The Actual Retail Price of Equity Trades: Schwarz, Barber, Huang, Jorion & Odean (2025)

What this is. The paper’s core results, the trading experiment design, and the regression specifications that identify the source of price execution variation across brokers: enough to know what was found and how, without reading all 35 pages. To replicate or extend, read the full source at the original.

TL;DR

The paper runs a controlled trading experiment opening six brokerage accounts at five major U.S. retail brokers (TD Ameritrade, Fidelity, E*Trade, Robinhood, Interactive Brokers Pro and Lite) and placing approximately 85,000 simultaneous identical market orders in 128 stocks over six months (December 2021 to June 2022). Mean price improvement (PI) relative to the National Best Bid and Offer (NBBO) ranges from 47% of the NBBO spread (TD Ameritrade) to 19% (IBKR Lite), and average round-trip costs range from 7 basis points to 46 basis points. The key finding is that all of this variation is attributable to the same market centers (off-exchange wholesalers) giving systematically different execution to different brokers for identical trades, not to broker routing decisions or payment for order flow (PFOF). PFOF, whose variation across brokers is an order of magnitude smaller than PI variation, explains almost none of the cross-broker execution differences.

Core results

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

#	Result	Locator	Magnitude
R1	Cross-broker PI ranges from 19% to 47% of NBBO, with mean round-trip costs from 7 to 46 bps; off-exchange execution averages 31% of NBBO vs. 17% for exchange trades	Table IV, p. 2527; Figure 1, p. 2510	TD: PI% = 47.2%, cost = -7.2 bps; FD: 35.8%, -19.7 bps; ET: 36.1%, -23.4 bps; RH: 26.8%, -31.4 bps; IB Lite: 19.5%, -44.3 bps; IB Pro: 18.8%, -46.2 bps
R2	Round-trip cost differences are economically large: TD-to-worst-broker gap is 39 bps; 1 bp of aggregate retail trading cost equals ~$2.8 billion annually	Table IV, p. 2527; p. 2529	Round-trip costs: TD -7.2 bps, IBKR Pro -46.2 bps; midpoint-execution benchmark = 0 bps; NBBO execution = -61.9 bps
R3	All pairwise broker differences are statistically significant at the 1% level (except FD vs. ET, insignificant); execution quality varies highly significantly across all six accounts	Table V, p. 2529	TD-RH diff = 20.5 pp (t=63.10**); TD-IBKR Pro = 28.5 pp (t=47.36**); FD-ET = -1.4 pp (t=-1.29, n.s.)
R4	The same market center (venue) gives systematically different execution to different brokers for the exact same trade; routing venue choice does not explain the execution gap	Table VII, p. 2532	TD vs. RH at same venue: PI diff = 22.5 pp; at different venues: PI diff = 22.5 pp; identical across venue conditions for all broker pairs
R5	OIB (order flow toxicity) can generate variation consistent with observed execution differences, but broker fixed effects remain after controlling for OIB and venue/stock fixed effects	Table VIII, p. 2536	Broker-FE model: R2 = 15.5%; OIB adds 10 pp; FE on RH vs. TD baseline = -0.204** (robust to venue and stock FEs)
R6	PFOF does not explain cross-broker PI variation: the rank order of execution quality is unrelated to PFOF levels, and PFOF per share is ~1/30th the size of PI per share	Figure 4, p. 2535; Table IV, p. 2527	PFOF range: $0.000-$0.215 cents/share; PI range: $2.78-$7.84 cents/share; Fidelity (PFOF=0) has better execution than Robinhood (PFOF=$0.215/share)

Overall (paper’s conclusion). Retail investors trading “free” through commission-zero brokers face real execution costs that differ by as much as 39 basis points per round trip across identical simultaneous orders. The source of this dispersion is not broker routing but wholesaler-level pricing: the same off-exchange market centers give different execution to different brokers for the same trades. Payment for order flow explains almost none of the variation. Better disclosure of execution quality at the broker level, particularly through an expansion of SEC Rule 605 and Rule 606 reporting, is the proposed remedy.

Theory / model

The paper has no formal model. The theoretical framework draws on three mechanisms from the market microstructure literature:

Adverse selection and order toxicity (Kyle (1985), Battalio and Holden (2001)). Off-exchange wholesalers profit from uninformed retail order flow. If order flow from a given broker is more “toxic” (more directional, more informed), wholesalers face higher inventory risk and protect themselves by offering lower PI. The paper tests whether observed OIB variation can account for the PI variation across brokers.

Inventory management (Stoll (1978)). Dealers face costs from absorbing order imbalances. High one-sided order flow from a broker creates inventory risk, leading to higher effective spreads.

Competitive incentives and scale. Wholesalers compete for order flow and cater to broker objectives. Brokers with larger order-flow volumes (e.g., TD Ameritrade, which has more than double the daily average trades of other brokers in the experiment) may receive better execution because wholesalers compete more aggressively for their flow.

Prior experimental evidence. Bakos et al. (2005) ran an earlier experiment at three brokers in 1999 and found no difference in overall PI, though total trading costs (including commissions) differed. Kothari, So & Johnson (2021) study Robinhood trades in TAQ and report average PI of 33% NBBO for small trades using the actual known trade direction. Levy (2022) compares 1,000 trades at Robinhood and TD Ameritrade, finding a similar ranking to this paper. Eaton et al. (2022) provide independent evidence that broker-level order flow toxicity affects market quality using Robinhood platform outages as a natural experiment.

Identification strategy. The paper explicitly trades simultaneously across all broker accounts in the same stock at the same time, so any latency difference is controlled by construction (and verified empirically). This design isolates broker-level treatment effects from stock and time variation. The within-venue test (Table VII) separates venue-routing from within-venue execution differences.

Method

The core identification relies on a controlled trading experiment rather than observational data.

Stock selection. The CRSP universe (4,037 names as of June 2021) is stratified into 128 bins by market capitalization, liquidity (share turnover), volatility, and price, with one stock randomly selected per bin. Stocks with share price below $1 are excluded. The paper also includes four high-retail-interest stocks (AMC, Tesla, Nio, Aurora Cannabis), several mega-caps (Apple, Bank of America, NVIDIA, ExxonMobil, Google, Visa), and the top four Robinhood “mover” stocks each week (pp. 2521-2522).

Trade execution. For API-accessible brokers (TD Ameritrade, Robinhood, E*Trade, IBKR Pro), trades are placed programmatically, randomizing submission order across buy and sell sides to remove latency bias. For Fidelity and IBKR Lite, trades are placed manually in parallel. Target order size is $100 per trade (full shares rounded to nearest whole share); robustness checks use $1,000 and $5,000 targets. Positions are closed within 30 minutes. The experiment runs from December 21, 2021 to June 9, 2022 (113 trading days). After filters (TAQ matchability, 2-second simultaneity window, price $>$1), the final sample is 74,801 trades (pp. 2522-2524).

Price improvement (PI) measures (equations 1a-b, p. 2518):

$$\text{PI\$}_{\text{buy}} = \text{NBO} - P \quad \text{and} \quad \text{PI\$}_{\text{sell}} = P - \text{NBB} \tag{1a}$$ $$\text{PI\%} = \frac{\text{PI\$}}{\text{NBBO Spread}} \tag{1b}$$

Effective spread (equations 2a-b, p. 2518):

$$\text{ES\$}_{\text{buy}} = 2 \times (P - P_{\text{mid}}) \quad \text{and} \quad \text{ES\$}_{\text{sell}} = 2 \times (P_{\text{mid}} - P) \tag{2a}$$ $$\text{ES\%} = \frac{\text{ES\$}}{\text{NBBO Spread}} \tag{2b}$$

where $P$ is the execution price, NBO (NBB) is the national best offer (bid), and $P_{\text{mid}}$ is the NBBO midpoint. Round-trip transaction costs are computed using midpoint prices at entry and exit as the benchmark.

Routing-venue data. Under SEC Rule 606(b)(1), brokers must provide customer-specific routing data upon request. The paper requests and obtains these data, identifying each trade’s execution venue, enabling the within-venue test in Table VII (p. 2532).

Empirical specifications

Pairwise broker comparison (R1-R3). The headline result compares unconditional means of PI% across broker accounts (Table IV, p. 2527). Statistical significance is assessed via Table V (pairwise differences), which reports mean PI% differences for matched parallel trades with standard errors clustered by stock:

$$\text{PI\%}_{i,b} - \text{PI\%}_{i,b'} \quad \text{for each matched trade pair } (b, b') \tag{}$$

The two-sample test uses only matched (simultaneous) trades between each broker pair.

Same-venue vs. different-venue decomposition (R4). Table VII splits all pairwise matched trades into “Same” (both brokers routed to the same wholesale venue) and “Different” (routed to distinct venues). The comparison tests whether the PI gap is driven by venue routing or by within-venue differences:

$$\overline{\text{PI\%}}_{\text{Broker A}} - \overline{\text{PI\%}}_{\text{Broker B}} \bigg|_{\text{Same venue}} \quad \text{vs.} \quad \overline{\text{PI\%}}_{\text{Broker A}} - \overline{\text{PI\%}}_{\text{Broker B}} \bigg|_{\text{Different venue}}$$

Standard errors are clustered by stock (Table VII, p. 2532).

Multivariate regression (R5). Table VIII (p. 2536) regresses PI% on broker account dummies (TD Ameritrade omitted) and deciles of off-exchange order imbalance (OIB), separately for buys and sells, with progressive addition of venue and stock fixed effects:

$$\text{PI\%}_{i,j,t} = \sum_{b \neq \text{TD}} \beta_b \cdot \mathbf{1}[\text{Broker} = b] + \sum_{k=1}^{10} \gamma_k^{\text{Buy}} \cdot \text{OIB}_{k,t}^{\text{Buy}} + \sum_{k=1}^{10} \delta_k^{\text{Sell}} \cdot \text{OIB}_{k,t}^{\text{Sell}} + \text{FE} + \varepsilon_{i,j,t} \tag{}$$

where OIB is computed as buy minus sell off-exchange orders in the same stock during the minute of the trade, scaled by their sum (from TAQ, code “D”), assigned to deciles. Four models are estimated: (1) broker FEs only, (2) broker FEs + OIB deciles, (3) + venue FEs, (4) + stock FEs. Standard errors are clustered by stock.

PFOF analysis (R6). PFOF is obtained from SEC Rule 606 reports. The per-share PFOF is plotted against per-share PI (Figure 4, p. 2535) to assess rank-order and magnitude correspondence. No regression is estimated; the test is visual and descriptive, motivated by the finding that PFOF ($0.001-$0.003/share) is an order of magnitude smaller than PI ($0.028-$0.078/share).

Datasets used

Dataset	Role in paper	Wiki page
Authors’ own trading experiment (85,417 trades in 6 broker accounts)	Primary outcome: execution price, PI, round-trip cost per trade	No page yet
TAQ (Trade and Quote Database)	Match trades to exchange/off-exchange execution; compute OIB; obtain execution venue codes	TAQ (licensed)
CRSP (Center for Research in Security Prices)	Stock selection: universe stratification by market cap, liquidity, volatility, price	WRDS / CRSP (licensed)
SEC Rule 606 reports (broker routing reports)	PFOF per share by broker and venue; routing shares to each wholesale venue	No page yet
SEC Rule 605 reports (market center execution reports)	Venue-level execution quality benchmarks (fraction with PI, average PI amounts)	No page yet

Sample: December 21, 2021 to June 9, 2022 (113 trading days). 128 stocks; 74,801 trades in the final cleaned sample ($15.4 million notional). Six broker accounts: TD Ameritrade, Fidelity, E*Trade, Robinhood, IBKR Lite, IBKR Pro. Order target: $100 per trade, market orders only.

When to read the full paper

Use the original if you are: studying retail market microstructure or the effects of PFOF; evaluating broker execution quality methodologies or designing similar trading experiments; assessing the policy implications of SEC Rule 605/606 disclosure reform; or interested in the within-venue vs. between-venue decomposition of execution quality (Table VII is the key table). The Internet Appendix contains additional robustness results (trade size sensitivity, latency tests, stock-by-stock distributions).

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(5), October 2025. This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. The CC BY-NC 4.0 licence permits sharing with attribution for non-commercial use; the verbatim PDF is not hosted in this batch.

Schwarz, Christopher, Brad Barber, Xing Huang, Philippe Jorion, and Terrance Odean. “The ‘Actual Retail Price’ of Equity Trades.” The Journal of Finance 80, no. 5 (October 2025): 2507-2541. DOI: 10.1111/jofi.13467. © 2025 The Author(s). Licensed under CC BY-NC 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.