Algorithmic Trading, Market Timing and Market Efficiency
Professor Robin Sickles, Chair of Economics; Rice University
1st Aug 2014 11:00 am - Rm 498 Merewether Bldg H04
In recent years large panel data models have been developed to make full use of the Information content of such datasets. Despite the large number of contributions, an important issue that is rarely pursued in much of the existing literature concerns the risk of neglecting structural beaks in the data generating process. While a substantial literature on structural break analysis exists for univariate time series, a relatively small number of techniques have been developed for panel data models. This paper provides a new treatment to deal with the problem of multiple structural breaks that occur at unknown date points in the panel model parameters. Our method is related to the Haar wavelet technique that we adjust according to the structure of the explanatory variables in order to detect the change points of the parameters consistently. We apply the technique to high frequency securities data to examine the effects of algorithmic trading (AT) on standard measures of market quality that proxy for some dimension of liquidity. Specifically, we examine whether AT has time varying effects on liquidity and discuss asset pricing implications.