Fast and slow cancellations and trader behavior

Abstract

We investigate how short-lived liquidity supply due to order cancellations affects the order-placement behavior of slow traders. When order cancellations increase, slow traders submit fewer and less aggressive orders. Both short-and long-lived liquidity supply have positive effects on the market overall, reducing spreads and increasing depth. We conclude that it is not necessary to require limit orders to have a minimum lifespan. We develop econometric and machinelearning frameworks that allow traders to predict whether a quote is likely to have a short or long life, increasing the ability of slow traders to respond strategically to changing order flow.

The dark side of IPOs: Examining where and who trades in the IPO secondary market

“…In our study, market quality is not impacted by dark trading (25.16%) on the offering day. However, dark trading (> 40%) in the extended secondary market has a negative impact on market quality.22 Our findings that the cancel-to-trade ratio is associated with higher spreads contrast with the findings ofMcInish et al (2020) who find cancellations are associated with all traders (i.e., fast or slow) and with lower spreads.23 We also analyze if trading dynamics such as AT, hidden liquidity, and market fragmentation change around key dates in the IPO secondary market (i.e., flipping, quiet period, penalty bid, and lockup). Only around the lockup date do we find any noticeable change in any of the trading dynamics, specifically AT.…”

Machine learning methods in finance: Recent applications and prospects

“…Finally, some studies focus on predicting certain aspects of the market microstructure with ML. McInish et al (2019) apply random forests to predict the lifespan of orders based on order characteristics and market data. Easley et al (2021) predict a variety of variables relevant for market participants, such as bid‐ask spreads, changes in volatility, and sequential return correlations from established microstructure measures with random forests.…”

Machine Learning Methods in Finance: Recent Applications and Prospects

“…McInish et al (2019) apply random forests to predict the lifespan of orders based on order characteristics and market data Easley et al (2021). predict a variety of variables relevant for market participants, such as bid-ask spreads, changes in volatility, and sequential return correlations from established microstructure measures with random forests.…”