Title

Trading With A Stock Chart Heuristic

Keywords

Efficient market hypothesis (EMH); Expert systems; Heuristics; Knowledge engineering; Stock market forecasting; Technical analysis

Abstract

The efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors cannot consistently ldquobeat the marketrdquo because stocks reside in perpetual equilibrium, making research efforts futile. This flies in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using knowlege engineering experimentation with one price history pattern - the ldquobull flag stock chartrdquo - which signals technical analysts of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years of daily closing price data for the New York stock exchange composite index. The experiment is then replicated using the horizontal rotation or mirror image pattern of the ldquobull flagrdquo (or ldquobear flagrdquo stock chart) that signals a future stock market decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions based on random choice. © 2007 IEEE.

Publication Date

11-3-2008

Publication Title

IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans

Volume

38

Issue

1

Number of Pages

93-104

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TSMCA.2007.909508

Socpus ID

54949134110 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/54949134110

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