Development and analysis of derivative trading systems using artificial intelligence

Author

Sunisa Amornwattana

Abstract

"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, page iii.

Advisor(s)

Enke, David Lee, 1965-

Committee Member(s)

Nystrom, Halvard E.
Samaranayake, V. A.
Grasman, Scott E. (Scott Erwin)
Dagli, Cihan H., 1949-

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Publisher

University of Missouri--Rolla

Publication Date

Spring 2007

Pagination

xii, 158 pages

Note about bibliography

Includes bibliographical references (pages 150-157).

Rights

© 2007 Sunisa Amornwattana, All rights reserved.

Document Type

Dissertation - Citation

File Type

text

Language

English

Subject Headings

Neural networks (Computer science) -- Economic aspectsOptions (Finance) -- Mathematical modelsSecurities -- Prices -- Mathematical modelsStock price forecasting -- Mathematical models

Thesis Number

T 9201

Print OCLC #

180701814

Recommended Citation

Amornwattana, Sunisa, "Development and analysis of derivative trading systems using artificial intelligence" (2007). Doctoral Dissertations. 1729.
https://scholarsmine.mst.edu/doctoral_dissertations/1729