regression for short term forecasting of stock market index daily returns. Two new HONNs, the Correlation HONN (CHONN) and the Horizontal HONN (HorizHONN) outperform all other models tested in terms of the Akaike Information Criterion, out-of-sample root mean square error, of FTSE100 and NASDAQ giving out-of-sample Hit Rates of up to 60% with AIC improvement up to 6.2%. New hybrid models for volatility estimation are formed by combining CHONN with E/GARCH are compared with conventional EGARCH, providing up to 2.1% and 2.7% AIC improvement for FTSE100 and NASDAQ.

Type:Conference item (Presentation)
Title:FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Talk
Event:Algorithmic Trading – Future Directions and Opportunities for Research
Location:University College London, London, UK
Dates:2010-06-11 - 2010-06-11
Open access status:An open access version is available from UCL Discovery
Publisher version:http://fc.cs.ucl.ac.uk/
Language:English
Keywords:Correlation, Higher Order, Neural Network, Finance, FTSE, Prediction, Estimation, Forecast, GARCH, volatility
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Electronic and Electrical Engineering

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