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, page iii.

Name thesis:degree_name

Ph. D. in Engineering Management

Grantor

University of Missouri--Rolla

Year dc:date.available

2016

Author dc:creator

• Amornwattana, Sunisa

• Operations Research, Systems Engineering and Industrial Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/1729

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-2731