Unmasking Mutual Fund Derivative Use: Kaniel & Wang (2025)

What this is. The paper’s core results, the data and method it contributes (SEC Form N-PORT derivative positions with monthly PnL plus K-Means Clustering of derivative strategies), and the empirical specifications behind each result: enough to know what it found and how, without reading all 47 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using newly available SEC Form N-PORT data (July 2019 to December 2022), Kaniel and Wang (2025) are the first to directly measure how mutual fund derivative positions contribute to fund returns. Contrary to the common belief that funds use derivatives to hedge, the paper finds that 59% of derivative users employ derivatives to amplify equity returns (positive derivative-nonderivative return correlation). Using K-Means Clustering on the allocation of derivative underlying assets, the paper identifies five persistent derivative strategy clusters. Long index users (41% of derivative users) dominate: they use long equity index derivatives to gain market exposure and amplify fund returns, and they contribute the bulk of the measured amplification. Despite this strategy, long index users do not outperform nonusers in normal times or during crisis periods. During COVID-19, they doubled derivative use to short indices, suffered losses when the Fed announced emergency measures on March 23, 2020, and then lost again on newly opened short positions as markets rebounded.

Core results

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

#	Result	Locator	Magnitude
R1	Derivatives contribute substantially to fund returns: average monthly DIR is -6.5 bps but with a standard deviation of 78 bps; the derivative relative contribution exceeds 0.1 for 30% of fund-month observations	Table 1 Panel B, p. 1130; Figure 1, p. 1132	mean DIR = -6.5 bps (std 78); mean non-DIR = 20.7 bps (std 531); 10% of obs have derivative relative contribution > 0.6
R2	59% of derivative users have a positive correlation between DIR and non-DIR, indicating amplification, not hedging; median correlation 0.17; equity derivative users 64% positive, median 0.43	Figure 4, p. 1144; Table 4 Panel A, p. 1144	median DIR-non-DIR correlation: long index = 0.67; long stock = 0.12; short index = -0.58; short stock = -0.25; nonequity = -0.06
R3	Derivative strategies are highly persistent: gross notional exposure auto-regresses at 0.776-0.956 across nontoken strategy groups; probability of staying in major user group 85-94% throughout sample	Table 3, p. 1139; Figure 2 Panel C, p. 1138	AR coefficient: long index 0.832*** (t=21.82); long stock 0.915*** (t=12.16); short index 0.883*** (t=22.53); short stock 0.956*** (t=18.94); token users only 0.0841*** (t=3.95)
R4	Long index users receive 0.2% more monthly flows than nonusers (2.4% annually), the additional flows are concentrated in institutional share classes; all other derivative users receive about 0.1% more institutional flows	Table 9 Panel B, p. 1161; Table 10 Panel A, p. 1163	long index dummy coef = 0.201** (t=2.21) in col 1; share-class results show higher institutional flows, with retail flows similar to nonusers
R5	Long index users underperform nonusers by 0.36-0.62 pp annually on all five risk-adjusted performance measures over 2011-2022; all derivative users underperform slightly but the gap is largest for long index	Table 9 Panel A, p. 1161	long index FF5 alpha: -1.45*** vs nonusers -0.83*; long index minus nonusers difference = -0.62** (t=-2.20) annually
R6	During COVID-19 outbreak, long index users underperformed other derivative users by 4.85% per month; derivatives accounted for 0.85% (18%) of the gap; active equity trading drove the rest	Table 8 Panel A, p. 1153; Figure 5, p. 1154	long index DIR contribution: -47.60 bps/month; all others DIR: +37.11 bps during outbreak; long index - all others DIR gap = -84.71*** bps
R7	During COVID-19 recovery, long-index funds earned only 6.3 bps per month from DIR, versus -54.92 bps for other derivative users; their total return advantage over those users came mainly from non-DIR	Table 8 Panel B, p. 1153; Figure 5, p. 1154	long index - all others DIR gap = +61.22** bps; active DIR gap = 5.42 bps (n.s.); total return difference = +260.29*** bps monthly, including +199.08** bps non-DIR
R8	Institutional investors allocated extra flows to high-tracking-error long index funds pre-COVID (consistent with a risk-shifting channel), which then shifted to short derivative positions during the crash but still failed to outperform	Table 10, p. 1163	high-CTE long index dummy coef = 0.284*** (t=3.16) for institutional flows; high-CTE users increased short notional exposure by 17.5*** pp vs low-CTE users
R9	Derivative use is common: 34.7% of active domestic equity funds use derivatives and manage 36% of total assets	Table 1 Panel A, p. 1130	1,079 of 3,106 funds (34.7%) use derivatives; derivative users manage 36% of total assets
R10	Derivative use is highly dispersed: half of users are token users, while nontoken users hold substantial notional exposure	Figure 1, p. 1132; text p. 1131	50% have gross notional exposure below 2%; the remaining half have median exposure above 15%
R11	Swap users have a higher median derivative relative contribution than non-swap users, and swap-only users exceed mixed-contract users	Table 1 Panel A, p. 1130; text p. 1134	swap gross notional exposure is 11.80%, the largest among contract types; median relative contribution = 0.17 for swap users vs 0.005 for nonswap users; swap-only = 0.39 vs 0.14 for mixed-contract users
R12	K-Means identifies five economically distinct strategies, each concentrated in its named underlying category	Table 2 Panel A, p. 1136	long index 41.4% of users, 96.4% long index allocation; long stock 12.6%, 95% long stock; short index 11.4%, 82% short index; short stock 8.5%, 84% short stock; nonequity 26.1%, 82% nonequity
R13	Prospectus language supports distinct amplification, cash-management, and information-trading motives by strategy	Figure 3, p. 1140; text p. 1141	amplification keywords: 42% of long-index users vs 6% of other users; cash-management terms: 14% vs 2%; individual-stock terms: 62% of long/short stock users vs 5% of others
R14	Short-stock derivatives are mostly naked, consistent with trading on firm-specific negative signals as well as hedging	text p. 1142	31% of short-stock positions are covered and 69% naked; 62% of short-stock users have over 80% of derivative positions in naked individual-stock derivatives
R15	Long-index users allocate flows more aggressively to equity and reduce excess cash as flows arrive	Table 5 Panels A-B, p. 1148	long-index flow coefficients: -0.0383** (t=-2.21) for change in excess cash and +0.0252* (t=1.76) for change in equity holdings
R16	Short-equity users show clearly higher flow-performance sensitivity; long-stock estimates are also larger but imprecise	Table 6, p. 1149	raw-return coefficients: long stock 13.78 (t=1.56), short equity 10.46*** (t=5.03), nonusers 2.383*** (t=10.65); short-equity coefficients on CAPM, FF4, FF5 alphas = 16.23***, 20.86***, 18.55***
R17	Long-index users increase short derivative exposure during the COVID outbreak and some switch to net short exposure	Table 7, p. 1151	short exposure rises 10.28 pp (0.69% to 10.97%) for long-index users vs 2.33 pp for other users; 31.6% of long-index users have negative net exposure in 2020/Q1
R18	Derivatives and active trading reduce tracking error during COVID, especially for other derivative users	Figure 6, p. 1156; text p. 1155	other users’ hypothetical tracking error peaks at 27% vs realized 17%; nonusers’ realized and hypothetical tracking errors both peak at 14%
R19	Funds respond to the 2022 rate hikes with delayed derivative adjustments	Figure 7, p. 1156; text p. 1157	long-index users increase use by 3.4 pp in Q2 (31% relative); other users increase 1.1 pp in Q1; no users adjusted before the hikes
R20	During the rate hikes, long-index performance is mixed and other derivative users underperform, driven partly by nonequity positions	Figure 8, p. 1157; text p. 1157	other derivative users underperform by over 3% in all risk-adjusted alphas; long-index users have slightly higher raw returns and CAPM alpha but lower FF4 and FF5 alphas; long-interest-rate derivatives are 36% of nonequity exposure
R21	Derivatives reduce tracking error during the Fed rate-hike period	Figure 9, p. 1158; text p. 1157	realized tracking error is below hypothetical tracking error for all derivative users; no difference for nonusers
R22	Long-index funds do not outperform nonusers during the COVID outbreak and recovery, despite large market losses	Figure 5, p. 1154; text p. 1153	long-index users lose nearly 35% in returns and 5% in risk-adjusted alphas; no outperformance in returns, CAPM, FF4, or FF5 alpha

Overall (paper’s conclusion). The majority of derivative-using mutual funds, especially long index users, employ derivatives to amplify equity returns rather than hedge. This amplification strategy does not yield superior performance in normal times or in crises, yet these funds attract abnormally high institutional flows. The paper tests the prediction of Glode (2011) that mutual funds underperform in normal times but outperform in crises: long index users fail the crisis-period outperformance prediction. The evidence is consistent with a risk-shifting rationale: high-CTE long-index funds receive extra institutional flows and increase short exposure in the crisis, but the strategy fails on the realized price path after the unexpected Fed intervention during COVID-19.

Theory / model

The paper has no formal theoretical model. It develops and tests the following testable hypotheses against the N-PORT data:

H1 (Amplification vs. Hedging). Prior work by Koski and Pontiff (1999) surveyed mutual fund managers and found most claimed to use derivatives for hedging, with only a small minority reporting amplification. Cao, Ghysels, and Hatheway (2011) use N-SAR data and find hedging evidence by comparing return distributions. Frino, Lepone, and Wong (2009) study derivative use and fund flows with options and futures. All three relied on coarse data that could not directly estimate the derivative PnL contribution. This paper tests the hedging hypothesis directly: if funds amplify, the derivative-induced return (DIR) and the nonderivative-induced return (non-DIR) will be positively correlated. If funds hedge, the correlation will be negative. The paper defines the signed derivative relative contribution as the ratio of DIR to non-DIR, and the (unsigned) derivative relative contribution as its absolute value (p. 1133):

$$\text{DIR}_t = \frac{\text{PnL}_t^{\text{Realized}} + \text{PnL}_t^{\text{Unrealized}} - \text{PnL}_{t-1}^{\text{Unrealized}}}{\text{TNA}_{t-1}}$$ $$\text{Signed Derivative Relative Contribution}_t = \frac{\text{DIR}_t}{\text{non-DIR}_t}$$ $$\text{Derivative Relative Contribution}_t = \left| \frac{\text{DIR}_t}{\text{non-DIR}_t} \right|$$

where $\text{PnL}^{\text{Realized}}$ and $\text{PnL}^{\text{Unrealized}}$ are monthly realized and unrealized profit-and-loss from all derivative positions as reported in N-PORT, scaled by total net assets in the previous month (p. 1133). Non-DIR is defined as fund return minus DIR.

H2 (Strategy Clustering). Funds with similar derivative strategies will cluster along the dimension of their underlying-asset allocation. K-Means Clustering on the 12-dimensional allocation vector should yield economically interpretable clusters corresponding to recognized trading motives (amplification, hedging, information trading, nonequity risk management).

H3 (Performance). Amplifying derivative users should not necessarily outperform in normal markets (the strategy adds risk without guaranteed alpha). In crises, performance depends on the realized path; a strategy that bets against the market could succeed or fail depending on the crisis trajectory.

Identification. There is no causal identification strategy. All results are descriptive. The paper exploits the granularity of N-PORT (monthly fund-level PnL by derivative position, including swaps not previously covered in CRSP or N-SAR) to document facts not measurable with prior data.

Economic channels. The paper distinguishes information-based trading in individual-stock derivatives, amplification and hedging of market exposure, liquidity and cash management through index derivatives, and interest-rate/currency risk management through nonequity derivatives (Sections 3.1-3.2, pp. 1134-1142). Its flow tests also assess a risk-shifting channel: institutional investors allocate flows to funds expected to change risk during a crisis, while a reverse-causality flow-management channel would have funds use index derivatives to equitize cash (Section 5, pp. 1162-1163). The evidence is consistent with risk shifting but does not fully separate the channels.

Method

K-Means Clustering (Section 3.1, pp. 1134-1136). The key input for each fund-quarter is the 12-dimensional allocation vector $x = (x_1, \ldots, x_{12})$ of notional amounts across 12 categories (equity index long/short, individual stock long/short, interest rate long/short, FX long/short, commodity long/short, other long/short). K-Means minimizes intracluster Euclidean distances and maximizes intercluster distances:

$$\min_{C_1,\ldots,C_k} \sum_{j=1}^{k} \sum_{x_i \in C_j} \| x_i - \mu_j \|^2$$

where $\mu_j$ is the centroid of cluster $j$. The optimal number of clusters $k$ is chosen by the Silhouette Method, yielding $k = 5$. The five clusters are labeled: long index (41.4% of derivative users), long stock (12.6%), short stock (8.5%), short index (11.4%), and nonequity (26.1%) (Table 2, p. 1136). This builds on panel-regression for the persistence and performance analyses that follow.

Derivative Performance Measurement. N-PORT provides monthly realized and unrealized PnL for each derivative instrument. The paper hand-collects daily security-level returns for each derivative position by matching security names in N-PORT to Yahoo Finance and Bloomberg, allowing construction of hypothetical DIR (return assuming static quarterly holdings). The difference between actual DIR and hypothetical DIR isolates active within-quarter derivative trading (p. 1152).

Extended Sample via CRSP (Section 5, pp. 1158-1161). For performance and flow analysis, the paper extends to 2011-2022 using CRSP mutual fund holdings. Because CRSP does not provide gross notional exposure, the paper identifies long index users as funds where over 80% of derivative positions are long equity index contracts, using intensive manual matching of security names. Equal-weighted portfolio returns are then regressed on Fama-French factor models.

Empirical specifications

Unless noted otherwise, regressions report standard errors clustered at the fund level; Tables 9 and 10 use two-way clustering by fund and time. The main specifications are:

Derivative strategy persistence (Table 3, p. 1139). For each derivative user type $g$:

$$\text{GrossNotionalExposure}_{f,t} = \alpha + \beta \cdot \text{GrossNotionalExposure}_{f,t-1} + \varepsilon_{f,t}$$

The regressions are estimated separately by derivative-user type with standard errors clustered by fund; Table 3 does not report fund or Lipper-style fixed effects. N-PORT quarterly observations cover September 2019 to December 2022. Results (R3): $\hat{\beta}$ ranges from 0.776 to 0.956 across nontoken types; token users’ coefficient is 0.0841*** (t=3.95). Gross- and net-exposure regressions are both reported in Table 3.

Excess cash and equity holdings response to flows (Table 5, p. 1148). Separate panel regressions by derivative strategy group:

$$\Delta \text{ExcessCash}_{f,t} = \alpha + \beta \cdot \text{Flow}_{f,t} + \text{Controls}_{f,t} + \text{TimeFE}_t + \text{StyleFE}_f + \varepsilon_{f,t}$$

where excess cash is fund cash minus 20% of gross notional exposure (excluding call/put purchases and short equity positions), following An and others (2021). Long index users show $\hat{\beta} = -0.0383^{**}$ (t=-2.21), the only group with a negative relation, while all others show positive relations consistent with standard cash management.

Flow-performance sensitivity (Table 6, p. 1149). Fund next-month flows on past-year performance, controlling for lagged flows, expense ratio, turnover ratio, log TNA, past-year return volatility, with Lipper-style and time fixed effects:

$$\text{Flow}_{f,t+1} = \alpha + \gamma \cdot \text{Perf}_{f,t-12:t} + \text{Controls}_{f,t} + \text{TimeFE}_t + \text{StyleFE}_f + \varepsilon_{f,t}$$

Short equity users show the highest flow-performance sensitivity ($\hat{\gamma} = 10.46^{***}$ on raw return), consistent with hedge-fund-like investor base.

Long-run performance (Table 9, p. 1161). Equal-weighted portfolios formed by derivative user type; excess returns regressed on Fama-French factor returns using a 2011-2022 CRSP sample. Long index users show FF5 alpha of -1.45*** (t=-2.91) vs nonusers -0.83* (t=-1.90), a -0.62** difference (t=-2.20) annually (R5).

Fund flows by strategy (Table 9 Panel B, p. 1161). Monthly fund-level flow regressions on derivative strategy dummies and their interaction with a retail-share-class indicator:

$$\text{Flow}_{f,t} = \alpha + \delta_{\text{LI}} \cdot \mathbf{1}[\text{LongIndex}]_f + \delta_{\text{AO}} \cdot \mathbf{1}[\text{AllOthers}]_f + \text{Controls}_{f,t} + \text{TimeFE}_t + \text{StyleFE}_f + \varepsilon_{f,t}$$

Two-way clustered standard errors at fund and time levels. Long index coefficient: 0.201** (t=2.21) through 0.184** (t=2.15) across performance-measure variants.

COVID return decomposition (Table 8, p. 1153). Monthly fund returns decomposed into DIR and non-DIR; each further split into hypothetical (passive) and active components using hand-collected daily security returns. Comparisons made separately for outbreak (Feb-Mar 2020) and recovery (Apr-Jun 2020) periods. Long index - all others DIR gap = -84.71*** bps during outbreak.

Additional main-text specifications and measurement equations. In Table 5 Panel B, the dependent variable is the change in equity holdings rather than excess cash. The paper estimates separate regressions by strategy group:

$$\Delta \text{EquityHolding}_{f,t} = \alpha + \beta \text{Flow}_{f,t} + \boldsymbol{\gamma}'\text{Controls}_{f,t} + \tau_t + \lambda_s + \varepsilon_{f,t}$$

The controls are expense ratio, turnover, and fund size; time and Lipper-style fixed effects are included, with standard errors clustered by fund. The panel is quarterly N-PORT data, September 2019-December 2022. The long-index coefficient is 0.0252* (t=1.76; Table 5, p. 1148).

Table 9 Panel A forms equal-weighted monthly portfolios by derivative-user type in the extended CRSP sample and estimates factor-model alphas:

$$R_{g,t}-R_{f,t} = \alpha_g + \boldsymbol{\beta}_g'\mathbf{F}_t + \varepsilon_{g,t}$$

The coefficients are annualized percentage points and t-statistics are reported. There are no fixed effects; the sample is 2011-2022 (Table 9, p. 1161).

The share-class specifications in Table 9 Panel B and Table 10 Panel A include interactions between the strategy indicators and a retail-share-class indicator. Table 10 replaces long-index users with high- and low-change-in-tracking-error (CTE) indicators:

$$\text{Flow}_{i,t} = \alpha + \delta_H\text{HighCTE}_{i} + \delta_L\text{LowCTE}_{i} + \theta\text{Retail}_{i,t} + \phi_H(\text{HighCTE}_{i}\times\text{Retail}_{i,t}) + \phi_L(\text{LowCTE}_{i}\times\text{Retail}_{i,t}) + \boldsymbol{\gamma}'\text{Controls}_{i,t} + \tau_t + \lambda_s + \varepsilon_{i,t}$$

Table 10 Panel A uses monthly share-class flows from 2010-2019, the Table 9 fund controls, time and style fixed effects, and two-way clustered standard errors by fund and time. Panel B reports 2019/Q4 to 2020/Q1 exposure differences, not regression estimates (Table 10, p. 1163).

Table 7 reports mean gross notional exposures by strategy in 2019/Q4 and 2020/Q1, first differences, and tests of within- and across-group differences. Figure 7 plots mean quarter-to-quarter exposure changes with 95% confidence intervals by long-index versus other users. These are descriptive comparisons; the paper does not specify a separate regression equation (Table 7, p. 1151; Figure 7, p. 1156).

For Table 8, hypothetical DIR is the sum of derivative returns multiplied by reported notional exposures, assuming positions remain in place over the following quarter. Active DIR is realized DIR minus hypothetical DIR. The table reports strategy-group monthly means and between-group differences for February-March and April-June 2020; significance is reported on the long-index minus all-other-user differences, with no fixed effects (Table 8, p. 1153).

For Figures 5-9, daily alphas use factor loadings estimated on a prior one-year rolling window. Tracking error is the annualized 30-day rolling standard deviation of fund-minus-benchmark returns; hypothetical tracking error uses reported equity positions at quarter start, so the difference reflects derivatives and active trading. These are portfolio paths, not regressions with fixed effects or tabulated standard errors (Figures 5-9, pp. 1154-1158).

Datasets used

Dataset	Role in paper	Wiki page
SEC Form N-PORT (monthly, quarterly)	Primary data: derivative holdings, notional amounts, monthly realized and unrealized PnL by instrument; fund total net assets; portfolio weights	No page yet
CRSP Mutual Fund Database (2010-2022)	Extended sample for performance and flow analysis; fund characteristics and returns; derivative identification via holdings	WRDS / CRSP (licensed)
Morningstar Direct	Fund reported benchmarks (Lipper investment styles)	No page yet
SEC EDGAR Form N-1A (prospectus)	Principal Investment Strategy section; textual analysis of derivative-related discussions and keywords	SEC EDGAR
Yahoo Finance and Bloomberg (hand-collected)	Daily security-level returns for individual derivative positions; matched to N-PORT security names manually	No page yet
County-level COVID-19 statistics (New York Times)	Pandemic severity measures for cross-sectional variation analysis (Section IA.2)	No page yet

Sample: N-PORT primary sample July 2019 to December 2022 (3,106 active domestic equity funds, 1,079 derivative users). Extended CRSP sample 2011-2022.

When to read the full paper

Read the original if you are studying: (i) mutual fund derivative regulation, since the paper documents that most amplification is unhedged and questions whether derivative access benefits investors; (ii) constructing fund classification schemes using N-PORT data (Section 3 with the K-Means approach); (iii) measuring how derivatives affect fund tracking error during crises (Figures 6 and 9 in the source); or (iv) analyzing the flow-performance puzzle for derivative-using funds, particularly the risk-shifting channel evidence (Section 5 and Table 10). The Internet Appendix contains additional cross-sectional variation tests (SAH orders, industry concentration) and robustness checks for the COVID analysis.

Attribution and rights

Source: peer-reviewed, The Review of Financial Studies 38(4), 2025, pp. 1120-1166. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. Standard OUP publication-reuse rights; not CC-licensed. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced.

Kaniel, Ron, and Pingle Wang. “Unmasking Mutual Fund Derivative Use.” The Review of Financial Studies 38, no. 4 (2025): 1120-1166. DOI: 10.1093/rfs/hhaf001. Replication code: Harvard Dataverse, https://doi.org/10.7910/DVN/TQCGER. Extract-only; paywalled source.