Does Board Composition Matter to Institutional Investors?

Abstract

This study examines the resource dependency and signalling role of independent directors from the perspective of institutional investor’s and also investigates if the presence of large blockholder moderates the signalling effect. This study uses the quasi-natural experiment to examine this relationship. The difference-in-difference (DiD) analysis of 5,298 firm observations covering 618 National Stock Exchange (NSE) listed Indian firms for the period 2001–2011 provides empirical evidence that board composition does matter to institutional investors. We find that non-compliant firms who adopted the board independence requirement experience a significant increase in institutional ownership relative to previously compliant firms. We also find that institutional investors have invested more in family-owned firms during post-mandate period compared to government-, private- and foreign-owned firms. Overall, this study contributes to the existing literature on resource dependency theory and signalling theory and shows that the board independence acts as a signal to institutional investors and decreases the agency cost and cost of monitoring.

JEL Codes: G3, G11, G34, G38, G23

Keywords

Board composition independent directors institutional investors corporate governance signalling theory resource dependency theory

1. Introduction

The concentrated shareholders act opportunistically and take advantage of available information at the expense of other shareholders (Roe, 2005). The presence of significant ownership enables them to have information advantage compared to rest of shareholders. In the presence of restricted information, other investors tend to use secondary information like board composition and governance parameter as a signal for firm’s attributes (Deutsch & Ross, 2003; Sanders & Boivie, 2004) and consider independent director as a critical resource for the firm (Hillman, Cannella, & Paetzold, 2000). The independent director plays an important role and can solve the agency problem of controlling shareholders and non-controlling shareholders (Helland & Sykuta, 2005). Markets such as India where ownership is concentrated and the average promoters (non-institutional) ownership is 48.01 per cent (Sarkar, 2010), the role of the independent board becomes more crucial as the board is responsible for protecting the rights of both depressed shareholders and institutional investors. Hence, in this article, we examine the resource dependency and signalling role of independent directors on institutional investor’s ownership and see if the presence of large blockholders moderates the impact of independent directors on institutional investor’s ownership.

The conventional belief in managerial theory suggests that independent directors of board monitor and control discretionary power and self-serving behaviour of management to reduce agency cost (Helland & Sykuta, 2005; Shleifer & Vishny, 1997). Board independence controls the CEO’s discretion over the board and is more likely to take strict action against non-performing CEO’s (Guo & Masulis, 2015; Weisbach, 1988). The board independence is positively related to firm performance (Liu, Miletkov, Wei, & Yang, 2015) and reduces its variability (Pearce, Robinson, & Schepker, 2014). The recent literature on board independence also highlights the role of independent directors in increasing the corporate innovation (Balsmeier, Fleming, & Manso, 2017; Lu & Wang, 2018). There are many studies which highlight the role of independent directors on crucial board decisions such as anti-takeover defences (Byrd & Hickman, 1992), restructuring (Johnson, Hoskisson, & Hitt, 1993), corporate social responsibility (Zhang, Zhu, & Ding, 2013) executive compensation (Sonenshine, Larson, & Cauvel, 2015; Vafeas, 2000) and impacts stock volatility (Christy, Matolcsy, Wright, & Wyatt, 2013), IPO’s valuation (Bertoni, Meoli, & Vismara, 2014) and cost of debt (Bradley & Chen, 2015), but no study has examined their signalling role on institutional investor’s ownership.

Security Exchange Board of India (SEBI) enacts the rules and regulations of corporate governance to supervise and ratify the managerial decisions and the board monitoring rule of revised Clause 49 (2005) compel firms to have minimum 50 per cent of independent directors and 33 per cent in case chairman is a non-executive director. This particular mandate may drive institutional investors’ attention to board composition as an indicator of better governance. Besides, it becomes critical to analyse the signalling role of independent directors taking into consideration the presence of large blockholders, who reduce the agency cost. Thus, this study is the first to contribute to the literature by examining the resource dependency and signalling role of board composition towards institutional investors with the help of exogenous shock to board independence in the Indian public listed firms arising from the change in Clause 49 of listing agreement. This will also help us in addressing the important issue of endogeneity. Generally, institutional investors have information disadvantage compared to insiders and they use information like board composition and governance parameter as a signal for firm’s financial and non-financial attributes like profitability and environmental responsibility (Deutsch & Ross, 2003; Sanders & Boivie, 2004). McCahery, Sautner, and Starks (2016) provide survey evidence that institutional investors give due importance to the governance and exit the firms which have a poor governance structure. Particularly, institutional investors tend to prefer independent board (Schnatterly & Johnson, 2014) and give importance to the role of independent directors in arbitration among internal management in case of any disagreement and in monitoring any conflict of interest between shareholders and management (Fama & Jensen, 1983). The board also provides a critical resource in the form of networks and guidance to management in strategic decisions of the firms (Hillman et al., 2000; Zahra & Pearce, 1989). This resource supplying capacity of the board may be used as an effective way to mitigate the resource constraint enforced by the highly competitive environment (resource dependency theory). The board’s social capital will also act as an asset to board and help in improving overall board effectiveness (Kim & Cannella, 2008). The existing literature on board composition highlights board as resource provider and a medium to signal in the presence of restricted information. In India where concentrated ownership is significantly present, the function of the independent director becomes more important as controlling shareholders restrict the information and investors largely rely on independent directors for the information. Hence, in this study, we analyse the nexus between the existence of independent directors and institutional investors’ ownership in the Indian context.

We use pooled and panel OLS regression model by adopting difference-in-difference (DiD) analysis technique on a sample which consists of 5,298 firm observations covering over 618 National Stock Exchange (NSE) listed Indian firms for the period 2001–2011 and spanning over 113 different industries. The independent director mandate was not applied to all firms at the same point in time and different classes of firms have implemented it at different point in time. Hence, there are firms which have already complied with it and others who are yet to comply with the norms and this creates the treatment (firms yet to comply) and control (firms complied) groups for the study.

The analysis shows that non-complaint firms had lower institutional investors’ shareholding but they have experienced higher institutional investors’ shareholding after adopting the board independence requirement in the post-mandate period compared to other firms. The results indicate that the institutional investors welcome board independence norm and they reward the affected firms by investing more in the post-mandate period. Thus, our results show that the presence of more independent directors is linked to larger institutional investors’ ownership. We also document that institutional investors have invested more in family-owned firms in the post-mandate period compared to government-, private- and foreign-owned firms. Several robustness tests are also performed to test the sensitivity of results to firm-, industry- and time-specific effects. Our analysis shows that institutional investors perceive board independence positively and they have rewarded the firms by increasing their shareholding in the post-mandate period.

2. Development of Hypothesis

The extant literature in the field of institutional investors and corporate governance shows mixed results. While Aggarwal, Erel, Ferreira, and Matos (2011) show that the changes in institutional ownership over time enhances firm-level governance, Sarkar and Sarkar (2000) and Bushee, Carter, and Gerakos (2013) find no association between governance and institutional investors. However, Mohanty (2003) and Chung and Zhang (2011) observed that institutional investors invest in companies that have better governance records considering the mixed evidence in the literature on the relationship between institutional investors and corporate governance. In this study, we examine the causal relation between board independence and institutional investors’ holdings using an exogenous shock to board independence in the Indian public firms arising from Clause 49. Clause 49 (2005) mandate of SEBI compels firms to have minimum 50 per cent of independent directors and 33 per cent in case the chairman is a non-executive director; this will bring substantial change in the board composition of firms and compel them to change their existing board structure. Each firm chooses the structure of its board, which maximises its value (Coles, Daniel, & Naveen, 2008; Lehn, Patro, & Zhao, 2009). In agreement with this view, if all the firms have adopted the best optimal and beneficial board structure in the pre-mandate era, we should not find any inconsistency (differences) in the institutional investors’ shareholding after controlling all the relevant firm-specific variables in the post-mandate era. Moreover, firms that made substantial changes in their board structure to comply with norms (affected Firms) will experience decrease in the institutional investors’ ownership in the post-reform era over the remaining firms because the mandate compels them to shift from their suitable governance structure. On the contrary, if the mandate is beneficial for the firm and decreases the agency cost and cost of monitoring for the institutional investors, the affected firms will experience increase in the institutional investors’ ownership over the remaining firms in the post-reform era. In other words, if the resource dependency theory and signalling theory hold in the market, we will find an increase in the institutional shareholding of the firms. Hence, we propose the following hypothesis:

H_1a: There is a significant positive relationship between the proportion of independent directors and institutional investors’ ownership.

The presence of large blockholders in a firm assures better monitoring (Admati, Pfleiderer, & Zechner, 1994) and acts as a signal of good governance to the market (Ayres & Cramton, 1993). However, large blockholders may lead to intense monitoring, which may result in poor performance and inefficiency in the firm (Bebchuk, Kraakman, & Triantis, 2000; La Porta, Lopez-De-Silanes, Shleifer, & Vishny, 2002). Nevertheless, the role of blockholders in governance is critical and can impact the signalling role of board composition to institutional investors. Better monitoring by blockholders will decrease the agency cost and the cost of monitoring for the institutional investor. Hence, institutional investors’ shareholding may be affected by the presence of blockholders and the form of blockholders in the firm. Probably, the presence of large family ownership leads to an increase in agency cost between the promoter and minority shareholder, while the government blockholder guards the interest of all stakeholders. Therefore, we examine whether the impact of board independence on institutional investors’ shareholding varies with the types of firms and blockholder categories, namely, state-, private-, family- and foreign-owned, and propose the following hypothesis:

H_1b: There is a significant impact of board independence on institutional investors’ ownership for different types of blockholders.

3. Data and Methodology

The sample is constructed from the Prowess database managed by the Centre for Monitoring Indian Economy (CMIE). Our initial sample consists of all NSE listed firms, but after eliminating the financial and utility firms and depending on the availability of data, our final sample comprises of 5,298 firm observations covering 618 NSE listed Indian firms for the period 2001–2011 and spanning to 113 different industries.

The primary dependent variable is institutional investors’ shareholding (INSTI), which is evaluated as the percentage of shares held by the non-promoter institutions. The ratio of the independent directors to total directors (Non-Ex/Dir) data is not available from the published databases, but the author from the audited annual reports of each firm has compiled it for each year. Similar to Schnatterly and Johnson (2014), we use the following firm-specific variables as control variables, that is, log firm size is measured as the natural logarithm of total assets to control for the institutions’ preferences for larger firms hoping for the higher liquidity and the presence of lower information asymmetries (Gompers & Metrick, 2001). Firm’s recent good performance act as an attractive mechanism for some institutional investors, so to control for that we use cash flow measured as profit after tax before depreciation and amortisation by total assets. Cash flow volatility is measured as rolling standard deviation of cash flow taken 3 years at a time. Dividend is measured as the dummy variable equal to 1 if a firm declared the dividend or else 0 to control the institutions preferences for firms that pay dividends (Falkenstein, 1996; Gompers & Metrick, 2001). We use market to book ratio measured as book value of assets less book value of equity plus market value of equity divided by book value of assets to control for the preferences for growth firms. We proxy the risk of losses in case of bankruptcy by the leverage ratio, which is measured as the total borrowings to total assets net of cash holding because prior research has shown that institutional ownership is negatively associated with the level of leverage in the firm (Badrinath, Gay, & Kale, 1989; Gompers & Metrick, 2001). We also control for the presence of liquidity in the firm by controlling for the net working capital, which is measured as a difference of current assets and current liabilities net of cash to total assets. Finally, we control for the research and development ratio, which is measured as the research and development expenses over total assets as research and development (R&D) expenses have been used in the literature (Hessel & Norman, 1992; Wahal & McConnell 2000) for analysing institutional myopia and find that institutional ownership prefers the firm having high R&D intensity.

In order to examine if institutional investors’ holding is influenced by the presence of large blockholders, we define blockholders as those who hold more than 20 per cent of the shareholding in line with La Porta et al. (2002). We manually compiled the data on the family-owned firms from the individual balance sheet of each firm for each year and other categories based on the CMIE Prowess database. The identified mutually exclusive large blockholders are (a) government firms which are the public sector undertakings or government-owned corporations, which are ‘not privatised’, (b) the family firms which are non-government family owned firms, (c) private firms which are non-government-owned firms comprising non-family-owned firms and (d) foreign firms which are non-government-owned firms by foreign origin promoters.

We examine the relationship between board independence and institutional investors by using the pooled and static panel regression model with firm, industry and time fixed effects based on the Hausman specification test (χ² = 29.14). Similar to Aggarwal, Schloetzer, and Williamson (2014), we follow the DiD approach to analyse the relationship between independent directors and institutional investors. The DiD approach helps us to examine how an exogenous shock to board independence in the form of Clause 49 affects institutional investor’s ownership and helps us in catering to the problem of endogeneity. Clause 49 compels firms to have minimum 50 per cent of independent directors on a board and 33 per cent in case chairman is a non-executive director. So, the DiD approach helps us in examining the effect of changing board structure on institutional investor’s ownership. The DiD approach uses the firms in compliance with Clause 49 as control firms and firms that make change in the board composition to comply with Clause 49 as affected firms. This will help us in isolating the problem of endogeneity (Guo, Lach, & Mobbs, 2015) and in analysing the effect of board independence on institutional investor’s ownership that exceeds this common time trend. The criterion for Clause 49 implementation is based on firms paid up share capital and net-worth, and the firms have to implement the norms by the year 2005. Hence, we chose the year 2004 to identify the firms, which will make substantial changes in their board structure to comply with the listing norms. The firm with a presence of less than 50 per cent of independent directors and less than 33 per cent in case the chairman is a non-executive are classified as sub-optimal board firms (affected firms) and the remaining firms are classified as optimal board firms (remaining firms). We exclude the year 2005 from our analysis, as it was the year of transition. The following regression model is used for analysing the effect of board independence on the institutional investor’s ownership:

\begin{matrix} \begin{matrix} I N S T I_{i t} = β_{0} + β_{1} A F F E C T E D_{i, t} + ​ β_{2} P O S T_{i, t} \\ + β_{3} A F F E C T E D ​_{i, t} * P O S T_{i, t} \\ + β_{4} S I Z E_{i, t} + β_{5} M K T B_{i, t} \end{matrix} \\ \begin{matrix} + β_{6} C F L O W_{i, t} + β_{7} N W C_{i, t} \\ + β_{8} L E V_{i, t} + β_{9} R D_{i, t} ​ ​ ​ + β_{10} D I V_{i, t} \\ + β_{11} C F V_{i, t} + U_{i} + V_{t} ​ ​ + ​ ε_{i, t} \end{matrix} \end{matrix}

(1)

where the dependent variable INSTI is evaluated as the percentage of equity held by non-promoters’ institutions. AFFECTED is a variable representing all firms that will make substantial changes in their board structure to comply with the mandate. The POST is a dummy variable equal to 1 from year 2006 to 2011 indicating post-mandate period and equal to 0 from year 2001 to 2004 indicating the pre-mandate period. AFFECTED × POST is the interaction term of AFFECTED and POST which indicate the effect of board composition norms on institutional investors’ ownership for affected firms in the post-reform period compared to remaining firms. SIZE, MKTB, CFLOW, NWC, LEV, RD, DIV and CFV are the firm-specific controlled variables as defined previously, U_i represents the industry/firm fixed effects while V_t absorbs the time fixed effects and Ɛ_i,t is the error term. The analysis has been done for all the firms together and then for each blockholder group separately to see if the signalling effect changes with the presence of a type of large blockholder, that is, for government-, family-, private- and foreign-owned firms.

4. Results

The descriptive statistics reported in Table 1 shows that on average institutional investors hold 11 per cent of the shareholding in Indian firms. On analysing the pre- and post-mandate eras, we find that the average institutional investor ownership has increased from 10 per cent to 12 per cent in the post-mandate period. Table 1 also reveals that the average ratio of independent directors to the total directors on the board is 33.6 per cent. On separating the firms, we observe that the ratio of affected firms is 28 per cent and for the remaining firms, it is 38 per cent. The ratio in the pre-mandate period was just 17 per cent and it increased to 43 per cent in the post-mandate period, which shows that there is a significant increment in the number of independent directors after the implementation of the mandate. The correlation matrix in Table 2 shows that all the variables are significantly correlated with the institutional investors. Firm size, market to book ratio, cash flow and R&D are positively related, but leverage ratio, networking capital and cash flow volatility are inversely related to institutional investors’ shareholding. The correlation among independent variables suggests that there is no problem of multicollinearity among the variables.

Table 1.
Descriptive Statistics

All Firms Affected/Remaining Firms
Pre/Post-mandate
Affected Firm
Remaining Firms

Variables Mean Std. Dev. Min Max Affected Firms Remaining Firms FStatistics Premandate Post-mandate FStatistics Premandate Post-mandate FStatistics Premandate Post-mandate FStatistics

Institutional ownership 0.118 0.125 0 0.763 0.119 0.116 0.60 0.1015 0.127 53.06* 0.097 0.131 43.65* 0.105 0.123 13.86*

Log firm size 3.699 0.667 0.819 5.987 3.722 3.677 6.05 3.449 3.838 450.18* 3.458 3.868 229.39* 3.440 3.811 220.94***

Leverage ratio 0.207 0.285 –0.983 1.535 0.214 0.201 3.08* 0.221 0.199 6.91* 0.230 0.206 4.07 0.213 0.194 2.93*

Market to book ratio 1.728 1.226 0.193 29.724 1.727 1.730 0.01 1.546 1.830 65.75* 1.515 1.844 44.35* 1.575 1.817 24.08***

Networking capital 0.038 0.281 –0.981 0.984 0.0379 0.0389 0.02 0.042 0.036 0.53 0.0375 0.0381 0.00 0.0464 0.0347 1.20

Cash flow 0.082 0.114 –2.374 2.852 0.080 0.083 1.18 0.083 0.081 0.25 0.084 0.077 2.93* 0.081 0.084 0.48

Cash flow volatility 0.037 0.071 0.000 1.918 0.037 0.037 0.00 0.036 0.038 0.96 0.035 0.038 0.69 0.036 0.038 0.35

Research and development to asset 0.396 1.654 0 40 0.329 0.457 7.87* 0.377 0.407 0.39 0.298 0.347 1.46 0.448 0.462 0.03

Independent director to total director 0.336 0.275 0 1 0.285 0.382 169.99* 0.167 0.430 1399.38* 0.051 0.412 1576.31* 0.269 0.446 314.13***

No. of observation 5298 5298 5298 5298 2529 2769 1895 3403 899 1630 996 1773

All Firms					Affected/Remaining Firms	Pre/Post-mandate	Affected Firm	Remaining Firms
Institutional ownership	0.118	0.125	0	0.763	0.119	0.116	0.60	0.1015	0.127	53.06***	0.097	0.131	43.65***	0.105	0.123	13.86***
Log firm size	3.699	0.667	0.819	5.987	3.722	3.677	6.05**	3.449	3.838	450.18***	3.458	3.868	229.39***	3.440	3.811	220.94***
Leverage ratio	0.207	0.285	–0.983	1.535	0.214	0.201	3.08*	0.221	0.199	6.91***	0.230	0.206	4.07**	0.213	0.194	2.93*
Market to book ratio	1.728	1.226	0.193	29.724	1.727	1.730	0.01	1.546	1.830	65.75***	1.515	1.844	44.35***	1.575	1.817	24.08***
Networking capital	0.038	0.281	–0.981	0.984	0.0379	0.0389	0.02	0.042	0.036	0.53	0.0375	0.0381	0.00	0.0464	0.0347	1.20
Cash flow	0.082	0.114	–2.374	2.852	0.080	0.083	1.18	0.083	0.081	0.25	0.084	0.077	2.93*	0.081	0.084	0.48
Cash flow volatility	0.037	0.071	0.000	1.918	0.037	0.037	0.00	0.036	0.038	0.96	0.035	0.038	0.69	0.036	0.038	0.35
Research and development to asset	0.396	1.654	0	40	0.329	0.457	7.87***	0.377	0.407	0.39	0.298	0.347	1.46	0.448	0.462	0.03
Independent director to total director	0.336	0.275	0	1	0.285	0.382	169.99***	0.167	0.430	1399.38***	0.051	0.412	1576.31***	0.269	0.446	314.13***
No. of observation	5298	5298	5298	5298	2529	2769		1895	3403		899	1630		996	1773

Source: The authors.

Notes: The variables in the table are measured as follows. Institutional Ownership: the percentage of equity held by non-promoters’ institutions. Log Firm Size: natural log of total assets. Leverage Ratio: total borrowings to total assets net of cash holding. Market To Book Ratio: book value of assets less book value of equity plus market value of equity divided by book value of assets. Networking Capital: difference of current assets and current liabilities net of cash to total assets. Cash Flow: profit after tax before depreciation and amortisation by total assets. Cash Flow Volatility: rolling standard deviation of cash flow taken three years at a time. Research and Development to Asset: research and development expenditure to total assets. Independent Director to Total Director: ratio of the number of independent directors to total number of directors in the board. Affected Firms are the firms with a presence of less than 50% of independent directors in the board and less than 33% in case the chairman is a non-executive or it is a Remaining Firm. Post-Mandate indicates the post-mandate period from 2006 to 2010 and Pre-Mandate indicates the pre-mandate period from 2001 to 2004.

***, ** and * indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

Table 2.

Correlation Matrix

	Institutional Ownership	Log Firm Size	Leverage Ratio	Market to Book Ratio	Networking Capital	Cash Flow	Cash Flow Volatility	Research and Development to Asset
Institutional ownership	1
Log firm size	0.569***	1
Leverage ratio	–0.119***	–0.050***	1
Market to book ratio	0.207***	0.245***	–0.399***	1
Networking capital	–0.071***	–0.175***	–0.598***	0.195***	1
Cash flow	0.069***	0.036***	–0.281***	0.308***	0.161***	1
Cash flow volatility	–0.054***	–0.116***	–0.057***	–0.006	–0.020	–0.023	1
Research and development to asset	0.080***	0.095***	–0.0238	0.105***	0.005	0.031**	–0.001	1

Source: The authors.

Notes: The table reports the correlation matrix, variables in the table are measured as follows. Institutional Ownership: percentage of equity held by non-promoters’ institutions. Log Firm Size: natural log of total assets. Leverage Ratio: total borrowings to total assets net of cash holding. Market to Book Ratio: book value of assets less book value of equity plus market value of equity divided by book value of assets. Networking Capital: difference of current assets and current liabilities net of cash to total assets. Cash Flow: profit after tax before depreciation and amortisation by total assets. Cash Flow Volatility: rolling standard deviation of cash flow taken three years at a time. Research and Development to Asset: research and development expenditure to total assets. Independent Director to Total Director: ratio of the number of independent directors to total number of directors in the board.

*** and ** indicate significance at 0.01 and 0.05 levels.

4.1 Baseline Results

Models 1 and 2 given in Table 3 report the results of linear pooled regression. Model 1 shows that firm size, R&D and market to book ratio are positively associated with institutional investors’ ownership. Hence, institutional investors are more attracted towards the larger firms and firms with better investment opportunities, and they reward the firms that invest in innovation and new product development. Moreover, institutional investors’ shareholding is negatively related to high leveraged firms and firms with high working capital, thus indicating that institutional investors are not encouraging high leverage and holding of excess cash in the firms.

Table 3.
Impact of Board Composition on Institutional Investors Ownership

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Log firm size 0.106*
(43.08) 0.106*
(43.05) 0.082*
(16.74) 0.082*
(16.63) 0.118*
(22.25) 0.117*
(22.12) 0.108*
(39.56) 0.108*
(39.53) 0.114*
(41.35) 0.114*
(41.31)

Leverage ratio –0.042*
(–6.32) –0.042*
(–6.34) –0.025*
(–2.80) –0.024*
(–2.76) –0.026*
(–3.04) –0.026*
(–3.01) –0.029*
(–4.07) –0.029*
(–4.07) –0.027*
(–3.86) –0.027*
(–3.86)

Market to book ratio 0.004*
(3.02) 0.003*
(2.98) 0.004*
(3.58) 0.004*
(3.48) 0.004*
(3.52) 0.004*
(3.43) 0.001
(1.15) 0.001
(1.10) 0.001
(0.66) 0.001
(0.61)

Networking capital –0.017*
(–2.65) –0.017*
(–2.68) –0.018
(–2.05) –0.019
(–2.11) 0.002
(0.29) 0.002
(0.22) –0.006
(–0.81) –0.0063
(–0.84) 0.001
(0.26) 0.001
(0.23)

Cash flow 0.019
(1.41) 0.019
(1.47) 0.006
(0.67) 0.007
(0.76) 0.006
(0.62) 0.007
(0.70) 0.024*
(1.86) 0.025*
(1.92) 0.022*
(1.76) 0.023*
(1.81)

Cash flow volatility 0.009
(0.46) 0.009
(0.46) –0.090*
(–5.38) –0.089*
(–5.35) –0.078*
(–4.77) –0.077*
(–4.74) –0.005
(–0.30) –0.005
(–0.29) 0.004
(0.24) 0.004
(0.24)

Dividend dummy –0.002
(–0.63) –0.002
(–0.6) –0.001
(–0.23) –0.001
(–0.20) –0.006*
(–1.86) –0.006*
(–1.84) –0.003
(–0.96) –0.003
(–0.93) –0.006*
(–1.70) –0.006*
(–1.68)

Research and development to asset 0.001*
(1.72) 0.001*
(1.72) 0.001
(0.86) 0.001
(0.81) 0.001
(1.22) 0.001
(1.19) –0.001
(–0.36) –0.001
(–0.37) –0.001
(–0.51) –0.001
(–0.52)

Affected firms –0.001
(–0.45) –0.009*
(–1.94) –0.057
(–0.80) –0.065
(–0.90) –0.073
(–1.04) –0.079
(–1.13) 0.001
(0.05) –0.007
(–1.62) 0.001
(0.06) –0.007
(–1.55)

Post-mandate –0.017*
(–5.76) –0.023*
(–5.68) –0.007
(–2.53) –0.012*
(–3.51) –0.015*
(–2.76) –0.019*
(–3.27) –0.017*
(–5.79) –0.022*
(–5.75) –0.0090
(–1.26) –0.014*
(–1.86)

Affected firms × Post-mandate 0.012
(2.09) 0.010*
(2.58) 0.008
(2.17) 0.011
(2.12) 0.011
(2.04)

Constant –0.262*
(–29.13) –0.258*
(–28.07) 0.154*
(–3.93) –0.148
(–3.79) –.249*
(–6.41) –0.244*
(–6.28) –0.269*
(–27.42) –0.265*
(–26.57) –0.266*
(–23.86) –0.263*
(–23.25)

Firm dummy YES YES YES YES

Industry dummy YES YES YES YES

Time dummy YES YES YES YES

F-test 272.99 248.33 61.12 56.23 50.52 48.15 222.72 203.02 135.78 128.93

R ² 0.34 0.34 0.27 0.27 0.29 0.29 0.43 0.43 0.44 0.44

Adjusted R² 0.34 0.34 0.42 0.42 0.43 0.43

RMSE 0.1017 0.1016 0.0955 0.0955 0.0926 0.0942

N 5298 5298 5298 5298 5298 5298 5298 5298 5298 5298

Sigma_u 0.084 0.084 0.086 0.086

Sigma_e 0.068 0.068 0.066 0.066

Rho 0.603 0.605 0.625 0.626

Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10
Log firm size	0.106*** (43.08)	0.106*** (43.05)	0.082*** (16.74)	0.082*** (16.63)	0.118*** (22.25)	0.117*** (22.12)	0.108*** (39.56)	0.108*** (39.53)	0.114*** (41.35)	0.114*** (41.31)
Leverage ratio	–0.042*** (–6.32)	–0.042*** (–6.34)	–0.025*** (–2.80)	–0.024*** (–2.76)	–0.026*** (–3.04)	–0.026*** (–3.01)	–0.029*** (–4.07)	–0.029*** (–4.07)	–0.027*** (–3.86)	–0.027*** (–3.86)
Market to book ratio	0.004*** (3.02)	0.003*** (2.98)	0.004*** (3.58)	0.004*** (3.48)	0.004*** (3.52)	0.004*** (3.43)	0.001 (1.15)	0.001 (1.10)	0.001 (0.66)	0.001 (0.61)
Networking capital	–0.017*** (–2.65)	–0.017*** (–2.68)	–0.018** (–2.05)	–0.019** (–2.11)	0.002 (0.29)	0.002 (0.22)	–0.006 (–0.81)	–0.0063 (–0.84)	0.001 (0.26)	0.001 (0.23)
Cash flow	0.019 (1.41)	0.019 (1.47)	0.006 (0.67)	0.007 (0.76)	0.006 (0.62)	0.007 (0.70)	0.024* (1.86)	0.025* (1.92)	0.022* (1.76)	0.023* (1.81)
Cash flow volatility	0.009 (0.46)	0.009 (0.46)	–0.090*** (–5.38)	–0.089*** (–5.35)	–0.078*** (–4.77)	–0.077*** (–4.74)	–0.005 (–0.30)	–0.005 (–0.29)	0.004 (0.24)	0.004 (0.24)
Dividend dummy	–0.002 (–0.63)	–0.002 (–0.6)	–0.001 (–0.23)	–0.001 (–0.20)	–0.006* (–1.86)	–0.006* (–1.84)	–0.003 (–0.96)	–0.003 (–0.93)	–0.006* (–1.70)	–0.006* (–1.68)
Research and development to asset	0.001* (1.72)	0.001* (1.72)	0.001 (0.86)	0.001 (0.81)	0.001 (1.22)	0.001 (1.19)	–0.001 (–0.36)	–0.001 (–0.37)	–0.001 (–0.51)	–0.001 (–0.52)
Affected firms	–0.001 (–0.45)	–0.009* (–1.94)	–0.057 (–0.80)	–0.065 (–0.90)	–0.073 (–1.04)	–0.079 (–1.13)	0.001 (0.05)	–0.007 (–1.62)	0.001 (0.06)	–0.007 (–1.55)
Post-mandate	–0.017*** (–5.76)	–0.023*** (–5.68)	–0.007** (–2.53)	–0.012*** (–3.51)	–0.015*** (–2.76)	–0.019*** (–3.27)	–0.017*** (–5.79)	–0.022*** (–5.75)	–0.0090 (–1.26)	–0.014* (–1.86)
Affected firms × Post-mandate		0.012** (2.09)		0.010*** (2.58)		0.008** (2.17)		0.011** (2.12)		0.011** (2.04)
Constant	–0.262*** (–29.13)	–0.258*** (–28.07)	0.154*** (–3.93)	–0.148 (–3.79)	–.249*** (–6.41)	–0.244*** (–6.28)	–0.269*** (–27.42)	–0.265*** (–26.57)	–0.266*** (–23.86)	–0.263*** (–23.25)
Firm dummy			YES	YES	YES	YES
Industry dummy							YES	YES	YES	YES
Time dummy					YES	YES			YES	YES

F-test	272.99	248.33	61.12	56.23	50.52	48.15	222.72	203.02	135.78	128.93
R ²	0.34	0.34	0.27	0.27	0.29	0.29	0.43	0.43	0.44	0.44
Adjusted R²	0.34	0.34					0.42	0.42	0.43	0.43
RMSE	0.1017	0.1016					0.0955	0.0955	0.0926	0.0942
N	5298	5298	5298	5298	5298	5298	5298	5298	5298	5298
Sigma_u			0.084	0.084	0.086	0.086
Sigma_e			0.068	0.068	0.066	0.066
Rho			0.603	0.605	0.625	0.626

Source: The authors.

Notes: Values in the table represent coefficients for each variable and t-statistics are in the parentheses.

This table examines the impact of board composition on institutional investors’ ownership for a period 2001–2011. Models 1 and 2 are the results of pooled regression. Models 3–10 are results of fixed effects panel regression, controlling for firm, industry and time. The dependent variable is Institutional Ownership measured as the percentage of equity held by non-promoters institutions. Log Firm Size: natural log of total assets. Leverage Ratio: total borrowings to total assets net of cash holding. Market to Book Ratio: book value of assets less book value of equity plus market value of equity divided by book value of assets. Networking Capital: difference of current assets and current liabilities net of cash to total assets. Cash Flow: profit after tax before depreciation and amortisation by total assets. Cash Flow Volatility: rolling standard deviation of cash flow taken 3 years at a time. Research and Development to Asset: research and development expenditure to total assets. The variable in the table are measured as: Affected Firms is a dummy variable equal to 1for the firms with a presence of less than 50% of independent directors in the board and less than 33% in case the chairman is a non-executive or else 0. Post-Mandate is a dummy variable equal to 1 from 2006 to 2010 indicating the post-mandate period and equal to 0 from 2001 to 2004 indicating the pre-mandate period. Affected Firms × Post-Mandate is the interaction term between Affected and Post.

***, ** and * indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

Model 2 presents that the coefficient of affected firms and the post-mandate period is negative and significant, which indicates that firms affected by the mandate have lower institutional investors’ shareholding compared to the remaining firms. But the coefficient of the DiD term (interaction term) AFFECTED × POST is positive and statistically significant thus indicating that the firms which enacted the board composition norms experienced the higher institutional investors’ ownership in the post-reform period than the remaining firms. It shows that the board mandate was beneficial for the firms and helped the firms to shift their board structure towards optimal board structure for the institutional investors. The results highlight the board’s role as resource provider and a medium to signal the institutional investors. The result shows that board independence decreases the agency cost and cost of monitoring for the institutional investors.

To ensure the robustness of our results across time, firms and industry, we use the fixed effect panel regression model to examine the impact of mandate on institutional investors’ ownership. Models 3–6 report the panel regression results after controlling for firm-specific effects and time trend. The results indicate that affected firms have higher institutional investors’ shareholding after the governance enactment relative to the other controlled firms even after controlling for time and firm effects. We further test by controlling for industry-specific factor and time trend (models 7–10). The coefficient of affected firms × post-mandate remains positive and is statistically significant for all the models, that is, it indicates that the results support the conjecture that governance mandate focussed on board structure had a positive implication on the institutional investors’ shareholding even after controlling for industry effect, firm effect and time trend. However, we need to further probe whether this effect changes with the presence of a large blockholder.

4.2 Impact of Type of Blockholders on Institutional Investors’ Shareholdings

In this section, we study the role of blockholders in the relationship between board composition and institutional investors’ shareholdings and analyse this using the DiD method for blockholders categories namely, government-, private-, family- and foreign-owned firms. Models 11–15 (Table 4) report the results of government as a blockholder. The coefficient of affected firms is negative and significant, whereas the coefficient of post-mandate is positive and significant. This shows that the government controlled affected firms have lower institutional investors’ shareholding compared to unaffected firms and these firms have higher institutional investors’ shareholding in the post-mandate period than the pre-mandate period. The coefficient of affected firms × post-mandate is positive and insignificant, and shows that affected government-controlled firms have not experienced any significant increase in the institutional investors’ shareholding after the mandate enactment relative to the other controlled firms. This may be due to the complex bureaucratic system that might act as a discouraging factor for the institutional investors.

Table 4.
Impact of Board Composition on Institutional Investors Ownership with Respect to Government and Family Firms

Model 11 Mode 12 Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model 19 Model 20

Log firm size 0.079*
(5.68) 0.064
(2.34) 0.060*
(3.83) 0.050
(1.57) 0.053*
(2.93) 0.120*
(33.19) 0.105*
(14.33) 0.129
(30.73) 0.141*
(18.01) 0.136*
(32.21)

Leverage ratio 0.187*
(4.78) 0.040
(1.14) 0.028
(0.78) 0.042
(1.14) 0.024
(0.62) –0.040*
(–4.07) –0.027
(–2.03) –0.026
(–2.52) –0.028
(–2.12) –0.026
(–2.46)

Market to book ratio –0.011*
(–3.48) –0.003
(–1.66) –0.003
(–1.40) –0.003*
(–1.75) –0.003
(–1.45) 0.014*
(5.66) 0.009*
(3.67) 0.012
(4.81) 0.010*
(4.23) 0.012*
(4.89)

Networking capital 0.187*
(4.44) 0.125*
(2.9) 0.117
(2.68) 0.123
(2.71) 0.115
(2.52) –0.006
(–0.63) –0.003
(–0.22) 0.022
(2.08) 0.019
(1.46) 0.032*
(3.01)

Cash flow –0.068
(–0.39) –0.323*
(–3.11) –0.337*
(–3.21) –0.295
(–2.59) –0.302
(–2.63) –0.020
(–1.13) 0.010
(0.68) –0.007
(–0.45) 0.011
(0.77) –0.007
(–0.42)

Cash flow volatility 0.595
(1.41) 0.301
(1.21) 0.269
(1.06) 0.371
(1.36) 0.334
(1.20) –0.015
(–0.57) –0.118*
(–5.41) –0.016
(–0.66) –0.101*
(–4.76) –0.003
(–0.13)

Dividend dummy 0.047
(1.35) 0.005
(0.26) 0.002
(0.11) 0.003
(0.15) –0.003
(–0.14) –0.006
(–1.15) –0.006
(–1.03) –0.022
(–3.95) –0.010*
(–1.89) –0.025***
(–4.64)

Research and development to asset 0.003
(0.47) 0.000
(–0.07) 0.000
(–0.06) 0.000
(–0.04) 0.000
(–0.06) 0.000
(0.10) 0.003
(1.34) –0.001
(–1.30) 0.004*
(1.84) –0.001
(–1.28)

Affected firms –0.074
(–2.07) –0.054
(–0.79) –0.224*
(–6.21) –0.042
(–0.66) –0.211*
(–5.41) –0.015
(–2.37) –0.059
(–0.80) –0.016
(–2.66) –0.072
(–0.99) –0.015
(–2.53)

Post-mandate 0.048
(1.4) 0.055*
(2.73) 0.057*
(3.12) 0.057
(1.47) 0.054
(1.56) –0.031*
(–5.33) –0.023*
(–4.82) –0.032
(–6.11) –0.036*
(–4.67) –0.080*
(–8.60)

Affected firms × Post-mandate 0.011
(0.25) –0.018
(–0.88) –0.020
(–0.95) –0.018
(–0.82) –0.019
(–0.89) 0.010
(1.28) 0.012
(2.04) 0.010
(1.34) 0.010*
(1.79) 0.009
(1.24)

Constant –0.232*
(–3.32) –0.108
(–0.95) 0.018
(0.30) –0.057
(–0.45)
0.046
(0.64) –0.309*
(–22.86) –0.228*
(–5.02) –0.328
(–21.42) –0.330*
(–7.27) –0.329***
(–7.27)

Government firms Yes Yes Yes Yes Yes

Family-owned
firms Yes Yes Yes Yes Yes

Firm dummy Yes Yes Yes Yes

Industry dummy Yes Yes Yes Yes

Time dummy Yes Yes Yes Yes

F-test 8.92 13.88 7.84 146.36 116.12 74.24

R ² 0.410 0.884 0.886 0.381 0.516 0.533

Adjusted R² 0.364 0.860 0.854 0.378 0.497 0.513

RMSE 0.107 0.050 0.051 0.100 0.090 0.088

N 153 153 153 153 153 2626 2626 2626 2626

Sigma_u 0.130 0.113 0.0802 0.082

Sigma_e 0.050 0.051 0.069 0.067

Rho 0.870 0.828 0.570 0.596

	Model 11	Mode 12	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18	Model 19	Model 20
Log firm size	0.079*** (5.68)	0.064** (2.34)	0.060*** (3.83)	0.050 (1.57)	0.053*** (2.93)	0.120*** (33.19)	0.105*** (14.33)	0.129 (30.73)	0.141*** (18.01)	0.136*** (32.21)
Leverage ratio	0.187*** (4.78)	0.040 (1.14)	0.028 (0.78)	0.042 (1.14)	0.024 (0.62)	–0.040*** (–4.07)	–0.027** (–2.03)	–0.026 (–2.52)	–0.028** (–2.12)	–0.026** (–2.46)
Market to book ratio	–0.011*** (–3.48)	–0.003 (–1.66)	–0.003 (–1.40)	–0.003* (–1.75)	–0.003 (–1.45)	0.014*** (5.66)	0.009*** (3.67)	0.012 (4.81)	0.010*** (4.23)	0.012*** (4.89)
Networking capital	0.187*** (4.44)	0.125*** (2.9)	0.117** (2.68)	0.123** (2.71)	0.115** (2.52)	–0.006 (–0.63)	–0.003 (–0.22)	0.022 (2.08)	0.019 (1.46)	0.032*** (3.01)
Cash flow	–0.068 (–0.39)	–0.323*** (–3.11)	–0.337*** (–3.21)	–0.295** (–2.59)	–0.302** (–2.63)	–0.020 (–1.13)	0.010 (0.68)	–0.007 (–0.45)	0.011 (0.77)	–0.007 (–0.42)
Cash flow volatility	0.595 (1.41)	0.301 (1.21)	0.269 (1.06)	0.371 (1.36)	0.334 (1.20)	–0.015 (–0.57)	–0.118*** (–5.41)	–0.016 (–0.66)	–0.101*** (–4.76)	–0.003 (–0.13)
Dividend dummy	0.047 (1.35)	0.005 (0.26)	0.002 (0.11)	0.003 (0.15)	–0.003 (–0.14)	–0.006 (–1.15)	–0.006 (–1.03)	–0.022 (–3.95)	–0.010* (–1.89)	–0.025*** (–4.64)
Research and development to asset	0.003 (0.47)	0.000 (–0.07)	0.000 (–0.06)	0.000 (–0.04)	0.000 (–0.06)	0.000 (0.10)	0.003 (1.34)	–0.001 (–1.30)	0.004* (1.84)	–0.001 (–1.28)
Affected firms	–0.074** (–2.07)	–0.054 (–0.79)	–0.224*** (–6.21)	–0.042 (–0.66)	–0.211*** (–5.41)	–0.015** (–2.37)	–0.059 (–0.80)	–0.016 (–2.66)	–0.072 (–0.99)	–0.015** (–2.53)
Post-mandate	0.048 (1.4)	0.055*** (2.73)	0.057*** (3.12)	0.057 (1.47)	0.054 (1.56)	–0.031*** (–5.33)	–0.023*** (–4.82)	–0.032 (–6.11)	–0.036*** (–4.67)	–0.080*** (–8.60)
Affected firms × Post-mandate	0.011 (0.25)	–0.018 (–0.88)	–0.020 (–0.95)	–0.018 (–0.82)	–0.019 (–0.89)	0.010 (1.28)	0.012** (2.04)	0.010 (1.34)	0.010* (1.79)	0.009 (1.24)
Constant	–0.232*** (–3.32)	–0.108 (–0.95)	0.018 (0.30)	–0.057 (–0.45)	0.046 (0.64)	–0.309*** (–22.86)	–0.228*** (–5.02)	–0.328 (–21.42)	–0.330*** (–7.27)	–0.329*** (–7.27)
Government firms	Yes	Yes	Yes	Yes	Yes
Family-owned firms						Yes	Yes	Yes	Yes	Yes
Firm dummy		Yes		Yes			Yes		Yes
Industry dummy			Yes		Yes			Yes		Yes
Time dummy				Yes	Yes				Yes	Yes
F-test	8.92		13.88		7.84	146.36		116.12		74.24
R ²	0.410		0.884		0.886	0.381		0.516		0.533
Adjusted R²	0.364		0.860		0.854	0.378		0.497		0.513
RMSE	0.107		0.050		0.051	0.100		0.090		0.088
N	153	153	153	153	153	2626		2626	2626	2626
Sigma_u		0.130		0.113			0.0802		0.082
Sigma_e		0.050		0.051			0.069		0.067
Rho		0.870		0.828			0.570		0.596

Source: The authors.

Notes: Values in the table represent coefficients for each variable and t-statistics are in the parentheses.

This table examines the impact of board composition on institutional investors’ ownership with respect to government firms and family-owned firms for a period 2001–2011. Models 11–15 represent the results of government firms and models 16–20 are the results of family-owned firms. The dependent variables are as follows. Institutional Ownership: measured as the percentage of equity held by non-promoters institutions. Log Firm Size: natural log of total assets. Leverage Ratio: total borrowings to total assets net of cash holding. Market to Book Ratio: book value of assets less book value of equity plus market value of equity divided by book value of assets. Networking Capital: difference of current assets and current liabilities net of cash to total assets. Cash Flow: profit after tax before depreciation and amortisation by total assets. Cash Flow Volatility: rolling standard deviation of cash flow taken three years at a time. Research and Development to Asset: research and development expenditure to total assets. The variable in the table are measured as: Affected Firms is a dummy variable equal to 1 for the firms with a presence of less than 50% of independent directors in the board and less than 33% in case the chairman is a non-executive or else 0. Post-Mandate is a dummy variable equal to 1 from 2006 to 2010 indicating the post-mandate period and equal to 0 from 2001 to 2004 indicating the pre-mandate period. Affected Firms × Post-Mandate is the interaction term between Affected and Post.

***, ** and * indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

Models 16–20 (Table 4) report the results of family-owned firms. Model 17 shows that coefficient of affected firms is negative and insignificant and the coefficient of post-mandate is negative and statistically significant, indicating family-owned firms in the post-mandate era have lower institutional investors’ shareholding compared to the pre-mandate era. However, the coefficient of the interaction term affected firms × post-mandate is positive and statistically significant, and shows that family-owned affected firms that enacted the governance mandate experienced higher institutional investors’ ownership in the post-mandate period compared to remaining firms. It shows that family-owned group firms got a positive and significant reward from institutional investors in the form of investment in their firms. In general, the board structure of family-owned firms is predominantly controlled and influenced by family members and executives. However, the mandate ensures board independence, and it has compelled the family-owned firms to change the board composition. The institutional investors’ community welcomes this particular move by the family-owned firms and invested more in affected family-owned firms in the post-mandate period with respect to other firms.

Models 21–25 and models 26–30 (Table 5) report the results of private firms and foreign firms, respectively. The co-efficient of affected firms is positive and significant, but the co-efficient of post-mandate is negative and significant, suggesting that institutional investors are maintaining significant shareholding of affected private and foreign firms and experiencing a drop in its shareholding in the post-mandate period relative to pre-mandate period. Since the coefficient of affected firms × post-mandate is positive and insignificant, the affected private and foreign controlled firms have not experienced any significant increase in the institutional investors’ shareholding after the governance enactment compared to other firms. One of the reasons for this may be the prior presence of significant shareholding by institutional investors in the private and foreign firms, institutional investors are already maintaining higher shareholding in the firms in the pre-mandate period.

Table 5.

Impact of Board Composition on Institutional Investors Ownership with Respect to Private and Foreign Firms

	Model 21	Model 22	Model 23	Model 24	Model 25	Model 26	Model 27	Model 28	Model 29	Model 30
Log firm size	0.095*** (22.56)	0.081*** (14.06)	0.082*** (17.52)	0.102*** (17.13)	0.090*** (18.92)	0.134*** (13.61)	0.073*** (4.72)	0.129*** (8.98)	0.114*** (7.05)	0.158*** (10.65)
Leverage ratio	–0.064*** (–5.93)	–0.046*** (–3.59)	–0.046*** (–3.80)	–0.043*** (–3.44)	–0.043*** (–3.64)	–0.052** (–2.37)	0.031 (1.23)	0.046* (1.83)	0.004 (0.16)	0.036 (1.48)
Market to book ratio	0.013*** (4.91)	0.006** (2.55)	0.007** (2.84)	0.004* (1.68)	0.006** (2.22)	–0.001 (–0.28)	0.012*** (3.31)	0.002 (0.60)	0.011** (2.78)	0.000 (0.02)
Networking capital	–0.035*** (–3.52)	–0.039*** (–3.14)	–0.054*** (–4.51)	–0.023* (–1.85)	–0.044*** (–3.69)	–0.049** (–2.34)	–0.054* (–1.96)	–0.056 (–1.67)	–0.053* (–1.99)	–0.054 (–1.65)
Cash flow	0.026 (1.24)	–0.001 (–0.06)	0.029 (1.42)	–0.003 (–0.18)	0.029 (1.42)	0.120* (1.70)	0.075 (1.24)	0.164** (2.43)	0.013 (0.22)	0.126* (1.90)
Cash flow volatility	0.020 (0.61)	–0.024 (–0.87)	–0.039 (–1.21)	–0.011 (–0.43)	–0.029 (–0.91)	–0.166 (–1.28)	–0.095 (–0.88)	–0.279** (–2.31)	–0.130 (–1.24)	–0.316** (–2.68)
Dividend dummy	–0.010* (–1.83)	–0.003 (–0.55)	0.000 (0.02)	–0.009* (–1.79)	–0.004 (–0.67)	–0.001 (–0.11)	0.022* (1.73)	0.039*** (3.04)	0.016 (1.30)	0.032** (2.48)
Research and development to asset	0.002 (1.12)	0.000 (0.09)	0.003 (1.15)	0.000 (–0.02)	0.002 (0.97)	0.006 (1.56)	0.005 (0.83)	–0.001 (–0.16)	0.000 (0.04)	–0.004 (–1.00)
Affected firms	0.007 (0.92)	0.005 (0.47)	0.001 (0.18)	0.007 (0.69)	0.002 (0.29)	0.004 (0.25)	0.001 (0.05)	–0.029* (–1.77)	0.001 (0.06)	–0.022 (–1.37)
Post-mandate	–0.018** (–2.70)	–0.010** (–2.02)	–0.012* (–1.89)	–0.054*** (–5.02)	–0.051*** (–3.75)	–0.036** (–2.73)	–0.013 (–1.28)	–0.029** (–2.53)	–0.060** (–2.37)	–0.082*** (–2.93)
Affected firms × Post-mandate	0.006 (0.70)	0.005 (0.76)	0.006 (0.73)	0.003 (0.51)	0.006 (0.66)	0.020 (1.10)	0.019 (1.40)	0.019 (1.23)	0.020 (1.55)	0.022 (1.47)
Constant	–0.230*** (–15.79)	–0.180*** (–9.09)	–0.189*** (–12.01)	–0.234*** (–10.82)	–0.197*** (–10.27)	–0.360*** (–9.74)	–0.187*** (–3.29)	–0.367*** (–7.22)	–0.323*** (–5.41)	–0.457*** (–8.22)
Private firms	Yes	Yes	Yes	Yes	Yes
Foreign firms						Yes	Yes	Yes	Yes	Yes
Firm dummy		Yes		Yes			Yes		Yes
Industry dummy			Yes		Yes			Yes		Yes
Time dummy				Yes	Yes				Yes	Yes
F-test	80.34		56.78		37.27	30.01		18.50		13.38
R ²	0.300		0.414		0.433	0.432		0.647		0.677
Adjusted R²	0.296		0.388		0.405	0.418		0.607		0.633
RMSE	0.098		0.091		0.090	0.091		0.075		0.072
N	2074	2074	2074	2074	2074	445	445	445	445	445
Sigma_u		0.069		0.068			0.066		0.063
Sigma_e		0.068		0.066			0.062		0.061
Rho		0.510		0.518			0.536		0.515

Source: The authors.

Notes: Values in the table represent coefficients for each variable and t-statistics are in the parentheses.

This table examines the impact of board composition on institutional investors’ ownership with respect to private firms and foreign firms for a period 2001–2011. Models 21–25 represents the results of private firms and models 26–30 represents the results of foreign firms. The dependent variable is Institutional Ownership measured as the percentage of equity held by non-promoters institutions. Log Firm Size: natural log of total assets. Leverage Ratio: total borrowings to total assets net of cash holding. Market to Book Ratio: book value of assets less book value of equity plus market value of equity divided by book value of assets. Networking Capital: difference of current assets and current liabilities net of cash to total assets. Cash Flow: profit after tax before depreciation and amortisation by total assets. Cash Flow Volatility: rolling standard deviation of cash flow taken three years at a time. Research and Development to Asset: research and development expenditure to total assets. The variable in the table are measured as: Affected Firms is a dummy variable equal to 1 for the firms with a presence of less than 50% of independent directors in the board and less than 33% in case chairman is a non-executive or else 0. POST-MANDATE is a dummy variable equal to 1 from 2006 to 2010 indicating post-mandate period and equal to 0 from 2001 to 2004 indicating pre-mandate period. Affected Firms × Post-Mandate is the interaction term between Affected and Post.

***, ** and * indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

4.3 Robustness Check

Stock market dynamics may play a significant role in the investment decisions of institutional investors and invest more in firms during the economic boom and sell off their shares in the period of financial crisis. Hence, it becomes crucial to examine the relationship between institutional investors’ shareholding and board structure after controlling for the stock market movement. We use market return as a proxy for the market condition and include that in the existing model (Table 6). To make our results more robust, we used two alternative ways to calculate the market return. First, we calculate the market return by taking the yearly Nifty return. Second, we calculate the average annual return after taking an average of the daily return for each year of Nifty. We find that the inclusion of market return does not change the results which indicate that our findings are robust, and market factor does not affect the relationship between institutional investors and board structure. We also repeat all the analysis after including data pertaining to the year 2005 and find that the result remains unchanged after using this new sample (results are available on request). In addition, we also perform the same analysis by using an alternative definition of affected firms to check the sensitivity of result, we repeat the analysis after defining affected firms as the firms which have the ratio of the independent directors to total directors below the median ratio. Table 7 shows that the coefficients of the DiD term affected firms × post-mandate are still positive and statistically significant in all sets of analysis. It indicates that our results are robust to the change in the definition of the affected firms as well.

Table 6.
Impact of Board Composition on Institutional Investors Ownership After Controlling for Market Return Average and Market Return Yearly

Model 31 Model 32 Model 33 Model 34 Model 35 Model 36 Model 37 Model 38 Model 39 Model 40

Log firm size 0.106*
(42.98) 0.082*
(16.68) 0.117*
(22.12) 0.109*
(39.48) 0.114*
(41.31) 0.106*
(42.93) 0.081*
(16.61) 0.117*
(22.12) 0.108*
(39.41) 0.114*
(41.31)

Net leverage ratio –0.042*
(–6.36) –0.025*
(–2.77) –0.026*
(–3.01) –0.029]*
(–4.09) –0.027*
(–3.86) –0.042*
(–6.3) –0.025*
(–2.79) –0.026*
(–3.01) –0.029*
(–4.05) –0.027*
(–3.86)

Market to book ratio 0.003*
(2.86) 0.004*
(3.18) 0.004*
(3.43) 0.001
(0.93) 0.001
(0.61) 0.004*
(3.1) 0.005*
(3.7) 0.004*
(3.43) 0.001
(1.23) 0.001
(0.61)

Networking capital –0.017*
(–2.66) –0.018
(–2.07) 0.002
(0.22) –0.006
(–0.8) 0.001
(0.23) –0.017*
(–2.69) –0.019
(–2.15) 0.002
(0.22) –0.006
(–0.86)– 0.001
(0.23)

Cash flow 0.019
(1.42) 0.007
(0.69) 0.007
(0.7) 0.024*
(1.86) 0.023*
(1.81) 0.020
(1.54) 0.008
(0.85) 0.007
(0.7) 0.025**
(1.98) 0.023*
(1.81)

Cash flow volatility 0.009
(0.44) –0.090*
(–5.39) –0.077*
(–4.74) –0.006
(–0.31) 0.004
(0.24) 0.009
(0.49) –0.088*
(–5.29) –0.077*
(–4.74) –0.005
(–0.26) 0.004
(0.24)

Dividend dummy –0.002
(–0.63) –0.001
(–0.29) –0.006*
(–1.84) –0.003
(–0.99) –0.006*
(–1.68) –0.002
(–0.57) –0.001
(–0.15) –0.006*
(–1.84) –0.003
(–0.9) –0.006*
(–1.68)

Research and development to asset 0.001*
(1.72) 0.001
(0.8) 0.001
(1.19) –0.001
(–0.38) –0.001
(–0.52) 0.001*
(1.72) 0.001
(0.84) 0.001
(1.19) –0.00
(–0.36) –0.001
(–0.52)

Market return average 0.007
(0.80) 0.008
(1.35) 0.044**
(1.97) 0.010
(1.21) 0.034
(1.10)

Market return yearly –0.005
(–1.5) –0.004*
(–1.830 0.037**
(1.97) –0.005
(–1.38) 0.029
(1.10

Affected firms –0.009*
(–1.95) –0.066
(–0.92) –0.079
(–1.13) –0.0074
(–1.63) –0.007
(–1.55) –0.009*
(–1.92) –0.064
(–0.89) –0.079
(–1.13) –0.007
(–1.59) –0.007
(–1.55)

Post-mandate –0.023*
(–5.6) –0.011*
(–3.45) –0.064*
(–10.86) –0.022*
(–5.64) –0.065*
(–9.14) –0.024*
(–5.81) –0.012*
(–3.69) –0.065*
(–10.85) –0.023*
(–5.87) –0.065*
(9.11)

Affected firms × Post-mandate 0.012
(2.1) 0.010
(2.59) 0.008
(2.17) 0.011
(2.13) 0.011
(2.04) 0.012
(2.07) 0.010
(2.55) 0.008
(2.17) 0.011
(2.11) 0.011
(2.04)

Constant –0.259*
(–28.00) –0.150*
(–3.82) –0.241*
(–6.21) –0.266*
(–26.55) –0.261*
(–22.82) –0.256*
(–27.72) –0.148
(–3.78) –0.242*
(–6.22) –0.263*
(–26.29) –0.261*
(–22.90)

Firm dummy Yes Yes Yes Yes

Industry dummy Yes Yes Yes Yes

Time dummy Yes Yes Yes Yes

F-test 228.04 48.15 186.24 128.93

R ² 0.3411 0.7489 0.4301 0.4464 228.24 48.15 186.29 128.93

Adjusted R² 0.3396 0.7146 0.4163 0.4323 0.3413 0.7489 0.4301 0.4464

RMSE 0.10163 0.0668 0.0955 0.0942 0.3398 0.7146 0.4164 0.4323

N 5298 5298 5298 5298 5298 0.10161 0.0668 0.0955 0.0942

Sigma_u 0.0850 5298 5298 5298 5298 5298

Sigma_e 0.0686 0.0847

Rho 0.6059 0.0685

	Model 31	Model 32	Model 33	Model 34	Model 35	Model 36	Model 37	Model 38	Model 39	Model 40
Log firm size	0.106*** (42.98)	0.082*** (16.68)	0.117*** (22.12)	0.109*** (39.48)	0.114*** (41.31)	0.106*** (42.93)	0.081*** (16.61)	0.117*** (22.12)	0.108*** (39.41)	0.114*** (41.31)
Net leverage ratio	–0.042*** (–6.36)	–0.025*** (–2.77)	–0.026*** (–3.01)	–0.029]*** (–4.09)	–0.027*** (–3.86)	–0.042*** (–6.3)	–0.025*** (–2.79)	–0.026*** (–3.01)	–0.029*** (–4.05)	–0.027*** (–3.86)
Market to book ratio	0.003*** (2.86)	0.004*** (3.18)	0.004*** (3.43)	0.001 (0.93)	0.001 (0.61)	0.004*** (3.1)	0.005*** (3.7)	0.004*** (3.43)	0.001 (1.23)	0.001 (0.61)
Networking capital	–0.017*** (–2.66)	–0.018** (–2.07)	0.002 (0.22)	–0.006 (–0.8)	0.001 (0.23)	–0.017*** (–2.69)	–0.019** (–2.15)	0.002 (0.22)	–0.006 (–0.86)–	0.001 (0.23)
Cash flow	0.019 (1.42)	0.007 (0.69)	0.007 (0.7)	0.024* (1.86)	0.023* (1.81)	0.020 (1.54)	0.008 (0.85)	0.007 (0.7)	0.025** (1.98)	0.023* (1.81)
Cash flow volatility	0.009 (0.44)	–0.090*** (–5.39)	–0.077*** (–4.74)	–0.006 (–0.31)	0.004 (0.24)	0.009 (0.49)	–0.088*** (–5.29)	–0.077*** (–4.74)	–0.005 (–0.26)	0.004 (0.24)
Dividend dummy	–0.002 (–0.63)	–0.001 (–0.29)	–0.006* (–1.84)	–0.003 (–0.99)	–0.006* (–1.68)	–0.002 (–0.57)	–0.001 (–0.15)	–0.006* (–1.84)	–0.003 (–0.9)	–0.006* (–1.68)
Research and development to asset	0.001* (1.72)	0.001 (0.8)	0.001 (1.19)	–0.001 (–0.38)	–0.001 (–0.52)	0.001* (1.72)	0.001 (0.84)	0.001 (1.19)	–0.00 (–0.36)	–0.001 (–0.52)
Market return average	0.007 (0.80)	0.008 (1.35)	0.044** (1.97)	0.010 (1.21)	0.034 (1.10)
Market return yearly						–0.005 (–1.5)	–0.004* (–1.830	0.037** (1.97)	–0.005 (–1.38)	0.029 (1.10
Affected firms	–0.009* (–1.95)	–0.066 (–0.92)	–0.079 (–1.13)	–0.0074 (–1.63)	–0.007 (–1.55)	–0.009* (–1.92)	–0.064 (–0.89)	–0.079 (–1.13)	–0.007 (–1.59)	–0.007 (–1.55)
Post-mandate	–0.023*** (–5.6)	–0.011*** (–3.45)	–0.064*** (–10.86)	–0.022*** (–5.64)	–0.065*** (–9.14)	–0.024*** (–5.81)	–0.012*** (–3.69)	–0.065*** (–10.85)	–0.023*** (–5.87)	–0.065*** (9.11)
Affected firms × Post-mandate	0.012** (2.1)	0.010** (2.59)	0.008** (2.17)	0.011** (2.13)	0.011** (2.04)	0.012** (2.07)	0.010** (2.55)	0.008** (2.17)	0.011** (2.11)	0.011** (2.04)
Constant	–0.259*** (–28.00)	–0.150*** (–3.82)	–0.241*** (–6.21)	–0.266*** (–26.55)	–0.261*** (–22.82)	–0.256*** (–27.72)	–0.148** (–3.78)	–0.242*** (–6.22)	–0.263*** (–26.29)	–0.261*** (–22.90)
Firm dummy		Yes	Yes				Yes	Yes
Industry dummy				Yes	Yes				Yes	Yes
Time dummy			Yes		Yes			Yes		Yes
F-test	228.04		48.15	186.24	128.93
R ²	0.3411		0.7489	0.4301	0.4464	228.24		48.15	186.29	128.93
Adjusted R²	0.3396		0.7146	0.4163	0.4323	0.3413		0.7489	0.4301	0.4464
RMSE	0.10163		0.0668	0.0955	0.0942	0.3398		0.7146	0.4164	0.4323
N	5298	5298	5298	5298	5298	0.10161		0.0668	0.0955	0.0942
Sigma_u		0.0850				5298	5298	5298	5298	5298
Sigma_e		0.0686					0.0847
Rho		0.6059					0.0685

Source: The authors.

Notes: Values in the table represent coefficients for each variable and t-statistics are in the parentheses.

This table examines the impact of board composition on institutional investors’ ownership after controlling for market returns for a period 2001–2011. Market return average is measured as the average daily return of Nifty and market return yearly is measured as the yearly return of Nifty. The dependent variable is Institutional Ownership measured as the percentage of equity held by non-promoters institutions. Log Firm Size: natural log of total assets. Leverage Ratio: total borrowings to total assets net of cash holding. Market to Book Ratio: book value of assets less book value of equity plus market value of equity divided by book value of assets. Networking Capital: difference of current assets and current liabilities net of cash to total assets. Cash Flow: profit after tax before depreciation and amortisation by total assets. Cash Flow Volatility: rolling standard deviation of cash flow taken three years at a time. Research and Development to Asset: research and development expenditure to total assets. The variable in the table are measured as: Affected Firms is a dummy variable equal to 1 for the firms with a presence of less than 50% of independent directors in the board and less than 33% in case the chairman is a non-executive or else 0. Post-Mandate is a dummy variable equal to 1 from 2006 to 2010 indicating the post-mandate period and equal to 0 from 2001 to 2004 indicating the pre-mandate period. Affected Firms × Post-Mandate is the interaction term between Affected and Post.

***, ** and * indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

Table 7.

Impact of Board Composition on Institutional Investors Ownership (Below Median)

	Model 41	Model 42	Model 43	Model 44	Model 45	Model 46
Log firm size		0.106*** (43.12)	0.082*** (16.74)	0.117*** (22.22)	0.108*** (39.59)	0.114*** (41.38)
Net leverage ratio		–0.042*** (–6.38)	–0.025*** (–2.8)	–0.026*** (–3.04)	–0.029*** (–4.08)	–0.027*** (–3.87)
Market to book ratio		0.003*** (2.98)	0.004*** (3.44)	0.004*** (3.38)	0.001 (1.08)	0.001 (0.59)
Networking capital		–0.017*** (–2.69)	–0.019** (–2.12)	0.001 (0.2)	–0.006 (–0.85)	0.001 (0.22)
Cash flow		0.019 (1.45)	0.008 (0.77)	0.007 (0.72)	0.025* (1.92)	0.023* (1.81)
Cash flow volatility		0.010 (0.51)	–0.088*** (–5.27)	–0.076*** (–4.66)	–0.004 (–0.23)	0.006 (0.31)
Dividend dummy		–0.002 (–0.59)	–0.001 (–0.13)	–0.006* (–1.76)	–0.003 (–0.91)	–0.006* (–1.65)
Research and development to asset		0.001* (1.74)	0.001 (0.83)	0.001 (1.19)	–0.001 (–0.37)	–0.001 (–0.52)
Below median	–0.015*** (–2.67)	–0.013*** (–2.97)	–0.066* (–1.82)	–0.059* (–1.67)	–0.011** (–2.47)	–0.010** (–2.4)
Post-mandate	0.015*** (2.96)	–0.027*** (–6.12)	–0.015*** (–4.29)	–0.022*** (–3.82)	–0.026*** (–6.2)	–0.070*** (–9.13)
Below medina × Post-mandate	0.018*** (2.61)	0.017*** (2.97)	0.015*** (3.78)	0.014*** (3.6)	0.016*** (3.01)	0.016*** (2.94)
Constant	0.109*** (25.89)	–0.2548*** (–27.33)	–0.144*** (–5.47)	–0.250*** (–9.30)	–0.262*** (–26.08)	–0.260*** (–22.92)
Firm dummy			Yes	yes
Industry dummy					Yes	Yes
Time dummy				Yes		Yes

F-test	20.29		57.20	48.72	203.62	129.28
R ²	0.0114	0.3417			0.4304	0.4469
Adjusted R²	0.0108	0.3403			0.4168	0.4328
RMSE	0.12439	0.10158			0.0955	0.0942
N	5298	5298	5298	5298	5298	5298
Sigma_u			0.0840	0.0820
Sigma_e			0.0685	0.0667
Rho			0.6007	0.6015

Source: The authors.

Notes: Values in the table represent coefficients for each variable and t-statistics are in the parentheses.

This table examines the impact of board composition on institutional investors’ ownership for a period 2001–2011. The variable Below Median is a dummy variable equal to 1 for the firms which are below the median ratio of Independent Director to Total Director or else 0. Below Median × Post is an interaction term of below median and post. The dependent variable is Institutional Ownership measured as the percentage of equity held by non-promoters institutions. Log Firm Size: natural log of total assets. Leverage Ratio: total borrowings to total assets net of cash holding. Market to Book Ratio: book value of assets less book value of equity plus market value of equity divided by book value of assets. Networking Capital: difference of current assets and current liabilities net of cash to total assets. Cash Flow: profit after tax before depreciation and amortisation by total assets. Cash Flow Volatility: rolling standard deviation of cash flow taken three years at a time. Research and Development to Asset: research and development expenditure to total assets. Post-Mandate is a dummy variable equal to 1 from 2006 to 2010 indicating the post-mandate period and equal to 0 from 2001 to 2004 indicating the pre-mandate period.

***, ** and * indicate significance at 0.01, 0.05 and 0.10 levels, respectively.

5. Conclusion

We differ from previous studies by focusing on firms that have to make substantial changes in their board structure (affected firms) to comply with the governance norms and compare their institutional shareholdings to the firms that are already compliant by the mandate. Our study is the first study to examine the resource dependency and signalling role of board composition towards institutional investors. SEBI revised Clause 49 to improve the corporate governance standard, and we use this setup as a quasi-natural experiment to examine if the composition of the board signals information to institutional investors is in the post-mandate era. We find that non-compliant firms have experienced significantly higher institutional investors’ shareholding in the post-reform period compared to previously compliant firms. Thus, board independence is linked to higher institutional investors’ ownership and it decreases the agency cost and cost of monitoring for the institutional investor. As a result, institutional investors have increased their shareholding in the affected firms in the post-mandate period. Our results are similar to Rhe and Lee (2008), who finds that demographic characteristics of non-executive directors serve as signal to foreign investors in the Korean market and Schnatterly and Johnson (2014) which infer that institutional investors prefer independent board.

We further find that our results are consistent with the resource dependency theory, which states that the board of directors enable easy access to external resources for the firms; they also minimise the transaction cost, improve exchange relationships and bring specialised skills and expertise. These linkages and board independence might decrease the agency cost and cost of monitoring for the institutional investors and could improve the performance of the firms. Therefore, the institutional investors have increased their shareholding in the firms in the post-mandate period. We also find evidence that institutional investors have invested more in family-owned firms in the post-mandate period, than the government-, private- and foreign-owned firms. Historically, the board structure of family firms is controlled and influenced by family members and insiders, but in the post-mandate era family firms have considerably increased the number of independent directors on the board. This has reduced the information asymmetry between institutional investors and insiders.

Overall, our study contributes to the existing literature on the resource dependency theory and signalling theory and shows that board independence act as a signal to institutional investors and reduces agency and monitoring cost. This study is the first study that uses the quasi-natural experiment to study the relationship between the board independence and institutional ownership. The use of exogenous shock to board independence also enables us to address the crucial issue of endogeneity which is present in the existing literature. Our study has policy implications as it provides empirical evidence about the importance of independent board and role of independent directors in improving corporate governance and strengthening the impact of regulatory measures. The finding that institutional investors pay attention to the board independence may influence the firm board strategy and operations, and a firm’s top management can structure their board including independent directors to signal better governance. Future research may be done to examine if these findings are valid for different types of institutional investors, that is, domestic and foreign institutional investors and explore if the demographic composition of the board of directors also signals institutional investors.

6. Summary of the Finding

Main Finding
Board composition and institutional investors	Board composition matter to institutional investors and board independence acts as a signal to institutional investors and decreases the agency cost and cost of monitoring. Institutional investors increase their ownership in the firm which increases the number of independent directors on the board.
Blockholders and intuitional investors	Institutional investors have invested more in family-owned firms which increases the number of independent directors on the board compared to government-, private- and foreign-owned firms.
Contribution
Methodological	The study contributes by examining the causal relation between board independence and institutional investors’ holdings using an exogenous shock to board independence in the Indian public firms arising from Clause 49. The use of exogenous shock to board independence also enables us to address the crucial issue of endogeneity which was present in the literature.
Theoretical	Our study is the first study to examine the resource dependency and signalling role of board composition towards institutional investors. We also contribute by examining the moderation effect of the presence of large blockholders on independent directors and institutional investors.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Admati

A. R.

, Pfleiderer

, & Zechner

(1994). Large shareholder activism, risk sharing, and financial market equilibrium. Journal of Political Economy, 102(6), 1097–1130.

Aggarwal

, Erel

, Ferreira

, & Matos

(2011). Does governance travel around the world? Evidence from institutional investors. Journal of Financial Economics, 100(1), 154–181.

Aggarwal

, Schloetzer

J. D.

, & Williamson

(2014). Economic consequences of corporate governance mandates for poorly governed firms. SSRN 891411.

Ayres

, & Cramton

(1993). Relational investing and agency theory. Cardozo Law Review, 15, 1033–1066.

Badrinath

S. G.

, Gay

G. D.

, & Kale

J. R.

(1989). Patterns of institutional investment, prudence, and the managerial “safety-net” hypothesis. Journal of Risk and Insurance, 56(4), 605–629.

Balsmeier

, Fleming

, & Manso

(2017). Independent boards and innovation. Journal of Financial Economics, 123(3), 536–557.

Bebchuk

L. A.

, Kraakman

, & Triantis

(2000). Stock pyramids, cross-ownership, and dual class equity: The mechanisms and agency costs of separating control from cash-flow rights. In Mork

R. K.

(Ed.), Concentrated corporate ownership (pp. 295–318). Chicago, IL: University of Chicago Press.

Bertoni

, Meoli

, & Vismara

(2014). Board independence, ownership structure and the valuation of IPOs in continental Europe. Corporate Governance: An International Review, 22(2), 116–131.

Bradley

, & Chen

(2015). Does board independence reduce the cost of debt? Financial Management, 44(1), 15–47.

10.

Bushee

B. J.

, Carter

M. E.

, & Gerakos

(2013). Institutional investor preferences for corporate governance mechanisms. Journal of Management Accounting Research, 26(2), 123–149.

11.

Byrd

J. W.

, & Hickman

K. A.

(1992). Do outside directors monitor managers? Evidence from tender offer bids. Journal of Financial Economics, 32(2), 195–221.

12.

Christy

J. A.

, Matolcsy

Z. P.

, Wright

, & Wyatt

(2013). Do board characteristics influence the shareholders’ assessment of risk for small and large firms? Abacus, 49(2), 161–196.

13.

Chung

K. H.

, & Zhang

(2011). Corporate governance and institutional ownership. Journal of Financial and Quantitative Analysis, 46(1), 247–273.

14.

Coles

J. L.

, Daniel

N. D.

, & Naveen

(2008). Boards: Does one size fit all? Journal of Financial Economics, 87(2), 329–356.

15.

Deutsch

, & Ross

T. W.

(2003). You are known by the directors you keep: Reputable directors as a signaling mechanism for young firms. Management Science, 49(8), 1003–1017.

16.

Falkenstein

E. G.

(1996). Preferences for stock characteristics as revealed by mutual fund portfolio holdings. The Journal of Finance, 51(1), 111–135.

17.

Fama

E. F.

, & Jensen

M. C.

(1983). Separation of ownership and control. Journal of Law and Economics, 26(2), 301–325.

18.

Gompers

P. A.

, & Metrick

(2001). Institutional investors and equity prices. The Quarterly Journal of Economics, 116(1), 229–259.

19.

Guo

, & Masulis

R. W.

(2015). Board structure and monitoring: New evidence from CEO turnovers. The Review of Financial Studies, 28(10), 2770–2811.

20.

Guo

, Lach

, & Mobbs

(2015). Tradeoffs between internal and external governance: Evidence from exogenous regulatory shocks. Financial Management, 44(1), 81–114.

21.

Helland

, & Sykuta

(2005). Who’s monitoring the monitor? Do outside directors protect shareholders’ interests? Financial Review, 40(2), 155–172.

22.

Hessel

C. A.

, & Norman

(1992). Financial characteristics of neglected and institutionally held stocks. Journal of Accounting, Auditing & Finance, 7(3), 313–330.

23.

Hillman

A. J.

, Cannella

A. A.

, & Paetzold

R. L.

(2000). The resource dependence role of corporate directors: Strategic adaptation of board composition in response to environmental change. Journal of Management Studies, 37(2), 235–256.

24.

Johnson

R. A.

, Hoskisson

R. E.

, & Hitt

M. A.

(1993). Board of director involvement in restructuring: The effects of board versus managerial controls and characteristics. Strategic Management Journal, 14(S1), 33–50.

25.

Kim

, & Cannella

A. A.

(2008). Toward a social capital theory of director selection. Corporate Governance: An International Review, 16(4), 282–293.

26.

La Porta

, Lopez-De-Silanes

, Shleifer

, & Vishny

(2002). Investor protection and corporate valuation. The Journal of Finance, 57(3), 1147–1170.

27.

Lehn

K. M.

, Patro

, & Zhao

(2009). Determinants of the size and composition of US corporate boards: 1935–2000. Financial Management, 38(4), 747–780.

28.

Liu

, Miletkov

M. K.

, Wei

, & Yang

(2015). Board independence and firm performance in China. Journal of Corporate Finance, 30(1), 223–244.

29.

, & Wang

(2018). Managerial conservatism, board independence and corporate innovation. Journal of Corporate Finance, 48(1), 1–16.

30.

McCahery

J. A.

, Sautner

, & Starks

L. T.

(2016). Behind the scenes: The corporate governance preferences of institutional investors. The Journal of Finance, 71(6), 2905–2932.

31.

Mohanty

(2003). Institutional investors and corporate governance in India (National Stock Exchange of India Research Initiative Paper No. 15). Retrieved from http://nseindia.com/content/research/Paper42.pdf

32.

Pearce

J. A.

, Robinson

R. B.

, & Schepker

D. J.

(2014). Board stability and independence reduce volatility in firm revenues. Academy of Management Proceedings, 2014(1), 12188.

33.

Rhee

, & Lee

J. H.

(2008). The signals outside directors send to foreign investors: Evidence from Korea. Corporate Governance: An International Review, 16(1), 41–51.

34.

Roe

M.J.

(2005) The Institutions of Corporate Governance. In Handbook of New Institutional Economics (pp. 371–399). Boston, MA: Springer.

35.

Sanders

W. M.

, & Boivie

(2004). Sorting things out: Valuation of new firms in uncertain markets. Strategic Management Journal, 25(2), 167–186.

36.

Sarkar

(2010). Ownership and corporate governance in Indian firms. In Corporate governance: An emerging scenario (pp. 217–267). National Stock Exchange of India Ltd.

37.

Sarkar

, & Sarkar

(2000). Large shareholder activism in corporate governance in developing countries: Evidence from India. International Review of Finance, 1(3), 161–194.

38.

Schnatterly

, & Johnson

S. G.

(2014). Independent boards and the institutional investors that prefer them: Drivers of institutional investor heterogeneity in governance preferences. Strategic Management Journal, 35(10), 1552–1563.

39.

Shleifer

, & Vishny

R. W.

(1997). A survey of corporate governance. The Journal of Finance, 52(2), 737–783.

40.

Sonenshine

, Larson

, & Cauvel

(2015). The effect of mergers, divestitures, and board composition on CEO compensation before and after the financial crisis (Working Paper No. 2015–08). Washington, DC: American University, Department of Economics.

41.

Vafeas

(2000). The determinants of compensation committee membership. Corporate Governance: An International Review, 8(4), 356–366.

42.

Wahal

, & McConnell

J. J.

(2000). Do institutional investors exacerbate managerial myopia? Journal of Corporate Finance, 6(3), 307–329.

43.

Weisbach

M. S.

(1988). Outside directors and CEO turnover. Journal of Financial Economics, 20(1), 431–460.

44.

Zahra

S. A.

, & Pearce

J. A.

(1989). Boards of directors and corporate financial performance: A review and integrative model. Journal of Management, 15(2), 291–334.

45.

Zhang

J. Q.

, Zhu

, & Ding

H. B.

(2013). Board composition and corporate social responsibility: An empirical investigation in the post Sarbanes-Oxley era. Journal of Business Ethics, 114(3), 381–392.