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Item ESSAYS ON MARKET MICROSTRUCTURE AND HIGH FREQUENCY TRADING(2014) Li, Wei; Kyle, Albert S.; Business and Management: Finance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation includes two chapters on topics related to market microstruc- ture and high frequency trading. In the first chapter, I explore the effects of speed differences among front-running high frequency traders (HFTs) in a model of one round of trading. Traders differ in speed and their speed differences matter. I model strategic interactions induced when HFTs have different speeds in an extended Kyle (1985) framework. HFTs are assumed to anticipate incoming orders and trade rapidly to exploit normal-speed traders' latencies. Upon observing a common noisy signal about the incoming order flow, faster HFTs react more quickly than slower HFTs. I find that these front-running HFTs effectively levy a tax on normal-speed traders, making markets less liquid and prices ultimately less informative. Such negative effects on market quality are more severe when HFTs have more heterogeneous speeds. Even when infinitely many HFTs compete, their negative effects in general do not vanish. I analyze policy proposals concerning HFTs and find that (1) lowering the frequency of trading reduces the negative impact of HFTs on market quality and (2) randomizing the sequence of order execution can degrade market quality when the randomizing interval is short. Consistent with empirical findings, a small number of HFTs can generate a large fraction of the trading volume and HFTs' profits depend on their speeds relative to other HFTs. In the second chapter, I study the effects of higher trading frequency and front-running in a dynamic model. I find that a higher trading frequency improves the informativeness of prices and increases the trading losses of liquidity driven noise traders. When the trading frequency is finite, the existence of HFT front-runners hampers price efficiency and market liquidity. In the limit when trading frequency is infinitely high, however, information efficiency is unaffected by front-running HFTs and these HFTs make all profits from noise traders who do not smooth out their trades.Item Essays on Empirical Market Microstructure(2011) Tuzun, Tugkan; Kyle, Albert S; Business and Management: Finance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The first essay examines the events of May 6, 2010: the ``Flash Crash". The Flash Crash, a brief period of extreme market volatility on May 6, 2010 raised questions about the current structure of the U.S. financial markets. Audit-trail data from U.S. Commodity Futures Trading Commission (CFTC) is used to describe the structure of the E-mini S\&P 500 stock index futures market on May 6. In this study, three questions are asked. How did High Frequency Traders (HFTs) trade on May 6? What may have triggered the Flash Crash? What role did HFTs play in the Flash Crash? There is evidence which supports that HFTs did not trigger the Flash Crash, but their responses to the unusually large selling pressure on that day exacerbated market volatility. The second essay examines the relationship between mutual fund trading and liquidity consumption in financial markets. Using Thompson Mutual Funds holdings data and the Trade and Quotes (TAQ) data, we relate the mutual fund trading to liquidity consumption. Mutual fund trading is positively correlated with liquidity consumption. Mutual fund sensitivity to liquidity consumption differs based on mutual fund investment style. Large trades reveal the trading activity of actively managed mutual funds whereas the trading activity of index funds can be explained by small trades. This is consistent with a plausible explanation that index funds need to use small trades to rebalance their portfolios and information motivates the large trades of active mutual funds. The third essay tests the predictions of trading game invariance using the sample of trades from TAQ dataset from 1993 to 2008. The theory of trading game invariance predicts that the distribution of trade sizes as a fraction of trading volume should vary across stocks proportionally to their trading activity in -2/3 power and that the number of trades should vary across stocks proportionally to their trading activity in 2/3 power. The data supports predictions of the invariance theory. For the number of trades, the estimated power coefficient of 0.69 (with standard errors of 0.001) is especially close to the predicted one of 2/3 on the subsample before 2001. These estimates increases to 0.79 (with standard errors of 0.004) after 2001 following a structural break related to a reduction in tick size and a consequent spread of algorithmic trading. Furthermore, the entire distribution of trade size shifts with the trading activity in a manner predicted by invariance theory. When trade sizes are adjusted for differences in trading activity, then their distribution is stable across stocks and similar to the distribution of a log-normal variable, truncated at the 100-share threshold.