Relative Tick Size and the Trading Environment

O’Hara, Maureen; Saar, Gideon; Zhong, Zhuo

doi:10.1093/rapstu/ray009

Cited by 77 publications

(36 citation statements)

References 30 publications

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“…We find mixed results regarding the tick size change and the level of algorithmic trading in the pilot firms on taker‐maker fee models. While our results do not support recent works of Ye and Yao () and O'Hara, Saar and Zhong (), who suggest that a tick size increase provides an advantage for faster algorithmic traders, our results align with Weild, Kim and Newport () and others who suggest that a larger tick size constrains algorithmic activity . Last, we find that an increase in the tick size results in a decrease in hidden liquidity.…”

Section: Introductioncontrasting

confidence: 97%

“…While the evidence conflicts with the motivation and hypotheses of Ye and Yao () and O'Hara, Saar and Zhong (), our findings are consistent with predictions made by Harris (), Bartlett and McCrary (), and Buti, Consonni, Rindi, Wen and Werner (), who state that algorithmic traders are worse off in a larger relative tick size environment. Our results are consistent with the Tick Size Pilot's aim at curtailing algorithmic traders (Weild, Kim and Newport, ).…”

Section: Resultssupporting

confidence: 76%

“…Foucault, Kadan, and Kandel () suggest that a higher (lower) relative tick size implies a shift to taker‐maker (maker‐taker) models to adjust the optimal tick size. Consistent with this notion, O'Hara, Saar and Zhong () show that the activity of stocks with a large relative tick size is more fragmented—with considerable trading activity occurring on venues where sub‐penny pricing occurs. Such an outcome manifests since the optimal tick size, one that produces the highest trading rate, is now constrained.…”

Section: Empirical Predictionsmentioning

confidence: 85%

“…For example, Ye and Yao () argue that a larger tick size hinders price competition and increases the role of time priority, in which traders with speed benefit. Likewise, O'Hara, Saar and Zhong () posit and find that a larger relative tick size allows high‐frequency market makers to become more aggressive, undercutting resting limit orders. Further, Foucault, Kadan and Kandel () suggest that the trading frequency on market fee models may influence the level of algorithmic trading, indicating a positive relation between overall trading activity and algorithmic trading.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Increasing the Tick: Examining the Impact of the Tick Size Change on Maker‐Taker and Taker‐Maker Market Models

Cox

Ness

2019

The Financial Review

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We investigate the effects of an increase in tick size on order and trading flow across market fee models. Using the pilot firms in the U.S. Securities and Exchange Commission's Tick Size Pilot Program, we document that trade and order volume declines on maker‐taker fee models after the tick size implementation. We find that the inverted fee models (taker‐maker) experience an increase in both trade and order volume. Additionally, we find that a tick size adjustment has a substantial influence on market participation in maker‐taker fee models. We also find that measures of both hidden and algorithmic trading decline with an increasing tick size, which is strongly moderated by the differences in the maker‐taker and taker‐maker fee models.

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Section: Introductioncontrasting

confidence: 97%

Section: Resultssupporting

confidence: 76%

Section: Empirical Predictionsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Increasing the Tick: Examining the Impact of the Tick Size Change on Maker‐Taker and Taker‐Maker Market Models

Cox

Ness

2019

The Financial Review

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“…This feature may largely simplify the LOB modelling. More importantly, market conditions and trading strategies for large-tick stocks are deemed to be different from those for small-tick stocks [39]. Therefore, the trading strategies for these two categories of stocks should be studied separately.…”

Section: Introductionmentioning

confidence: 99%

Optimal Liquidation in a Level-I Limit Order Book for Large-Tick Stocks

Jacquier¹,

Líu²

2018

SIAM J. Finan. Math.

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We propose a framework to study the optimal liquidation strategy in a limit order book for large-tick stocks, with the spread equal to one tick. All order book events (market orders, limit orders and cancellations) occur according to independent Poisson processes, with parameters depending on the most recent price move direction. Our goal is to maximise the expected terminal wealth of an agent who needs to liquidate her positions within a fixed time horizon. By assuming that the agent trades (through both limit and market orders) only when the price moves, we model her liquidation procedure as a semi-Markov decision process, and compute the semi-Markov kernel using Laplace method in the language of queueing theory. The optimal liquidation policy is then solved by dynamic programming, and illustrated numerically.Date: November 16, 2017.2010 Mathematics Subject Classification. 91G60, 91G99, 60K15, 60K20. Key words and phrases. limit order book, optimal liquidation, semi-Markov decision process, queueing theory, dynamic programming.The authors would like to thank Martin Gould and Fabrizio Lillo for useful discussions. AJ acknowledges financial support from the EPSRC First Grant EP/M008436/1 and HL acknowledges financial support from SHELL. . 1 A priority rule regulates how limit orders stored in the LOB will get executed. By far the most common priority rule is 'price-time' [24, Section 3.4], that is, limit orders posted closer to the mid price will get priority and limit orders posted at the same price follow the 'first come first serve' rule.

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Tick size and market quality: Simulations based on agent‐based artificial stock markets

Yang

Zhang

2020

Intell Sys Acc Fin Mgmt

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Summary This paper investigates the way that minimum tick size affects market quality based on an agent‐based artificial stock market. Our results indicate that stepwise and combination systems can promote market quality in certain aspects, compared with a uniform system. A minimal combination system performed the best to improve market quality. This is the first study to analyse tick size systems that remain at the theory stage and compare four types of system under the same experimental environment. The results suggests that a minimal combination system could be considered a new direction for market policy reform to improve market quality.

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Relative Tick Size and the Trading Environment

Cited by 77 publications

References 30 publications

Increasing the Tick: Examining the Impact of the Tick Size Change on Maker‐Taker and Taker‐Maker Market Models

Increasing the Tick: Examining the Impact of the Tick Size Change on Maker‐Taker and Taker‐Maker Market Models

Optimal Liquidation in a Level-I Limit Order Book for Large-Tick Stocks

Tick size and market quality: Simulations based on agent‐based artificial stock markets

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