Do High-Frequency Traders Anticipate Buying and Selling Pressure?

Hirschey, Nicholas H.

doi:10.2139/ssrn.2238516

Cited by 136 publications

(102 citation statements)

References 35 publications

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“…Hirschey (2016) provides perhaps the most direct evidence. He reports that HFT aggressive flow at a given second predicts OT aggressive flow in the following 30 s. He interprets this finding as anticipatory trading by HFTs.…”

Section: Evidencementioning

confidence: 94%

The Economics of High-Frequency Trading: Taking Stock

Menkveld

2016

Annu. Rev. Financ. Econ.

252

100

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Section: Evidencementioning

confidence: 94%

The Economics of High-Frequency Trading: Taking Stock

Menkveld

2016

Annu. Rev. Financ. Econ.

252

100

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“…For each trade, the liquidity demander and liquidity supplier of a trade is identified as an HFT or nHFT. The same dataset is used by Brogaard et al (2014), O'Hara, Yao, and Ye (2014), Hirschey (2013), andCarrion (2013). Datasets used in research often employ proxies for HFT and AT (such as Hendershott et al, 2011, Hasbrouck & Saar (2013).…”

Section: Data and Sample Selectionmentioning

confidence: 99%

Need for speed: Hard information processing in a high‐frequency world

Zhang

2017

Journal of Futures Markets

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I study the role of high-frequency traders (HFTs) and non-high-frequency traders (nHFTs) in transmitting hard price information from the futures market to the stock market using an index arbitrage strategy. Using intraday transaction data with HFT identification, I find that HFTs process hard information faster and trade on it more aggressively than nHFTs. In terms of liquidity supply, HFTs are better at avoiding adverse selection than nHFTs. Consequently, HFTs enhance the linkage between the futures and stock markets, and significantly contribute to information efficiency in the stock market by reducing the delay between the stock and the futures markets. J E L C L A S S I F I C A T I O N G10, G14 | INTRODUCTIONIn recent years, the volume and complexity of information accessible to participants in financial markets has grown to exceed human information processing capacity. As a result, computer algorithms are now used to process large amounts of information more quickly. One specific group of computer trading algorithms is that used by high-frequency traders (HFTs). HFTs distinguish themselves from other groups of traders through their use of high speed trading and information processing, their high trading volume, as well as their sophisticated algorithms.1 A major concern of regulatory authorities, such as the U.S.Securities and Exchange Commission (SEC) and the U.S. Commodity Futures Trading Commission (CFTC), is the influence of HFTs on market quality and price discovery (cf. the call for comments of the SEC, 2010). HFTs contribute to price discovery through the application of different information processing strategies. Common information processing strategies of HFTs include arbitrage trading strategies. Index arbitrage focuses on mispricings between an index (such as the S&P 500) and its components. It can thus be categorized as "hard" quantitative information processing. To address the regulatory concerns relating to price discovery, I analyze the role of HFTs and non-high-frequency traders (nHFTs) in interpreting "hard" futures price information for index arbitrage strategies, and study the implications for information efficiency. This paper provides an empirical test of hard information processing strategies used by HFTs, specifically index arbitrage strategies between the E-mini futures and the U.S. stock markets.The results show that HFTs use their competitive advantage to react to hard quantitative information shocks faster and more strongly than nHFTs. Specifically, they trade in the direction of hard information shocks within the first few seconds and quickly start selling off their trading position in order to realize their trading profits. This trading behavior translates into HFTs increasing wileyonlinelibrary.com/journal/fut

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“…Under the informed trader hypothesis we expect to see HFT profits being small, or even negative, when trading with Fundamental traders. However, a growing literature shows that Fundamental traders may trade in a way that makes their order flow noticeable (Hirschey, 2011). Heston, Korajczyk, and Sadka (2010) show that institutional traders leave a detectable pattern in their trading activity.…”

Section: A Hft Profits By Counterpartymentioning

confidence: 99%