Navigating dark liquidity (How Fisher catches Poisson in the Dark)
Ilija I. Zovko

TL;DR
This paper introduces a real-time, dynamic methodology to profile dark liquidity and manage trading exposure effectively, balancing liquidity capture with signaling risk in dark venues.
Contribution
It presents a novel per-fill profiling approach that enables traders to adaptively control dark liquidity access, improving upon static historical analyses.
Findings
Effective real-time dark liquidity profiling method
Enhanced control of trading exposure based on dynamic signals
Potential reduction in signaling and slippage risks
Abstract
In order to reduce signalling, traders may resort to limiting access to dark venues and imposing limits on minimum fill sizes they are willing to trade. However, doing this also restricts the liquidity available to the trader since an ever increasing quantity of orders are traded by algos in clips. An alternative is to attempt to monitor signalling in real time and dynamically make adjustments to the dark liquidity accessed. In practice, price slippage against the order is commonly taken as an indication of signalling. However, estimating slippage is difficult and requires a large number of fills to reliably detect it. Ultimately, even if detected, it fails to capture an important element of causality between dark fills and lit prints - a signature of information leakage. In the extreme, this can lead to scaling back trading at a time when slippage is caused by a competing trader…
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Taxonomy
TopicsFinancial Markets and Investment Strategies · Complex Systems and Time Series Analysis · Stock Market Forecasting Methods
