Investment efficiency is an important driver of firm growth. In a perfect frictionless world, capital would be efficiently allocated to optimal projects with positive net present values (Modigliani and Miller, 1958). In the real world, however, firms often deviate from optimal investments due to frictions in the capital market, such as agency problems (Jensen and Meckling, 1976), information asymmetry (Myers and Majluf, 1984) and managerial optimism (Malmendier and Tate, 2005). Agency theory predicts that the misalignment between managerial incentives and shareholder interests and the gap of information lead managers to engage in self-maximizing behavior and make suboptimal investment decisions (i.e., overinvestment and underinvestment), especially in firms with poor corporate governance (Richardson, 2006). Prior work considers that high-quality financial information is associated with high investment efficiency (Biddle *et al.*, 2009; Gomariz and Ballesta, 2014; Boubaker *et al.*, 2018). In addition, prior studies document that the private information acquisition and interpretation by financial analysts (Chen *et al.*, 2017b; Choi *et al.*, 2020), institutional investors (Cao *et al.*, 2020), and foreign investors (Chen *et al.*, 2017a) improve investment efficiency by mitigating information asymmetry.

To date, a growing number of studies has documented the profound impacts of institutional investors on various corporate decisions and outcomes, such as executive pay levels (Hartzell and Starks, 2003), corporate investment (Alvarez *et al.*, 2018), earnings management (Chung *et al.*, 2002), mergers and acquisitions (Chen and Harford, 2007), corporate financing decisions (Klein and Zur, 2009; Boubaker *et al.*, 2017) and corporate philanthropy (Fu and Qin, 2021).

Our paper links these two research strands and examines the effect of large institutional investors (i.e., institutional blockholders with at least a 5% ownership stake) on corporate investment efficiency. It also explores the impact of Institutional investor horizon and nationality on investment efficiency and whether information asymmetry and agency problems matter for this relation.

Recent studies argue that not all institutional investors are effective monitors (Chen *et al.*, 2007; Borochin and Yang, 2017; Brav *et al.*, 2018; Trinh *et al.*, 2023). Institutional investors with large shareholdings are more likely to engage in costly monitoring for long-term benefits (Alvarez *et al.*, 2018; Ge *et al.*, 2021). In contrast, small dispersed institutional shareholders with relatively few shares in a firm cannot afford to engage in costly monitoring. They are more likely to liquidate their investment if the firm performs poorly (Jensen and Meckling, 1976). Therefore, we only focus on those institutional investors with significant ownership holdings. Following Chen *et al.* (2007), we define institutional blockholders as institutional investors who hold at least 5% of shares outstanding.

Our empirical analysis uses a sample of 283 French-listed firms over 2002–2016. Consistent with the monitoring argument, we find that institutional blockholders increase investment efficiency and reduce the likelihood of overinvesting. Our results are robust to endogeneity concerns, alternative proxies of the variables of interest, and different regression frameworks. We also explore the heterogeneity of institutional blockholders’ monitoring incentives by controlling for investor horizon and institutional nationality. The results show that long-term investing institutions continue to negatively and significantly affect firms’ investment inefficiency, owing to their monitoring role. In contrast, the relations become insignificant for short-term institutional investors. Besides, when we split blockholders into those held by domestic and foreign institutional investors, we provide evidence that both foreign and domestic institutions reduce the extent of investment inefficiency. In additional analyses, we find that the negative relationship between institutional blockholders and inefficient investment mainly manifests in firms with higher information asymmetry and agency problems.

This paper has two main contributions. First, it contributes to the ongoing debate regarding whether institutional blockholders are effective. As stressed earlier, many scholars have analyzed the role of institutional investors on CEO compensation (e.g., Hartzell and Starks, 2003), corporate governance and market outcomes (Chen *et al.*, 2007), earnings management (Chung *et al.*, 2002, Trinh *et al.*, 2023); corporate investment (Alvarez *et al.*, 2018), capital structure decisions (Klein and Zur, 2009; Boubaker *et al.*, 2017) and corporate philanthropy (Fu and Qin, 2021). We extend this line of research by examining the relationship between institutional blockholders and investment efficiency.

This study also complements Cao *et al.* (2020), who document that institutional ownership, particularly pressure-resistant institutional investors, can improve investment efficiency by alleviating under and overinvestment problems. While institutional investors are not homogeneous and several characteristics differentiate between them, our study extends the work of Cao *et al.* (2020) by focusing on institutional blockholders (i.e., institutional ownership above 5% of outstanding common shares).

Finally, our paper sheds light on the role of institutional blockholders in improving the investment efficiency of French-listed firms and fills the gap of research outside the US on this subject. To the best of our knowledge, this topic has remained unexplored in France. Investment efficiency is a relevant topic in France, a concentrated ownership environment, where firms are mainly family-owned and the protection for outside investors is relatively weak (Faccio and Lang, 2002). Therefore, it is worth investigating whether better corporate governance mechanisms improve investment efficiency for French-listed firms.

The rest of the paper is organized as follows. Section 2 reviews the literature and develops the hypotheses. Section 3 describes the data and the main variables. Section 4 presents the empirical results. Section 5 provides robustness checks and additional analyses. Section 6 concludes.
# Literature review and hypotheses development

## Determinants of investment efficiency

In perfect financial markets, managers are expected to invest efficiently and accurately assess the intrinsic value of investment opportunities. Under Neoclassical theory, a firm’s investment policy solely depends on its perceived investment opportunities as measured by the marginal Q ratio (Tobin, 1969; Hayashi, 1982). When markets are perfect, a firm obtains funding for all projects with a positive net present value at the prevailing cost of capital and continues to invest until the marginal return from investment equals the marginal cost. However, prior literature recognizes that, in the presence of market imperfections, such as information asymmetry, and agency problems, a firm may deviate from its optimal investment (Chen *et al.*, 2017a), resulting in over- or underinvestment problems (Richardson, 2006; Biddle *et al.*, 2009). According to Myers and Majluf (1984), the adverse selection problem can affect the cost of raising funds and the optimal investment decisions of firms. In the presence of information asymmetry, the “lemons problem” of managers selling overpriced securities can lead to over-investment (Baker *et al.*, 2003; Biddle *et al.*, 2009). Alternatively, if outside investors with informational disadvantages detect such behaviors and respond rationally by increasing the firm’s cost of external financing, managers can refrain from raising funds, leading to under-investment (Stiglitz and Weiss, 1981; Biddle *et al.*, 2009).

As for moral hazard, distortions in investment decisions are attributed to the conflict of interests between managers and shareholders. Managers accurately assess the intrinsic value of investment opportunities; however, they may invest firm resources inefficiently to derive private benefits and follow personal agendas (Jensen and Meckling, 1976). For instance, self-interested managers may over-invest and use the firm’s cash in projects for empire-building, perquisite consumptions, and management entrenchment (Jensen, 1986). Alternatively, some behavioral traits of managers can also induce underinvestment. Bertrand and Mullainathan (2003) suggest that managers’ preference for a “quiet life” leads to a downward deviation from a firm’s optimal investment level. Furthermore, when they are not closely monitored, risk-averse managers tend to reject profitable projects, which in turn causes underinvestment (Ross, 1973).

Consistent with agency cost explanations, Richardson (2006) finds that firms with the highest levels of free cash flow are associated with more investment distortions in the form of over-investment. Such an effect can be mitigated through effective monitoring from activist shareholders. In support of this view, some recent work provides evidence that transparent information environments and effective monitoring mechanisms reduce the likelihood of over- and underinvestment (e.g., Biddle *et al.*, 2009; Chen *et al.*, 2011; Gomariz and Ballesta, 2014; Cao *et al.*, 2020).

## Institutional ownership and investment efficiency

A large strand of literature documents that institutional investors can effectively restrain information asymmetry (Shleifer and Vishny, 1997) and improve investment efficiency (Cao *et al.*, 2020). Chung *et al.* (2002) show that earnings management is less likely in the presence of institutional investors. Ajinkya *et al.* (2005) report that firms with higher institutional ownership issue management earnings forecasts and provide better corporate disclosure. Burns *et al.* (2010) find that a higher concentration of holdings by institutional investors imposes greater monitoring and reduces the incentives for firms to misreport.

Institutional ownership also serves as an important external monitoring mechanism. Research shows that higher institutional ownership increases the performance sensitivity of executive pay and decreases the level of managerial compensation (Hartzell and Starks, 2003). Chen *et al.* (2007) also point out that monitoring and activism by independent long-term institutions lead to better post-merger performance. Consistently with the active monitoring hypothesis, Callen and Fang (2013) report that stable institutional groups reduce future stock price crash risk by curbing managerial bad news hoarding activities. Gaspar and Massa (2007) show that shareholder activism by local financial institutions is associated with better value-enhancing decisions (less over-investment and fewer but better acquisitions). In addition, Cheng *et al.* (2013) document that private information acquisition and interpretation by outside market participants, such as institutional investors and financial analysts, improve investment efficiency. In a cross-country study, Chen *et al.* (2017) show that foreign institutional investors in privatized firms are associated with better investment efficiency. Cao *et al.* (2020) provide evidence that pressure-resistant institutional investors prevent managers from making non-optimal investment decisions, leading to less under-investment and over-investment decisions. For Ward *et al.* (2020), the motivation for institutions to engage in monitoring alleviates the problem of both over-investing free cash flows and under-investment arising from managers’ career concerns. Moradi *et al.* (2022) use data from listed firms in Iran and further report that institutional ownership improves investment efficiency by decreasing both under- and over-investment problems. McCahery *et al.* (2016) suggest that institutional investors first intervene more intensively behind the scenes by engaging management privately through direct negotiations with top management and discussions with the board of directors but without management’s presence. If these private interventions fail, institutional investors can take public measures, such as voting against management, submitting shareholder proposals, public criticism, or aggressively questioning management on a conference call. By doing so, institutional investors can limit opportunistic managerial behaviors and consequently improve corporate governance practices.

Another strand of the literature claims that when institutional investors are dissatisfied with their portfolio companies due to poor stock performance, they can also express their dissatisfaction through the exit by “voting with their feet.” (Admati and Pfleiderer, 2009; Edmans, 2009; Edmans and Manso, 2011). Parrino *et al.* (2003) document a strong relationship between institutional investors’ exit and subsequent CEO replacement, suggesting that massive sale of shares by institutional investors has a real impact on corporate decisions. Admati and Pfleiderer (2009) claim that institutional blockholders can effectively perform a governance role through the threat of exiting as a form of activism. When managers deviate from their responsibility and do not act in the best interest of their shareholders, informed institutional blockholders can express their dissatisfaction by simply selling their stocks, which negatively impacts stock prices. However, equity-based compensation and takeover threats make managers work to avoid the exit of informed blockholders. Edmans (2009) further shows that the threat of exit disciplines managers’ actions, inducing them to undertake more efficient real investments, such as research and development.

Based on the above discussions, we hypothesize that *ceteris paribus*, the presence of institutional blockholders, comes with better informational environments and monitoring, leading to higher firm investment efficiency. More specifically, we form the following hypothesis.

```
H<sub>1</sub>: Institutional blockholdings come with lower corporate
              investment inefficiency
```

## Institutional investment horizon and investment efficiency

An important factor that should affect the willingness of an institutional investor to engage in monitoring is the time horizon of their investment. The literature shows that the investment horizon of institutional investors helps mitigate investment inefficiencies and reduce agency problems associated with research and development (Bushee, 1998) and acquisitions (Gaspar *et al.*, 2005; Chen *et al.*, 2007). Bushee (1998) shows that dedicated investors with long-term institutional holdings cannot sell their assets easily and are highly motivated to foster information sharing and monitoring to protect their assets. Furthermore, McCahery *et al.* (2016) report that investor horizon matters for engagement. Investors with long-term horizons less concerned about stock liquidity engage more intensively and have strong incentives to monitor firms.

On the other hand, Bushee (1998, 2001) argues that some institutional investors, coined “transient” or “myopic,” have a short-term focus and passive behavior. According to Porter (1992), passive investors face strong regulatory and legal restrictions on their investments and often have difficulty assessing the companies’ long-term performance. Bushee (2001) provides evidence that institutional momentum traders with high portfolio turnovers (“transient investors”) prefer short-term earnings over long-run value. Gaspar *et al.* (2005) find that firms held by short-term investors exhibit negative abnormal returns around the merger announcement and higher long-run underperformance.

In other words, Institutional monitoring is well-grounded in large ownership positions and a long-term horizon. Dedicated institutions with concentrated investments and long investment horizons are more vigilant and have sufficient incentives to monitor firms. However, short-horizon institutions are uninvolved in corporate governance activism and have no incentive to control management (Gaspar *et al.*, 2005; Bushee, 2001; Harford *et al.*, 2018). Accordingly, as the monitoring incentives of institutional investors are hindered by short-termism, we hypothesize that monitoring by long-term investors should result in lower investment inefficiency.

More specifically, we form the following hypothesis

```
H2: Dedicated institutional investors have stronger effects on improving
              investment efficiency than transient institutional investors
```

## Institutional Investor nationality and investment efficiency

Another stream of research investigates the monitoring effectiveness of foreign investors vis-a-vis domestic investors and provides mixed evidence. One view is that foreign institutional investors play a significant role in monitoring the companies they invest in by voicing their dissatisfaction or indirectly selling their shares (Gillan and Starks, 2003). Consistent with this view, foreign institutions are effective monitors for at least two potential reasons. First, foreign institutional investors often possess a wide range of knowledge and experience in governance framework and best practices through global investing (Ferreira and Matos, 2008; Lel, 2019) and can therefore serve as catalysts for promoting effective corporate governance on an international scale (Aggarwal *et al.*, 2011). Second, due to their arm’s-length relationships with the investee firm, foreign institutional investors are less constrained by close business ties with insiders (Brickley *et al.*, 1988). Their independent perspectives and management skills can lead to greater accountability, transparency regarding governance practices, and long-term value creation (Aggarwal *et al.*, 2011; Ferreira and Matos, 2008). Recent studies provide supporting evidence that the monitoring ability of foreign institutions improves the timeliness of accounting disclosure (Jiang and Kim, 2004), reduces earnings management (Lel, 2019; Gu *et al.*, 2023), urges companies to hire top-quality auditors (Guedhami *et al.*, 2009; Kim *et al.*, 2019), reduces stock liquidity commonality (Deng *et al.*, 2018), and enhances voluntary disclosure (Tsang *et al.*, 2019). Luo *et al.* (2014) find evidence that foreign investors are more informed and outperform their domestic counterparts due to their lack of business ties with local firms (Ferreira and Matos, 2008). In a similar vein, Dang *et al.* (2023) provide evidence that foreign institutional ownership is associated with low synchronicity of stock prices.

In contrast, the information disadvantage view suggests that foreign investors have access to less sophisticated and reliable information and may face more information disadvantages due to geographical, cultural language, and legal systems constraints (La Porta *et al.*, 1998; Dvořák, 2005; Kang and Kim, 2010). Liu *et al.* (2018) provide evidence that local investors outperform foreign investors simply due to their geographical proximity to management or interpersonal networking. As such, local institutional investors are better positioned to obtain superior firm-specific information, reduced information gathering, and lower communication costs (Gaspar and Massa, 2007; Chhaochhria *et al.*, 2012). Additionally, Miller *et al.* (2021) show that domestic institutional investors face lower monitoring costs than their foreign peers and are thus more effective at constraining earnings management. Thus, we expect that the importance of both foreign and domestic institutional investors in monitoring and improving firm information environment reduces a company’s inefficient investment, leading to the following hypotheses:

```
H3: Corporate investment inefficiency decreases with the intensity of monitoring
              by foreign and domestic institutional blockholders.
```

## Sample and data sources

We construct our sample by collecting data from several sources. We obtain Data on the portfolio holdings of institutional investors from the Thomson-Reuters Institutional Holdings (13F) database. Accounting and financial data are from the Worldscope database. To construct our regression variables, we remove firms operating in the financial industry (SIC codes between 6000 and 6999) due to the specific nature of their activity and observations with missing data. These screening processes led to 3796 firm-year observations representing 283 French-listed firms over the sample period from 2002 to 2016.

## Variable descriptions

### Estimation of investment efficiency

Investment efficiency reflects a firm’s ability to carry out all projects with positive net present value (Gomariz and Ballesta, 2014). Following Biddle *et al.* (2009) and Chen *et al.* (2011), we analyze investment efficiency through the sensitivity of investment expenditures to growth opportunities. We First estimate expected investment as a function of investment opportunities (measured by sales growth) and then use the regression residuals as our proxies for firm-specific inefficient investment:

where *Investment*<sub>*i,t*</sub> is the total investment expenditure<fnref n="1" /> of firm *i*in the year t, deflated by the lagged total assets. *SalesGrowth*<sub>*i,t–1*</sub> is the annual sales growth rate of firm *i* from year t–2 to year t–1, which proxies for growth opportunities. Based on acceleration theory, the authors assume that firms can adjust their investment levels to meet growth in product demand or sales revenue. By analyzing past sales growth patterns, firms can anticipate future demand and adjust their investment accordingly. Therefore, the lag of sales growth is a key determinant for the optimal investment. We estimate the investment model cross-sectionally for each year and industry. The residual term of the investment model captures the deviation from the expected level of investment and thus represents the magnitude of inefficient investment. Our dependent variable (*INVT_INEFF*) is the absolute value of the residuals. A higher value of *INVT_INEFF* indicates a higher degree of investment inefficiency and *vice versa*.

To provide a more comprehensive assessment of firms’ investment behavior, we further classify under and over-investing firms based on the magnitude of residuals obtained from Equation (1) estimation. Firms that invest above their optimal (with positive residuals) are classified as over-investing firms. In contrast, those that do not carry out all profitable projects (with negative residuals) are considered under-investing firms.

#### Institutional ownership variables

Following previous research (Brickley *et al.*, 1988; Burns *et al.*, 2010; Chen *et al.*, 2007), We define institutional blockholders as institutional investors who hold at least 5% of shares outstanding. In this study, we focus on two measures of institutional ownership: (1) we measure institutional block ownership (*BLOCK_DUM*) as a dummy variable that is equal to one if a firm has at least one or more institutional investors blockholders, and zero otherwise. (2) the percentage ownership by institutional blockholders with at least 5% stake of shares (*BLOCK%**)*.

To analyze the effect of institutional investment horizon on investment efficiency, we adopt the approach used by Bushee (2001). As such, we consider that “Dedicated” institutions are those with low portfolio turnover and more concentrated portfolio holdings. We classify them as long-term institutional investors. In contrast, “transient” institutions have the highest turnover and diversified portfolios (Bushee, 2001). We classify them as short-term institutional investors. *LONG_IO* (*SHORT_IO*) is the percentage of dedicated (Transient) institutional owners relative to the total outstanding shares.

Furthermore, to accurately reflect the heterogeneous monitoring incentives depending on institutional blockholders’ national origins, we divide *BLOCK%* into foreign institutional ownership (*FOREIGN*) and domestic institutional ownership (*DOMESTIC*). *FOREIGN* (*DOMESTIC*) is the fraction of shares owned by foreign (domestic) institutions with at least a 5% equity stake.

#### Control variables

Following prior literature (Biddle *et al.*, 2009; Chen *et al.*, 2011; Gomariz and Ballesta, 2014; Choi *et al.*, 2020; Boubaker *et al.*, 2022).), we control for a range of firm characteristics that might affect investment efficiency: Leverage (*LEVERAGE*) is defined as total debt divided by total assets and proxies for financial distress. Firm size (*SIZE*) is the natural logarithm of sales. Tangibility (*TANGIBILITY*) proxies for capital intensity and is measured by the total property, plant, and equipment divided by total assets. Cash holdings (*CASH_HOLDING*) is computed as the cash and short-term investments divided by total assets. We also control for economic conditions using Loss (*LOSS*), a dummy variable that is equal to one if earnings before extraordinary items in year t are negative, and zero otherwise. The regressions control for growth opportunities and profitability using the market-to-book ratio (*MTB*) and the return on asset ratio (*ROA*), respectively. We also include the volatility of return on assets (*SDROA*), defined as the standard deviation of *ROA* calculated over the preceding three years, including the current year. All regressions also include the aggregate ownership held by all types of institutional investors in the 13F database (denoted “*IO*”) to control for overall institutional investment. The variable *IO* captures the effect of dispersed institutional investors (i.e., diversified ownership).

## Model specification

We estimate the relationship between institutional investors blockholders and investment efficiency using the following model.

where *i* and *t* denote the firm and year, respectively. *INVT_INEFF*<sub>*it*</sub>is the main proxy for investment inefficiency. It captures the deviation of actual investment from its optimal level. *BLOCK_DUM*<sub>*it*</sub>*/BLOCK%*<sub>*it*</sub> are the main proxies for institutional ownership. Following previous studies (Borochin and Yang, 2017; Harford *et al.*, 2018, and Ge *et al.*, 2021), we include the aggregate ownership held by all types of institutional investors to control for institutional ownership dispersion *(**IO**)* in our regression. Lastly, we control for a range of firm characteristics that are deemed to affect investment efficiency in prior studies, including firm-level leverage (*LEVERAGE*), firm size (*SIZE*), tangibility ratio (*TANGIBILITY*), Financial slack (*CASH_HOLDING*), economic conditions (*LOSS*), growth opportunities (*MTB*), firms’ profitability (*ROA*), and the volatility of return on assets (*SDROA*). We also include industry and year dummies in all regressions. All continuous variables are winsorized at the 1% and 99% levels to mitigate the effect of outliers. We use generalized least squares (*GLS*) regression to account for heteroskedasticity and serial correlation<fnref n="2" />.

## Summary statistics

Table 1 (Panel A) presents the summary statistics of the variables used in the analysis. The main proxy for firm investment inefficiency is the absolute value of the residuals (*INVT_INEFF*). Panel A shows that the mean value for *INVT_INEFF* is 5.5%. The mean value of *Inv_Over* (*Inv_Under*) reflects that the level of over-investment (under-investment) on average represents 7,7% (–4,2%), suggesting that overinvestment is more prominent for French firms than the under-investment problem. The mean value of *BLOCK%* is 2.305, indicating that, on average, 2% of a firm’s outstanding shares are owned by institutional blockholders. Panel B shows that 20% of the sample firms are owned by at least one institutional Blockholder. Further, the institutional ownership percentage shows a high ownership level, with a mean of 10,7%. This table also shows that the average firm in our study is relatively highly indebted (a mean leverage ratio of 20.1%), profitable (*ROA*=3.5%), and with acceptable growth opportunities (the mean *MB* ratio is 1.967). Similarly, the mean values for *TANGIBILITY* and *CASH_HOLDING* are 0.162 and 0.155, respectively.

Table 1 (Panel C) reports the correlation matrix for the variables in the analysis. The correlations between the independent variables are moderately low, and the highest variance inflation factor value is 2.29. Therefore, multicollinearity is not likely to be a problem in our study.

<footnote n="1">In our main regression analysis and in line with prior literature (e.g. Li *et al.*, 2024; Khediri, 2021; Naeem, and Li, 2019; Boubaker *et al.*, 2018; Chen *et al.* 2011; McNichols and Stubben, 2008; Biddle and Hilary, 2006; Hubbard, 1998) our investment proxy, denoted by Investment, is measured by the ratio of capital expenditures to lagged total assets. In measuring investment expenditures, we consider only capital expenditures (CAPEX) which refers to cash outflow for purchase of fixed assets, intangible assets, and other long-term assets, minus cash receipts from asset sales (Biddle *et al.*, 2009). Furthermore, acquisitions are less frequent than capital expenditure for French companies, which does not affect investment long-term trends (McNichols and Stubben, 2008)</footnote>

<footnote n="2">We use the Generalized Least Square (GLS) model as our main specification after conducting a number of formal tests: All assumptions of ordinary least squares (OLS) regression are violated, which make the GLS regression better suited to this study and should produce better results (Gujarati, 2002).</footnote>
# Additional analysis

## Does the severity of information asymmetries and agency problems matter?

One would expect that the severity of information asymmetry and agency problems indeed matters in altering the relation between the presence of institutional blockholders and corporate investment efficiency. Previous research argues that firms suffering from adverse selection and moral hazards exhibit more deviation from the optimal level of investment and are more vulnerable to inefficient investment (Jensen and Meckling, 1976; Myers and Majluf, 1984). Recent studies stress that high-quality financial information is associated with high investment efficiency (Biddle *et al.*, 2009; Gomariz and Ballesta, 2014). In particular, private information acquisition and interpretation by financial analysts (Chen *et al.*, 2017b; Choi *et al.*, 2020), institutional investors (Cao *et al.*, 2020), and foreign investors (Chen *et al.*, 2017a) are shown to improve investment efficiency by mitigating information asymmetry. In this vein, Ajinkya *et al.* (2005) further report that firms with higher institutional ownership are more inclined to issue management forecasts and provide better disclosure. Furthermore, Rajkovic (2020) reports that the presence of a lead independent director on the corporate board is associated with higher investment efficiency. Such effects are stronger for firms with weaker corporate governance standards, lower financial disclosure, and greater financial constraint. In a similar vein, Chen *et al.* (2017) find that high-quality forecast increases firm-level investment efficiency, particularly in firms with higher information asymmetry and lower institutional stock ownership. Jiang *et al.* (2018) document that multiple large shareholders improve the efficiency of firms’ investment decisions and this positive effect is more pronounced in firms with poor governance and greater information asymmetry.

<table><caption>Table 8 — Alternative regression frameworks — Notes: This table presents the results of testing Alternative regression frameworks. The dependent variable is the inefficiency of investment (*INV_INEFF*), measured as the deviation of the actual investment from the optimal investment level estimated as a function of a firm’s sales growth. The sample includes 283 French-listed firms over 2002-2016. *, **, *** denote significance at the 10%, 5% and 1% threshold levels, respectively. t–statistics are given in parentheses.</caption><tr><td>VariablesDependent Variable = INVT_INEFFFama Mac-Beth (1973)Newey-WestCluster
            effect(1)(2)(3)(4)(5)(6)BLOCK_DUM-0.042*(-2.00)-0.049*(-1.73)-0.049*(-1.73)BLOCK%-0.004**(-2.16)-0.004*(-1.78)-0.004*(-1.78)IO0.002*(2.02)0.002**(2.12)0.002*(1.64)0.002*(1.70)0.002*(1.64)0.002*(1.70)LEVERAGE0.092*(1.77)0.091*(1.74)0.094*(1.70)0.097*(1.70)0.094*(1.70)0.097*(1.70)SIZE-0.023**(-2.19)-0.025**(-2.20)-0.023*(-1.86)-0.024*(-1.86)-0.023*(-1.86)-0.024*(-1.86)TANGIBILITY0.113***(3.02)0.115***(3.08)0.122**(2.26)0.124**(2.26)0.122**(2.26)0.124**(2.26)CASH_HOLDING-0.054**(-2.60)-0.047**(-2.77)-0.056**(-2.04)-0.050**(-1.99)-0.056**(-2.04)-0.050**(-1.99)LOSS-0.007(-1.64)-0.002(-0.98)-0.015*(-1.77)-0.014*(-1.75)-0.015*(-1.77)-0.014(-1.75)MTB0.012**(2.83)0.011**(2.79)0.011**(2.13)0.010**(2.12)0.011**(2.13)0.010**(2.12)ROA0.104(2.17)0.111**(2.17)0.065(1.46)0.071(1.52)0.065*(1.46)0.071(1.52)SDROA-0.446(-1.58)-0.472(-1.56)-0.342(-1.34)-0.340(-1.34)-0.342(-1.34)-0.340(-1.34)Constant0.017(1.16)0.005*(1.97)0.127***(2.92)0.129***(2.91)0.127**(2.92)0.129**(2.91)Year_FEYesYesYesYesYesYesIndustry_FEYesYesYesYesYesYesSample
            size35233523352335233,5233,523F-value45.02***42.37***7.83***7.46***7.83***7.46***</td></tr></table>

Based on this reasoning, if institutional blockholders engage in monitoring activities, their governance role is expected to be stronger in firms facing greater information asymmetry and those with higher expropriation risk (e.g., firms with higher free cash flow levels or weak internal governance mechanisms). Accordingly, we expect the negative relationship to be more pronounced in firms facing greater information asymmetry and agency problems.

To investigate this hypothesis, we first measure information asymmetry using analyst coverage (*COV*), analyst forecast errors (*Error*), and bid-ask spreads (*Spread*). Analyst coverage is from the IBES database and is defined as the number of analysts following a firm in a given year. Higher analyst coverage reflects low information asymmetry (Lang and Lundholm, 1996). *Error* is a proxy for information opacity. Forecast errors indicate a lack of precise information that helps analysts provide accurate and less biased forecasts. It is calculated as the absolute value of the difference between the actual and the forecasted earnings per share (*EPS*) deflated by stock Price. The higher the forecast error, the higher the information asymmetry between insiders and outsiders. The bid-ask spread (*Spread*) is another measure of information opacity, calculated by annually averaging the ratio of the daily bid-ask spread to the closing price. A high level of bid-ask spread indicates a poor information environment. We partition our sample into high (low) information asymmetry groups based on the median value of these variables and then perform the regressions in Equation (2) for these subsamples.

We report the results of our subsample analysis in Table 9. The findings show a significant negative effect of institutional blockholders on investment inefficiency only for companies facing greater information asymmetry as proxied by lower analyst following (*Analysts*) in Panel A), higher forecast error (*Error*) in Panel B, and higher bid-ask spreads (*Spread*) in Panel C.

<table><caption>Table 9 — The effect of information asymmetry — Notes: This table reports regression results on the impact of information asymmetry on the relation between institutional blockholdings and investment efficiency. In each specification, the dependent variable is the inefficiency of investment (*INV_INEFF*), measured as the deviation of the actual investment from the optimal investment level, estimated as a function of a firm’s sales growth. The sample includes 283 French-listed firms covered over 2002‒ 2016. *, **, *** denote significance at the 10%, 5% and 1% threshold levels, respectively. The Z–statistics are given in parentheses.</caption><tr><td>VariablesDependent Variable = INVT_INEFFPanel B:
            ErrorLowHighLowHighLowHighLowHigh(1)(2)(3)(4)(5)(6)(7)(8)BLOCK_DUM0.001(0.31)-0.091**(-2.40)BLOCK%-0.000(-0.65)-0.011***(-3.48)LONG_IO-0.000(-0.35)-0.007***(-3.00)SHORT_IO-0.000(-0.11)-0.003(-0.39)FOREIGN0.000(1.01)-0.007**(-2.57)DOMESTIC-0.000(-0.94)-0.007**(-2.41)ControlsYesYesYesYesYesYesYesYesYear_FEYesYesYesYesYesYesYesYesIndustry_FEYesYesYesYesYesYesYesYesSample
            size960183116921831169225639602563Chi–sq454.09***94.74***521.40***101.36***521.02***106.28***457.88***107.24***Chow
            test1.13(0.330)1.09(0.359)1.00(0.445)0.964(0.480)VariablesPanel C:
            SpreadLowHighLowHighLowHighLowHigh(1)(2)(3)(4)(5)(6)(7)(8)BLOCK_DUM-0.002(-0.50)-0.091**(-2.40)BLOCK%-0.000(-0.65)-0.011***(-3.48)LONG_IO-0.000(-0.35)-0.012***(-3.35)SHORT_IO-0.000(-0.11)-0.009(-0.85)FOREIGN-0.000(-0.61)-0.010**(-2.44)DOMESTIC-0.000(-0.48)-0.013***(-3.11)ControlsYesYesYesYesYesYesYesYesYear_FEYesYesYesYesYesYesYesYesIndustry_FEYesYesYesYesYesYesYesYesSample
            size16921831169218311692183116921831Chi–sq521.17***94.74***521.40***101.36***521.02***100.58***521.49***100.99***Chow
            test3.93(0.000)4.35(0.000)3.78(0.000)3.82(0.000)</td></tr></table>

Second, we examine whether the effect of institutional investors on investment efficiency depends on the severity of the agency problem. The first proxy for agency problems is the firm’s free cash flow (*Free Cash Flow)*. High free cash flows leave more room for agency problems, leading managers to over-invest in negative NPV projects (Jensen and Meckling, 1976; Jensen, 1986). Free cash flow (*FCF*) is measured as the income before extraordinary items plus depreciation expense divided by total assets. Higher FCF translate into higher agency problems. We use CEO duality as an additional proxy for agency problems (Jensen, 1993). According to Forker (1992), when the CEO is also chair of the board, the monitoring quality decreases and firms make inefficient investments. *CEO Duality* is a dummy variable that takes the value of one if the CEO is also the board’s chair and zero otherwise. We partition our sample into high (low) agency problems based on FCF and CEO duality, respectively. We then regress our main model for these subsamples and present the estimation results in Table 10. The results show that the negative impact of institutional blockholders on investment inefficiency prevails only among firms that face high agency problems, as captured by high free cash flows (Panel A)) and the presence of CEO duality (Panel B).

Overall, our findings support the previously reported findings, and we find strong evidence that institutional blockholders lead to significantly lower investment inefficiencies. The negative relationship identified is mainly driven by long-term institutional ownership. Besides, foreign and domestic institutions reduce the extent of inefficiency in investments. We additionally demonstrate that the effect is more pronounced for firms facing more information asymmetry and agency problems, further supporting the monitoring view.

<table><caption>Table 10 — The effect of agency problems — Notes: This table reports regression results on the impact of agency problems on the relation between institutional blockholdings and investment efficiency. In each specification, the dependent variable is the inefficiency of investment (*INV_INEFF*), measured as the deviation of the actual investment from the optimal investment level, estimated as a function of a firm’s sales growth. The sample includes 283 French-listed firms covered over 2002-2016. *, **, *** denote significance at the 10%, 5% and 1% threshold levels, respectively. The Z–statistics are given in parentheses.</caption><tr><td>VariablesDependent Variable = INVT_INEFFPanel A:
            FCFLowHighLowHighLowHighLowHigh(1)(2)(3)(4)(5)(6)(7)(8)BLOCK_DUM-0.000(-0.02)-0.099***(-2.62)BLOCK%-0.000(-0.15)-0.008***(-3.07)LONG_IO-0.000(-0.15)-0.010***(-3.03)SHORT_IO-0.000(-0.24)-0.010(-0.95)FOREIGN0.000(0.38)-0.011***(-2.77)DOMESTIC-0.000(-0.19)-0.009***(-2.69)ControlsYesYesYesYesYesYesYesYesYear_FEYesYesYesYesYesYesYesYesIndustry_FEYesYesYesYesYesYesYesYesSample
            size17171806171718061717180617171806Chi–sq454.26***94.63***454.29***97.30***454.35***97.60***454.35***99.63***Chow
            test2.59(0.000)3.89(0.000)2.59(0.000)3.59(0.000)VariablesPanel B: CEO DualityThe CEO is
            not the board chairThe CEO is the board chairThe CEO is not the board chairThe CEO is
            the board chairThe CEO is not the board chairThe CEO is the board chairThe CEO is not
            the board chairThe CEO is the board
            chair(1)(2)(3)(4)(5)(6)(7)(8)BLOCK_DUM–0.002(-0.48)-0.082**(-2.41)BLOCK%0.000(0.43)-0.008***(-3.00)LONG_IO0.000(0.68)-0.007***(-2.66)SHORT_IO0.004*(1.73)-0.009(-0.98)FOREIGN-0.000(-0.81)-0.007**(-2.07)DOMESTIC0.000(1.45)-0.008**(-2.49)ControlsYesYesYesYesYesYesYesYesYear_FEYesYesYesYesYesYesYesYesIndustry_FEYesYesYesYesYesYesYesYesSample
            size14932000149320001493200014932000Chi–sq375.39***101.13***375.33***104.47***375.34***102.99***379.35***103.67***Chow
            test2.35(0.006)2.59(0.002)2.27(0.007)2.29(0.006)</td></tr></table>
# Conclusion

In this paper, we examine the effect of institutional blockholders on corporate investment efficiency in France using 3796 firm-year observations from 2002 to 2016. Firm-specific inefficient investment is defined as the deviation of actual investment from the expected level of investment estimated as a function of sales growth (Biddle *et al.*, 2009). Our empirical work adds several notable and interesting key findings to the exiting literature. First, consistent with the monitoring effectiveness prediction, we provide evidence that institutional blockholders (shareholders with ownership ≥ 5%) improve investment efficiency by decreasing the magnitude of over-investment. This finding stands up to a range of robustness tests, including mitigating endogeneity concerns, using alternative proxies, and alternative regression frameworks. Second, among institutions with differing investment horizons, long-term investing institutions play a more important role in enhancing investment efficiency via their monitoring compared with short-term institutions. Finally, when we split institutional blockholdings into those held by domestic and foreign institutional investors, we provide evidence that both foreign and domestic institutions reduce the extent of inefficiency in investments. Third, in cross-sectional analysis, we also show that the role of institutional investors’ monitoring in reducing investment inefficiency is stronger for firms with greater information asymmetry and higher agency problems, providing further empirical support for the mechanism driving our results.

Overall, our study’s results support the evidence that institutional blockholders can reduce enterprise investment inefficiency through monitoring and activism (Chen *et al.*, 2017a; Cao *et al.*, 2020), leading to high firms’ subsequent performance. We, therefore, identify a potential channel through which institutional blockholders can increase firm value in the French stock market. Our empirical evidence has practical implications for investors and policymakers. Consistent with the bright side of institutional investors, our results strongly suggest that monitoring by institutional blockholders can directly benefit investors in the form of more efficient investments. Thus, our findings should be of direct interest to investors and portfolio managers to identify potential investees with better investment efficiency. Moreover, the study offers practical implications for policymakers and financial market regulators in France, aiming to reform corporate governance practices. They should promote institutional investors’ active participation, which comes with a better corporate investment environment.

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