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Machines vs. Machines: High Frequency Trading and Hard Information
In today's markets where high frequency traders (HFTs) act as both liquidity providers and takers, I argue that information asymmetry induced by liquidity-taking HFTs' use of machine-readable information is important. This particular type of information asymmetry arises because some machines may access the information before other machines or because of randomness in relative speed. Applying a novel statistical approach to measure HFT activity through limit order book data and using a natural experiment of index inclusion, I show that liquidity-providing HFTs supply less liquidity to stocks ...
Price Discovery in the U.S. Treasury Cash Market: On Principal Trading Firms and Dealers
We explore the following question: does the trading activity of registered dealers on Treasury interdealer broker (IDB) platforms differ from that of principal trading firms (PTFs), and if so, how and to what effect on market liquidity? To do so, we use a novel dataset that combines Treasury cash transaction reports from FINRAâ€™s Trade Reporting and Compliance Engine (TRACE) and publicly available limit order book data from BrokerTec. We find that trades conducted in a limit order book setting have high permanent price impact when a PTF is the passive party, playing the role of ...
First to \"Read\" the News: New Analytics and Algorithmic Trading
Exploiting a unique identification strategy based on inaccurate news analytics, we document a causal effect of news analytics on the market irrespective of the informational content of the news. We show that news analytics speed up the stock price and trading volume response to articles, but reduce liquidity. Inaccurate news analytics lead to small price distortions that are corrected quickly. The market impact of news analytics is greatest for press releases, which are timelier and easier to interpret algorithmically. Furthermore, we provide evidence that high frequency traders rely on the ...