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Li, Wei
Kyle, Albert S.
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.