Development and analysis of derivative trading systems using artificial intelligence
"This dissertation proposes a methodology that utilizes a generalized regression neural network to develop hybrid option trading systems that incorporate both volatility and return forecasting. This study focuses on the S&P 500 stock index as a representative for the market. The three different trading methods are discussed: stock return forecasting using a simple call and put option strategy, volatility forecasting applying a straddle option strategy, and the combination of volatility and stock return forecasting applying advanced strategies, such as strip, strap, bull, and bear spread strategies. The results show that the hybrid options trading model can improve the overall trading return and outperform trading models using merely return forecasting or volatility forecasting in isolation"--Abstract, leaf iii.
Enke, David Lee, 1965-
Nystrom, Halvard E.
Samaranayake, V. A.
Grasman, Scott E. (Scott Erwin)
Dagli, Cihan H., 1949-
Engineering Management and Systems Engineering
Ph. D. in Engineering Management
University of Missouri--Rolla
xii, 158 leaves
© 2007 Sunisa Amornwattana, All rights reserved.
Dissertation - Citation
Library of Congress Subject Headings
Neural networks (Computer science) -- Economic aspects
Options (Finance) -- Mathematical models
Securities -- Prices -- Mathematical models
Stock price forecasting -- Mathematical models
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b6125816~S5
Amornwattana, Sunisa, "Development and analysis of derivative trading systems using artificial intelligence" (2007). Doctoral Dissertations. 1729.