Using rough set to support investment strategies of real-time trading in futures market

Authors:
Suk Jun Lee;Jae Joon Ahn;Kyong Joo Oh;Tae Yoon Kim
Affiliations:
Department of Information and Industrial Engineering, Yonsei University, Seoul, South Korea 120-749;Department of Information and Industrial Engineering, Yonsei University, Seoul, South Korea 120-749;Department of Information and Industrial Engineering, Yonsei University, Seoul, South Korea 120-749;Department of Statistics, Keimyung University, Daegu, South Korea 704-701
Venue:
Applied Intelligence
Year:
2010

Citing 7
Cited 5

A rough set approach to attribute generalization in data mining

Information Sciences: an International Journal
Applying rough sets to market timing decisions

Decision Support Systems - Special issue: Data mining for financial decision making
Forecasting stock market movement direction with support vector machine

Computers and Operations Research
A hybrid genetic-neural architecture for stock indexes forecasting

Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
A generalized model for financial time series representation and prediction

Applied Intelligence
Soft computing techniques applied to finance

Applied Intelligence
The use of data mining and neural networks for forecasting stock market returns

Expert Systems with Applications: An International Journal

Enhanced rule extraction and classification mechanism of genetic network programming for stock trading signal generation

Proceedings of the 13th annual conference on Genetic and evolutionary computation
How many reference patterns can improve profitability for real-time trading in futures market?

Expert Systems with Applications: An International Journal
FAST: Fundamental Analysis Support for Financial Statements. Using semantics for trading recommendations

Information Systems Frontiers
Automated asset management based on partially cooperative agents for a world of risks

Applied Intelligence
Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Finding proper investment strategies in futures market has been a hot issue to everyone involved in major financial markets around the world. However, it is a very difficult problem because of intrinsic unpredictability of the market. What makes things more complicated is the advent of real-time trading due to recent striking advancement of electronic communication technology. The real-time data imposes many difficult tasks to futures market analyst since it provides too much information to be analyzed for an instant. Thus it is inevitable for an analyst to resort to a rule-based trading system for making profits, which is usually done by the help of diverse technical indicators. In this study, we propose using rough set to develop an efficient real-time rule-based trading system (RRTS). In fact, we propose a procedure for building RRTS which is based on rough set analysis of technical indicators. We examine its profitability through an empirical study.