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Automated high-frequency trading has grown tremendously in the past 20 years and is responsible for about half of all trading activities at stock exchanges worldwide. Geography is central to the rise of high-frequency trading due to a market design of “continuous trading” that allows traders to engage in arbitrage based upon informational advantages built into the socio-technical assemblages that make up current capital markets. Enormous investments have been made in creating transmission technologies and optimizing computer architectures, all in an effort to shave milliseconds of order travel time (or latency) within and between markets. We show that as a result of the built spatial configuration of capital markets, “public” is no longer synonymous with “equal” information. High-frequency trading increases information inequalities between market participants.
- High-frequency trading and the construction of information inequality.pdf
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- A Practical Real Options Approach to Valuing High-Frequ... R&D Projects.pdf
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- Correlated High-Frequency Trading.pdf
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- Trading Rules Over Fundamentals - A Stock Price Formula...s and Crashes.pdf
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- High Frequency Trading.pdf
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We define low-latency activity as strategies that respond to market events in the millisecond environment, the hallmark of proprietary trading by high-frequency trading firms. We propose a new measure of low-latency activity that can be constructed from publicly-available NASDAQ data to investigate the impact of high-frequency trading on the market environment. Our measure is highly correlated with NASDAQ-constructed estimates of high-frequency trading, but it can be computed from data that are more widely-available. We use this measure to study how low-latency activity affects market quality both during normal market conditions and during a period of declining prices and heightened economic uncertainty. Our conclusion is that increased low-latency activity improves traditional market quality measures — lowering short-term volatility, decreasing spreads, and increasing displayed depth in the limit order book. Of particular importance is that our findings suggest that increased low-latency activity need not work to the detriment of long-term investors in the current market structure for U.S. equities.
- Low-Latency Trading.pdf
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High frequency trading dominates trading in financial markets. How it affects the low frequency trading, however, is still unclear. Using NASDAQ order book data, we investigate this question by categorizing orders as either high or low frequency, and examining several measures. We find that high frequency trading enhances liquidity by increasing the trade frequency and quantity of low frequency orders. High frequency trading also reduces the waiting time of low frequency limit orders and improves their likelihood of execution. Our results indicate that high frequency trading has a liquidity provision effect and improves the execution quality of low frequency orders.
- How Does High Frequency Trading Affect Low Frequency Trading.pdf
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We study performance and competition among high-frequency traders (HFTs). We construct measures of latency and find that differences in relative latency account for large differences in HFTs’ trading performance. HFTs that improve their latency rank due to colocation upgrades see improved trading performance. The stronger performance associated with speed comes through both the short-lived information channel and the risk management channel, and speed is useful for a variety of strategies including market making and cross-market arbitrage. We explore implications of competition on relative latency and find support for various theoretical predictions.
- Risk and Return in High-Frequency Trading.pdf
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A limit order market enables an early seller to trade with a late buyer by leaving a price quote. Information arrival in the interim period creates adverse selection risk for the seller and therefore hampers trade. Entry of high-frequency traders (HFTs) might restore trade as their machines can refresh quotes quickly on (hard) information. Empirically, HFT entry reduced adverse selection by 23% and increased trade by 17%. Model calibration shows that one percentage point more of the gains from trade were realized. Finally, we show that a well-designed double auction raises this to ten percentage points.
- Middlemen in Limit Order Markets.pdf
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