Principios Rectores de los Sistemas Fiscales

I now discuss whether the improvement in liquidity as found in Hendershott et al. (2011) is more attributable to fast or slow trades. Kim and Murphy (2013) argue fast trades rather than slow trades are better proxies for speed-sensitive algorithms, which trade upon order book changes. The underlying intuition is that the combination of AT speed advantage and the incentive to trade on new information quickly imply traders employing AT are more likely to react swiftly to order book imbalance. It is argued this speed advantage imposes higher adverse selection costs on slower traders while algorithmic traders themselves are better positioned to avoid adverse selection (Biais, Foucault and Moinas, 2011). Further, because HFTs generate high turnover and are a substantial source of liquidity provision their cost base is lowered by large exchange fee rebates and therefore HFTs potentially quote tighter spreads (Carrion, 2013). I consider these arguments by testing the model specification in Hendershott et al. (2011) on fast and slow trades.

I take the time interval (TI) between two consecutive trades as the metric for trading speed.

This is computed by taking, for each stock, a trade’s timestamp minus the timestamp of the

108 _{I was unable to obtain complete NYSE trade and quote data for pre-2003 period.} 109 _{See Figure 2 in Hendershott, et al. (2011, p.9).}

133 immediate previous trade. TI is only measured if both trades are computed on the same day and therefore, across all stocks, the first trade of each day is removed. For each stock I further assign trades into a TI quintile. A high quintile proxies slow trades and a low quintile proxies fast trades. The break points are computed based on each stock’s TI computed across the entire sample period. To account for the large variation in TI across firms I compute break-points within-firm rather than at the aggregated level.

I present summary statistics on firm TI in Table 4.4 and show the median TI for the largest firms is 0.125 minutes (7.53 seconds) before Autoquote and 0.109 minutes (6.53 seconds) after Autoquote. The median TI for the smallest firms is 2.08 minutes (125 seconds) before Autoquote and 1.76 minutes (106 seconds) after Autoquote. Even the fastest trades are subject to large variance across stocks. For example for large firms the TI at the 1st percentile is 0.0003 minutes (0.02 seconds) before Autoquote and 0.0002 minutes (0.01 seconds) after Autoquote, while for small firms the TI is 0.018 minutes (1.08 seconds) before Autoquote and 0.014 minutes (0.85) seconds after Autoquote. This suggests the speed of AT is potentially firm- specific (for example, the speed of order insertion and cancellation can depend on a stock’s correlation to activity in equities indices, commodity prices, futures or listed ETFs).

4.5.1. Results

I re-test Equation 4.5 but on the cross-section of trading speed quintiles. My results show improvements in liquidity are concentrated among faster trades, but the fastest trades contribute relatively less improvement to liquidity. I also show adverse selection declined the least for the fastest trades while realised spreads increased the most for the slowest trades. This supports findings that higher AT is effective in reducing adverse selection costs but also

134 Table 4.4. Summary Statistic of Time Between Trades by Size Quintile

The table shows the mean and percentile breakpoints of time interval (TIj,n) between two trades (minutes) for before and after Autoquote. TIj,n is computed by taking, for each

stock j, the nth trade’s timestamp minus the timestamp of the immediate previous trade. TI

j,n is computed only if both trades occurred on the same day. Values are computed

based on 913 NYSE-listed stocks sorted into market capitalisation quintiles where 1 is the smallest firms and 5 is the largest firms. The sample period is 1st _{January 2003 to}

31st _{July 2003.}

1 (smallest cap) 2 3 4 5 (largest cap)

Before After Before After Before After Before After Before After

Mean of TIj,n 4.877 3.819 1.574 1.285 0.866 0.752 0.486 0.427 0.261 0.234 1st _{0.018 0.014 0.017 0.009 0.007 0.007 0.001 0.006 0.00003 0.00002} 5th _{0.037 0.030 0.024 0.022 0.019 0.021 0.018 0.015 0.017 0.014} 25th _{0.460 0.388 0.177 0.135 0.106 0.085 0.070 0.057 0.046 0.041} median 2.083 1.760 0.723 0.613 0.414 0.355 0.230 0.197 0.125 0.109 75th _{5.967 4.866 2.006 1.656 1.109 0.976 0.637 0.562 0.336 0.301} 95th _{18.413 14.024 5.888 4.807 3.234 2.821 1.792 1.586 0.942 0.859} 99th _{33.094 24.067 10.435 8.338 5.605 4.903 3.179 2.821 1.662 1.529}

135 suggests the fastest trades are not associated with the greatest reduction in adverse selection costs. My results are consistent with liquidity providers generating higher revenues from the slowest traders.

Results in Panel C of Table 4.3 show the decline in effective spreads is concentrated in the three fastest TI quintiles. The fastest trades experienced a decline in spreads by 8.8 basis points (significant at the 1% level); the second and third quintile experienced larger declines of 22.3 (significant at the 1% level) and 33.0 (significant at the 1% level) basis points respectively. These results are evidence the fastest trades are not associated with the most improvement in liquidity. The fourth quintile shows insignificant change and the fifth quintile show a significant increase in spreads. These results suggest slower trades did not experience an improvement in liquidity. The sign of the coefficient for turnover is positive for the fastest trades but is increasingly negative for slower trades. This suggests, ceteris paribus, during days with high turnover the fastest trades are not associated with improvement in liquidity. The signs of the coefficients for volatility, tick-size and size remain largely unchanged with my findings in Section 4.4.

Adverse-selection significantly declined across all speed quintiles and tended to decline further for slower trades. The fastest trades declined by 22.8 basis points (significant at the 1% level) and the slowest trades declined by 106.8 basis points (significant at the 1% level). This suggests algorithmic traders are better at avoiding adverse selection from slow traders. Realised spreads significantly increased across all quintiles, and tended to increase more for slower trades. The fastest trades increased by 14.0 basis points (significant at the 1% level) and the slowest trades by 176.0 basis points (significant at the 1% level). This suggests slow traders pay relatively more for liquidity after the Autoquote phase-in. A higher realised spread potentially suggests

136 liquidity providers are better at anticipating mispricing and interpreting signals from order- book imbalance.

My finding of a simultaneous reduction in adverse selection and increase in realised spreads is consistent with the results in Hendershott et al. (2011). Lower adverse selection cost is consistent with theories that AT gives rise to more efficient quoting and a reduction in the probability of liquidity providers being “picked off” by slow quotes. This reduction is concentrated among fast trades which are more associated with AT. Hendershott et al. (2011) attribute the higher realised spreads to a one-off competitive advantage enjoyed by algorithmic traders as a result of the Autoquote phase-in and this advantage ought to decline as new entrants introduce competing algorithms. I show this advantage is highest when transacting against slow trades. The dual finding that slower trades have less price impact and pay more for liquidity also suggests such behaviour is more likely uninformed liquidity trades or random noise trades (see Kyle, 1985; Easley, Engle, O’Hara and Wu, 2008).

Last, irrespective of trade speed, the estimated coefficient for volatility is positive with respect to adverse selection and negative with respect to realised spreads. In other words, during periods of high volatility market-makers in aggregate are subject to greater adverse selection costs and generate less market-making revenue. This provides empirical evidence for why liquidity provision may be fleeting during high volatility and suggests switching from liquidity provision to liquidity taking strategies can be potentially more profitable in such periods.