Does Floor Trading Matter: Brogaard, Ringgenberg & Roesch (2025)

What this is. The paper’s core results, identification design, estimating equations, and mechanism tests: enough to know what it found and how, without reading all 40 pages. To replicate or extend it, read the full source at the original.

TL;DR

On March 23, 2020, the NYSE suspended floor trading because of COVID-19, moving to fully electronic trading. Using this abrupt, exogenous shock in a difference-in-differences framework, the paper shows that human floor traders improve market quality. Removing them raises proportional effective spreads by roughly 9 basis points (more than 70% relative to the pre-closure mean) and increases Hasbrouck pricing errors by about 6%, with the effects largest immediately after the open when information arrival is highest. Two partial reopenings (May 26 and June 17, 2020) allow the authors to distinguish between two non-mutually-exclusive mechanisms: (i) in-person information transfer between floor brokers and designated market makers (DMMs), and (ii) floor-only D-order types. Continuous trading quality improves only after the second reopening (DMMs return in person), while auction quality improves after the first (D orders resume). Floor trading matters in the age of algorithms.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Effective spreads increase significantly for NYSE stocks after floor closure, under both identification strategies	Table III Panel A col. 2 (p. 394); Table III Panel B col. 2 (p. 395)	NYSE vs. NASDAQ: +8.92 bps (t=7.90); within-stock NYSE vs. off-NYSE: +1.46% (t=2.68); stable across all fixed-effect specs
R2	Pricing errors increase for NYSE stocks after floor closure, indicating worse price efficiency	Table IV Panel A cols. 1-2 (p. 396); Table IV Panel B col. 1 (p. 396)	NYSE vs. NASDAQ: +6.28*** (t=4.68) in log pricing error; within-stock: +2.39* (t=1.84); approx. 6% and 2% increases respectively
R3	Effects are strongest immediately after the opening auction (9:30-10:00 am) and decline monotonically through the trading day	Table V Panels A-B (pp. 398-399)	9:30-10:00: +8.16*** (t=5.38); 10:00-10:30: +5.69*** (t=6.69); coefficients near zero from 12:00 onward
R4	Floor traders matter more when stock-specific information is higher: triple interaction with information complexity is positive and significant	Table V Panel C (p. 399)	Complex x Treated x After = +17.45*** (t=7.33); confirms information-transfer channel
R5	Opening and closing auction quality deteriorates after floor closure; price deviations increase for both auction types	Table VI (pp. 400-401)	Opening: +34 to +47 bps (Treated x After, all significant); Closing: +32 bps (approx. 100% increase relative to unconditional mean)
R6	Continuous trading spreads improve only at the second reopening (DMMs return in person), not the first (D orders resume); D orders are not the continuous-trading mechanism	Table VII Panel A col. 2 (p. 404); Table VII Panel B col. 2 (p. 405)	Treated x Open1 = +0.27 (t=0.72, insignificant); Treated x Open2 = -4.85*** (t=-6.69)
R7	Auction quality improves at the first reopening (when D orders resume) but not the second; D orders are the auction mechanism	Table VIII (pp. 407-408)	Treated x Open1 = -20.50** to -20.68** for opening auction (significant); Treated x Open1 for closing auction = -3.74*** to -4.00***; second reopening insignificant for auctions

Overall (paper’s conclusion). The NYSE floor suspension degraded market quality across multiple dimensions: liquidity (spreads), price efficiency (pricing errors), and auction quality. The partial reopening evidence shows two non-mutually-exclusive mechanisms are at work. During continuous trading, the floor facilitates in-person information transfer between floor brokers and DMMs that electronic markets cannot replicate; this channel reverses with the second reopening, consistent with theoretical predictions by Benveniste, Marcus and Wilhelm (1992) and the empirical evidence in Battalio, Ellul and Jennings (2007) that personal relationships lower informational asymmetries. During auctions, D orders (accessible only to floor traders) improve auction outcomes; this channel reverses with the first reopening, consistent with Jegadeesh and Wu (2022). The results address the debate sparked by Hu and Murphy (2020), who found NYSE closing auction quality worse than NASDAQ and attributed it to floor traders; here, floor traders are shown to improve auction quality and liquidity. Bessembinder, Hao and Zheng (2020) and Clark-Joseph, Ye and Zi (2017) document the value of DMM participation; this paper separately identifies floor brokers as an additional, distinct channel. The price-discovery role of designated dealers confirmed here is consistent with Madhavan and Panchapagesan (2000). Overall, human floor traders remain valuable intermediaries even in the algorithmic trading era.

Theory / model

The paper has no formal structural model. The identification argument begins with an additive model of market quality (eq. 1, p. 387):

$$E[\text{MarketQuality}_{0,i,e,t} \mid i, e, t] = \rho_e + \lambda_t + \Gamma_i + \alpha_{i,t} \tag{1}$$

where $\rho_e$ captures the exchange effect, $\lambda_t$ an aggregate time effect, $\Gamma_i$ a time-invariant firm effect, and $\alpha_{i,t}$ time-varying firm-level shocks. Observed market quality is then (eq. 2, p. 387):

$$\text{MarketQuality}_{i,e,t} = \rho_e + \lambda_t + \Gamma_i + \alpha_{i,t} + \beta \cdot \mathbf{1}_{\text{FloorTrading}} + \xi_{i,e,t} \tag{2}$$

where $\mathbf{1}_{\text{FloorTrading}}$ equals 1 if floor trading is suspended and 0 otherwise; $\beta$ is the treatment effect of interest. The canonical DiD estimator compares treated minus control before and after (eqs. 3-4, p. 387-388), recovering $\beta$ when the parallel trends assumption holds, i.e., when time-varying shocks to control firms ($\alpha_{j,t}$) evolve as in treatment firms ($\alpha_{i,t}$).

The two tested mechanisms follow from three observable changes caused by the floor closure: (i) loss of in-person interaction between DMMs and floor brokers, (ii) loss of manual auction operation, and (iii) loss of special floor-only order types (D orders). The staggered partial reopenings isolate each channel: first reopening restores D orders but not in-person interaction; second reopening restores in-person interaction and manual auctions.

Method

The paper applies two difference-in-differences strategies using the same additive model, differing in the control group.

Approach 1: NYSE vs. matched NASDAQ stocks (eq. 5, p. 389). The control group is NASDAQ-listed stocks matched on price, trading volume, market capitalization, and Fama-French 48-industry classification using one-to-one nearest-neighbor propensity score matching (PSM) without replacement. The treatment indicator is 1 if the stock is listed on NYSE after March 23, 2020:

$$y_{i,e,t} = \beta \mathbf{1}_{i,e,t} + \lambda_t + \Gamma_i + \gamma C_{i,e,t} + \epsilon_{i,e,t} \tag{5}$$

where $y_{i,e,t}$ is the market quality measure, $\mathbf{1}_{i,e,t}$ equals 1 for NYSE-listed stocks after March 23, $\lambda_t$ and $\Gamma_i$ are date and firm fixed effects, and $C_{i,e,t}$ is a vector of controls (log trading volume, parity trading volume). Standard errors are clustered by firm.

Approach 2: Within-stock variation across exchanges (eq. 6, p. 391). The same NYSE-listed stocks are compared across trading venues, using trades in the same stock on other exchanges (IEX, NASDAQ, BZX, etc.) as the control. This includes firm x date fixed effects ($\kappa_{it}$) to absorb time-varying firm-level shocks:

$$y_{i,e,t} = \beta D_{i,e,t} + \kappa_{i,t} + \gamma C_{i,e,t} + \epsilon_{i,e,t} \tag{6}$$

where $D_{i,e,t}$ equals 1 if firm $i$ is NYSE-listed, trading on the NYSE, after March 23, 2020. The firm x date fixed effects absorb any time-varying firm-level response to COVID-19, so confounders must differentially affect NYSE vs. off-NYSE trading in the same stock around March 23 to bias the estimate.

Both strategies use difference-in-differences and panel-regression building blocks. The matching step uses matching (PSM). The market quality measures are: proportional effective spread (PESPR), Hasbrouck (1993) pricing error, and auction price deviation (eq. 7, p. 400):

$$|\text{Deviation\%}| = 2 \times |\log(\text{trade}) - \log(\text{mid})| = 2 \times |\log(\text{trade}/\text{mid})| \tag{7}$$

where log(trade) is the log auction trade price and log(mid) is the last midpoint price from continuous trading. Higher values indicate worse auction quality. Information complexity (Panel C of Table V) is measured as $1 - R^2$ from a regression of each stock’s return on the contemporaneous SPY return within each half-hour interval (from Morck, Yeung, and Yu (2000)).

Empirical specifications

Main closure window (Tables III-VI): two-week window from March 16 to March 27, 2020 (one week before, one week after the March 23 floor closure). Sample: approximately 1,600 NYSE-listed stocks (PSM approach) or 552-553 NYSE-listed stocks with sufficient off-exchange trading (within-stock approach). Results are robust to a larger window encompassing all of 2020-2021 (Internet Appendix Table IA.II).

Spread regression (R1, Table III): Dependent variable is PESPR in percentage of midpoint. PESPR on NYSE is computed from NYSE quotes and trades only; PESPR off-NYSE is from NBBO excluding NYSE. Specifications range from stock fixed effects only (col. 1) to stock + day fixed effects (col. 2) to adding price and log volume controls (col. 3-4 for Panel A; col. 3-5 for Panel B). The treatment coefficient of 8.92 bps in Panel A and 1.46% in Panel B are stable across all specifications, supporting identification assumptions.

Pricing error regression (R2, Table IV): Dependent variable is $\log|\text{PricingError}_{i,e,t}|$, the Hasbrouck (1993) measure estimated following Rosch, Subrahmanyam, and Van Dijk (2017). Specifications mirror Table III. Panel A coefficient of 6.28 (t=4.68) implies approximately 6% increase in pricing errors; Panel B coefficient of 2.39 (t=1.84) implies approximately 2% increase.

Intraday spread regression (R3-R4, Table V): Equation mirrors eq. 6 with PESPR computed within each 30-minute interval from 9:30 to 16:00. Panel A (morning) and Panel B (afternoon) show results by half-hour. Panel C includes a triple interaction with information complexity (1 - R2), with stock x day fixed effects.

Auction deviation regression (R5, Table VI): Dependent variable is $|\text{Deviation\%}|$ for opening and closing auctions. Equation follows eq. 5 with stock and day fixed effects; additional control for D-order volume in cols. 3-4 (opening) and cols. 7-8 (closing). Specification confirms D orders are not the primary driver of auction deterioration.

Reopening regressions (R6-R7, Tables VII-VIII): Two-week window centered around each of the two partial reopenings (May 26 and June 17, 2020). The estimating equation for continuous trading (Table VII) includes two treatment indicators (Open1, Open2) interacted with Treated; stock x event fixed effects absorb different aggregate conditions across reopenings:

$$\text{PESPR}_{i,e,t} = \beta_1(\text{Treated}_{i,e,t} \times \text{Open1}_{i,e,t}) + \beta_2(\text{Treated}_{i,e,t} \times \text{Open2}_{i,e,t}) + \beta_3 \text{Treated} + \beta_4 \text{Open1} + \beta_5 \text{Open2} + \gamma C_{i,e,t} + FE + \epsilon_{i,e,t}$$

The analogous form applies to auctions (Table VIII). A negative and significant $\beta_2$ (Open2) with insignificant $\beta_1$ (Open1) for continuous trading identifies in-person interaction as the mechanism. A negative and significant $\beta_1$ (Open1) with insignificant $\beta_2$ for auctions identifies D orders as the auction mechanism.

Datasets used

Dataset	Role in paper	Wiki page
CRSP (Center for Research in Security Prices)	Stock prices, dollar trading volume (thousands USD), market capitalization; used to construct the matched sample and identify NYSE vs. NASDAQ stocks	WRDS / CRSP (licensed)
NYSE TAQ (Trade and Quotes) via WRDS Intraday Indicators	Proportional quoted spreads (PQSPR), proportional effective spreads (PESPR), and Hasbrouck pricing errors; the main market quality measures for both identification strategies	TAQ (licensed)
OneMarket Data (OneTick software)	Intraday TAQ data for within-exchange comparisons (PESPR computed separately by exchange for each 30-minute interval); NYSE data provided directly	no page yet

Sample: main closure window March 16-27, 2020 (10 trading days); reopening windows May 18-June 1 and June 10-23, 2020 (10 trading days each). The sample starts with 3,447 US common stocks (CRSP share code 10 or 11) listed on NYSE (1,256) or NASDAQ (2,191) in December 2019, excluding stocks with more than one share class (431) or market cap below $500 million (1,412), yielding approximately 1,600 equities (Table I, p. 386).

When to read the full paper

Use the original if you are: comparing market quality across trading mechanisms (hybrid vs. fully electronic exchanges); designing or evaluating floor-trading regulation; studying the role of human intermediaries and in-person information exchange in modern markets; or extending the DiD design to other exchange structure changes. The partial reopening evidence (Tables VII-IX) is especially useful for separating information-transfer from order-type mechanisms. The Internet Appendix (20 robustness measures from WRDS Intraday Indicators) provides a comprehensive falsification suite.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(1), February 2025. This distillation was extracted by an LLM on 2026-06-06 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). Brogaard, Jonathan, Matthew C. Ringgenberg, and Dominik Roesch. “Does Floor Trading Matter?” The Journal of Finance 80, no. 1 (February 2025): 375-414. DOI: 10.1111/jofi.13401. (C) 2024 The Author(s). 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.