Institutional Investor Attention: Kwan, Liu & Matthies (2026)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 38 pages. To replicate or extend it, read the full source: the verbatim PDF (machine-accessible) or the original.

TL;DR

Using a proprietary dataset of institutional investors’ Internet news reading (Nov 2017 to Jun 2022; ~482M fund-firm-quarters, 4,075 funds), the paper measures fund attention to macro vs firm-specific news. Funds reallocate attention to macro news when aggregate volatility rises; funds that reallocate more strongly earn higher future returns. Firm-specific attention tracks holdings (“attention habitats”), and attention to a stock predicts that position’s value-add, most so for value-relevant news and for buying hedge funds, whose attention predicts stock returns.

Core results

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

#	Result	Locator	Magnitude
R1	Funds shift attention toward macro news when aggregate volatility is high	Table III, p. 804	β = 0.25** on VIX²ₜ₋₁; robust to VIX and realized vol; ≈ 5% of sample-SD in macro-attention share per 1-SD VIX²
R2	Funds with higher attention-reallocation sensitivity (β^VIX²) earn higher future returns	Table IV, p. 806	coef 0.31→0.36 (sig 5–1%); ≈ +0.36%/qtr (~1.4%/yr) per 1-SD; ~2× stronger in top VIX quartile (interaction 0.73**)
R3	High-β^VIX² funds look more efficient	§III.C	0.78–1.74% less attention-weighted salience (sig 5%); +17% advanced-degree staff; trait persistent (62–66% stay vs 25% random); hedge funds >2× mutual funds’ β^VIX²
R4	Firm-specific attention strongly tracks portfolio holdings (“attention habitats”)	Table V, p. 809	held read 5–6× more than non-held (t sig 1%); with firm×time FE, 1-SD holdings ≈ 1.02-SD attention; fund×firm FE dominate the variance
R5	Attention to a stock predicts that position’s value-add	Table VI, p. 813	1-SD attention ≈ +3.3% SD position value-add; trade-based coef 0.074**; ×trade-size 0.58*** (bigger trades, more value)
R6	Value-add is concentrated in value-relevant news (business/financial newswires)	Table VIII, p. 817	biz/fin-newswire attention×holdings 0.107** / 0.123***; retail and general news insignificant
R7	Funds attend more to buys than sells; attentive buys outperform	Table IX, p. 819	residualized attention: buy ≈ 1.6 vs sell ≈ 0.6 vs hold ≈ 0 (buy>sell sig 1%); attentive buys add value, sells mixed/insignificant
R8	Attention by buying hedge funds predicts future stock returns	Tables X–XI, pp. 821–823	Fama-MacBeth: buying-HF attention 0.51*/0.56*** (MF negative/insignificant, other-fund negative/significant at 5%); ≈ +0.35%/mo (~4%/yr) per 1-SD; HF long-short 0.53%/mo EW (t=2.75), 0.80%/mo VW; FF5 α ≈ 0.45%*** EW; no predictability for held/sold

Overall (paper’s conclusion). Attention is a resource that funds allocate, and the allocation contributes to performance. Funds that reallocate attention to macro news in volatile times, and that attend to value-relevant firm news, do better; the strongest stock-return signal is attention by buying hedge funds.

Datasets used

Dataset	Role in paper	Wiki page
Proprietary Internet news-reading data (“Data Partner”, anonymized analytics firm), Nov 2017–Jun 2022	The attention measure itself	Proprietary; not public or redistributable; no page
RavenPack 1.0	News topic / subject / sentiment; stock-ticker mapping	RavenPack (licensed)
FactSet LionShares	Institutional holdings (13-F), institution classification	FactSet LionShares (licensed)
CRSP & Compustat	Returns, fundamentals, stock characteristics	WRDS / CRSP / Compustat (licensed)
VIX (CBOE)	Aggregate-volatility measure (VIX²)	FRED, free, series VIXCLS
SEC Form ADV, Form N-1A	Fund descriptions for classification	Form ADV (via SEC IAPD, not EDGAR); N-1A via SEC EDGAR
LinkedIn / Revelio Labs	Fund human capital (advanced-degree share) for R3	Revelio Labs (licensed)

Sample: 481,820,400 fund-firm-quarters; 4,075 distinct funds.

Theory / model

The paper has no original structural model. It is purely empirical, testing predictions from the limited- and rational-inattention literature.

The primary theoretical framework is Peng and Xiong (2006) and Kacperczyk, Van Nieuwerburgh and Veldkamp (2016), whose models predict that attention-constrained investors allocate more attention to macroeconomic news when aggregate uncertainty is high. In those models, information about macro conditions becomes more valuable during high-VIX periods, so a rational fund reallocates limited attention toward it. A competing interpretation (also from Peng and Xiong (2006), Kacperczyk et al. (2014)) is that funds with more binding constraints react more to macro news when volatility rises, which would predict lower, not higher, future returns. The paper tests both predictions against data.

For firm-specific attention, the theoretical baseline is Van Nieuwerburgh and Veldkamp (2010): in equilibrium, investors prefer to hold assets they are more informed about and have incentives to acquire information about assets they expect to hold. This generates a positive feedback between attention and holdings. Further, a fund’s attention to a stock should be positively associated with the value the position contributes to the fund’s performance (position-level value-add), because learning about a stock becomes more valuable when the fund expects to hold a larger position.

There is no identification via a natural experiment. The paper controls for potential confounds through rich fixed-effect structures (see Method and Empirical specifications).

Method

The paper applies standard panel-regression, Fama-MacBeth, and portfolio-sort methods (panel-regression, fama-macbeth, portfolio-sort); it proposes no new estimator.

Attention measures. The core attention measure is the fraction of a fund’s reading of news articles covering either macroeconomic conditions ($\text{InstAttn}^{\text{macro}}_{it}$) or a specific firm ($\text{InstAttn}_{ist}$), derived from event-level Internet news-reading data matched to fund identities via the Data Partner (p. 796-799). Article topics and firm-stock mappings come from RavenPack 1.0 (p. 797).

Macroeconomic attention beta. For each fund-quarter, the macroeconomic attention sensitivity to aggregate volatility ($\beta^{\text{VIX}^2}_{it}$) is estimated by regressing weekly attention to macro news on contemporaneous $\text{VIX}^2$ over a trailing 52-week window (p. 804). This fund-quarter-level beta is then used as a predictor in quarterly fund-return regressions.

Position-level value-add. The outcome $h_{ist-1} \times R_{st}$ weights the stock return by the fund’s prior-period dollar holding share, following Berk and Van Binsbergen (2015) in spirit (p. 811).

Trade-based value-add. The outcome $\Delta\text{Position}_{ist-1} \times R_{st}$ multiplies the future stock return by the dollar change in holdings from $t-2$ to $t-1$, isolating performance attributable to trading (p. 814). These trade-based tests build on Akepanidtaworn et al. (2023), who find that institutional buying adds value while selling does not (p. 815 of this paper).

Stock-level return predictability. Fama-MacBeth cross-sectional regressions at the monthly frequency, with controls for size, book-to-market, profitability, investment, and news coverage (p. 821). Portfolio sorts rank stocks into quintiles by attention from buying funds within each investor-type category (p. 822). Newey-West standard errors with two lags are used in both (pp. 821, 822).

Empirical specifications

Specification 1: Macro attention and aggregate volatility (eq. 1, p. 803)

$$\text{InstAttn}^{\text{macro}}_{it} = \beta \cdot \text{VIX}^2_{t-1} + \mu_i + \text{Controls} + \epsilon_{it}$$

• LHS: fraction of fund $i$‘s reading in month $t$ about macro news.
• RHS: $\text{VIX}^2_{t-1}$, the average VIX-squared in the prior month (normalized to mean 0, SD 1); $\mu_i$ = fund fixed effects; controls include market return.
• SE: clustered by fund and time (p. 804).
• Sample: 234,934 fund-month observations (Table III, p. 804).
• Robustness: replaces $\text{VIX}^2$ with VIX and with realized S&P 500 daily volatility (columns 3-4 of Table III).

Specification 2: Fund-level return predictability (Table IV, p. 806)

$$\text{Fund Ret}_{it} = \gamma \cdot \beta^{\text{VIX}^2}_{it-1} + \lambda \cdot [\beta^{\text{VIX}^2}_{it-1} \times \text{VIX}_{t-1}] + \text{FundFE} + \text{TimeFE} + \text{Controls} + \epsilon_{it}$$

• LHS: quarterly fund return weighted by holdings at $t-1$.
• RHS: $\beta^{\text{VIX}^2}_{it-1}$ (attention-reallocation sensitivity, normalized); interaction with VIX level; controls include log AUM and log articles read.
• SE: clustered by fund and time (p. 806).
• Sample: 51,207 fund-quarter observations (Table IV, p. 806).

Specification 3: Attention and holdings - intensive margin (eq. 2, p. 810)

$$\text{InstAttn}_{ist} = \alpha + \beta \cdot h_{ist-1} + \epsilon_{ist}$$

• LHS: share of fund $i$‘s reading devoted to firm $s$ in quarter $t$.
• RHS: $h_{ist-1}$ = dollar share of firm $s$ in fund $i$‘s portfolio at end of quarter $t-1$; $\alpha$ denotes fixed effects (fund $\times$ time, firm $\times$ time in various columns).
• SE: clustered by fund, firm, and time (p. 809).
• Sample: held stocks only ($h_{ist-1} > 0$); 11,910,288 fund-stock-quarter observations (Table V, p. 809).

Specification 4: Position-level value-add (eq. 3, p. 812)

$$h_{ist-1} \times R_{st} = \alpha + \beta \cdot \text{InstAttn}_{ist-1} + \delta \cdot h_{ist-2} + \epsilon_{ist}$$

• LHS: position-level value-add (holding weight times next-quarter return, scaled by 100).
• RHS: $\text{InstAttn}_{ist-1}$ = fraction of reading about firm $s$ in $t-1$ relative to total fund reading; $h_{ist-2}$ controls for prior-period holdings.
• FE: fund $\times$ time; or fund $\times$ time + firm $\times$ time (columns 1-3, Table VI, p. 813).
• SE: clustered by fund, firm, and time.
• Sample: held stocks ($h_{ist-1} > 0$); ~11.9M fund-stock-quarter obs.

Specification 5: Trade-based value-add (eq. 4, p. 814)

$$\Delta\text{Position}_{ist-1} \times R_{st} = \alpha + \beta \cdot \text{InstAttn}_{ist-1} + \delta \cdot h_{ist-2} + \epsilon_{ist}$$

• LHS: trade-based value-add (dollar change in holdings from $t-2$ to $t-1$ times return at $t$, scaled by 100).
• RHS: same regressors as specification 4; also interacted with trade size.
• FE: firm $\times$ time; fund $\times$ time (Table VI, Panel B, p. 813).
• SE: clustered by fund, firm, and time.
• Sample: traded positions only; ~11.1M fund-stock-quarter observations.

Specification 6: Stock-level Fama-MacBeth return predictability (Table X, p. 821)

$$R_{st+1} \times 100 = a_t + b_t \cdot \text{AttnBuying}_{st} + \text{controls}_t + u_{st}$$

• LHS: monthly stock return times 100.
• RHS: attention by buying (or holding/selling) hedge funds, mutual funds, or other funds; controls include size, book-to-market, gross profitability, investment, log news coverage, and log number of funds in the action category.
• Coefficients: time-series average of cross-sectional OLS slopes.
• SE: Newey-West with two lags (p. 821).
• Sample: 54 monthly periods (Nov 2017 to Jun 2022); each period ~500-3000 stocks with adequate news coverage.

Specification 7: Portfolio sorts by buying-fund attention (Table XI, p. 822-823)

Stocks are ranked into five quintiles each quarter by the attention-to-buys measure from the Fama-MacBeth specification, separately for hedge funds, mutual funds, and other funds. Equal-weighted (EW) and value-weighted (VW) returns to each quintile and the long-short (H-L) spread are computed over the next three months. Factor alphas (CAPM, FF3, FF3+MOM, FF5) are estimated with Newey-West two-lag adjustment (Table XI, Panel B, p. 823).

When to read the full paper

Use the mirrored PDF if you are: replicating (code in the journal’s Supporting Information); extending the attention measure or the value-add tests; doing a literature review where the Internet Appendix robustness matters; or auditing a specific coefficient. The locators above point you to the exact table. For “what did this paper find,” the table above is sufficient and is the intended default.

Attribution & rights

Source: peer-reviewed, The Journal of Finance 81(2). This distillation was extracted by an LLM on 2026-05-17 and is not human-verified or independently reproduced. Licence, verification trail, and takedown policy: Open Library.

Attribution (CC BY 4.0). Kwan, Alan, Yukun Liu, and Ben Matthies. “Institutional Investor Attention.” The Journal of Finance 81, no. 2 (April 2026): 791–827. DOI: 10.1111/jofi.70009. © 2026 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. The verbatim, unmodified PDF is mirrored in the Open Library.