The Good, the Bad, and the Ugly: How algorithmic traders impact institutional trading costs

Abstract

We show that behind the aggregate effects of algorithmic and high-frequency traders (AT/HFT) is substantial heterogeneity in how individual algorithms impact institutional trading costs. Using unique trader-identified regulatory data, we find that the cluster of “harmful” algorithmic traders doubles institutional trading costs. “Beneficial” algorithmic traders offset much of this increase. We find no evidence that fast traders (HFTs) are more harmful. Traders that hold inventory overnight are more likely to benefit institutional investors by providing more sustained liquidity. The heterogeneity explains why AT/HFT appear detrimental to some investors despite being beneficial or benign in aggregate.

Keywords: Algorithmic Trading, High-Frequency Trading, Liquidity, Transaction Costs, Implementation Shortfall, Predatory Trading

JEL Classification: G14