Incentives matter: Domestic funds and price informativeness improvement: Chen, Wu, Yang & Zhong (2026)

What this is. The paper’s core results, the empirical design, and the two proposed mechanisms (information processing and information provision) with their estimating equations: enough to know what it found and why, without reading the full article. To replicate or extend it, read the original at https://doi.org/10.1016/j.finmar.2025.101027.

TL;DR

Using a panel of Chinese A-share listed companies from 2005 to 2019 (21,242 firm-level observations), the paper finds no significant association between plain domestic institutional fund ownership and stock price informativeness, consistent with Kacperczyk, Sundaresan and Wang (2021). Incentive-weighted domestic ownership is positively associated with informativeness: a greater weighted shareholding ratio corresponds to higher future earnings forecastability from stock prices. Following Bai, Philippon and Savov (2016) and Kacperczyk, Sundaresan and Wang (2021), the baseline measure is the sensitivity of future earnings to current stock prices. The mechanism tests provide evidence consistent with two channels: incentivized funds’ ownership changes better predict future stock returns (information processing), and incentive-weighted ownership is associated with more frequent and more positively toned site visits (information provision). A firm-level MO-OLS decomposition attributes approximately 43-44% of the incentive-weighted domestic fund contribution to the fund manager, followed by year fixed effects and fund-stock matching, consistent with Fang, Kempf and Trapp (2014).

Core results

Magnitudes and significance as reported; \*/\*\*/\*\*\* = 10%/5%/1%.

#	Result	Locator	Magnitude
R1	Domestic fund ownership (Dom) has no significant effect on price informativeness at either horizon	Table 2, cols 2 and 7, p. 6	Dom x log(M/A) = -0.014 (0.034) [h=1]; -0.013 (0.025) [h=3]; neither significant
R2	Incentive-weighted domestic ownership (Dom_Inc) significantly raises price informativeness	Table 2, cols 4 and 9, p. 6	Dom_Inc x log(M/A) = 0.038** (0.015) [h=1]; 0.032** (0.016) [h=3]
R3	Synchronicity robustness: Dom_Inc reduces price synchronicity (more idiosyncratic, more informative)	Table 3, Panel A, col 2, p. 8	Dom_Inc on SYNCH = -0.149*** (0.032); Dom insig at -0.042 (0.029)
R4	PIN robustness: Dom_Inc raises probability of informed trading	Table 3, Panel B, col 8, p. 8	Dom_Inc on PIN = 0.053*** (0.004)
R5	Information processing channel: changes in Dom_Inc predict future returns (funds buy/sell before price moves)	Table 5, cols 2 and 4, p. 11	delta Dom_Inc on Ret_{t+1} = 0.023** (0.010); on Ret_{t+4} = 0.144** (0.062); delta Dom is negative (-0.088***, col 1)
R6	Information provision: Dom_Inc raises site visit quantity and quality	Table 6, cols 2 and 8, p. 12	VisitNum coeff = 4.532*** (0.696); VisitTone coeff = 0.320*** (0.122)
R7	Decomposition: fund manager explains ~43% of the incentive-driven price informativeness improvement	Fig. 1, p. 14	Fund manager: 43.74% (h=1), 43.49% (h=3); year FE: ~30-35%; matching: ~22-25%
R8	Foreign fund ownership is positively associated with earnings-forecasting price informativeness	Table 2, cols 2 and 7, p. 6	For x log(M/A) = 0.350* (0.163) at h=1; 0.249** (0.119) at h=3
R9	Incentive-weighted foreign ownership has a stronger positive association with price informativeness	Table 2, cols 5 and 10, p. 6	For_Inc x log(M/A) = 0.800*** (0.303) at h=1; 0.484* (0.269) at h=3
R10	Revelatory price efficiency results support incentive-weighted domestic ownership across investment outcomes	Table 4, Panels A-C, p. 10	Dom_Inc x log(M/A): total investment 0.400* (0.215), 0.300** (0.144); R&D 0.003** (0.001), 0.046*** (0.001); CAPE 0.537** (0.241), 0.316** (0.150), for h=1 and h=3 respectively; plain Dom estimates are not significant
R11	The synchronicity result holds above and below mean incentive-weighted ownership	Table 3, Panel A, cols 3-4, p. 8	Dom_HiInc = -0.168** (0.073); Dom_LoInc = -0.136*** (0.029)
R12	Lagged ownership tests retain the negative synchronicity association for incentive-weighted domestic and foreign funds	Table 3, Panel A, cols 5-6, p. 8	Lagged Dom_Inc = -0.092*** (0.028); lagged Dom = 0.075** (0.033); lagged For = -0.733*** (0.169) and -1.038*** (0.151)
R13	Changes in foreign fund ownership also predict subsequent stock returns	Table 5, cols 2 and 4, p. 11	Delta For on Ret(t+1) = 0.093** (0.036); on Ret(t+4) = 0.142* (0.074)
R14	Site visit quantity and tone interact positively with stock prices in forecasting future earnings	Table 6, cols 3-6 and 9-12, p. 12	VisitNum x log(M/A) = 0.032 (0.024), 0.063*** (0.017) at h=1; 0.088*** (0.019), 0.086*** (0.019) at h=3. VisitTone x log(M/A) = 0.003 (0.004), 0.002* (0.001) at h=1; 0.004** (0.002), 0.004*** (0.001) at h=3
R15	Aggregate institutional fund incentives are positively associated with price informativeness	Table 2, cols 3 and 8, p. 6	Inc x log(M/A) = 0.016*** (0.007) at h=1; 0.014** (0.006) at h=3
R16	Foreign ownership predicts more price-responsive investment across RPE outcomes	Table 4, Panels A-C, p. 10	For x log(M/A): total investment 5.742** (2.620), 6.346** (2.944) at h=1 and 2.700*** (0.835), 2.585*** (0.776) at h=3; R&D 0.207** (0.086), 0.249*** (0.064) at h=1 and 0.144** (0.058), 0.147*** (0.056) at h=3; CAPE 5.896* (2.834), 5.882** (2.716) at h=1 and 2.570*** (0.830), 2.476*** (0.760) at h=3

Overall (paper’s conclusion). Incentives help reconcile the puzzle that domestic funds appear passive despite holding local informational advantages. Once direct and flow incentives are folded into a weighted ownership measure, domestic funds are associated with greater price informativeness. The mechanism tests are consistent with improved information processing and active information provision to firms.

Theory / model

The paper has no formal theoretical model. It proposes two empirical hypotheses motivated by prior theory (Grossman and Stiglitz (1980), Kyle (1985), Holmstrom and Tirole (1993)):

Hypothesis 1: Information processing channel. Incentive-aligned fund managers process information more efficiently. Under this channel, an increase in incentive-weighted domestic fund ownership should align with subsequent stock-price movements, i.e., the change in Dom_Inc should positively predict future stock returns (p. 9). Formally: if $\beta_1 > 0$ in the return-prediction regression (equation 11, p. 9), funds are proficient at anticipating future price movements.

Hypothesis 2: Information provision channel. Incentive-aligned funds actively seek and transmit private information to firm managers through corporate site visits (Chen, Goldstein and Jiang (2007); Bond, Edmans and Goldstein (2012)). Under this channel, higher Dom_Inc should increase (a) the number of site visits (VisitNum) and (b) the quality of those visits (VisitTone), which in turn should improve the earnings-forecasting content of stock prices. A positive coefficient of Dom_Inc in the visit regression and a significant interaction of VisitNum with log(M/A) in the earnings-forecastability regression (Table 6, cols 3-6) would support this channel (p. 11-12).

Identification caveat. The paper uses panel OLS with industry and period fixed effects plus firm-level controls; no instrument or quasi-natural experiment is employed. The identification rests on selection-on-observables. Results are robust to alternative price informativeness measures (synchronicity, PIN, RPE) and to lagged ownership, but a causal reading requires this conditional-ignorability assumption.

Method

Price informativeness is measured following Carpenter, Lu and Whitelaw (2021) as the sensitivity of future earnings to current stock prices.

Price informativeness measure (MO-OLS). Following Keane and Neal (2020), the paper estimates a mean-observation OLS (MO-OLS) framework, building on mo-ols, that allows the coefficient linking stock prices to future earnings to vary over both firm and time dimensions. The procedure nests three regression levels:

Pooled regression to obtain $\tilde{b}^{\text{Dom\_Inc},t+h}$ (equation 16, p. 13):

$$\frac{E_{i,t+h}}{A_{it}} = a_{t+h} + b^{\text{Dom\_Inc},t+h} \log\!\left(\frac{M}{A}\right)_{it} \times Dom\_Inc_{it} + \gamma' X_{it} + \varepsilon_{it} \tag{16}$$

Time-specific cross-sectional regression to obtain $\hat{b}_t^{\text{Dom\_Inc},t+h}$ (equation 17, p. 13):

$$\frac{E_{i,t+h}}{A_{it}} = a_{t+h} + b_t^{\text{Dom\_Inc},t+h} \log\!\left(\frac{M}{A}\right)_{it} \times Dom\_Inc_{it} + \gamma_t' X_{it} + v_{it} \tag{17}$$

Unit-specific time-series regression to obtain $\hat{b}_i^{\text{Dom\_Inc},t+h}$ (equation 18, p. 13):

$$\frac{E_{i,t+h}}{A_{it}} = a_{t+h} + b_i^{\text{Dom\_Inc},t+h} \log\!\left(\frac{M}{A}\right)_{it} \times Dom\_Inc_{it} + \gamma_i' X_{it} + u_{it} \tag{18}$$

The paper first scales the time-varying interaction slope by the cross-sectional dispersion of log market capitalization to assets (equation 13, p. 13):

$$\text{FPE}_{t}^{\text{Dom\_Inc},t+h} = b_{t}^{\text{Dom\_Inc},t+h} \times \sigma_t\!\left(\log\!\left(\frac{M}{A}\right)\right) \tag{13}$$

The cross-sectional regression defining that slope includes industry indicators (equation 14, p. 13):

$$\frac{E_{i,t+h}}{A_{it}} = a_{t+h}^{t} + b_{t}^{\text{Dom\_Inc},t+h}\log\!\left(\frac{M_{it}}{A_{it}}\right)\times \text{Dom\_Inc}_{it} + c_{t+h}^{t}\left(\frac{E_{it}}{A_{it}}\right) + d_{t+h,s}^{t}\mathbf{1}_{s,it} + \varepsilon_{i,t+h}^{t} \tag{14}$$

The firm-year price informativeness measure replaces the common slope by the MO-OLS firm-specific estimate (equation 15, p. 13):

$$\text{FPE}_{it}^{\text{Dom\_Inc},t+h} = b_{it}^{\text{Dom\_Inc},t+h} \times \sigma_t\!\left(\log\!\left(\frac{M}{A}\right)\right) \tag{15}$$

The preliminary MO-OLS coefficient combines pooled, time-specific, and firm-specific estimates (equation 19, p. 14):

$$\beta_{it}^{\text{Dom\_Inc},t+h(\text{Prel})} = \widehat b_i^{\text{Dom\_Inc},t+h} + \widehat b_t^{\text{Dom\_Inc},t+h} - \widehat b^{\text{Dom\_Inc},t+h} \tag{19}$$

The authors then apply their iterative correction (equation 20, p. 14):

$$\begin{aligned} \beta_{it}^{\text{Dom\_Inc},t+h} ={}& \widehat b_i^{\text{Dom\_Inc},t+h} + \widehat b_t^{\text{Dom\_Inc},t+h} - \widehat b^{\text{Dom\_Inc},t+h} \\ &+ \sum_{\ell=0}^{L}(-1)^{\ell+1}\Bigg( Q_{xx,N}^{-1}\frac{1}{N}\sum_{i=1}^{N}x_{it}x_{it}'\Gamma_{1,\ell} + Q_{xx,T}^{-1}\frac{1}{T}\sum_{t=1}^{T}x_{it}x_{it}'\Gamma_{2,\ell} \\ &\qquad - Q_{xx,NT}^{-1}\frac{1}{NT}\sum_{i=1}^{N}\sum_{t=1}^{T}\left(x_{it}x_{it}'\Gamma_{1,\ell}+x_{it}x_{it}'\Gamma_{2,\ell}\right)\Bigg), \end{aligned} \tag{20}$$

where $\Gamma_{1,\ell}=Q_{xx,T}^{-1}\left(\frac{1}{T}\sum_{t=1}^{T}x_{it}x_{it}'\Gamma_{2,\ell-1}\right)$ and $\Gamma_{2,\ell}=Q_{xx,N}^{-1}\left(\frac{1}{N}\sum_{i=1}^{N}x_{it}x_{it}'\Gamma_{1,\ell-1}\right)$. Equation 14 is estimated cross-sectionally with industry indicators; the corresponding baseline sample has 21,242 firm-period observations at $h=1$ and 15,532 at $h=3$. Equations 16-18 use pooled, time-specific, and firm-specific regressions on the paper’s firm-period sample. The displayed MO-OLS equations do not specify additional fixed effects or standard-error treatment, and the paper does not report a separate sample count for each of these stages.

Fund $j$‘s share of firm $i$‘s total incentive-weighted domestic ownership allocates the firm-level contribution to that fund (equation 21, p. 14):

$$\text{FPE}_{i,j,t}^{t+h} = \text{FPE}_{i,t}^{\text{Dom\_Inc},t+h} \times \text{Dom\_Inc}_{i,j,t} \tag{21}$$

Decomposition. Following Abdulkadiroglu, Pathak and Schellenberg (2020), the fund-level contribution of fund $j$ to firm $i$‘s FPE is decomposed via a cross-sectional regression of fund-level contributions on fund-year intercepts and firm characteristics (equation 23, p. 15, using the fund-pi-decomposition approach):

$$\text{FPE}_{ijt}^{t+h} = \alpha_{jt} + X_{it} \beta_{jt} + \varepsilon_{ijt} \tag{23}$$

The decomposition of equation 22 (p. 15) into three additive components then yields:

$$\text{FPE}_{ijt}^{t+h} = \underbrace{\bar{\alpha}_t}_{\text{Year}_t} + \underbrace{(\alpha_{jt} - \bar{\alpha}_t)}_{\text{Manager}_{jt}} + \underbrace{X_{it}\beta_{jt} + \varepsilon_{ijt}}_{\text{Match}_{it}} \tag{22}$$

where $\bar{\alpha}_t = \frac{1}{J}\sum_{j=1}^J \alpha_{jt}$ is the year-average fund intercept. Equation 23 is a year-specific cross-sectional regression over observed fund-firm holdings, with firm characteristics as regressors; the paper gives no separate fixed effects, standard-error treatment, or sample count for these decompositions (p. 15).

Empirical specifications

Baseline (Table 2, equations 6, pp. 5-6). The price informativeness regression follows Kacperczyk, Sundaresan and Wang (2021) and Bai, Philippon and Savoy (2016):

$$\left(\frac{E}{A}\right)_{i,t+h} = \alpha + \beta_1 \log\!\left(\frac{M}{A}\right)_{it} + \beta_2 \log\!\left(\frac{M}{A}\right)_{it} \times \text{Fund\_ownership}_{it} + \beta_3 \text{Fund\_ownership}_{it} + \gamma' X_{it} + \lambda_{is} + \tau_t + \varepsilon_{it} \tag{6}$$

where $(E/A)_{i,t+h}$ is firm $i$‘s earnings/assets in period $t+h$ ($h=1,3$); $\log(M/A)_{it}$ is the log price-to-asset ratio; Fund_ownership is Dom, For, Dom_Inc, or For_Inc depending on the column; $X_{it}$ includes current earnings, insider ownership, leverage, tangibility, listed years, cash, ROA, and domestic- and foreign-fund ownership status; $\lambda_{is}$ are industry fixed effects; $\tau_t$ are period fixed effects. The text specifies standard errors clustered at industry-period level, while Table 2 notes report robust standard errors. The coefficient of interest is $\beta_2$: the average price informativeness conditional on fund ownership type.

Return and synchronicity specifications (equations 7 and 8, pp. 7-8). The daily return regression used to construct firm-year $R^2$ is:

$$\text{RET}_{it} = \alpha + \beta_1 \text{MKTRET}_t + \beta_2 \text{MKTRET}_{t-1} + \beta_3 \text{INDRET}_t + \beta_4 \text{INDRET}_{t-1} + \varepsilon_{it} \tag{7}$$ $$\text{SYNCH}_{it} = \log\!\left(\frac{R^2_{it}}{1-R^2_{it}}\right) \tag{8}$$

The return model uses daily A-share returns with at least 200 trading days per fiscal year; Table 3 uses industry and period fixed effects, the Table 2 controls, and robust standard errors. The paper also logistic-transforms the bounded $R^2$ as described on p. 7.

Revelatory price efficiency specification (equation 10, pp. 8-9). Investment sensitivity to prices is estimated for total investment, R&D, and capital expenditure, each scaled by period-$t$ assets:

$$\left(\frac{\text{Invest}}{A}\right)_{i,t+h} = \alpha + \beta_1 \log\!\left(\frac{M}{A}\right)_{it} + \beta_2 \log\!\left(\frac{M}{A}\right)_{it} \times \text{Fund\_ownership}_{it} + \beta_3 \text{Fund\_ownership}_{it} + \beta_4 \log\!\left(\frac{M}{A}\right)_{it} \times \text{FPE}_{it} + \gamma' X_{it} + \lambda_{is} + \tau_t + \varepsilon_{it} \tag{10}$$

Here $h=1,3$, and fund ownership is Dom, Dom_Inc, or For in the reported comparisons. Table 4 includes industry and period fixed effects, controls for earnings, investment, insider ownership, leverage, tangibility, listing age, cash, ROA, and their price interactions, with robust standard errors. The observations are 23,795 and 19,395 for the total-investment panels at $h=1$ and $h=3$; samples vary for R&D and CAPE (Table 4, p. 10).

Synchronicity robustness (Table 3, Panel A, equation 7-8, pp. 7-8). Price synchronicity is estimated as the logistic transformation of $R^2$ from regressing firm $i$‘s A-share return on market and industry factors:

Equation 8 transforms the return-regression $R^2$ to synchronicity. A lower SYNCH implies more firm-specific information in prices. Fund ownership enters as a level regressor; a significantly negative coefficient on Dom_Inc confirms the baseline result (Table 3 Panel A, col 2: -0.149***).

PIN robustness (Table 3, Panel B, equation 9, p. 7). The probability of informed trading (VPIN) follows Easley, Kiefer, O’Hara and Paperman (1996):

$$\text{VPIN} = \frac{1}{nV} \sum_{\tau=1}^{n} \left| V^{\tau}_{\text{buy}} - V^{\tau}_{\text{sell}} \right| \tag{9}$$

where $V^{\tau}_{\text{buy}}$ and $V^{\tau}_{\text{sell}}$ are buy and sell volumes in volume bucket $\tau$, $n$ is the number of buckets, and $V$ is the uniform bucket volume. The coefficient of Dom_Inc on PIN (0.053***, Table 3 Panel B, col 8) confirms the baseline.

Information processing channel (Table 5, equation 11, pp. 9-10). A return-prediction regression assesses whether fund ownership changes predict future stock returns:

$$\text{Return}_{i,t+h} = \alpha + \beta_1 \Delta\text{Fund\_ownership}_{it} + \gamma' X_{i,t} + \lambda_{is} + \tau_t + \varepsilon_{it} \tag{11}$$

where $h = 1$ or $4$ periods; $\Delta\text{Fund\_ownership}_{it}$ is $\Delta\text{Dom}_{it}$, $\Delta\text{For}_{it}$, or $\Delta\text{Dom\_Inc}_{it}$; $X_{it}$ includes log size, book-to-market, past 12-month volatility, and momentum. Table 5 uses industry and period fixed effects, robust standard errors, and 19,908 observations at $h=1$ and 19,636 at $h=4$. A positive $\beta_1$ on $\Delta\text{Dom\_Inc}$ (0.023**, col 2; 0.144**, col 4) indicates forward-looking information content.

Information provision channel (Table 6, equation 12, p. 11). Firm site visits are regressed on fund ownership:

$$\text{Visit}_{it} = \alpha + \beta_1 \text{Fund\_ownership}_{it} + \gamma' X_{i,t} + \lambda_{is} + \tau_t + \varepsilon_{it} \tag{12}$$

where Visit is proxied by VisitNum (annual count of site visits from all domestic funds, 2011-2019 from SZSE) or VisitTone (tone of the Investor Relations Activity Log, constructed using a Chinese Financial Sentiment Dictionary following Gordon, Loeb and Shu (2013) and Brockman, Cicon, Li, Price and Shu (2017)). Table 6 includes industry and period fixed effects, the stated firm controls and their interactions with log(M/A), and robust standard errors. The visit-outcome samples have 22,642 observations for VisitNum and 8,954 for VisitTone; the earnings forecast regressions have between 3,072 and 16,733 observations. A positive coefficient on Dom_Inc in both (4.532*** and 0.320***) supports the information provision channel.

Fund incentive construction. Fund $j$‘s incentive to promote firm $i$‘s value is the total management fee gain from a 1% increase in firm $i$‘s value (equation 1, p. 3):

$$\text{Incentives}_{ijt} = \text{Direct incentives}_{ijt} + \text{Flow incentives}_{ijt} \tag{1}$$ $$\text{Direct incentives}_{ijt} = p \times \text{AUM}_{jt} \times w_{ijt} \tag{2}$$ $$\text{Flow incentives}_{ijt} = p \times \text{AUM}_{jt} \times \beta \times (w_{ijt} - v_{(-j)it}) \tag{3}$$

where $p = 0.828\%$ is the average management fee rate, $w_{ijt}$ is the value weight of stock $i$ in fund $j$‘s portfolio, $\beta$ is the estimated inflow-to-performance sensitivity, and $v_{(-j)it}$ is the period-$t$ average value weight of stock $i$ across peer funds. The incentive-weighted domestic ownership variable is:

$$\text{Dom\_Inc}_{it} = \sum_{j=1}^{J} \text{Incentives}_{ijt} \times \text{Dom}_{ijt} \tag{5}$$

Net fund inflows are estimated via (equation 4, p. 4):

$$\text{Net Inflow}_{jt} = \frac{\text{AUM}_{jt} - \text{AUM}_{j,t-1}(1 + R_{jt})}{\text{AUM}_{j,t-1}} \tag{4}$$

Datasets used

Dataset	Role in paper	Wiki page
CSMAR (China Stock Market Accounting Research Database)	Firm financial characteristics, stock returns, market capitalization; Chinese A-listed non-financial corporates, 2005-2019	no page yet
Resset (Renmin University CSMAR Economic Research Data System)	Semi-annual open-end fund holdings (3,400+ equity, hybrid, and index funds), 2005-2019	no page yet
SZSE Investor Relations Activity Log	Firm site visit records (quantity and tone), 2011-2019; Shenzhen Stock Exchange only	no page yet

Sample: 21,242 firm-period observations (Table 1), semi-annual frequency, 2005-2019 for baseline; site visit subsample 2011-2019. Observations with missing data, delisted firms, and firms with fewer than 90 trading days in the prior six months are excluded.

When to read the full paper

Use the original if you are: studying why domestic institutional investors in emerging markets fail to improve price efficiency despite having local informational advantages; extending the fund-incentive and price-discovery literature to non-US markets; replicating or extending the MO-OLS firm-level FPE measure or the three-component decomposition; or benchmarking the relative magnitudes of foreign vs. domestic institutional investors’ impact on price efficiency in China.

Attribution and rights

Source: peer-reviewed, Journal of Financial Markets 78 (2026) 101027. All rights reserved (© 2025 Elsevier B.V.). The initial distillation was extracted by an LLM on 2026-06-25, with additional results and formal specifications added on 2026-10-04; it is not human-verified or independently reproduced. Extract-only: no verbatim PDF is hosted here.

Chen, Shaoling, Xi Wu, Haisheng Yang, and Jiaying Zhong. “Incentives matter: Domestic funds and price informativeness improvement.” Journal of Financial Markets 78 (2026) 101027. DOI: 10.1016/j.finmar.2025.101027. © 2025 Elsevier B.V. All rights reserved.