eprintid: 19924
rev_number: 56
eprint_status: archive
userid: 608
dir: disk0/00/01/99/24
datestamp: 2011-07-29 14:01:18
lastmod: 2021-10-29 22:53:37
status_changed: 2011-07-29 14:01:18
type: poster
metadata_visibility: show
item_issues_count: 0
creators_name: Shawash, J
creators_name: Selviah, DR
title: FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Poster Paper
divisions: UCL
divisions: B04
divisions: C05
divisions: F46
keywords: Correlation, Higher Order, Neural Network, volatility, returns, FTSE, finance, financial, prediction, forecast, estimation
abstract: This poster paper 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.
date: 2010-06-11
vfaculties: VENG
oa_status: green
primo: open
primo_central: open_green
elements_source: Manually entered
elements_id: 239925
lyricists_name: Selviah, David
lyricists_id: DRSEL91
full_text_status: public
event_title: Algorithmic Trading - Future Directions and Opportunities for Research
event_location: University College London, London, UK
citation:        Shawash, J;    Selviah, DR;      (2010)    FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Poster Paper.                   Presented at: Algorithmic Trading - Future Directions and Opportunities for Research, University College London, London, UK.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/19924/2/Poster_Algorithmic_Trading_ver2.pdf