Tick Size and Market Quality: Barardehi, Dixon, Liu & Lohr (2026)

What this is. The paper’s core results, the economic framework, and the DiD specification used: enough to know what was found and how, without reading all 17 pages. To replicate or extend, read the full source at the original.

TL;DR

Using the U.S. Tick Size Pilot (TSP), which raised the minimum tick from 1 cent to 5 cents for roughly 1,200 small-cap pilot stocks in 2016, the paper shows that tick size effects on market quality are heterogeneous and depend on a stock’s prevailing quoted spread. For stocks with average quoted spreads below approximately 10 cents, the larger tick harmed liquidity: quoted and effective spreads widened, consistent with pricing fidelity deteriorating. For stocks with spreads above 15 cents, the larger tick improved liquidity: spreads narrowed, consistent with undercutting being reduced. Stocks in between experienced indeterminate or conflicting effects. These opposing effects explain why prior TSP studies that pool narrow- and wide-spread stocks together in a single non-tick-constrained group find muddled or conflicting results. The paper recommends that researchers using the TSP for causal inference bifurcate samples at the 10 cent prevailing spread threshold.

Core results

Magnitudes are as reported; \*\*/\*\*\* = 5%/1%. QR = quantile (median) regression; OLS results are similar unless noted. All columns correspond to spread bins by prevailing May-June 2016 quoted spread: (1) Bin 1 = tick-constrained (<5 cents), (2) Bin 2 = near-tick-constrained (5-10 cents), (3) Bin 3 = intermediate-spread (10-15 cents), (4) Bin 4 = wide-spread (>15 cents). Pilot x Event is the DiD coefficient from Eq. (1).

#	Result	Locator	Magnitude
R1	TSP widened quoted spreads for tick-constrained and near-tick-constrained stocks; narrowed them for wide-spread stocks; effect indeterminate for intermediate stocks	Table 3, Panel A, p. 9	QR Pilot x Event: Bin 1 +0.032*** (t=19.34); Bin 2 +0.033*** (t=12.81); Bin 3 +0.0079* (t=1.69); Bin 4 -0.048*** (t=-3.63)
R2	TSP widened effective spreads for stocks with spreads below 10 cents and narrowed them for wide-spread stocks	Table 3, Panel A, p. 9	QR Pilot x Event: Bin 1 +0.033*** (t=26.19); Bin 2 +0.025*** (t=16.46); Bin 3 +0.0075** (t=2.84); Bin 4 -0.019*** (t=-3.13)
R3	TSP reduced undercutting (QID ratio) across all four spread bins; QID rebounded upon TSP conclusion, consistent with the undercutting channel	Table 2, Panels A-B, p. 8	Imposition: Bin 1 -0.27*** (t=-17.90); Bin 2 -0.23*** (t=-15.01); Bin 3 -0.16*** (t=-10.83); Bin 4 -0.11*** (t=-10.81); all four bins significantly positive at TSP conclusion
R4	TSP increased NBBO quoted depth for all spread bins; largest log-increases for tick-constrained and near-tick-constrained stocks	Table 3, Panel A, pp. 9-10; Fig. 2, p. 10	Ln(NBBO Depth) QR: Bin 1 +1.06*** (t=20.89); Bin 2 +0.91*** (t=20.34); Bin 3 +0.49*** (t=9.88); Bin 4 +0.21*** (t=5.99)
R5	TSP increased round-trip transaction costs (CRT) for tick-constrained stocks and reduced them for wide-spread stocks; at very large trade sizes the TSP reduced CRT near-uniformly (Bin 1 remains positive at 2500 shares; paper: “almost uniformly” with that exception)	Table 4, Panel A, p. 12; Fig. 5, p. 15	10-round-lot CRT QR: Bin 1 +0.023*** (t=13.98); Bin 4 -0.075*** (t=-4.10); 2500-share (y=25) Bin 1 +0.008** (Table 4); Bins 2-4 negative at 2500 shares
R6	Rolling-bin threshold analysis shows ~10 cents as the key breakpoint where the TSP switches from harming to benefiting effective spreads; the threshold for small-trade CRT is also approximately 10 cents	Fig. 3, p. 13; §4.4, pp. 11-13	TSP effect on effective spread positive and significant for pre-shock spreads below ~10 cents; switches to negative above ~15 cents; 10-15 cent zone indeterminate in both imposition and conclusion windows
R7	Applying the Werner et al. (2023) median-bifurcation methodology to TSP imposition data yields a null result for the non-tick-constrained group; this reflects the cancellation of near-tick-constrained (+) and wide-spread (-) effects when pooled	Table 5, p. 16	TSC (tick-constrained) QR +0.046*** (t=23.57); TSU (non-tick-constrained) -0.0046 (t=-1.17, insignificant)

Overall (paper’s conclusion). The TSP provides direct evidence of the tick size tradeoff between pricing fidelity and undercutting. Narrow-spread stocks were harmed because the larger tick distorted the bid-ask spread relative to the latent competitive spread, worsening pricing fidelity. Wide-spread stocks benefited because the larger tick made undercutting less attractive, encouraging displayed liquidity provision. The opposing effects within the broad non-tick-constrained category explain why Hu et al. (2018), Griffith and Roseman (2019), Rindi and Werner (2019), Chung et al. (2020), and Werner et al. (2023) reach different conclusions: they employ different sample constructions that weight the two opposing groups differently.

Theory / model

The paper has no formal theoretical model of its own. It tests the “tick size tradeoff” framework of Werner et al. (2023), which characterizes tick size effects as a tradeoff between two competing channels.

Pricing fidelity channel. A smaller tick enables a finer price grid, allowing the equilibrium bid-ask spread to narrow toward the latent competitive spread. When a tick is too large relative to the competitive spread, the tick forces the quoted spread above its competitive level, inflating transaction costs and reducing allocative efficiency. This channel dominates for stocks whose competitive spreads are narrow (tick-constrained or near-tick-constrained): a tick increase makes pricing fidelity worse.

Undercutting channel. A smaller tick lowers the cost of front-running a resting limit order with a trivially better price: posting a quote that is just one tick better is cheaper when the tick is smaller. Greater undercutting risk discourages displayed liquidity provision (adverse selection for slower traders and higher monitoring costs), reduces fill rates on limit orders, and lowers overall depth. A larger tick reduces this activity and can thereby improve market quality. This channel dominates for stocks with wide spreads (many ticks intra-spread): a tick increase reduces undercutting and improves market quality.

Testable prediction. The two channels predict opposite effects for stocks with different prevailing spreads. The paper identifies two empirical thresholds: at ~10 cents, the net effect on effective spreads and small-trade CRT switches from negative (pricing fidelity channel dominates) to ambiguous; at ~15 cents, the net effect turns positive (undercutting channel dominates).

Identification. The TSP is a natural experiment: the SEC randomly assigned approximately 2,400 stocks with market capitalization below $3 billion to pilot (tick = 5 cents) and control (tick = 1 cent) groups. This design satisfies the parallel trends assumption: within each spread bin, treated and control stocks are comparable in market capitalization, dollar volume, and return volatility before the TSP (Table 1, p. 7).

Method

The paper uses difference-in-differences (DiD) quantile regressions (Callaway and Li, 2019) to estimate the causal effect of the tick size change. Quantile (median) regression is used as the primary estimator because microstructure outcomes such as quoted spreads are positively skewed: OLS means are sensitive to large outliers and can diverge from medians in ways that depend on how researchers handle extreme observations. The paper also reports OLS estimates after winsorizing at the 5th and 95th percentiles by tick-constraint category; results are qualitatively similar.

The DiD specification (equation 1, p. 6) is:

$$Y_{j,t} = \alpha_0 + \alpha_p \text{Pilot}_j + \alpha_e \text{Event}_{j,t} + \beta \left( \text{Pilot}_j \times \text{Event}_{j,t} \right) + u_t + \varepsilon_{j,t} \tag{1}$$

where $\text{Pilot}_j$ equals 1 for treated stocks (G1 or G2) and 0 for control stocks; $\text{Event}_{j,t}$ equals 0 before the tick size change and 1 after it (with enforcement-date-based assignment for the staggered TSP rollout across October 2016); $u_t$ is a date fixed effect; and $\varepsilon_{j,t}$ is the error. Standard errors are double-clustered at the stock and date level.

The coefficient of interest is $\beta$, the DiD estimate of the treatment effect. Because G1 and G2 differed only in whether the 5-cent increment applied to trading in addition to quoting (the authors find no reliable differences between G1 and G2 and so pool them), while G3 included a trade-at rule, the primary analysis uses G1 and G2 pilot stocks versus control stocks.

The paper estimates equation (1) separately for each of four spread bins based on prevailing pre-shock quoted spreads: bin 1 (tick-constrained, below 5 cents), bin 2 (near-tick-constrained, 5-10 cents), bin 3 (intermediate, 10-15 cents), and bin 4 (wide-spread, above 15 cents). For the threshold analysis in Section 4.4, the paper uses overlapping 6-cent bins shifted by 1 cent ({(0, 6c), (1c, 7c), …}) and plots the DiD coefficient versus the bin’s median pre-shock quoted spread. The resulting figure traces the spread-level transition from harmful to beneficial TSP effects.

Empirical specifications

Spread and depth outcomes (R1, R2, R4). The dependent variable $Y_{j,t}$ is, in turn: time-weighted average quoted dollar spread; size-weighted average effective dollar spread; and natural log of time-weighted average quoted depth at the NBBO (using WRDS Intraday Indicators). Equation (1) is estimated via quantile regression (median) with date fixed effects and double-clustered SEs. Sample for TSP imposition: 08/11/2016-12/15/2016 (pre-period before 10/03/2016, treatment period from 10/24/2016 onwards). Sample for TSP conclusion: 08/07/2018-11/20/2018 (pre-period before 10/01/2018). Results in Table 3 (p. 9).

Undercutting and cancel-to-trade (R3). The primary undercutting measure QID (quote-improvement-to-deterioration ratio) is from Barardehi et al. (2025): the frequency of NBBO quote improvements divided by the sum of improvement and trade-driven NBBO deterioration frequencies, computed daily from TAQ data. A higher QID indicates more undercutting. The cancel-to-trade ratio (from MIDAS data) is total canceled orders divided by total executed trades. Equation (1) estimated the same way. Results in Table 2 (p. 8).

Round-trip transaction costs (R5). The dependent variable $\text{CRT}_{y,j,t}$ is the average per-share cost of a round-trip trade of $y$ round lots (where $y \in \{1, 2.5, 5, 10, 25, 50, 100\}$), computed from MIDAS order book snapshots taken every 15 minutes. The CRT is the absolute difference between the average per-share purchase price and the midpoint, averaged across buy and sell sides and across the trading day. Equation (1) estimated separately for each $y$. Results in Table 4 (p. 12) and Figs. 4-5.

Threshold analysis (R6). Rolling overlapping bins of width 6 cents shifted by 1 cent across the pre-shock quoted spread distribution. For each bin, equation (1) yields a DiD coefficient on effective spread and on 500-share CRT. A bin is classified as having a determinable positive (negative) effect when both the imposition and conclusion estimates have the same sign and at least one is significant; otherwise the effect is labelled indeterminate. Results in Fig. 3 (p. 13).

Bifurcation reconciliation (R7). The paper replicates the Werner et al. (2023) sample-bifurcation approach: stocks split by median quoted spread, with stocks below the median classified as tick-constrained (TSC) and above as non-tick-constrained (TSU). Equation (1) is estimated for the TSP imposition on quoted and effective spreads. Results in Table 5 (p. 16).

Datasets used

Dataset	Role in paper	Wiki page
TAQ (Trade and Quote) daily data via WRDS	Daily time-weighted quoted spreads; size-weighted effective spreads; daily QID undercutting measure; computed from tick-level trades and quotes during regular trading hours	TAQ
WRDS Intraday Indicators	Daily time-weighted NBBO depth measures for ~2,400 TSP pilot and control stocks	WRDS
MIDAS (SEC Market Information Data Analytics System)	15-minute order book snapshots used to compute cumulative depth at price levels, round-trip transaction costs (CRT), and cancel-to-trade ratios; covers all exchanges and price levels	no page yet

Sample: TSP imposition window 08/11/2016-12/15/2016 (with enforcement dates 10/03/2016-10/23/2016 as staggered treatment onset); TSP conclusion window 08/07/2018-11/20/2018 (simultaneous return to 1-cent tick 10/01/2018). Approximately 2,400 securities: ~1,200 pilot stocks (G1+G2 combined) and ~1,200 control stocks.

When to read the full paper

Use the original if you are: (i) designing or evaluating tick size reform policies and need the specific threshold estimates (10 cents, 15 cents) and the spread-bin CRT gradients; (ii) using the TSP as a causal instrument for microstructure or price efficiency questions and need guidance on sample construction; (iii) reconciling conflicting TSP findings in the literature (Section 5 provides a systematic comparison to Hu et al. (2018), Griffith and Roseman (2019), Rindi and Werner (2019), Chung et al. (2020), and Werner et al. (2023)); or (iv) studying undercutting dynamics and the cancel-to-trade channel using MIDAS data.

Attribution and rights

Source: peer-reviewed, Journal of Financial Markets 78 (2026) 101024. This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Barardehi, Yashar H., Peter Dixon, Qiyu Liu, and Ariel Lohr. “When does the tick size help or harm market quality? Evidence from the Tick Size Pilot.” Journal of Financial Markets 78 (2026) 101024. DOI: 10.1016/j.finmar.2025.101024. © 2025 The Authors. Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.