Development and analysis of derivative trading systems using artificial intelligence
type: CreativeWork
description: "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.
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.
author: Amornwattana, Sunisa
keywords: Operations Research, Systems Engineering and Industrial Engineering
date published: 2016-02-10T08:00:00Z
date modified: 2026-07-24T03:18:50Z
provider: Missouri University of Science and Technology
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Missouri--Rolla