Mean Ratio Statistic for measuring predictability

Oliver Linton and Katja Smetanina
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Katja Smetanina: Institute for Fiscal Studies

No CWP08/15, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We propose an alternative Ratio Statistic for measuring predictability of stock prices. Our statistic is based on actual returns rather than logarithmic returns and is therefore better suited to capturing price predictability. It captures not only linear dependence in the same way as the variance ratio statistics of Lo and MacKinlay (1988) but also some nonlinear dependencies. We derive the asymptotic distribution of the statistics under the null hypothesis that simple gross returns are unpredictable after a constant mean adjustment. This represents a test of the weak form of the Efficient Market Hypothesis. We also consider the multivariate extension, in particular, we derive the restrictions implied by the EMH on multiperiod portfolio gross returns. We apply our methodology to test the gross return predictability of various financial series.

Keywords: Variance Ratio Tests; Martingale; Predictability (search for similar items in EconPapers)
JEL-codes: C10 C22 G10 G14 (search for similar items in EconPapers)
Date: 2015-02-20
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Working Paper: Mean Ratio Statistic for measuring predictability (2015)
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