Equilibrium High Frequency Trading
HEC Paris - Finance Department
IAE - Université de Toulouse 1 Capitole; Toulouse School of Economics
Centre for Economic Policy Research (CEPR)
International Conference of the French Finance Association (AFFI), May 2011
Algorithms enable investors to locate trading opportunities, which raises gains from trade. Algorithmic traders can also process information on stock values before slow traders, which generates adverse selection. We model trading in this context and show that, for a given level of algorithmic trading, multiple equilibria can arise, some of which generate market exclusion for slow traders and sharp increases in the price impact of trades. We offer a theoretical interpretation for the "flash-crash" of may 2010. Next, we analyze the equilibrium level of investment in algorithmic trading. Because when others become fast it increases adverse selection costs for slow investors, algo-trading generates negative externalities. Therefore the equilibrium level of algo-trading exceeds its utilitarian welfare maximizing counterpart. Furthermore, since it involves fixed costs, investment in algorithmic trading is more protable for large institutions than for small ones. This generates equilibrium informational asymmetries between large fast traders and small slow traders.
Number of Pages in PDF File: 36
JEL Classification: G10, G18, G20Accepted Paper Series
Date posted: May 8, 2011 ; Last revised: March 15, 2013
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