Avoiding data mining bias when testing technical analysis strategies - a methodological study

Douglas, Rowan

Avoiding data mining bias when testing technical analysis strategies - a methodological study

Master Thesis

2020

Abstract

When seeking to identify a profitable technical analysis (TA) strategy, a na¨ıve investigation will compare a large number of possible strategies using the same set of historical market data. This process can give rise to a significant data mining bias, which can cause spurious results. There are various methods which account for this bias, with each one providing a different set of advantages and disadvantages. This dissertation compares three of these methods, the step wise Superior Predictive Ability (step-SPA) method of P.-H. Hsu, Y.-C. Hsu and Kuan (2010), the False Discovery Rate (FDR) method of Benjamini and Hochberg (1995) and the Monte Carlo Permutations (MCP) method of Masters (2006). The MCP method is also extended, using a step wise algorithm, to allow it to identify multiple profitable strategies. The results of the comparison show that while both the FDR and extended MCP methods can be useful under certain circumstances, the stepSPA method is ultimately the most robust, making it the best choice in spite of its significant computational requirements and stricter set of assumptions.

Keywords

Actuarial Science

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