Research

Market Microstructure

We develop two new methods for matching trades and bid-ask quotes that account for information latency in the era of fast trading. The first method adjusts for exchange-to-SIP latency. The second method constructs exchanges' Relative Best Bid and Offer (RBBO) based on exchange-to-exchange latency and data center co-locations. We test these trade classification methods using over 650 million TAQ trades matched with order executions in TAQ Integrated Feed. We find that the first method improves the Lee Ready (1991) trade classification accuracy from 86% to 92%. Our preferred method, the RBBO method, further improves accuracy by another 58 bp but is computationally expensive. Using an exogenous technological shock, we find that adjusting for latency could alter research inferences when measuring liquidity. 

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Key message in one chart: 

As exchange routing activity rises (green), existing trade classification methods (SIP time in orange, Direct NBBO time in purple) that assume zero exchange-to-exchange latency deliver lower accuracy. The latency adjusted methods proposed in our paper (grey and navy) persistently output superior buy/sell trade classification accuracy results and outperform existing methods every day.

Researchers using the Boehmer et al. (2021) methodology implicitly assume that the sample of retail trades identified as retail has the same characteristics as retail trades that are not identified as retail. Moreover, researchers must also assume the algorithm does not falsely identify institutional trades to be retail trades. We demonstrate that neither of these assumptions are valid in practice. We also show that the subset of known retail trades identified by the algorithm to be retail differs significantly along many dimensions from the subset of known retail trades that are not classified as retail.

Investments