Domestic Funds and Price Informativeness: 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 shows that plain domestic institutional fund ownership has no significant effect on stock price informativeness, consistent with prior literature (Kacperczyk, Sundaresan and Wang (2021)). However, once incentives are accounted for, incentive-weighted domestic fund ownership significantly improves price informativeness: a greater incentive-weighted shareholding ratio is associated with higher future earnings forecastability of stock prices. Price informativeness is measured following Carpenter, Lu and Whitelaw (2021) as the sensitivity of future earnings to current stock prices. Two mechanisms drive this: (1) incentivized funds process information better (their shareholding changes predict future stock returns more accurately), and (2) they provide more and higher-quality information to firms (more frequent and more positively toned site visits). A novel MO-OLS-based firm-level decomposition traces approximately 43-44% of the improvement attributable to the fund manager, consistent with Fang, Kempf and Trapp (2014) who find fund managers play a predominant role in funds’ price discovery; year fixed effects and fund-stock matching account for the remainder.

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%

Overall (paper’s conclusion). Incentives 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 demonstrably improve price efficiency through both superior 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 publicly available 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 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):

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

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

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

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

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

A preliminary estimate (equation 19, p. 13) averages these three, and an iterative correction (equation 20) yields the consistent coefficient $\hat{\beta}_{it}^{\text{Dom\_Inc},t+h}$. The firm-level FPE measure is then $\text{FPE}_{it}^{\text{Dom\_Inc},t+h} = \hat{\beta}_{it}^{\text{Dom\_Inc},t+h} \times \sigma_t(\log(M/A))$ (equations 13-15, pp. 12-13), where $\sigma_t(\log(M/A))$ is the cross-sectional standard deviation of log price-to-asset ratios in year $t$.

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.

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, and ROA; $\lambda_{is}$ are industry fixed effects; $\tau_t$ are period fixed effects. Standard errors are clustered at industry-period level. The coefficient of interest is $\beta_2$: the average price informativeness conditional on fund ownership type.

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:

$$\text{SYNCH}_{it} = \log\!\left(\frac{R^2_{it}}{1 - R^2_{it}}\right) \tag{8}$$

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. 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)). 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):

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

where $p = 0.828\%$ is the average management fee rate, $w_{ijt}$ is the value weight of stock $i$ in fund $j$‘s portfolio, $\beta^j$ is fund $j$‘s 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.). This distillation was extracted by an LLM (claude-sonnet-4-6) on 2026-06-25 and 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.