Stock market trading rule discovery using pattern recognition and technical analysis

Authors:
Jar-Long Wang;Shu-Hui Chan
Affiliations:
Department of Management Information System, Fortune Institute of Technology, Kaohsiung, Taiwan, ROC;Department of Finance, Fortune Institute of Technology, Kaohsiung, Taiwan, ROC and Institute of Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

Citing 3
Cited 7

Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural networks, and genetic algorithm: a case study in romantic decision support

Decision Support Systems
A computational implementation of stock charting: abrupt volume increase as signal for movement in New York stock exchange composite index

Decision Support Systems - Special issue: Data mining for financial decision making
Neural network techniques for financial performance prediction: integrating fundamental and technical analysis

Decision Support Systems - Special issue: Data mining for financial decision making

Trading rule discovery in the US stock market: An empirical study

Expert Systems with Applications: An International Journal
Forecasting stock market short-term trends using a neuro-fuzzy based methodology

Expert Systems with Applications: An International Journal
Profitability of technical chart pattern trading on FX rates: analyzed by wavelet transform

IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An inter-market arbitrage trading system based on extended classifier systems

Expert Systems with Applications: An International Journal
A novel, rule-based technical pattern identification mechanism: Identifying and evaluating saucers and resistant levels in the US stock market

Expert Systems with Applications: An International Journal
How many reference patterns can improve profitability for real-time trading in futures market?

Expert Systems with Applications: An International Journal
Mining longitudinal network for predicting company value

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three

Quantified Score

Hi-index	12.06

Visualization

Abstract

This study examines the potential profit of bull flag technical trading rules using a template matching technique based on pattern recognition for the Nasdaq Composite Index (NASDAQ) and Taiwan Weighted Index (TWI). To minimize measurement error due to data snooping, this study performed a series of experiments to test the effectiveness of the proposed method. The empirical results indicated that all of the technical trading rules correctly predict the direction of changes in the NASDAQ and TWI. This finding may provide investors with important information on asset allocation. Moreover, better bull flag template price fit is associated with higher average return. The empirical results demonstrated that the average return of trading rules conditioned on bull flag significantly better than buying every day for the study period, especially for TWI.