Do Proprietary Algorithmic Traders Withdraw Liquidity During Market Stress?

Nawn, Samarpan; Banerjee, Ashok

doi:10.2139/ssrn.3059559

Cited by 2 publications

(4 citation statements)

References 36 publications

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“…HFTs, due to the commonality in their liquidity provision, may withdraw large amounts of liquidity simultaneously (Malceniece et al, 2019;Anagnostidis and Fontaine, 2020) engaging in the formation of liquidity shocks in the market. On the other hand, few studies identify that HFTs also help the recovery from liquidity shocks (see, for instance, Nawn and Banerjee, 2019;Clapham et al, 2019). Further research on this issue covering more extended periods and under different market designs is needed.…”

Section: Discussionmentioning

confidence: 99%

“…They explain this pattern by the possible unprofitability or riskiness of the aggressive trading strategies in the presence of very quick price adjustments. Nawn and Banerjee (2019) identify HFTs' role as liquidity suppliers and detect a statistically significant increase in their liquidity provision following extreme price movements. Brogaard et al (2018) conduct single-stock and multi-stock analyses.…”

Section: Extreme Price Movementsmentioning

confidence: 93%

“…See, for instance,Benos et al (2017) andBoehmer et al (2018).11 Nawn and Banerjee (2019) detect a scenario supporting this logic. Authors divide the short-term volatility into two components: volatility arising from noise trading and volatility arising from information trading (driven by efficient prices).…”

mentioning

confidence: 92%

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High-Frequency Trading and its Impact on Market Liquidity: A Review of Literature

Ersan

Dalgic

Ekinci

et al. 2021

Alanya Akademik Bakış

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High-frequency trading (HFT) has been dominating the activity in developed financial markets in the last two decades. Despite its recent formation, the literature on the impacts of HFT on financial markets and participants is broad. However, there are ongoing debates and unanswered questions within many subtopics. We survey through the research towards HFT effects on liquidity in an attempt to explain the coexistence of evidence regarding both the positive and the negative impacts of HFT. We name two main factors leading to mixed results. Former concerns the negative market conditions such as intraday shocks, through which HFT trading patterns may sharply change. Latter regards the certain characteristics of HFT liquidity provision with the potential to present externalities for the market.2 BYX exchange utilized by CBOE reduced order processing times by around sevenfold from 445 microseconds in 2009 to 64 microseconds in 2018 (Baldauf and Mollner, 2020). HFT firms are in an arms race that leads them to send data from exchanges to electronic traders in latencies as small as 4 nanoseconds (Sprothen, 2016). 3 Throughout the text, we use 'high-frequency trading (trader)' and 'low-latency trading (trader)' interchangeably. We use 'HFT' as the acronym for high-frequency trading; 'HFTs' as the acronym for high-frequency traders; and non-HFTs as the acronym for low-frequency traders (traders other than high-frequency traders).

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Section: Discussionmentioning

confidence: 99%

Section: Extreme Price Movementsmentioning

confidence: 93%

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High-Frequency Trading and its Impact on Market Liquidity: A Review of Literature

Ersan

Dalgic

Ekinci

et al. 2021

Alanya Akademik Bakış

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“…Bohemer and Shankar (2014) find that AT reduces the overall probability of systemic shocks in the Indian equity markets. Nawn and Banerjee (2018) find that proprietary algorithmic traders continue to supply liquidity even during periods of stress in the markets.…”

Section: High Frequency Trading and Regulatory Interventionsmentioning

confidence: 99%

When do Regulatory Interventions Work?

2020

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Previous studies find mixed results about how a fee on high order-to-trade (OTR) ratios impacts market quality. Using a natural experiment where such a fee was introduced twice for different reasons, this paper finds evidence of impact only when the implementation matched the motive. We use a difference-in-difference regression, that exploits microstructure features, to find causal evidence of lower aggregate OTR and higher market quality when the fee was used to manage limited exchange infrastructure, but little to no change in the OTRs or market quality when it was used for a regulatory need to slow down high frequency trading.

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Do Proprietary Algorithmic Traders Withdraw Liquidity During Market Stress?

Cited by 2 publications

References 36 publications

High-Frequency Trading and its Impact on Market Liquidity: A Review of Literature

High-Frequency Trading and its Impact on Market Liquidity: A Review of Literature

When do Regulatory Interventions Work?

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