Very Fast Money: High-Frequency Trading on the NASDAQ

By Allen Carrion. 2013

291 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/556671d32fba7ce4e7d1c2b880109ed111864e38

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Citations in the corpus

High frequency trading and the new market makers (2013, background): “contribute to price efficiency; Carrion (2013, this issue) shows that HFTs “provide liquidity when it is scarce and consume liquidity when plentiful,” and Zhang (2012) studies what type of information HFTs and nonHFTs are best at processing. Hasbrouck and Saar (2013, this issue) propose a new measur”
The Economics of High-Frequency Trading: Taking Stock (2016, background): “In particular, HFTs are more likely to add limit orders to the book when the 10 Menkveld spread is wide, and thus supply liquidity (Carrion 2013; Jarnecic & Snape 2014; Hagströmer, Nordén & Zhang 2014).”
Quantifying the High-Frequency Trading "Arms Race" (2021, unknown): “First, we only have data from the LSE, so we do not observe when positions are closed by trades on other venues (see also Carrion 2013, who notes the same concern).”
High-Frequency Trading and Extreme Price Movements (2018, unknown): “These data have been previously used by Carrion (2013), Brogaard, Hendershott and Riordan (2014), and O’Hara, Yao and Ye (2014), among others.”
Low-latency trading $ (2013, background): “Carrion (in this issue) uses the NASDAQ HFT dataset to show that the aggregate profit patterns of high-frequency traders point to intraday market timing skill, but not necessarily the ability to profit from minute-by-minute movements. High-frequency traders also appear to supply liquidity when it is”
High‐Frequency Trading and Market Performance (2020, unknown): “Thus, faster HFT speeds do affect the nature 18Other empirical work similarly argues that HFT improves price informativeness (Brogaard (2010), Carrion (2013), Brogaard, Hendershott, and Riordan (2014), Chaboud et al. (2014), Conrad, Wahal, and Xiang (2015), Boehmer, Fong, and Wu (2018)).”
A Dynamic Limit Order Market with Fast and Slow Traders (2014, unknown): “ = ⎨⎩  2  ∗ |− ∗ | +(1−) 12 0 for  ∈ Σ for  ∈ Σ (8)”
High Frequency Trading and Price Discovery (2014, background): “18 Using a different benchmark price for HFTs’ end of day order flow imbalance Carrion (2013) finds that HFTs’ liquidity supplying trades have positive revenues before the inclusion of NASDAQ’s liquidity rebates.”
Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, background): “Publicly available data upon request for academic purposes can be found in [8], [24], [10].”
High-Frequency Market Making to Large Institutional Trades (2018, unknown): “Carrion (2013) finds that spreads are higher when HFTs supply more liquidity and narrower when HFTs demand more liquidity, and also finds that price efficiency is higher when HFT participation is higher.”
High Frequency Quoting, Trading, and the Efficiency of Prices (2015, background): “For example, Brogaard, Hendershott and Riordan (2014), Carrion (2013), and Hirschey (2013) all use Nasdaq-identified high frequency traders (the so-called “Nasdaq dataset”) and collectively find that HFT are modestly profitable in aggregate, that they both demand and supply liquidity, and that they…”
Price Discovery without Trading: Evidence from Limit Orders (2019, background): “Carrion (2013) finds that market-wide information is incorporated quickly into prices on days HFTs trade more.”
High frequency market microstructure. (2015, background): “There is general, but not universal, agreement that HFT market making enhances market quality by reducing spreads and enhancing informational efficiency (see Jones, 2012; Brogaard, Hendershott, and Riordan, 2013; Carrion, 2013).”