Market Making in Large-Tick Markets and the Limits of One-Tick Reversion
Summary
The discussion considers market making when the quoted spread is one tick, where ordinary bid-ask bounce intuition may describe only a one-tick move. It points to research on large-tick assets that relates the sequence of trade signs—continuations versus alternations—to an implicit spread measure. The cited work also examines how this measure can help assess the effects of tick-size changes and how tick size and make-take fees relate to information asymmetry between investors and market makers.
A further response describes negative autocorrelation in successive price moves as a common pattern: an uptick may be followed more often by a downtick. That observation alone does not create reliable trading profits. Other market makers may compete for the same favorable fills, while a trader may be more likely to receive adverse fills. Adding orders at more price levels can increase fill opportunities, but also raises open-order exposure and strategy complexity. The discussion offers intuition and research leads, not a tested strategy or performance evidence.
Key ideas
- Large-tick market behavior can be studied through the balance of continuing and alternating trade signs.
- A one-tick spread can coexist with an implicit spread that reflects market dynamics.
- Successive price moves may show negative autocorrelation in a large-tick setting.
- A simple strategy based on this pattern may face low favorable fill rates and high adverse fill rates.
- Layering orders may improve fill opportunities while increasing exposure and complexity.
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# Market making in one tick markets?
# Market making in one tick markets?
I've searched, but found no literature on market making in single tick markets. I'd appreciate any references.
Given that most literature on MM assumes micro-structure is mean-reverting due to the bid-ask bounce, none of this applies in the case of a single tick market since the 'mean-reversion' is a single tick.
## Answer by lehalle (score -1, accepted)
https://quant.stackexchange.com/a/73672
The paper you are looking for is Large tick assets: implicit spread and optimal tick size by Dayri and Mathieu Rosenbaum 2013. they explain the role of the tick in the relation between market makers and investors. In short their main measure (coming from another paper on "uncertainty bounds") is made of the number of "continuation of trade signs" divided by "alternation of trade signs": $$\eta=\frac{N^{cont}}{2N^{alt}}.$$ and they show how it can efficiently be used to complement a "one tick spread" with a measure of the implicit spread.
Later, in How to predict the consequences of a tick value change? Evidence from the Tokyo Stock Exchange pilot program, with Mathieu and Webbing Huang (2015), we have shown how it can be used to predict the impact of a tick change by exchanges (using the Japan as a use case).
And finally Mathieu again developed a theoretical framework to show how exchanges could us the tick size to balance the asymmetry of information between investors and market makers; see Optimal make-take fees for market making regulation, El Euch, Mastrolia, Rosenbaum, Touzi (2019)
## Answer by databento (score 2)
https://quant.stackexchange.com/a/73936
Instead of "mean reversion" in the classical sense, you should expect to see negative autocorrelation in the next price move: You would typically see that if it upticks (i.e. midprice goes up on next best bid or best offer level formation), it is more likely to downtick on the next price move. The next midprice change should be negatively autocorrelated.
Many still refer to this as a "mean-everting behavior", so it's really what you want to call it.
I think what you're looking for is literature on how to capture this as an alpha and the truth is that there's no free lunch because the stylized observation here is too simple:
- The most naive reaction to this microstructural observation is to join the best bid after a downtick and vice versa.
- However, most MMs are aware of this, so you should also expect your "desired" fill rate to be low on the best bid after a downtick, and low on the best offer after an uptick.
- Moreover most aggressive participants are aware of this as well, so you should expect your "adverse" fill rate to be high on the best bid after a downtick, and high on the best offer after an uptick.
- This partly shifts the focus to how you can improve your fill rate, which is a hard open problem that there's no good academic literature on: the most obvious follow-up to increase your fill rate is to layer more levels ahead of time, but then this also means your open order risk is higher and the strategy state space becomes much more complex.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.