Price Discovery on Multiple Exchanges

Price Discovery on Multiple Exchanges

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Andreas Vig Logermann & Johan Emil Rasmussen January 21, 2015

Abstract The study of price discovery is motivated by the recent trend of securities being traded at multiple venues. The literature revolves around the issue of finding an efficient way to estimate the contribution of each market to the implicit efficient price process. This article estimates the information share using the Cholesky decomposition originally proposed by Hasbrouck (1995) and seeks to compare this measure with a recent extension, the spectral decomposition of Fernandes and Scherrer (2014). The spectral decomposition approach effectively eliminates the problem of the information share measure being order-variant. The conclusion of this paper is that taking the mean of the different Cholesky permutations yields results that are somewhat equal to results given by the spectral decomposition method. Empirically, these two methods are compared using three different stocks, YHOO, AAPL and INTC, and their quotes from three liquid exchanges, NASDAQ, BATS and NYSE Arca. Overall, NYSE Arca seems to be the most information dominant market, followed by NASDAQ. BATS seems to be the least information dominant market. To confirm the full-sample results, daily VECM models are estimated, yielding information shares upon daily subsamples. We construct an alternative full-sample information share measure using the daily estimates, which yield somehow different results. NASDAQ becomes less information dominant, while the information share of BATS increase, such that BATS in general becomes more informationally dominant than NASDAQ. Our results show no consistent significant correlations between daily information shares and daily realized variance, daily volumes and daily mean spreads, respectively, although some negative correlation is present. The information shares are used in a trading strategy application which exploits buy and sell signals from the observed...