Liquidity Withdrawal and the 'Flash Crash' on May 6, 2010

Wing Bernard LEE, Singapore Management University
Shih-Fen CHENG, Singapore Management University
Annie KOH, Singapore Management University


On May 6, 2010, the US equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (DJIA) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. Earlier research by Lee, Cheng and Koh 2010) identified the conditions under which a “flash crash” can be triggered by systematic traders running highly similar trading strategies, especially when they are “crowding out” other liquidity providers in the market. The authors contend that the events of May 6, 2010 exhibit patterns consistent with the type of “flash crash” observed in their earlier study (2010). While some commentators assigned blame to high-frequency trading, the authors show that the issue may be less about high-frequency trading per se, but rather the domination of market activities by trading strategies that are responding to the same set of market variables in similar ways, as well as various pre-existing schemes that modify the “rules of the game” in the middle of trading. The consequent lack of market participants interested in the “other side” of their trades may result in a significant liquidity withdrawal during extreme market movements. This paper describes an attempt to reconstruct the critical elements of the market events of May 6, 2010 based on the five hypotheses posed initially by the Joint CFTC-SEC Preliminary Report, by using a large-scale computer simulation model. The resulting price distribution provides a reasonable resemblance to the descriptive statistics of the second-by-second prices of S&P500 e-Mini futures from 14:30 to 15:00 on May 6, 2010. There are no a priori assumptions made on asset price distributions, and our description of market dynamics is purely based on the structure of the market and the key types of market participants involved. This type of simulation avoids “over-fitting” historical data, and can therefore provide regulators with deeper insights on the possible drivers of the “flash crash”, as well as what type of responses they may consider under comparable market circumstances in the future.