Abstract: The NYSE Trade and Quote (TAQ) dataset, used throughout finance research and securities regulation, is generated by a Securities Information Processor (SIP), which aggregates quotes and trades from all U.S. stock exchanges at a central location. We show that the SIP systematically reports events out of sequential order: quote changes that occur after a trade are frequently reported as occurring before the trade. The source of the issue is that trades and quotes are recorded with variable latency (due to, e.g., geography) and are extremely clustered in time. The result is a look-ahead bias: the prevailing SIP quotes, ubiquitous in signing trades and measuring spreads, incorporate price impact from the trade itself. We document that the look-ahead bias leads to incorrect trade signing and downward-biased effective spreads and price impact. The errors are extreme for the large fraction of trades with high reporting latency: approximately 20% of trades are incorrectly signed, and effective spreads and price impact are understated by more than 40%. We propose a signing methodology based on exchange rules that yields 100% accuracy for a significant majority of volume and, over all trades, outperforms the current standard. We also introduce a new benchmark to measure effective spreads and price impact that is free from bias.
Discussant: Stacey Jacobsen, Southern Methodist University
Abstract: In a dynamic model of large traders who manage inventory risk, we show that a daily market closure coordinates liquidity. Some length of closure is welfare-improving relative to 24/7 trade, as the coordination of liquidity improves allocative efficiency, fully offsetting the costs of the closure. A long closure is optimal for traders in small markets, while traders in large markets would benefit from extending trading hours to near 24/7. A calibration of our model to several large equity exchanges that have proposed extending trading hours suggests that implementing such proposals would benefit traders.
Discussant: Sophie Moinas, Toulouse School of Economics
Dmitriy Muravyev, University of Illinois-Urbana-Champaign
Abstract: Market makers traditionally compete for client orders by posting better bid and ask prices. But internalization mechanisms – such as price improvement auctions in options – let market makers trade without posting competitive quotes. We examine how internalization affects quote competition in the options markets, where major market makers are primarily responsible for both posting quotes and internalizing retail order flow. We find that internalization reduces quote competition. Market makers route trades to auctions to match best prices when their quotes are uncompetitive: auction trades are 31 percentage points more likely to occur, but 17 percentage points less likely to receive price improvement when an exchange is not posting the best price. Exchanges that offer auctions are 12 percentage points less likely to quote the best price. To examine how internalization affects spreads, we analyze a market-wide rule change that restricted auctions for certain options. For affected options, quoted spreads narrowed by 23% while effective spreads show much smaller changes. We also document a long-term widening of the gap between quoted and effective spreads alongside an increase in auction use, consistent with our findings of internalization reducing quote competition.
