The 100-Second Dash:
The Episodic Dynamics of Liquidity
Buy when there is supply and sell when there is demand.
It’s a simple mantra, but it can have huge implications. To have less impact, traders need to be able to take advantage when liquidity is abundant and remain patient when it is not.
Liquidity is not evenly spread throughout the day — it is concentrated in short “episodes.” Those moments are when traders typically want to be buying and selling.
To demonstrate the episodic nature of liquidity, we analyzed the volume that traded over the course of the trading day second by second. We looked at each stock and ranked the seconds by the percentage of volume trading in each and then averaged the top 100 seconds across all securities.
Excluding the open and close, we find that 23.8% of all S&P 500 volume and 56.8% of all Russell 2000 volume trades in the 100 most active seconds of the day… less than two minutes in total!
To get more granular, we bucketed the Russell 2000 into quintiles by ADV (average daily volume) and then did the same ranking of average percentage of daily volume.
What we found is that liquidity becomes even more episodic in less liquid names. For those names, not only is there less volume overall, but it’s also more sporadic! More than 85% of volume in the 5th ADV quintile of Russell 2000 names trades in the top 100 seconds of the day versus about 38% of volume for the 1st ADV quintile.
So, what are the takeaways? For a dataset that can be summarized in two short tables, there is a lot to unpack.
Sometimes traders are compelled to “do something” even when counterparty interest is absent, or held to a Percent of Volume (POV) benchmark that doesn’t reflect the reality that liquidity is episodic. That can handcuff traders when there is a liquidity opportunity and force them to pay more (or sell for less) when minimal volume is available. Not to mention it can dramatically increase the risk of information leakage.
Allowing a trader to buy when there is supply and sell when there is demand frees traders to source liquidity while minimizing impact. Giving traders discretion and aligning the proper benchmarks could prove a better path to improved performance overall.
We have seen brokers incorporate these liquidity dynamics into algos in insightful ways, by tapering venues, utilizing dynamic heat maps, etc.
Please reach out if you are interested in discussing the specific stocks you are focused on and the specifics of their typical liquidity “episodes.”
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