Crowded Spaces and Anomalies: Chincarini, Lazo-Paz & Moneta (2026)

What this is. The paper’s core results, the crowding measures it constructs, and the empirical specifications behind the main findings: enough to know what it found and how, without reading all 17 pages. To replicate or extend it, read the full source at the original.

TL;DR

This paper investigates whether crowded equity positions, those in which many institutional investors hold the same stocks and thereby exhaust the liquidity for normal exits, are associated with higher future returns and greater crash risk. Using Thomson/Refinitiv 13F institutional holdings from 1980 to 2021, the authors construct a Days-ADV crowding measure (the days of average daily trading volume needed for all institutions to exit a position). They find that more crowded anomaly stocks deliver significantly higher risk-adjusted returns across all 11 anomalies studied by Stambaugh, Yu, and Yuan (2012), that the anomaly alpha is entirely absent among non-crowded stocks, that the result persists after the anomaly publication dates identified by Mclean and Pontiff (2016), and that crowding increases institutional exposure to stock price crash risk. The paper extends the hedge-fund crowding-return result of Brown, Howard, and Lundblad (2021) to all 13F institutions and contradicts the mutual-fund finding of Zhong, Ding, and Tay (2017), and frames crowding as an additional channel within limits-to-arbitrage theory.

Core results

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

#	Result	Locator	Magnitude
R1	Most-crowded (Q5) stocks earn higher FF3 alphas than least-crowded (Q1); the VW spread is 1.44%/month	Table 3 Panels A-B, p. 8; Table 4, p. 9	VW Q5: FF3 alpha = 0.54%/month (t=8.87); Q1: -0.90%/month (t=-7.86); Q5-Q1 spread: 1.44%/month (t=9.67); EW spread: 1.57%/month (t=12.23)
R2	Bivariate double-sort aggregate anomaly portfolio (long crowded long-leg, short least-crowded short-leg) earns large EW alpha	Table 6 Panel B, p. 10-11	EW FF3 alpha = 1.69%/month (t=11.09) full sample; 1.96%/month in-sample; 1.61%/month post-publication (t=7.67)
R3	Non-crowded anomaly stocks earn near-zero or insignificant alphas across all factor models and periods	Table 7, p. 11	EW non-crowded portfolio FF3 alpha = 0.009%/month (t=0.18); VW = 0.008%/month (t=0.09); near zero across FF3, FF5P, FF5A, FF5AM
R4	Fama-MacBeth cross-sectional regressions confirm positive LADV-return association; stronger among anomaly stocks and post-publication	Table 9, p. 12	LADV coef = 0.546 (t=4.31) full sample (col 1); Long x LADV = 0.287 (t=3.12) full sample with interactions (col 4); Short x LADV = 0.485 (t=4.86) (col 4); post-pub spec (col 5): Long x LADV = 0.206 (t=3.01), Short x LADV = 0.308 (t=3.83)
R5	Crowding positively predicts future stock price crash risk, measured by NCSKEW and DUVOL	Table 10, p. 14	NCSKEW: LADV coef = 0.011 (t=3.29) full sample; DUVOL: LADV coef = 0.018 (t=5.13) full sample; robust to both subperiods
R6	Crowded anomaly portfolios declined significantly more than uncrowded portfolios during the 2007-2009 and COVID-19 crises	Figure 3, p. 13	CAR differences statistically significant for financial crisis (t=1.97) and COVID-19 crisis (t=2.03)

Overall (paper’s conclusion). Crowding is positively associated with future abnormal returns across all 11 stock market anomalies studied, and the anomaly alpha is generated almost entirely by the most crowded stocks. This result is robust to different factor model specifications (FF3, FF5, FF5 augmented with liquidity and momentum), persists after publication dates, and is stronger for transient and short-horizon institutions. Crowding also increases institutional exposure to crash risk, consistent with the idea that crowded positions impose additional risk for which investors require compensation and that crowding adds a new consideration to limits-to-arbitrage arguments.

Theory / model

The paper has no formal economic model. It tests two empirical hypotheses derived from limits-to-arbitrage theory.

Hypothesis 1 (returns). Crowded equity positions impose additional risks on investors, because correlated exit decisions can cause large price declines (coordination risk) and because the presence of many similarly positioned investors makes liquidity scarce when all try to exit simultaneously. Following the limits-to-arbitrage literature (Shleifer and Vishny (1997); De Long et al. (1990); Lam et al. (2011)), arbitrageurs must be compensated for this extra risk, so the long (short) leg of anomalies should earn positive (negative) abnormal returns specifically among the most crowded stocks.

Hypothesis 2 (crash risk). When too many institutional investors hold the same stock they create exposure to a correlated crash: if information or margin calls trigger simultaneous exit decisions, prices decline sharply. This is captured by the negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL) of firm-specific weekly returns, following Hutton et al. (2009) and Callen and Fang (2015).

Identification. The paper makes no causal claim. The empirical work is descriptive and correlational: it documents a positive association between lagged Days-ADV (institutional crowding) and subsequent returns or crash risk. Fama-MacBeth regressions control for known determinants of institutional demand (size, book-to-market, turnover, cumulative return), but there is no instrument, discontinuity, or difference-in-differences design.

Method

Days-ADV crowding measure (equation 3, p. 5). The main crowding proxy is the total dollar value of institutional holdings in a stock relative to its average daily dollar trading volume over the same quarter:

$$\text{Days-ADV}_{i,t} = \frac{\sum_{j=1}^{N} \text{InstHold}_{i,j,t-1}}{\text{ADV}_{i,t-1}} \tag{3}$$

where $\text{InstHold}_{i,j,t-1}$ is the total dollar value invested in security $i$ by institutional investor $j$ in quarter $t-1$, and $\text{ADV}_{i,t-1}$ is the average daily dollar trading volume of security $i$ over quarter $t-1$. Higher Days-ADV means institutions would need more trading days to fully unwind the position at normal volume. The paper uses the log of Days-ADV (LADV) in regressions to reduce the influence of outliers. A complementary portfolio-level similarity measure based on cosine similarity between institutional portfolio weight vectors is also constructed (equations 1-2, p. 5), but Days-ADV is the primary measure throughout because it directly links ownership magnitude to the liquidity of the individual security.

Activity Ratio crowding measure (equation 4, p. 5). As an alternative, the Activity Ratio (ActRatio) of Zhong et al. (2017) is used as a cross-check:

$$\text{ActRatio}_{i,t} = \frac{\sum_{j=1}^{N} \text{Shares}_{i,t-2}}{\text{AvgTurn}_{i,t-1}} \tag{4}$$

where the numerator is the percentage of shares held by active investors at $t-2$ and the denominator is the average share turnover of stock $i$ at $t-1$. The correlation between Days-ADV and ActRatio is 0.99, confirming they proxy the same construct.

Crash risk metrics (equations 5-7, p. 7-8). Firm-specific residual returns $R_{i,t}$ are first obtained by stripping market and industry effects from weekly returns via a market-model regression that includes lead and lag terms (equation 5, p. 7). Negative conditional skewness (NCSKEW) is then:

$$\text{NCSKEW}_{i,t} = -\frac{n(n-1)^{3/2} \sum R_{i,t}^3}{(n-1)(n-2)\left(\sum R_{i,t}^2\right)^{3/2}} \tag{6}$$

Down-to-up volatility (DUVOL) is:

$$\text{DUVOL}_{i,t} = \log\left(\frac{(n_u - 1)\sum_{\text{DOWN}} R_{i,t}^2}{(n_d - 1)\sum_{\text{UP}} R_{i,t}^2}\right) \tag{7}$$

where $n_u$ ($n_d$) is the count of up (down) weeks in the year and DOWN (UP) is the subsample of weeks with returns below (above) the mean. Higher values of both measures indicate greater crash risk.

Days-to-Cover (DTC, equation 8, p. 15). To test the short-leg alpha against a competing explanation (Hong et al. (2016)), DTC is computed as:

$$DTC = \frac{SR}{\text{Average Daily Turnover}} \tag{8}$$

where $SR$ is the short ratio (short interest divided by shares outstanding). DTC approximates the number of days required for all short sellers to cover at normal trading volume, capturing marginal-cost crowding for short positions.

Empirical specifications

Portfolio sorts (Section 3.1, p. 8). Each calendar quarter, stocks are ranked into quintiles by each crowding measure (Days-ADV, ActRatio, NI, PSO). Value- and equal-weighted quintile portfolio returns are computed for the following quarter and the time-series alpha is estimated via:

$$R^e_{p,t} = \alpha_p + \beta_1 \text{MktRf}_t + \beta_2 \text{SMB}_t + \beta_3 \text{HML}_t + \varepsilon_{p,t}$$

with variants that also include the Fama and French (1993) five-factor model (FF5), the Pastor and Stambaugh (2003) traded liquidity factor (FF5P), the Amihud (2019) illiquid-minus-liquid factor (FF5A), and further the Carhart (1997) momentum factor (FF5AM). Newey-West standard errors are used throughout.

For the bivariate anomaly analysis (Section 3.2, p. 10), stocks are sorted first on the anomaly variable into quintiles and then within the long (short) leg the top (bottom) 30% by Days-ADV are selected. The aggregate portfolio across anomalies takes an equally weighted average of the resulting anomaly-specific portfolio returns each month. Non-crowded sorted portfolios use the middle 40% of Days-ADV within each anomaly leg (Table 7).

Fama-MacBeth regressions (Section 3.3, p. 12). Each quarter, cumulative monthly returns over the following quarter are regressed cross-sectionally on LADV and a vector of controls $X_{i,t}$ (log size, age, return standard deviation, book-to-market, dividend yield, average monthly turnover, and cumulative returns over the past three and nine months). To test whether the anomaly channel amplifies the crowding-return link, indicator dummies for long-leg and short-leg anomaly membership and their interactions with LADV are added:

$$r_{i,t+1} = \alpha + \beta_1 \text{LADV}_{i,t} + \beta_2 \text{Long}_{i,t} + \beta_3 (\text{Long} \times \text{LADV})_{i,t} + \beta_4 \text{Short}_{i,t} + \beta_5 (\text{Short} \times \text{LADV})_{i,t} + \gamma' X_{i,t} + \varepsilon_{i,t}$$

The time-series average of quarterly cross-sectional coefficients gives the estimates. Standard errors are Newey-West with four lags.

Crash risk panel regression (Section 3.4.1, Table 10, p. 14). One-year-ahead crash risk is regressed on log Days-ADV and controls, with firm and year fixed effects. Standard errors are clustered by firm:

$$\text{CrashRisk}_{i,t+1} = \alpha + \beta \, \text{LADV}_{i,t} + \gamma' \text{Controls}_{i,t} + \text{FirmFE} + \text{YearFE} + \varepsilon_{i,t}$$

Controls include cumulative firm-specific daily returns, kurtosis and standard deviation of firm-specific daily returns, market-to-book ratio, book value of liabilities to total assets, ROA, log market cap, average monthly share turnover, number of analysts, and the lag of the crash risk variable. Anomaly-leg dummies and post-publication indicators are interacted with LADV in extended specifications.

Datasets used

Dataset	Role in paper	Wiki page
Thomson/Refinitiv (TR) 13F institutional holdings	Primary crowding measure (Days-ADV, cosine similarity); institution-type classification into transient, dedicated, quasi-indexer, hedge fund, mutual fund	WRDS (licensed)
CRSP monthly stock data	Stock returns, prices, trading volume, shares outstanding; anomaly variable construction	WRDS (licensed)
Compustat annual fundamentals	Accounting-based anomaly variables (accruals, NOA, asset growth, profitability, etc.) and short interest data (2003-2021)	WRDS (licensed)
I/B/E/S analyst data	Number of analysts following each stock (control variable in FM regressions)	WRDS (licensed)
Kenneth French Data Library	FF3, FF5, and momentum factor returns for risk adjustment	Ken French library
Brian Bushee institution classification	Transient, dedicated, quasi-indexer institution type labels	no page yet

Sample: US common stocks on NYSE, AMEX, and Nasdaq with price above $5, excluding utilities and financial firms. Main sample: 1980:Q1 to 2021:Q4 (quarterly rebalancing). Exception: momentum anomaly portfolios are rebalanced quarterly (not annually). DTC analysis restricted to 2003-2021, when Nasdaq short interest data becomes available.

When to read the full paper

Use the original if you are: investigating the relationship between institutional crowding and anomaly returns (Table 6 provides anomaly-by-anomaly bivariate-sort alphas for all 11 anomalies across five factor models); studying crash risk as a channel linking institutional crowding to limits-to- arbitrage; extending the results to the 97 anomalies of Mclean and Pontiff (2016) (Table 8 and the Internet Appendix); or assessing whether the DTC measure of Hong et al. (2016) accounts for the short-leg alpha in crowded spaces.

Attribution and rights

Source: peer-reviewed, Journal of Banking and Finance 182 (2026) 107579. This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. The CC BY-NC-ND 4.0 licence permits verbatim sharing but not derivative works; the verbatim PDF is not hosted in this batch.

Attribution (CC BY-NC-ND 4.0). Chincarini, Ludwig B., Renato Lazo-Paz, and Fabio Moneta. “Crowded spaces and anomalies.” Journal of Banking and Finance 182 (2026) 107579. DOI: 10.1016/j.jbankfin.2025.107579. © 2025 The Authors. Published by Elsevier B.V. Licensed under CC BY-NC-ND 4.0. This page is a distillation by the Institute for Automated Research: core results extracted and re-expressed as structured text. The licence does not permit derivative works; this page constitutes extract-only fair use documentation.