--- title: "Does Floor Trading Matter: Brogaard, Ringgenberg & Roesch (2025)" description: >- Distilled: Using the COVID-19 suspension of NYSE floor trading on March 23, 2020 as a natural experiment, this paper finds that human floor traders significantly improve market quality: their removal raises proportional effective spreads by roughly 9 basis points (more than 70% of the pre-closure mean) and increases Hasbrouck pricing errors by approximately 6%. J. Finance 2025, CC BY 4.0. Seven core results with source locators, datasets used, the DiD identification design, and the mechanism tests. sidebar: label: Brogaard-Ringgenberg-Roesch 2025 order: 1 tags: [paper-summary, market-microstructure, liquidity, algorithmic-trading, floor-trading, natural-experiment, difference-in-differences, open-access, cc-by, peer-reviewed, unreplicated, data:wrds, data:taq] paper: authors: Jonathan Brogaard, Matthew C. Ringgenberg, Dominik Roesch authorList: - { family: Brogaard, given: Jonathan, orcid: "0000-0001-5612-289X", affiliation: University of Utah } - { family: Ringgenberg, given: Matthew C., orcid: "0000-0002-3013-2085", affiliation: University of Utah } - { family: Roesch, given: Dominik, affiliation: State University of New York at Buffalo } year: 2025 venue: The Journal of Finance 80(1), February 2025, 375–414 venueShort: J. Finance 2025 doi: 10.1111/jofi.13401 jel: codes: [G14, G12, G10] assignedBy: claude-sonnet-4-6 date: 2026-06-06 topics: ['Financial Markets and Investment Strategies', 'Corporate Finance and Governance', 'Monetary Policy and Economic Impact'] dataAccess: licensed-commercial outcome: - proportional effective spreads (PESPR) on NYSE stocks - Hasbrouck (1993) pricing errors - auction price deviation from midpoint outcomeClass: [market-microstructure, asset-prices] license: >- CC BY 4.0 (confirmed via Crossref DOI metadata: content-version vor, URL http://creativecommons.org/licenses/by/4.0/, delay-in-days 0, start 2024-10-27; corroborated by artifact p. 375 Creative Commons Attribution License notice) licenseShort: CC BY 4.0 access: open machineAccess: "open-access PDF available (Wiley/OpenAlex direct PDF link confirmed 2026-06-06)" redistribution: extract-only (CC BY 4.0 permits mirroring; PDF not hosted in this batch) resultsCount: 7 citedByCount: 28 methods: role: applies-method family: reduced-form-causal buildsFrom: [difference-in-differences, panel-regression, matching] identification: natural-experiment contributionType: [new-fact, measurement] mechanisms: [information-asymmetry, liquidity] scope: region: US assetClass: US equities (NYSE and NASDAQ common stocks) period: 2020-03-16..2020-06-23 frequency: daily dataType: [market] granularity: [security, transaction] n: "approx. 1,600 NYSE-listed stocks (main closure window); 552-553 stocks in within-exchange specifications" findings: - ref: R1 outcome: proportional effective spreads (PESPR) on NYSE stocks metric: basis-points value: "8.92 bps increase (t=7.90) in PESPR for NYSE vs. matched NASDAQ stocks after floor closure (Table III, Panel A, col. 2); 1.46% increase (t=2.68) in within-stock NYSE-vs-off-NYSE specification (Table III, Panel B, col. 2)" direction: positive vsBenchmark: "pre-closure unconditional mean PESPR of 12 bps for NYSE stocks; 8.92 bps = more than 70% increase" - ref: R2 outcome: Hasbrouck (1993) pricing errors metric: coefficient value: "6.28*** (t=4.68) increase in Log|PricingError| for NYSE vs. NASDAQ matched stocks (Table IV, Panel A, col. 2); 2.39* (t=1.84) increase in within-stock specification (Table IV, Panel B, col. 1)" direction: positive vsBenchmark: "approx. 6% increase (Panel A) and approx. 2% increase (Panel B) relative to control; consistent across specifications" - ref: R3 outcome: proportional effective spreads (PESPR) on NYSE stocks metric: coefficient value: "Intraday: largest effects at 9:30-10:00 window (8.16***, t=5.38) and 10:00-10:30 (5.69***, t=6.69); effects decline monotonically and are insignificant from 12:00 onward (Table V, Panels A-B)" direction: positive vsBenchmark: "Floor traders matter most immediately after the opening auction; midday and afternoon coefficients near zero" - ref: R4 outcome: proportional effective spreads (PESPR) on NYSE stocks metric: coefficient value: "Triple interaction Complex x Treated x After = 17.45*** (t=7.33); floor traders matter more when stock-specific information complexity is high (Table V, Panel C)" direction: positive vsBenchmark: "Treated x After alone = -11.94*** (t=-7.84); floor trading value is concentrated in informationally complex stocks" - ref: R5 outcome: auction price deviation from midpoint metric: basis-points value: "Opening auction: Treated x After ranges from 34 to 47 bps (significant at 1-5%) across columns (1)-(4) of Table VI; Closing auction: Treated x After = 32 bps (Table VI, cols. 5-8)" direction: positive vsBenchmark: "Closing auction unconditional mean approx. 32 bps; floor closure implies approx. 100% increase in closing price deviation" - ref: R6 outcome: proportional effective spreads (PESPR) on NYSE stocks metric: coefficient value: "Around reopenings: Treated x Open1 = 0.27 (t=0.72, insignificant); Treated x Open2 = -4.85*** (t=-6.69) - spreads improve only after second reopening when DMMs return in person (Table VII, Panel A)" direction: negative vsBenchmark: "No reversal at first reopening (D orders resume); strong reversal at second reopening (in-person DMM interaction resumes)" - ref: R7 outcome: auction price deviation from midpoint metric: basis-points value: "Around reopenings: Treated x Open1 = -20.50** (t=-2.45) significant improvement at opening auction; no significant improvement at second reopening (Table VIII, cols. 1-4)" direction: negative vsBenchmark: "Auction quality improves at first reopening (D orders) but not second; continuous trading improves at second reopening only" resultType: new-finding relatesTo: - { cite: 'Bessembinder, Hao & Zheng (2020)', doi: '10.1093/rfs/hhz040', relation: builds-on, note: 'DMM participation leads to better market quality on NYSE; this paper isolates the floor broker channel separately from DMM activity' } - { cite: 'Clark-Joseph, Ye & Zi (2017)', doi: '10.1016/j.jfineco.2017.09.001', relation: builds-on, note: 'designated market makers still matter; this paper focuses on floor brokers as the separately identified treatment group' } - { cite: 'Hu & Murphy (2020)', doi: '10.2139/ssrn.3600230', relation: tests, note: 'Hu and Murphy find NYSE closing auction quality is worse than NASDAQ, attributing it to floor traders; this paper finds the opposite - floor closure worsens auction quality' } - { cite: 'Madhavan & Panchapagesan (2000)', doi: '10.1093/rfs/13.3.627', relation: extends, note: 'designated dealers improve price discovery at open over fully automated markets; confirmed here with floor broker/DMM in-person interaction as the channel' } - { cite: 'Battalio, Ellul & Jennings (2007)', relation: builds-on, note: 'reputation effects in floor trading; personal relationships between specialists and floor brokers lower informational asymmetries' } - { cite: 'Benveniste, Marcus & Wilhelm (1992)', doi: '10.1016/0304-405x(92)90025-s', relation: builds-on, note: 'personal relationships between specialists and brokers can lower informational asymmetries that improve market quality' } - { cite: 'Jegadeesh & Wu (2022)', doi: '10.1016/j.jfineco.2021.12.003', relation: extends, note: 'NYSE closing auctions better than NASDAQ due to D orders; consistent with first-reopening result here that D orders matter for auctions' } openQuestions: - "Whether algorithms would eventually adapt to replicate floor-trader information-transfer benefits over longer horizons than the two-year window examined (p. 410, Limitations section)." - "Whether the SUTVA assumption holds: other liquidity providers on off-NYSE venues may have changed behavior in response to the floor closure, which would bias the within-exchange treatment estimate (p. 410)." - "Whether effects differ in less liquid or smaller stocks outside the sample's $500 million market-cap floor (p. 385, §I.C)." replicationCode: status: available extraction: - by: paper-distiller (claude-sonnet-4-6) date: 2026-06-06 role: extracted note: >- Full text read (pp. 375-414, 40 pages); seven results extracted from Tables III-IX of the CC-BY PDF. Crossref and OpenAlex metadata fetched this session. Not human-verified. Not reproduced. - by: paper-verifier (claude-sonnet-4-6) date: 2026-06-06 role: verified note: >- Locators and reported magnitudes re-checked against the source PDF; all seven Core results rows confirmed against Tables III-VIII (pp. 394-408), all equations (eqs. 1-7 and reopening specification) verified term-by-term against the PDF; no errors found, no fixes required. licenceVerification: - source: Crossref REST API works/10.1111/jofi.13401 checked: 2026-06-06 by: paper-distiller (claude-sonnet-4-6) found: "license[].content-version=vor, URL=http://creativecommons.org/licenses/by/4.0/, delay-in-days=0, start=2024-10-27" rightsSignalConflict: false --- **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](https://doi.org/10.1111/jofi.13401). ## 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](/wiki/commercial/wrds/) (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](/wiki/commercial/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](https://doi.org/10.1111/jofi.13401) 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](https://creativecommons.org/licenses/by/4.0/). > This page is an **adaptation** by the Institute for Automated Research: > core results extracted and re-expressed; **changes were made**.