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Using Automated Pattern Recognition to Test Effectiveness of Technical Analysis

Student: Pustovalova Alena

Supervisor: Alexis V. Belianin

Faculty: International College of Economics and Finance

Educational Programme: Bachelor

Year of Graduation: 2014

<p>In this thesis, the automated pattern recognition algorithm will be present. &nbsp;Using the geometric Brownian motion we test the hypothesis of informativeness and effectiveness of chart patterns as basic tools of technical analysis. Moreover, the reliability rating is constructed on the basis of prices trend after the model formation. In total, 12 patterns are considered. Depending on the choice of pattern distributions either 6 or 1 of models is statistically significant.</p><p>&nbsp;</p><p>&nbsp;</p>

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