A Kernel Regression Method for Technical Pattern Recognition

Document Type : applied


1 Assistant Prof., Faculty of Management, Khatam University, Tehran, Iran

2 MSc. Student in financial engineering, Faculty of Management, University of Khatam, Tehran, Iran


During 1960-1970, several studies were done base on efficient market hypothesis but the researchers sought to challenge this hypothesis. One of the tools that use to reject this hypothesis is Technical analysis. Technical analysis is intuitive and visual approach based on past information. One of the tools of Technical analysis is patterns that are geometric shapes. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression and we apply this method to bank mellat, iran khodro and oil industry investment stocks from March 2011 to May 2016. Finally we assess the ability to predict correct trends by using patterns. We find that patterns do provide incremental information and can overbear the weak form of efficient market. The patterns that we investigate on, are head & shoulder top and bottom, ascending & descending flag, bullish & bearish pennant, rectangle tops & bottoms, double tops & bottoms, triple top & bottoms patterns.
JEL: G14, G17
How to cite this paper: Mousavi, M. M., & Pourebrahim, H. (2016). Kernel Regression Method for Technical Pattern Recognition. Quarterly Journal of Risk Modeling and Financial Engineering, 1(2), 166–184. (In Persian)


Main Subjects

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