Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias

Article history: Received 25 October 2008 Received in revised form 19 July 2009 Accepted 5 January 2010 Available online 18 January 2010 In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the “superior predictive ability” (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify predictive models without potential data snooping bias. It is shown analytically and by simulations that the stepwise SPA test is more powerful than the stepwise Reality Check test of Romano andWolf (2005, Econometrica).We then apply the proposed test to examine the predictive ability of technical trading rules based on the data of growth and emerging market indices and their exchange traded funds (ETFs). It is found that technical trading rules have significant predictive power for thesemarkets, yet such evidence weakens after the ETFs are introduced. Published by Elsevier B.V. JEL classification: C12 C32 C52 G11