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Relative Tick Size and the Trading Environment
We investigate how and why relative tick sizes influence traders’ order strategies, and how this affects liquidity provision in the market. Using unique NYSE data, we find that a larger relative tick size benefits high-frequency trading (HFT) market makers: they leave orders in the book longer, trade more aggressively, and have higher profit margins. In a tick-constrained (tick-unconstrained) environment, larger relative ticks result in greater (less) depth, which is consistent with greater adverse selection coming from increased undercutting of limit orders by informed HFT market makers. Received October 12, 2017; editorial decision August 21, 2018 by Editor Thierry Foucault.
Inverted fee structures, tick size, and market quality
Stock exchanges compete for order flow through their fee models. A traditional model pays rebates to liquidity suppliers, and an inverted model pays rebates to liquidity demanders. Using a regulatory intervention to examine the interaction between tick size, restrictions on dark trading, and exchange fees, we show that traders use inverted venues to adjust for suboptimal tick sizes. Increased inverted venue activity improves pricing efficiency and liquidity, especially when the tick size is binding. We show that the sub-tick price improvement offered by inverted venues enhances competition for liquidity provision and increases information impounded into prices through nonmarketable limit orders.
Innovation and Informed Trading: Evidence from Industry ETFs
We empirically examine the impact of industry exchange-traded funds (IETFs) on informed trading and market efficiency. We find that IETF short interest spikes simultaneously with hedge fund holdings on the member stock before positive earnings surprises, reflecting long-the-stock/short-the-ETF activity. This pattern is stronger among stocks with high industry risk exposure. A difference-in-difference analysis on the ETF inception event shows that IETFs reduce post-earnings-announcement drift more among stocks with high industry risk exposure, suggesting that IETFs improve market efficiency. We also find that the short interest ratio of IETFs positively predicts IETF returns, consistent with the hedging role of IETFs.
Nonstandard Errors
In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.