Anomalies and Their Short-Sale Costs: Muravyev, Pearson & Pollet (2025)

What this is. The paper’s core results, datasets, identification strategy, and the estimating equations: enough to know what it found and how without reading all 56 pages. To replicate or extend it, read the original at doi.org/10.1111/jofi.13501.

TL;DR

Using 162 anomalies from Chen and Zimmermann (2021) and stock borrow fee data from Markit (July 2006 to December 2020), the paper shows that the average long-short abnormal return of 0.14%/month is entirely due to high-fee stocks (borrow fee greater than 1%/year, roughly 12% of stock-month observations). Once high-fee stocks are excluded, or once returns are adjusted for the borrow fee, the average long-short abnormal return collapses to 0.04% or -0.01%/month, respectively, neither significantly different from zero. The result holds across microcap stocks, the 20 anomalies with the highest fees, four factor-mimicking portfolios (momentum, profitability, investment, book-to-market), and five individually named anomalies. Portfolios sorted on theoretically grounded risk measures (CAPM beta, tail-risk beta) are unaffected, serving as a placebo. A short-interest-to-institutional-holdings ratio (from Compustat/13F) works as a publicly available proxy for borrow fees, eliminating the need for Markit data to test exploitability.

Core results

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

#	Result	Locator	Magnitude
R1	Average long-short abnormal return across 162 anomalies is 0.14%/month before fees, significantly positive	Table III Panel A, p. 3659	Mean = 0.14%/mo, t-stat = 2.87***
R2	High-fee stocks drive the long-short return: excluding stocks with borrow fee > 1%/yr, long-short abnormal return drops to 0.04% and is insignificant	Table III Panel B, p. 3659	Mean = 0.04%/mo, t-stat = 0.84
R3	Fee-adjusted long-short return is near zero (borrow fee added back to short-side returns for full sample)	Table III Panel C, p. 3659	Mean = -0.01%/mo, t-stat = -0.24
R4	Unadjusted decile 1 abnormal returns are approximately linear in the borrow fee, slope -0.088 (approx. -1/12 per 1 ppt fee)	Table V col. 1, p. 3671	Slope = -0.0883, t-stat = -1.96*
R5	Microcap anomaly returns are also entirely due to high-fee stocks: before fees decile 1 = -0.48%/mo (t = -2.92); excluding high-fee stocks decile 1 = +0.09% (t = 0.76); fee-adjusted = +0.08% (t = 0.50)	Table IV, p. 3669	Before-fee L/S = +0.36% (t = 5.06); after drop = +0.11% (t = 1.39); net-of-fee = -0.05% (t = -0.62)
R6	High-fee anomaly subset (20 anomalies, avg fee > 4%/yr): before-fee long-short = 0.41%/mo (t = 2.08); excluding high-fee stocks = -0.04%/mo; fee-adjusted long-short = 0.02%/mo (near zero, insignificant)	Table VI, p. 3673	Exclud. high-fee L/S = -0.04% (t = -0.21); net-of-fee = 0.02% (t = 0.10)
R7	Momentum and profitability factor long-short returns are eliminated by borrow fees: momentum L/S drops from 0.21% to 0.03%/mo after excluding high-fee stocks, profitability from 0.61% to 0.09%/mo; net-of-fee momentum = -0.01%, profitability = 0.21%/mo (annualizes to 2.52%/yr, insignificant)	Table XI, p. 3688	Momentum net-of-fee L/S: -0.01% (t = -0.11); profitability net-of-fee: 0.21% (t = 0.59)
R8	CAPM-beta and tail-risk-beta sorted portfolios are unaffected by borrow fee exclusion or adjustment (placebo): CAPM L/S net-of-fee = 0.45%/mo (t = 0.81), tail-risk L/S net-of-fee = 0.43%/mo (t = 0.97)	Table XII, p. 3690	Net-of-fee CAPM L/S = 0.45%, tail-risk L/S = 0.43%; both insignificant
R9	Short-interest-to-institutional-holdings ratio (Compustat/13F, no Markit needed) replicates the main result: excluding stocks with SI/IO > 18% reduces decile 1 abnormal return by 0.27%/mo relative to Panel A	Table IX Panel D, p. 3682	SI/IO exclusion decile 1: 0.03% (t = 0.47) vs. -0.24% (t = -2.92) in Panel A

Overall (paper’s conclusion). Stock borrow fees function as a common limit to arbitrage that is sufficient to explain the persistence of anomaly returns for marginal investors. The average anomaly cannot be profitably exploited via long-short strategies once shorting costs are incorporated. The residual puzzle is why long-side investors continue to hold high-fee stocks despite bearing negative expected returns relative to low-fee benchmarks.

Theory / model

The paper has no original structural model. It is an applied empirical study operating in the limits-to-arbitrage tradition (Lee, Shleifer, and Thaler (1991); Nagel (2005); Stambaugh, Yu, and Yuan (2012)). The tested hypothesis is:

Stock borrow fees are a common, binding limit to arbitrage that prevents exploitation of cross-sectional return anomalies and explains their apparent out-of-sample persistence.

Conceptual identity: borrow fee as shadow dividend. A short seller pays the daily borrow fee for every day a short position is open. A long-side investor whose shares are lent receives the fee less prime-broker intermediation spreads, but only on the fraction of shares actually borrowed. The fee is therefore a shadow dividend not recorded in CRSP stock returns. The paper’s identification logic follows directly (pp. 3640-3641, 3650):

$$\begin{aligned} R^{\text{short,adj}} &= R_{\text{stock}} + f \\ R^{\text{long,adj}} &= R_{\text{stock}} + \text{utilization} \times (1 - \text{spread\_fraction}) \times f \\ &\approx R_{\text{stock}} + \text{utilization} \times 0.7 \times f \end{aligned}$$

• $f$ = indicative borrow fee (annualized, converted to monthly)
• $\text{utilization}$ = shares on loan / lendable shares
• D’Avolio (2002) estimates the spread fraction at approximately 0.3

In contrast to Drechsler and Drechsler (2021), who find positive net-of-fee returns on eight anomalies using lender-side fees, this paper uses buy-side fees over a longer and more recent sample and finds near-zero net-of-fee returns.

Identification design. Two complementary approaches test whether anomaly returns survive after accounting for fees (p. 3640):

1. Exclude high-fee stocks: drop stock-month observations with indicative borrow fee > 1%/year from the sorted portfolios (without resorting). Approximately 12% of stock-months qualify as high-fee; 21% of decile 1 observations do.
2. Fee-adjust returns: add the full fee to returns for short-side portfolios (deciles 1 and 2); add $\text{utilization} \times 0.7 \times f$ to returns for long-side portfolios (deciles 3-10).

Both approaches use a DGTW characteristics-matched benchmark that excludes high-fee stocks from the benchmark portfolios to avoid contaminating the abnormal-return calculation (p. 3654 fn. 16).

Placebo test. Portfolios sorted by CAPM beta and tail-risk beta (Kelly and Jiang (2014)) are theoretically grounded risk measures, not behavioral anomalies. If borrow fees reflect arbitrage frictions specific to mispriced stocks, these sorted portfolios should be insensitive to borrow-fee adjustments. The insensitivity result (Table XII, p. 3690) provides placebo support.

Method

The paper applies portfolio-sort and panel-regression methods. There is no newly proposed estimator; the methodological contribution is the systematic application of buy-side borrow fees to a comprehensive, out-of-sample anomaly universe.

DGTW abnormal return construction (pp. 3654-3655). For each stock $i$ and month $t$, the abnormal return is:

$$\text{DGTW\_ETB}_{i,t} = R_{i,t} - R_{\text{benchmark},i,t}$$

• $R_{\text{benchmark},i,t}$ is the equal-weighted return of the DGTW characteristics-matched portfolio, constructed excluding stocks with borrow fee > 1%/year (to prevent high-fee benchmark contamination). The benchmark portfolios match on market capitalization, book-to-market, and prior six-month momentum, following Daniel et al. (1997).

Portfolio-level aggregation. The abnormal return on a sorted decile portfolio in month $t$ is the cross-sectional average of stock-level abnormal returns within that decile. For each anomaly, the time-series average is computed over the performance evaluation period (July 2006 to December 2020, 14.5 years). The cross-sectional mean across the 162 anomalies is the headline statistic (p. 3654).

Fee adjustment procedure (pp. 3650, 3666-3667). The monthly fee is the simple average of daily indicative fees over the 21-trading-day return evaluation window. For decile 1 stocks (short side), the full fee is added to the stock return. For decile 3-10 stocks (long side, held or lent), the expected fee received is:

$$\text{fee\_received}_{i,t} = \text{utilization}_{i,t} \times 0.7 \times f_{i,t}$$

This is added to the stock return. Decile 2 uses the same adjustment as decile 1 in both the fee-exclusion analysis and the fee-adjustment analysis (i.e., the full fee is added to decile 2 stock returns, treating it as short-side).

Standard-error treatment. All t-statistics on cross-sectional averages are computed using a panel regression in which the monthly portfolio return for each anomaly is regressed onto decile fixed effects; standard errors are double-clustered by anomaly and month. The estimate for each decile fixed effect is the average return on the corresponding decile with the appropriate t-statistic (pp. 3655-3656).

Empirical specifications

The paper’s results come from three types of portfolio-performance constructions rather than a single OLS regression. Each is described below with the estimating equation.

Specification 1: Cross-sectional mean of decile abnormal returns (R1-R3, R5-R6, Table III p. 3659).

For each anomaly $a$, the time-series average abnormal return on decile $d$ is:

$$\mu_{a,d} = \frac{1}{T} \sum_{t=1}^{T} \text{DGTW\_ETB}_{a,d,t}$$

The cross-sectional mean across anomalies (the headline statistic) and the corresponding t-statistic are extracted from the panel regression:

$$\text{DGTW\_ETB}_{a,d,t} = \alpha_d + \epsilon_{a,d,t}$$

• LHS: monthly decile portfolio abnormal return for anomaly $a$, decile $d$, month $t$
• RHS: decile fixed effects $\alpha_d$ (one per decile 1-10)
• SE: double-clustered by anomaly $a$ and month $t$
• Sample: 162 anomalies, July 2006 to December 2020, varying N per anomaly

The long-short result is $\alpha_1 - \alpha_{10}$ (decile 1 minus decile 10, since anomaly signals are signed so decile 1 is the short side). The procedure is applied in three versions: (A) all stocks, no fee adjustment; (B) excluding high-fee stocks (fee > 1%/yr); (C) fee-adjusted returns.

Specification 2: Relation between anomaly returns and borrow fees (R4, Table V p. 3671).

For each anomaly, the average decile 1 borrow fee $f_a$ is regressed on the average abnormal return to show the approximately linear relationship:

$$\mu_{a,1} = \beta_0 + \beta_1 f_a + u_a$$

• LHS: time-series average abnormal return on decile 1 portfolio, anomaly $a$
• RHS: average indicative borrow fee for decile 1 stocks of anomaly $a$ at portfolio formation date
• SE: double-clustered by anomaly and month (panel version with month controls)
• Key result: $\beta_1 = -0.0883$ (t = -1.96*), approximately $-1/12$

The slope of approximately $-1/12$ means a 1 percentage-point increase in the annual fee corresponds to roughly $-1/12$ percentage points per month in abnormal return, consistent with complete fee absorption of the anomaly signal (p. 3671).

Specification 3: SI/IO exclusion as Markit substitute (R9, Table IX Panel D p. 3682).

The same Specification 1 panel regression is applied after excluding stock-months where short interest divided by institutional holdings (SI/IO, from Compustat monthly short interest and 13F filings) exceeds 18%:

$$\text{DGTW\_ETB}_{a,d,t} = \alpha_d + \epsilon_{a,d,t} \quad \text{[restricted sample]}$$

• Restriction: drop stock-months with $(\text{short\_interest}_{i,t-1} / \text{inst\_holdings}_{i,t-1}) > 0.18$
• Cutoff 18% chosen to match approximately 12% of stock-months excluded by fee > 1% cutoff
• SE: double-clustered by anomaly and month
• Key result: decile 1 $\alpha$ changes by +0.27%/mo relative to unadjusted baseline, decile 1 $\alpha$ = +0.03% (t = 0.47) vs. -0.24% (t = -2.92) with all stocks

Robustness: microcap extension (all stocks, market cap below 20th percentile NYSE, Table IV p. 3669), which tests the claim of Hou, Xue, and Zhang (2020) that anomaly returns are concentrated in microcaps and finds that even microcap anomaly returns disappear once borrow fees are accounted for; 20 highest-fee anomalies (Table VI p. 3673); subsets by t-statistic, pre-sample Sharpe ratio, and publication venue (Table VII p. 3674); long-side investor perspective with varying intermediation fractions (Table VIII p. 3678); five specific anomalies (Table X p. 3685); four factor-based long-short portfolios (Table XI p. 3688).

Datasets used

Dataset	Role in paper	Wiki page
Chen and Zimmermann (2021) anomaly signals (openassetpricing.com), Jan 2000 to Dec 2020; 202 anomalies, 162 retained	Anomaly signal construction; decile portfolio assignment	Open Source Asset Pricing
Markit Securities Finance Buy Side Analytics Data Feed, daily from Jun 28 2006	Stock borrow fees (indicative fee = buy-side expected borrow cost); utilization	no page yet
CRSP (via WRDS) common stocks, returns, delisting returns	Stock returns, market cap filters, sample construction	WRDS / CRSP / Compustat (licensed)
Compustat (via WRDS) short interest + 13F institutional holdings	Proxy for borrow fee (SI/IO ratio) for researchers without Markit access	WRDS / CRSP / Compustat (licensed)

Sample: 554,253 stock-months (162 anomaly signals, Jul 2006 to Dec 2020), after dropping stocks below $1 price or $50 mn market cap and requiring at least 4 days of borrow fee observations per month.

When to read the full paper

Use the original if you are: replicating (code in the journal’s Supporting Information); extending the borrow fee adjustment methodology; examining the specific anomaly-by-anomaly results in the Internet Appendix; or auditing factor return attrition (Section VI). The locators above point to the exact tables. For “what did this paper find,” the table above is the intended default.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6). This distillation was extracted by an LLM on 2026-05-31 and augmented on 2026-06-01; it is not human-verified or independently reproduced. CC BY 4.0 permits this adaptation; the verbatim PDF is not hosted in this batch but CC permits mirroring.

Attribution (CC BY 4.0). Muravyev, Dmitriy, Neil D. Pearson, and Joshua M. Pollet. “Anomalies and Their Short-Sale Costs.” The Journal of Finance 80, no. 6 (December 2025): 3639-3694. DOI: 10.1111/jofi.13501. Copyright 2025 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.