Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Aloud, Monira
Affiliations: Department of Management Information Systems, College of Business Administration, King Saud University, Saudi Arabia. E-mail: mealoud@ksu.edu.sa
Abstract: The development of computational intelligence based trading strategies for financial markets has been the focus of research over the last few years. To develop efficient and effective automated trading strategies, we need to understand the workings of the market and the patterns emerging as a result of the traders interactions. In this paper, we develop an adaptive Genetic Programming (GP) agent-based trading system under Intraday Seasonality Model (ISM), which is abbreviated as GP-ISM trading system. ISM is used for creating maps and visualizing the dynamic price evolution of the asset during the day. This new model permits the recognition of periodic patterns and seasonalities in the price time series and hence eliminates any unnecessary data input. We use a high-frequency dataset of historical price data from Saudi Stock Market, which enables us to run multiple market simulation runs and draw comparisons and conclusions for the developed trading strategies. The goal of our work is to develop automated computational intelligence-based strategies for real markets, and this study facilitates a more thorough understanding of a specific market's workings and constitutes the basis for further exploration into such strategies designed for the stock market. We evaluate the intelligence of the GP-ISM trading system through agent-based simulation market index trading. For comparison, we also include four other types of trading agents in this contest, namely, zero-intelligence agents, Buy-and-Hold agents, fundamental agents and technical analysis agents. As a result, GP-ISM performs the best, which provides a general framework for the further development of automated trading strategies and decision support systems.
Keywords: Adaptive GP trading system, high-frequency trading, agent-based model, intraday seasonality model, financial forecasting, decision support systems
DOI: 10.3233/IDT-170291
Journal: Intelligent Decision Technologies, vol. 11, no. 2, pp. 235-251, 2017
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl