Informational Tradeoffs From Banking Concentration and Institutional Ownership on Firm Investment: Evidence From Europe

Abstract

We examine the influence of institutional investors and banking concentration on financial constraints in 37 European countries from 2001 to 2022. Financial constraints, arising from market imperfections such as asymmetric information, affect corporate risk-taking and investment decisions. We posit that both banking concentration in performing loans and the presence of institutional investors play a central role in alleviating financial constraints. Consistent with the information hypothesis, our results show that banking concentration reduces financial constraints, with stronger effects among smaller firms, firms with medium or low credit ratings, and industries with low external finance dependence. This effect is attenuated by institutional blockholders, particularly independent ones with larger equity holdings. Contrary to evidence that institutional investors favor large, well-governed firms, we find that their direct and moderating effects are stronger in smaller firms and low external financial dependence industries. Results remain robust to heterogeneity analyses by institutional investor type and investment horizon.

JEL Classification: G32; G34

Keywords

financial constraints investment–cash flow sensitivity banking concentration institutional investors

Introduction

Over the past 20 years, the European banking industry has experienced substantial deregulation and consolidation, increasing its competitiveness, concentration, and market power.¹ At the same time, institutional investors have significantly expanded their participation and influence in European financial markets and corporate financing (Organisation for Economic Co-operation and Development [OECD], 2020). This expansion has improved the quality and flow of financial information (Bird & Karolyi, 2016; R. Wang, 2021), enhanced liquidity management (Chung et al., 2010; Chung et al., 2012), strengthened corporate governance, and increased capital efficiency (Alvarez et al., 2018; Bhagat & Hubbard, 2022), thereby contributing to better risk assessment and financial stability.

Regulatory initiatives such as MiFID I (2007), MiFID II (2018), and the Capital Markets Union (2015) have further strengthened the role of institutional investors as both shareholders and debtholders. According to the OECD (2020), institutional investors’ total assets increased from €14.9 trillion to €18.9 trillion between 2012 and 2018, driven by growth in equity holdings, investment fund shares, and debt securities.²

Both banks and institutional investors play a central role in allocating resources to corporate investment, but their effectiveness in relaxing a firm’s financial constraints depends on their respective abilities to assess credit risk. The literature offers two competing views on how banking industry structure affects financial constraints. The Information Hypothesis (IH) argues that greater banking concentration alleviates financial constraints by incentivizing banks to acquire private information and establish long-term lending relationships, thereby reducing agency costs and information asymmetries (Alvarez & Jara, 2016; Leon, 2015; Marquez, 2002; Ratti et al., 2008). Empirical evidence supports this view, showing enhanced monitoring and more efficient credit allocation (Beck et al., 2018; DeYoung et al., 2015).

In contrast, the Market Power Hypothesis (MPH) contends that consolidation increases banks’ market power, leading to higher borrowing costs and potentially tighter financial constraints. While some studies find improved credit access (Drechsler et al., 2021), most evidence highlights adverse effects associated with higher concentration and risk-taking behavior (Acharya & Steffen, 2015; Beck et al., 2004; Hussain Khan & Kutan, 2023; Love & Martínez Pería, 2015; Ryan et al., 2014).

Institutional investors also affect firms’ financial constraints, both directly through lower borrowing costs and indirectly via monitoring and improved information disclosure (Edmans & Holderness, 2017; R. Wang, 2021). Empirical studies show that greater institutional ownership, particularly by blockholders, is associated with higher firm value (Almazán et al., 2005; Ferreira & Matos, 2008), lower bond yield spreads (Elyasiani et al., 2010), stronger governance (Aggarwal et al., 2011), and higher disclosure quality (Bird & Karolyi, 2016). Importantly, the literature emphasizes investor heterogeneity, as more active and long-term-oriented institutional investors are better positioned to reduce agency costs and financial frictions.

We argue that institutional ownership generates an informational trade-off between banks’ incentives to acquire and preserve private information (Denis & Mihov, 2003). As institutional investors, especially blockholders, promote transparency through monitoring and disclosure, publicly available information improves. While this enhances market discipline, it may weaken banks’ incentives to invest in private information and maintain long-term lending relationships, thereby moderating the effect of banking concentration on financial constraints. We refer to this channel as a moderating effect. This moderating effect is likely to be heterogeneous across firms and industries. Accordingly, we examine whether it varies with firms’ degree of financial constraints, measured along three dimensions: firm size, industry-level financial dependence, and credit ratings.

To our knowledge, no prior study comprehensively analyzes how the interaction between banking concentration and institutional ownership shapes corporate investment. This issue is central to regulatory debates on information flows, corporate governance, and financial stability (Brunnermeier et al., 2013; Djankov et al., 2008; Fong et al., 2022; Gennaioli & Shleifer, 2007; La Porta et al., 1998).

Empirically, we analyze the influence of banking concentration and institutional investors on financial constraints, proxied by the investment–cash flow sensitivity (ICFS) relationship, using a sample of non-financial listed firms in 37 European countries between 2000 and 2022. We provide novel evidence on the informational trade-off between institutional ownership and relationship banking in alleviating firms’ financial constraints, where relationship lending is captured by the concentration of the credit establishment loan market. Consistent with the IH, we find that banking concentration alleviates financial constraints, particularly for smaller firms, firms operating in industries with low external financial dependence, and firms with high credit ratings. We further show that institutional investor blockholders alleviate financial constraints while attenuating the impact of banking concentration, in line with the proposed informational trade-off. These effects are strongest among smaller firms, financially less externally dependent industries, and firms with stronger credit ratings. Finally, we show that these results are primarily driven by independent, long-term-oriented institutional investors (e.g., mutual funds, investment advisors, and hedge funds) and are robust to alternative definitions of institutional investor heterogeneity.

The remainder of this article is organized as follows. Section “Analytical Framework and Hypotheses” reviews literature and presents the hypotheses. Section “Data and Method” outlines the data and methodology. Section “Main Results” reports the baseline results, while section “Additional Estimates and Extensions” presents extensions and heterogeneity analyses. Section “Conclusion” concludes.

Analytical Framework and Hypotheses

Banking Concentration and Financial Constraints

Since the Global Financial Crisis, the European banking sector has undergone major regulatory reforms aimed at strengthening financial stability and competitiveness. The European Central Bank (ECB) introduced negative interest rates in 2014 and quantitative easing (QE) in 2015 to stimulate lending, lower long-term interest rates, and support corporate investment (Acharya & Steffen, 2015; Drechsler et al., 2021; Eggertsson et al., 2024; Koijen et al., 2021). In parallel, reforms such as Basel III, the single supervisory mechanism (2014), and the single resolution mechanism (2015) centralized supervision and tightened capital requirements across the Eurozone.

These measures have contributed to increased consolidation and concentration in the banking industry, reshaping competition and banks’ market power, with potential implications for corporate access to bank credit. Delis et al. (2016) show that efficiency gains from market power are more pronounced in Europe, while Cruz-García et al. (2017) document rising banking market power and declining cross-country dispersion. Carletti et al. (2024) further show that market power increased more in Euro-area countries adopting negative interest rate policies in 2014, suggesting improved competitiveness through the lending channel. While these developments appear to ease for bank-dependent firms such as small and medium-sized enterprises (SMEs), their effects on large listed firms remain mixed.³

Our first baseline hypothesis builds on the IH, which posits that greater banking concentration strengthens relationship lending and banks’ incentives to acquire private information. This, in turn, reduces information asymmetries, credit rationing, and firms’ financial constraints. Accordingly, we propose the following hypothesis:

H1. Consistent with the IH, higher banking concentration reduces firms’ ICFS.

Empirical evidence supports this view. Petersen and Rajan (1995) show that bank concentration lowers borrowing costs in the United States, while Ratti et al. (2008) find that higher banking concentration reduces ICFS among European firms, particularly smaller firms and those in industries with low asset tangibility.

Evidence for SMEs is more nuanced. Mol-Gómez-Vázquez et al. (2019) show that banking concentration reduces borrowing discouragement among European SMEs, whereas X. Wang et al. (2020) show that although market power initially lowers debt costs through relationship lending, higher concentration ultimately raises borrowing costs. Supporting evidence from emerging markets also aligns with the IH, showing that banking concentration or reduced competition alleviates firms’ financing constraints (Alvarez & Jara, 2016; Ayalew & Xianzhi, 2019; Rakshit & Bardhan, 2023).

Institutional Investors and Corporate Investment

The literature emphasizes the role of institutional investors in improving corporate governance through monitoring and active engagement (Bird & Karolyi, 2016; Chung & Zhang, 2011; R. Wang, 2021). By gathering firm-specific information, enhancing liquidity, facilitating efficient transactions, and improving risk assessment, institutional investors contribute to capital market efficiency and corporate investment decisions. When institutional investors become blockholders, they exert influence voting (“the voice”) or the “threat of exit” (by selling their shares) (Gillan & Starks, 2007), thereby shaping managerial behavior and firm value (Edmans, 2014; Edmans & Manso, 2011).

Several dimensions of institutional investor heterogeneity are critical for understanding their effect on financial constraints. First, investor type matters: independent institutional blockholders, such as mutual funds, hedge funds and investment advisors, are more likely to actively monitor management and reduce asymmetric information (Almazán et al., 2005). Second, the presence of multiple institutional blockholders can strengthen oversight, particularly in European firms characterized by concentrated ownership structures and pronounced agency conflicts between controlling and minority shareholders (Attig et al., 2009; Cai et al., 2016; La Porta et al., 2000; Maury & Pajuste, 2005). Overall, empirical evidence shows interactions between large institutional investors enhance activism, governance quality, and investment efficiency (Bird & Karolyi, 2016; Elyasiani et al., 2010; Ferreira & Matos, 2008; Kang et al., 2018).

Third, foreign institutional investors may generate spillover effect by exporting stronger corporate governance practices to countries with weaker investor protection (Aggarwal et al., 2011). Ferreira et al. (2010) show that foreign institutional investors increase the likelihood of cross-border mergers and acquisitions, particularly in low-protection environments. Fourth, investment horizon plays a key role: long-term institutional investors provide stability, reduce managerial short-termism, and facilitate access to external finance, whereas short-term investors are associated with higher volatility and trading intensity (Dai et al., 2022; Derrien et al., 2013). Moreover, long-term investors also improve governance by curbing earnings management and supporting higher-quality R&D investment (Harford et al., 2018).

Evidence from emerging markets supports these arguments. Alvarez et al. (2018) show that local institutional investors increase investment rates and that institutional blockholders reduce firms’ financial constraints. Related evidence from China and other emerging economies find that long-term foreign investors enhance investment efficiency, particularly in firms prone to agency problems (Mian & Mian, 2023). Based on these arguments, we propose the following baseline hypothesis:

H2 : Firms’ ICFS decreases in firms with higher ownership stake by independent and long-term institutional ownership.

The Moderating Effect Between Institutional Ownership and Bank Concentration

Most European Union countries have traditionally been characterized by bank-oriented financial systems, in which banks play a dominant role in allocating resources, particularly to the corporate sector. In these systems, banks are the primary source of external finance, in contrast to market-oriented financial systems, where capital markets play a more prominent role.

According to the IH, banks in such systems have strong incentives to collect proprietary firm-level information, enabling them to establish long-term lending relationships. These relationships allow banks to extract informational rents and charge higher interest spreads. As relationships mature, spreads tend to decline (Álvarez-Botas & González, 2023; Rajan, 1992), while firms’ access to financing improves (Petersen & Rajan, 1994, 1995).

The ability of banks to exploit this informational advantage depends on the exclusivity of firm’s information. When financial and strategic information becomes more accessible to other investors, particularly institutional investors, banks’ incentives, and capacity to preserve this advantage weaken (Bharath et al., 2011). As a result, institutional investors play a more prominent role in monitoring firms and signaling investment quality, thereby enhancing firms’ creditworthiness and influencing financial decisions.

This moderating role is especially relevant for long-term and independent institutional investors, who possess the incentives and the capacity to engage in active monitoring (Cleary & Wang, 2017; Ferreira & Matos, 2008). Moreover, investor heterogeneity matters: institutional investors with significant ownership stakes and greater independence (such as mutual funds, hedge funds, family offices, venture capital funds, and investment advisors) are more likely to exert meaningful influence on corporate governance (Alvarez et al., 2018; McCahery et al., 2016).

Overall, these arguments suggest a trade-off between institutional monitoring and the effects of banking concentration. We therefore propose the following hypothesis:

H3: Institutional ownership reduces the impact of bank concentration on alleviating financial constraints by diminishing banks’ informational advantage. This trade-off is expected to be particularly pronounced for independent and long-term institutional ownership.

The trade-off effect of institutional investors on the interaction between firm financial constraints and banking concentration is likely to be heterogeneous across firms and industries. Firm characteristics such as size, dependence on external finance, and credit risk are key drivers of this heterogeneity. In addition, the moderating effect depends on institutional investors’ preferences. Ferreira and Matos (2008) show that institutional investors favor large firms with strong governance, while foreign investors tend to overweight US-cross-listed firms or those included in the MSCI World Index. McCahery et al. (2016) further document that long-term institutional investors value governance attributes such as management ownership, equity-based compensation, board independence, shareholder accountability, and a high free float, which enhance liquidity and price informativeness.

Firm size, in particular, may shape the relevance of the moderating effect. In smaller firms, where banks play a central financing role and relationship lending is more prevalent, institutional blockholders may more effectively substitute for banks’ monitoring function. Petersen and Rajan (1995) show that small firms borrowing from multiple banks face higher interest rates and reduced credit availability relative to those with more concentrated banking relationships. Similarly, Berger and Udell (2002) argue that informational opacity increases small firms’ reliance on banks and that relationship lending alleviates financial constraints through the accumulation of soft information. Consistent evidence indicates that firms maintaining stable banking relationships obtain more favorable loan terms (Bharath et al., 2011; Bonini et al., 2016). Accordingly, institutional investor activism is expected to exert a stronger trade-off effect among small firms, leading to the following hypothesis:

H4a: The trade-off effect between institutional ownership and banking concentration on firm financial constraints is more pronounced for small firms.

Second, industries with higher external financial dependence rely more heavily on bank financing. Using data from 16 countries, Claessens and Laeven (2005) show that greater banking competition promotes growth in these industries, while banking concentration has no significant effect. Similarly, Bucă and Vermeulen (2017), using firm-level data from six European countries, find that investment declines more sharply in bank-dependent manufacturing industries when credit supply tightens. While these studies underscore the importance of bank financing at the industry level, the moderating role of institutional blockholders may also reflect their preferences, which tend to favor firms with strong governance, solid performance, higher valuations, and lower capital expenditures. These considerations suggest that institutional investors’ moderating effect may vary across industries. Accordingly, we propose the following hypothesis:

H4b: The trade-off effect between institutional ownership and banking concentration on firm financial constraints is more pronounced for firms operating in industries with high external financial dependence.

Denis and Mihov (2003), along with subsequent studies such as Colla et al. (2013) and Lin et al. (2013), show that the choice between bank debt, non-bank private debt, and public debt is closely related to a firm’s credit quality. Higher-rated firms typically have better access to public debt markets and face lower financial frictions, resulting in lower financing costs. In contrast, medium-rated firms rely more heavily on bank debt, while low-rated firms often depend on internal funds or private debt. Accordingly, banks have strong incentives to collect information and engage in long-term lending relationships that help alleviate financial constraints, particularly for medium- and low-rated firms. However, reflecting institutional investors’ preferences, the moderating effect is expected to be more pronounced in high-rated firms. Thus, we propose the following hypothesis regarding firm credit-rating heterogeneity:

H4c: The trade-off effect between institutional ownership and banking concentration on firm financial constraints is more pronounced for high-rated firms.

Data and Method

Sample Construction

The dataset combines multiple sources. Firm-level information is obtained from Refinitiv Eikon. Second, bank-level information from Orbis (Bureau Van Dijk) is used to construct country-level banking concentration measures, and macroeconomic and institutional variables are drawn from the World Development Indicators and World Governance Indicators of the World Bank.

The initial sample includes approximately 5,800 non-financial firms from 37 European countries. We exclude financial firms, companies with fewer than 3 years of data, and observations with missing values for key variables, including ownership, investment, sales, assets, debt, cash flow, and stock prices. Outliers are trimmed by removing the top and bottom 1% of each variable. The final sample comprises an unbalanced panel of 59,754 observations from 4,557 non-financial firms from 37 European countries for the period 2001–2022.

Table 1 reports descriptive statistics for the variables used in the empirical analysis. The investment ratio is defined as total investment (capital expenditures, acquisitions, and R&D expenditures minus sales of property, plants, and equipment [PPE] scaled by lagged total assets). Institutional ownership measures the fraction of equity held by institutional investors. To capture investor heterogeneity, we distinguish institutions by ownership size (blockholders vs. minority investors) and by activism (independent vs. gray investors). Consistent with prior literature, independent investors, such as investment advisors, mutual funds, hedge funds, and investment firms, are more likely to engage in active monitoring, whereas gray investors, including insurance companies, pension funds, and bank trusts, tend to adopt a more passive stance due to potential business ties with investee firms (Ferreira & Matos, 2008).

Table 1.

Sample Structure, Investment Ratios, and Control Variables by Country (Mean Values).

Country	Iso-3	Obs.	Investment	Cash Flow	Bank Con.	P1	P2	B.IOwn	M.IOwn_t	B.Ind.IO	M.Ind.IO	B.Gray.IO	M.Gray.IO	O.Sources	Tobin’s Q	Size	Total Debt/Assets	LT. Debt/Total Debt
Austria	AUT	706	0.066	0.086	0.941	0.401	0.097	0.038	0.062	0.033	0.059	0.005	0.002	0.028	0.892	20.780	0.270	0.194
Belgium	BEL	1,320	0.056	0.079	0.920	0.362	0.070	0.026	0.053	0.022	0.047	0.003	0.006	0.038	1.074	20.004	0.264	0.181
Bulgaria	BGR	1,069	0.038	0.046	0.821	0.155	0.047	0.012	0.003	0.002	0.001	0.006	0.002	0.140	0.865	16.925	0.170	0.089
Bosnia and Herzegovina	BIH	665	0.039	0.047	0.690	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.022	0.573	17.878	0.131	0.078
Switzerland	CHE	2,195	0.065	0.091	0.907	0.275	0.072	0.054	0.089	0.041	0.077	0.006	0.012	0.030	1.353	20.279	0.205	0.149
Cyprus	CYP	586	0.027	0.039	0.942	0.022	0.009	0.000	0.001	0.000	0.001	0.000	0.000	0.022	0.542	18.035	0.270	0.151
Czech Republic	CZE	114	0.052	0.131	0.829	0.552	0.063	0.035	0.030	0.035	0.029	0.000	0.001	0.069	1.060	20.270	0.154	0.114
Germany	DEU	6,506	0.069	0.071	0.840	0.283	0.069	0.040	0.061	0.038	0.059	0.001	0.002	0.043	1.153	19.373	0.205	0.138
Denmark	DNK	1,224	0.071	0.078	0.940	0.218	0.097	0.098	0.047	0.072	0.043	0.025	0.004	0.031	1.280	19.143	0.234	0.152
Spain	ESP	1,429	0.044	0.065	0.900	0.295	0.098	0.039	0.050	0.036	0.048	0.002	0.002	0.044	1.165	20.534	0.307	0.209
Estonia	EST	226	0.039	0.073	0.989	0.372	0.100	0.032	0.036	0.027	0.034	0.005	0.003	0.039	1.021	18.216	0.299	0.201
Finland	FIN	1,768	0.061	0.086	0.979	0.192	0.082	0.069	0.084	0.041	0.052	0.025	0.025	0.031	1.256	19.448	0.234	0.158
France	FRA	6,814	0.052	0.063	0.796	0.376	0.084	0.041	0.049	0.038	0.047	0.002	0.001	0.054	0.989	19.640	0.223	0.148
United Kingdom	GBR	10,684	0.064	0.070	0.738	0.191	0.099	0.177	0.104	0.171	0.100	0.002	0.004	0.059	1.357	19.158	0.178	0.131
Greece	GRC	2,187	0.041	0.040	0.965	0.270	0.100	0.016	0.017	0.015	0.017	0.001	0.000	0.082	0.796	18.502	0.293	0.147
Croatia	HRV	788	0.053	0.062	0.875	0.408	0.096	0.047	0.009	0.003	0.004	0.017	0.005	0.071	0.788	18.783	0.222	0.136
Hungary	HUN	274	0.082	0.099	0.789	0.348	0.111	0.042	0.022	0.038	0.019	0.004	0.003	0.041	1.201	18.280	0.224	0.143
Ireland	IRL	232	0.054	0.087	0.892	0.182	0.067	0.120	0.092	0.117	0.089	0.002	0.003	0.029	1.250	20.496	0.224	0.190
Iceland	ISL	161	0.056	0.090	1.000	0.167	0.098	0.265	0.093	0.056	0.034	0.043	0.026	0.022	1.122	19.304	0.347	0.277
Italy	ITA	2,680	0.051	0.063	0.824	0.423	0.080	0.029	0.051	0.025	0.049	0.002	0.002	0.024	0.960	19.874	0.273	0.166
Lithuania	LTU	323	0.074	0.109	0.974	0.467	0.091	0.039	0.023	0.037	0.023	0.001	0.000	0.040	0.982	18.170	0.239	0.134
Luxembourg	LUX	125	0.056	0.096	0.828	0.501	0.106	0.018	0.015	0.018	0.012	0.000	0.003	0.019	0.815	20.479	0.173	0.124
Latvia	LVA	191	0.055	0.071	0.853	0.328	0.111	0.004	0.005	0.004	0.004	0.000	0.000	0.021	0.709	16.802	0.165	0.113
Macedonia	MKD	261	0.034	0.062	0.913	0.154	0.038	0.002	0.000	0.000	0.000	0.002	0.000	0.054	0.743	17.292	0.162	0.072
Malta	MLT	184	0.046	0.076	0.989	0.172	0.062	0.011	0.004	0.000	0.003	0.010	0.001	0.008	1.263	18.321	0.263	0.210
Montenegro	MNE	168	0.025	0.040	0.964	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.019	0.408	17.793	0.063	0.039
Netherlands	NLD	1,177	0.060	0.093	0.938	0.241	0.077	0.115	0.075	0.103	0.071	0.011	0.003	0.029	1.168	20.294	0.232	0.164
Norway	NOR	1,476	0.075	0.072	0.978	0.296	0.086	0.075	0.078	0.054	0.065	0.022	0.012	0.068	1.158	20.012	0.298	0.227
Poland	POL	4,300	0.047	0.072	0.716	0.328	0.101	0.088	0.045	0.045	0.028	0.029	0.010	0.071	0.937	17.938	0.171	0.087
Portugal	PRT	592	0.049	0.074	0.972	0.426	0.130	0.032	0.044	0.028	0.043	0.003	0.001	0.092	0.810	20.340	0.335	0.215
Romania	ROU	1,099	0.048	0.052	0.856	0.346	0.087	0.020	0.009	0.017	0.008	0.003	0.001	0.044	0.713	17.582	0.128	0.055
Russian Federation	RUS	2,890	0.070	0.083	0.684	0.351	0.082	0.010	0.007	0.007	0.007	0.002	0.001	0.192	0.821	19.720	0.229	0.128
Serbia	SRB	295	0.034	0.050	0.810	0.026	0.008	0.003	0.008	0.002	0.007	0.001	0.002	0.017	0.583	17.525	0.120	0.063
Slovak Republic	SVK	65	0.053	0.035	0.915	0.291	0.132	0.009	0.004	0.009	0.004	0.000	0.000	0.059	0.493	17.749	0.250	0.082
Slovenia	SVN	278	0.063	0.088	0.810	0.280	0.112	0.091	0.015	0.086	0.014	0.002	0.001	0.032	0.678	18.737	0.230	0.139
Sweden	SWE	4,414	0.060	0.052	0.977	0.173	0.077	0.093	0.072	0.076	0.057	0.016	0.014	0.078	1.503	18.596	0.194	0.129
Ukraine	UKR	288	0.059	0.068	0.763	0.032	0.007	0.001	0.002	0.001	0.002	0.000	0.000	0.236	0.803	19.227	0.128	0.055
Total Sample		59,754
M			0.059	0.068	0.836	0.273	0.083	0.072	0.058	0.061	0.051	0.007	0.005	0.061	1.104	19.183	0.214	0.139
SD			0.067	0.103	0.133	0.238	0.078	0.117	0.065	0.107	0.061	0.027	0.013	0.144	0.931	2.180	0.168	0.140
Min			−0.060	−0.499	0.291	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.107	13.894	0.000	0.000
Max			0.462	0.423	1.000	0.929	0.525	0.558	0.265	0.537	0.258	0.200	0.081	1.321	8.533	24.756	0.702	0.615

Note: This table displays the mean values of the variables included in the investment empirical model, reported by country. The sample covers 37 European countries over the 2000–2022 period, including 24 EU member states. Investment is defined as the investment ratio, calculated as capital expenditure (CAPEX) plus acquisitions and R&D spending, minus sales of property, plant, and equipment (PPE). The Bank Concentration Index is measured as the share of total bank assets held by the five largest banks in each country. Institutional ownership is disaggregated by size (blockholders vs. minority) and type (gray vs. independent). Complete variable definitions and methodological details are provided in Appendix Table 8.

In line with prior studies, an institutional blockholder is defined as an investor holding at least 5% of cash-flow rights (Claessens et al., 2000; La Porta et al., 1998). Following Aggarwal et al. (2011), institutional ownership is set to when a firm reports no institutional equity holdings. Banking concentration is measured as the share of total banking assets held by the five largest banks in each country-year. Firm-level control variables include Tobin’s Q, firm size, leverage, and other funding sources, defined as the sum of new equity, private debt issuances, and sales of PPE. Detailed variable definitions are provided in Appendix Table 8.

The sample exhibits several notable features. On average, European firms invest 5.9% of total assets. Mean institutional blockholder ownership is 7.2%, with independent institutional investors accounting for most blockholdings (an average of 6.1%), while minority institutional ownership averages 5.8%. Finally, the European banking sector is highly concentrated, with an average five-bank asset concentration ratio of 83.6%.

Methodology

We estimate the investment model proposed by Fazzari et al. (2000) to examine how banking concentration and institutional investor blockholders affect firms’ ICFS, a commonly used proxy for financial constraints. The model is grounded in the notion that, under financial frictions, external finance is more costly than internal funds, leading investment to depend more heavily on cash flow. Although the ICFS measure has been subject to debate (H. Chen & Chen, 2012; Fazzari et al., 2000; Kaplan & Zingales, 2000), it remains widely employed to identify cross-firm differences in financing constraints.

Our identification strategy introduces three moderating channels into the ICFS framework: (1) banking concentration, measured as the share of total commercial banking assets held by the five largest banks in each country-year; (2) institutional blockholder ownership, defined as equity held by institutional investors with significant voting power; and (3) their interaction, capturing their joint effect on financial constraints. These interaction terms allow us to assess whether banking concentration and institutional ownership (individually and jointly) mitigate or amplify firms’ sensitivity of investment to cash flow.

The empirical specification is given by

\begin{matrix} I n v e s t m e n t_{i, c, t} = β_{1} C a s h f l o w_{i, c, t} + β_{2} C a s h f l o w_{i, c, t} x B a n k C o n_{c, t} + \\ β_{3} C a s h f l o w_{i, c, t} x B . I . O w n_{i, c, t} + β_{4} C a s h f l o w_{i, c, t} x B a n k C o n ._{c, t} x B . I . O w n_{i, c, t} + \\ β_{5} B a n k C o n ._{c, t} x B . I . O w n_{i, c, t} + β_{6} B . I . O w n_{i, c, t} + β_{7} M . I . O w n_{i, c, t} + \\ β_{8} O . S o u r c e s_{i, c, t} + X_{i, c, t - 1} + f_{i} + y c_{c, t} + ε_{i, c, t} \end{matrix}

(1)

where i, c, and t index firm, country, and year, respectively. The dependent variable is the investment ratio, defined as total investment over lagged total assets. $C a s h F l o w_{i, c, t}$ denotes operating cash flow over lagged total assets; $B a n k C o n_{c, t}$ is the country-year banking concentration index; $B . I . O w n_{i, c, t}$ captures institutional blockholder ownership; and $O . S o u r c e s_{i, c, t}$ represents alternative funding sources (e.g., equity and debt issuance and sales of PPE), following Kim and Weisbach (2008); $X_{i, c, t - 1}$ is a vector of lagged firm-level controls. The model includes firm fixed ( $f_{i}$ ) and country-year fixed effects $(y c_{c, t})$ to control for unobserved heterogeneity and macroeconomic conditions. To address imbalances in country-level representation, we estimate the model using weighted least squares (WLS), with country-year weights.

Consistent with financial constraint models, we expect the baseline ICFS coefficient $β_{1}$ to be positive (Almeida & Campello, 2007). The interaction between cash flow and banking concentration ( $β_{2})$ is expected to be negative (Hypothesis 1), consistent with the IH, whereby higher concentration incentivizes banks to acquire borrower information and establish long-term lending relationships (Ratti et al., 2008).

To capture the role of institutional investors, we include the interaction between cash flow and institutional blockholder ownership. In line with Hypothesis 2, we expect a negative coefficient β₃, particularly for independent institutions that actively influence governance, transparency, and managerial decision-making, thereby easing access to external finance. Finally, we introduce a three-way interaction between cash flow, banking concentration, and institutional ownership. The coefficient β₄ captures whether institutional ownership moderates the effect of banking concentration on financial constraints. Hypothesis 3 and the related Hypotheses 4a–4c predict that institutional investors weaken banks’ informational advantage, implying a positive β₄ if supported.

Main Results

Institutional Blockholders, Banking Concentration, and Financial Constraints

The baseline estimates of equation (1) are displayed in Table 2. The analysis evaluates the effects of banking concentration (Columns 3–6), institutional blockholder ownership (Columns 2, and 4–6), and their joint impact (Columns 5 and 6) on the ICFS.

Table 2.

Banking Concentration, Institutional Ownership, and Financial Constraints.

		Dependent variable: $I n v e s t m e n t_{t}$
Variables		(1)	(2)	(3)	(4)	(5)	(6)
Cash Flow_t	β₁	0.092*** (0.005)	0.096*** (0.005)	0.200*** (0.027)	0.206*** (0.028)	0.233*** (0.031)	0.235*** (0.033)
Cash Flow_t × Bank Con._t	β₂			−0.127*** (0.032)	−0.129*** (0.032)	−0.161*** (0.036)	−0.157*** (0.038)
Cash Flow_t × B.IOwn_t	β₃		−0.077** (0.031)		−0.083*** (0.032)	−0.667*** (0.195)	−0.628*** (0.201)
Cash Flow_t × Bank Con._t × B.IOwn_t	β₄					0.703*** (0.232)	0.684*** (0.238)
B. IOwn_t × Bank Con._t	β₅					−0.035(0.030)	−0.031(0.030)
B. IOwn_t	β₆	0.005(0.004)	0.011** (0.005)	0.005(0.004)	0.011** (0.005)	0.039(0.025)	0.034(0.025)
Cash Flow_t × M.IOwn_t	β₇						−0.174(0.425)
Cash Flow_t × Bank Con._t × M.IOwn_t	β₈						0.027(0.501)
M. IOwn_t × Bank Con._t	β₉						−0.062(0.063)
M. IOwn_t	β₁₀	0.048*** (0.008)	0.048*** (0.008)	0.048*** (0.008)	0.048*** (0.008)	0.049*** (0.008)	0.114** (0.053)
O. Sources_t	β₁₁	0.065*** (0.004)	0.065*** (0.004)	0.066*** (0.004)	0.066*** (0.004)	0.066*** (0.004)	0.066*** (0.004)
Tobin’s Q_t_–1	β₁₂	0.009*** (0.001)	0.009*** (0.001)	0.009*** (0.001)	0.009*** (0.001)	0.009*** (0.001)	0.009*** (0.001)
Size_t–1	β₁₃	−0.009*** (0.001)	−0.010*** (0.001)	−0.009*** (0.001)	−0.009*** (0.001)	−0.009*** (0.001)	−0.009*** (0.001)
Total Debt/Assets_t–1	β₁₄	−0.071*** (0.005)	−0.071*** (0.005)	−0.071*** (0.005)	−0.071*** (0.005)	−0.071*** (0.005)	−0.072*** (0.005)
LT. Debt/Total Debt_t–1	β₁₅	0.024*** (0.005)	0.024*** (0.005)	0.024*** (0.005)	0.024*** (0.005)	0.024*** (0.005)	0.024*** (0.005)
Observations		58,307	58,307	58,307	58,307	58,307	58,307
R ²		0.554	0.554	0.555	0.555	0.555	0.555
Adj. R²		0.510	0.510	0.510	0.510	0.510	0.510
F statistic		185.6	165.5	168	151.7	126.1	102.6
Country-year FE		YES	YES	YES	YES	YES	YES
Firm FE		YES	YES	YES	YES	YES	YES
Marginal effects
β₁ + β₂*Bank Con.				0.094*** (0.005)	0.098*** (0.005)	0.099*** (0.005)	0.103*** (0.006)
β₁ + β₃*B.IOwn			0.091*** (0.005)		0.200*** (0.027)	0.186*** (0.027)	0.190*** (0.032)
β₂ + β₄*B.IOwn						−0.111*** (0.032)	−0.108*** (0.037)
β₃ + β₄*Bank Con.						−0.079*** (0.030)	−0.055* (0.032)
β₁ + β₇*M.IOwn							0.225*** (0.034)
β₂ + β₈*M.IOwn							−0.155*** (0.039)
β₇ + β₈*Bank Con.							−0.151** (0.067)

Note: This table presents the baseline OLS investment regressions (qaq. 1), estimated using weighted least squares (WLS) with country-year weights. The regressions control the simultaneous effects of firm-level cash flow, institutional ownership, and country-level bank concentration. The dependent variable is investment, estimated as the sum of capital expenditures, acquisitions, and research and development (R&D) expenses, minus sales of property, plant, and equipment (PPE), and scaled up by lagged total assets. Explanatory variables include B.IOwn, which represents blockholder institutional ownership—defined as the ownership held by institutional investors with equity rights equal to or greater than 5%—and M.IOwn, which denotes minority institutional ownership (equity rights below 5%). Cash Flow refers to the ratio of operating cash flow to lagged total assets. Complete definitions of the remaining control variables are provided in Appendix 1 (Table 8). Standard errors are clustered at the country-year level. Overall marginal effects are reported in the bottom rows of the table.

p < .1. **p < .05. ***p < .01.

Consistent with financial constraints models, columns (1) and (2) show positive and statistically significant coefficients for the cash flow–to–assets ratio (0.092 and 0.096, respectively). These results confirm the presence of ICFS in the sample and provide the benchmark against which the moderating effects of banking concentration and institutional ownership are evaluated.

Hypothesis 1 (H1) predicts that greater banking concentration alleviates firm-level financial constraints by encouraging relationship lending and reducing information asymmetries. Supporting H1, Columns 3–6 show that the interaction term $C a s h F l o w_{i, c, t} \times B a n k_{c, t}$ is negative and statistically significant, with coefficients ranging from −0.127 to −0.161. Economically, the effect is substantial. In Column 3, the marginal effect of cash flow on investment at the mean level of banking concentration (0.8) declines from 0.200 to 0.098, implying a reduction in ICFS of approximately 1,016 basis points. A one-percentage-point increase in banking concentration is associated with a 12.7 basis point decrease in ICFS, indicating improved access to external finance.

Hypothesis 2 (H2) posits that institutional blockholders mitigate financial constraints through enhanced monitoring and governance. Consistent with this prediction, Columns 2, 4, 5, and 6 report negative and statistically significant coefficients for the interaction term $C a s h F l o w_{i, c, t} \times B . I O w n_{i, c, t}$ , ranging from −0.077 to −0.667. Evaluated at the mean level of institutional blockholder ownership (7.2%), the estimates imply a reduction in ICFS of 55 basis points. Moreover, a one-percentage-point increase in institutional blockholder ownership reduces ICFS by about 7.7 basis points, supporting the view that institutional investors ease financing frictions by improving transparency and oversight.

Hypothesis 3 (H3) predicts that institutional ownership weakens the ability of banking concentration to alleviate financial constraints by reducing banks’ informational advantage. Columns 5 and 6 provide strong support for H3. The coefficients on the three-way interaction $C a s h F l o w_{i, c, t} x B a n k C o n_{c, t} x B . I O w n_{i, c, t}$ are positive and statistically significant ( $β_{4} = 0.703 a n d β_{4} = 0.684)$ , indicating that the beneficial effect of banking concentration on ICFS diminishes in the presence of institutional blockholders. For example, in column (5), the cross-partial effect implies that a one-percentage-point increase in banking concentration reduces ICFS by 11.1 basis points at the mean level of blockholder ownership—still negative, but notably attenuated relative to firms without strong institutional monitoring. This finding suggests that when institutional investors already mitigate information asymmetries, banks have weaker incentives to engage in costly relationship lending.

To assess the robustness of H1–H3, we conduct several additional tests. First, we re-estimate equation (1) using two-stage least squares (2SLS) to address potential endogeneity in institutional ownership. Second, we employ the System Generalized Method of Moments (GMM) estimator to account for dynamic investment behavior and endogeneity in the ICFS relationship. In all cases, the results remain qualitatively consistent with the baseline findings. The corresponding supplementary estimates and explanatory notes are reported in full in the online appendix.⁴

We examine Hypotheses 4a to 4c by exploring heterogeneity across firms with varying degrees of financial constraints. We re-estimate the baseline model across subsamples defined by credit ratings, firm size, and industry financial dependence. Firms are classified using median splits based on implied credit rating (Almeida et al., 2004), firm size (Arslan et al., 2006; Devereux & Schiantarelli, 1990; Kadapakkam et al., 1998), and industry-level financial dependence following Rajan and Zingales (1998).

Table 3 reports the estimation results across subsamples. Consistent with the baseline findings, the coefficient of Cash Flow remains positive and statistically significant in all specifications. When banking concentration is introduced as a moderator through the interaction term $C a s h F l o w_{i, c, t} x B a n k C o n_{c, t}$ , the interaction effect is negative and significant for smaller firms (Columns 1 and 2), firms operating in industries with low external financial dependence (Columns 5 and 6), and particularly strong for firms with implied credit ratings below the sample median (Columns 11 and 12).

Table 3.

Cross-Sectional Tests—Regressions for Investment Splitting the Sample by Industry and Firm Characteristics.

		Dependent variable: ${Investment}_{t}$
		Size				Industry financial dependence				Implied rating (median criteria)
		Lowp < 50		Highp > 50		Lowp < 50		Highp > 50		Unconstrainedp > 50		Constrainedp < 50
Variables		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Cash Flow_t	β₁	0.192*** (0.033)	0.236*** (0.037)	0.158*** (0.037)	0.157*** (0.040)	0.247*** (0.034)	0.276*** (0.039)	0.144*** (0.040)	0.173*** (0.044)	0.161*** (0.032)	0.191*** (0.035)	0.228*** (0.045)	0.245*** (0.051)
Cash Flow_t × Bank Con._t	β₂	−0.130*** (0.038)	−0.183*** (0.042)	−0.045(0.043)	−0.043(0.047)	−0.183*** (0.038)	−0.217*** (0.045)	−0.052(0.046)	−0.085* (0.050)	−0.077** (0.037)	−0.113*** (0.041)	−0.153*** (0.052)	−0.173*** (0.059)
Cash Flow_t × B.IOwn_t	β₃	−0.084** (0.039)	−1.064*** (0.234)	−0.082* (0.044)	−0.055(0.252)	−0.106*** (0.039)	−0.637*** (0.243)	−0.056(0.055)	−0.802** (0.319)	−0.088** (0.042)	−0.723*** (0.227)	−0.065(0.057)	−0.437(0.350)
Cash Flow_t × Bank Con._t × B.IOwn_t	β₄		1.180*** (0.278)		−0.032(0.307)		0.645** (0.291)		0.889** (0.387)		0.763*** (0.277)		0.450(0.421)
B. IOwn_t × Bank Con._t	β₅		−0.018(0.049)		−0.002(0.037)		−0.048(0.036)		−0.013(0.047)		−0.064(0.040)		−0.004(0.049)
B. IOwn_t	β₆	0.019*** (0.007)	0.031(0.041)	0.007(0.006)	0.009(0.030)	0.015*** (0.006)	0.054* (0.030)	0.000(0.008)	0.010(0.039)	0.016** (0.007)	0.069** (0.032)	0.002(0.008)	0.005(0.041)
M. IOwn_t	β₇	0.078*** (0.013)	0.080*** (0.013)	0.036*** (0.008)	0.036*** (0.008)	0.036*** (0.009)	0.036*** (0.009)	0.076*** (0.014)	0.078*** (0.014)	0.043*** (0.010)	0.044*** (0.010)	0.066*** (0.013)	0.066*** (0.013)
O. Sources_t	β₈	0.064*** (0.004)	0.064*** (0.004)	0.060*** (0.006)	0.060*** (0.006)	0.066*** (0.006)	0.066*** (0.006)	0.063*** (0.005)	0.063*** (0.005)	0.065*** (0.006)	0.065*** (0.006)	0.064*** (0.005)	0.064*** (0.005)
Tobin’s Q_t_–1	β₉	0.007*** (0.001)	0.007*** (0.001)	0.011*** (0.001)	0.011*** (0.001)	0.007*** (0.001)	0.007*** (0.001)	0.012*** (0.001)	0.012*** (0.001)	0.008*** (0.001)	0.008*** (0.001)	0.014*** (0.001)	0.014*** (0.001)
Size_t–1	β₁₀	−0.008*** (0.001)	−0.008*** (0.001)	−0.010*** (0.001)	−0.010*** (0.001)	−0.010*** (0.001)	−0.010*** (0.001)	−0.008*** (0.001)	−0.008*** (0.001)	−0.011*** (0.001)	−0.011*** (0.001)	−0.010*** (0.001)	−0.010*** (0.001)
Total Debt/Assets_t–1	β₁₁	−0.071*** (0.005)	−0.071*** (0.005)	−0.063*** (0.006)	−0.063*** (0.006)	−0.077*** (0.006)	−0.077*** (0.006)	−0.061*** (0.007)	−0.060*** (0.007)	−0.070*** (0.007)	−0.070*** (0.007)	−0.083*** (0.007)	−0.083*** (0.007)
LT. Debt/Total Debt_t–1	β₁₂	0.020*** (0.006)	0.019*** (0.006)	0.022*** (0.007)	0.022*** (0.007)	0.031*** (0.006)	0.031*** (0.006)	0.014* (0.007)	0.013* (0.007)	0.016** (0.007)	0.016** (0.007)	0.021*** (0.006)	0.021*** (0.006)
Observations		28,299	28,299	29,887	29,887	34,641	34,641	23,620	23,620	35,222	35,222	22,299	22,299
R ²		0.570	0.571	0.587	0.587	0.594	0.594	0.535	0.536	0.602	0.602	0.611	0.611
Adj. R²		0.516	0.517	0.543	0.543	0.551	0.551	0.476	0.476	0.541	0.541	0.527	0.527
F statistic		85.92	73.03	90.24	75.96	102.3	85.25	58.40	49.05	77.84	65.03	55.61	46.82
Country-year FE		YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Firm FE		YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Marginal effects
β₁ + β₂*Bank Con.		0.083*** (0.006)	0.083*** (0.006)	0.121*** (0.008)	0.121*** (0.008)	0.095*** (0.007)	0.095*** (0.007)	0.101*** (0.008)	0.101*** (0.008)	0.096*** (0.007)	0.096*** (0.007)	0.100*** (0.009)	0.100*** (0.009)
β₁ + β₃*B.IOwn		0.187*** (0.032)	0.172*** (0.033)	0.152*** (0.036)	0.153*** (0.036)	0.239*** (0.033)	0.226*** (0.032)	0.141*** (0.039)	0.124*** (0.040)	0.154*** (0.031)	0.139*** (0.030)	0.224*** (0.045)	0.214*** (0.045)
β₂ + β₄*B.IOwn			−0.111*** (0.038)		−0.046(0.044)		−0.166*** (0.037)		−0.031(0.047)		−0.058(0.036)		−0.140*** (0.052)
β₃ + β₄*Bank Con.			−0.076** (0.036)		−0.082* (0.045)		−0.099** (0.039)		−0.055(0.054)		−0.086** (0.041)		−0.059(0.057)
Trade off effect			0.059*** (0.014)		−0.002(0.021)		0.042** (0.019)		0.046** (0.020)		0.045*** (0.016)		0.027(0.025)

Note: This table presents the baseline OLS investment regressions (equation (1)), estimated using weighted least squares (WLS) with country-year weights. The regressions control the simultaneous effects of firm-level cash flow, institutional ownership, and country-level bank concentration. The trade-off effect at mean values is defined as $β_{4} \times B a C \times B . I O w n > 0$ ; The dependent variable is Investment, estimated as the sum of capital expenditures, acquisitions, and research and development (R&D) expenses, minus sales of property, plant, and equipment (PPE), and scaled up by lagged total assets. Explanatory variables include B.IOwn, which represents blockholder institutional ownership—defined as the ownership held by institutional investors with equity rights equal to or greater than 5%- and M.IOwn, which denotes minority institutional ownership (equity rights below 5%). Cash Flow refers to the ratio of operating cash flow to lagged total assets. Complete definitions of the remaining control variables are provided in the Appendix 1 (Table 8). For Size and Industry Financial Dependence criteria, we estimate the historical average at industry level financial dependence, computed as the difference between capital expenditures and operating cash flows, and at firm level for log of assets, and we split the sample median for each criterion. For credit rating splitting criteria, we use Smart Ratios Implied Rating from Refinitiv Eikon dataset and split the sample according to country-year median criteria. Standard Errors are clustered at the country-year level. Overall marginal effects are reported in the bottom rows of the table.

***

p < .01. **p < .05. *p < .1.

These findings support the IH and underscore the role of long-term lending relationships. Prior studies show that in countries with more developed banking systems, bank credit supply plays a particularly important role for SMEs, which face limited access to market-based finance. This setting strengthens incentives for stable bank–firm relationships, particularly for riskier and more financially constrained firms, as banks rely on private information and relational capital to sustain lending during adverse conditions (Beck et al., 2005, 2018; Chodorow-Reich, 2014; De Jonghe et al., 2020; Jiménez et al., 2014; Schnabl, 2012).

By contrast, the interaction between Cash Flow and institutional blockholder ownership $(C a s h F l o w_{i, t} x B . I O w n_{i, t})$ is negative and significant, indicating that institutional investors help relax financial constraints. To assess the trade-off between blockholder ownership and banking concentration on firms’ ICFS, we estimate a three-way interaction $(C a s h F l o w_{i, t} x B a n k C o n_{c, t} x B . I O w n_{i, t})$ . The results reveal a partial substitution effect whereby institutional monitoring weakens the marginal role of bank concentration in mitigating financial constraints. These effects are more pronounced for smaller firms (Columns 1 and 2) and firms in low financial dependence industries (Columns 5 and 6) and firms with stronger credit quality (Columns 9 and 10). For industry financial dependence, the triple interaction is positive (β₄ > 0) and significant for both low- and high-dependence industries (Columns 6 and 8).

The overall marginal effect, evaluated at mean values of banking concentration $(\bar{B a n k C o n})$ and blockholder institutional ownership $(\bar{B . I O w n})$ , captures this trade-off and is defined as $β_{4} x \bar{B a n k C o n} . x \bar{B . I O w n} > 0$ . For small firms, the trade-off effect equals 0.060 (Column 2), implying a 600-basis-point increase in ICFS relative to the sensitivity associated solely with banking concentration (β₁ + β₂ x $\bar{B a n k C o n}$ . = 0.083). Incorporating institutional ownership raises the overall ICFS to 0.143. Corresponding trade-off effects equal 0.045 for unconstrained firms (implied rating) and 0.046 for firms in highly financially dependent industries.⁵

Overall, these results support hypotheses H4a and H4c, and provide partial support for H4b. To assess the robustness of the findings reported in Table 3, we conduct additional estimations using 2SLS and the System GMM to address potential endogeneity concerns. The results, presented in Tables A1–A4 of the Online Appendix, are qualitatively consistent with those in Table 3, reinforcing our main conclusions.

We further implement a difference-in-differences (DiD) specification exploiting the introduction of the Markets in Financial Instruments Directive II (MiFID II), a comprehensive European Union financial regulation that came into force on January 3, 2018, and aims to enhance transparency, investor protection, and market functioning. The corresponding DiD results, reported in Tables A5 and A6 of the Online Appendix, confirm that our main findings, particularly those related to investor type heterogeneity, are not driven by endogeneity. Specifically, MiFID II is associated with a significant post-2018 reduction in ICFS, as indicated by the negative and statistically significant coefficient on $C a s h f l o w_{i, t} x T r e a t m e n t_{c, t}$ . This provides causal evidence that improved disclosure reduces firms’ reliance on internal funds by enhancing the information environment faced by financial intermediaries. At the same time, the interaction $C a s h f l o w_{i, t} x B a n k C o n ._{c, t} x T r e a t m e n t_{c, t}$ is positive and significant, suggesting that MiFID II attenuates the mitigating role of banking concentration in easing financial constraints. In contrast, the treatment interactions involving institutional blockholder ownership are not statistically significant, indicating that MiFID II does not materially affect their role.

Institutional Investor Types, Banking Concentration, and Financial Constraints

Table 4 extends the analysis by examining how heterogeneity among institutional investors, classified by activism orientation as independent or gray institutions, affects financial constraints. Following Ferreira and Matos (2008), independent (“pressure-resistant”) institutional investors, such as mutual funds, hedge funds, investment firms, and investment advisors, are more likely to actively monitor firms through their blockholder positions. By reducing asymmetric information, these investors can help alleviate financial constraints (Alvarez et al., 2018). In contrast, gray (“pressure-sensitive”) institutions, including bank trusts, insurance companies, and pension funds, tend to maintain business ties with management and adopt a more passive investment stance, prioritizing long-term relationships over active monitoring.

Table 4.

Institutional Investor Heterogeneity, Banking Concentration, and Financial Constraints.

Variables		Dependent variable: $I n v e s t m e n t_{t}$
		Total sample			Size		Industry financial dependence		Implied rating (median criteria)
		Total sample			Lowp < 50	Highp > 50	Lowp < 50	Highp > 50	Unconstrainedp > 50	Constrainedp < 50
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Cash Flow_t	β₁	0.093*** (0.005)	0.208*** (0.028)	0.232*** (0.031)	0.233*** (0.037)	0.162*** (0.041)	0.271*** (0.039)	0.178*** (0.044)	0.192*** (0.036)	0.240*** (0.050)
Cash Flow_t × Bank Con._t	β₂		−0.131*** (0.032)	−0.160*** (0.036)	−0.179*** (0.043)	−0.048(0.048)	−0.211*** (0.044)	−0.091* (0.050)	−0.115*** (0.041)	−0.166*** (0.058)
Cash Flow_t × B. Ind.IO_t	β₃		−0.124*** (0.033)	−0.646*** (0.209)	−0.954*** (0.244)	−0.102(0.277)	−0.729*** (0.268)	−0.399(0.363)	−0.764*** (0.239)	−0.272(0.375)
Cash Flow_t × Bank Con._t × B. Ind.IO_t	β₄			0.637** (0.253)	1.033*** (0.292)	−0.027(0.342)	0.737** (0.323)	0.305(0.445)	0.765** (0.297)	0.189(0.455)
Cash Flow_t × B. Gray.IO_t	β₅		0.204* (0.116)	−0.737(0.647)	−2.461*** (0.930)	0.132(0.912)	0.366(0.679)	−3.894*** (1.352)	−0.885(0.786)	−1.168(1.342)
Cash Flow_t × Bank Con._t × B. Gray.IO_t	β₆			1.048(0.768)	2.823** (1.109)	0.009(1.083)	−0.461(0.807)	5.005*** (1.649)	1.400(0.942)	1.435(1.657)
B. Ind.IO _t	β₇	0.009* (0.004)	0.017*** (0.005)	0.050* (0.028)	0.042(0.044)	0.018(0.038)	0.076** (0.032)	−0.008(0.044)	0.095** (0.037)	0.001(0.044)
M. Ind.IO _t	β₈	0.052*** (0.008)	0.052*** (0.008)	0.053*** (0.008)	0.086*** (0.014)	0.037*** (0.009)	0.038*** (0.009)	0.086*** (0.015)	0.049*** (0.011)	0.073*** (0.013)
B. Gray.IO _t	β₉	−0.036*** (0.013)	−0.052*** (0.015)	−0.120(0.079)	−0.104(0.127)	−0.088(0.116)	−0.097(0.087)	0.014(0.129)	−0.111(0.096)	−0.011(0.138)
M. Gray.IO _t	β₁₀	0.031(0.026)	0.030(0.026)	0.033(0.026)	0.069(0.046)	0.032(0.028)	0.061** (0.029)	−0.007(0.048)	0.029(0.031)	−0.009(0.043)
Bank Con._t × B. Ind.IO_t	β₁₁			−0.040(0.034)	−0.022(0.052)	−0.009(0.046)	−0.069* (0.039)	0.017(0.054)	−0.089* (0.047)	0.011(0.053)
Bank Con._t × B. Gray.IO_t	β₁₂			0.077(0.092)	0.054(0.150)	0.069(0.129)	0.079(0.105)	−0.103(0.151)	0.052(0.115)	−0.008(0.163)
O. Sources_t	β₁₃	0.065*** (0.004)	0.065*** (0.004)	0.065*** (0.004)	0.064*** (0.004)	0.060*** (0.006)	0.066*** (0.006)	0.062*** (0.005)	0.063*** (0.006)	0.065*** (0.005)
Tobin’s Q_t_–1	β₁₄	0.009*** (0.001)	0.009*** (0.001)	0.009*** (0.001)	0.008*** (0.001)	0.011*** (0.001)	0.007*** (0.001)	0.012*** (0.001)	0.008*** (0.001)	0.014*** (0.001)
Size_t–1	β₁₅	−0.010*** (0.001)	−0.010*** (0.001)	−0.009*** (0.001)	−0.008*** (0.001)	−0.011*** (0.001)	−0.010*** (0.001)	−0.008*** (0.001)	−0.011*** (0.001)	−0.010*** (0.001)
Total Debt/Assets_t–1	β₁₆	−0.071*** (0.005)	−0.071*** (0.005)	−0.071*** (0.005)	−0.072*** (0.005)	−0.063*** (0.006)	−0.077*** (0.006)	−0.059*** (0.007)	−0.069*** (0.007)	−0.082*** (0.007)
LT. Debt/Total Debt_t–1	β₁₇	0.024*** (0.005)	0.024*** (0.005)	0.024*** (0.005)	0.020*** (0.006)	0.021*** (0.007)	0.032*** (0.007)	0.011(0.007)	0.016** (0.008)	0.021*** (0.006)
Observations		56,954	56,954	56,954	27,925	28,891	33,795	23,096	34,355	21,783
R ²		0.555	0.556	0.556	0.572	0.589	0.596	0.537	0.604	0.614
Adj. R²		0.510	0.510	0.511	0.517	0.544	0.552	0.476	0.542	0.528
F statistic		147	116.3	88.42	52.05	53.73	62.01	34.41	44.96	33.77
Country-year FE		YES	YES	YES	YES	YES	YES	YES	YES	YES
Firm FE		YES	YES	YES	YES	YES	YES	YES	YES	YES
Marginal effects
β₁ + β₂* Bank Con._t			0.098*** (0.005)	0.099*** (0.005)	0.083*** (0.006)	0.121*** (0.008)	0.095*** (0.007)	0.101*** (0.008)	0.096*** (0.007)	0.100*** (0.009)
β₁ + β₃* B. Ind.IO_t			0.201*** (0.027)	0.193*** (0.028)	0.182*** (0.034)	0.155*** (0.036)	0.222*** (0.034)	0.158*** (0.041)	0.147*** (0.033)	0.223*** (0.045)
β₂ + β₄* B. Ind.IO_t				−0.121*** (0.033)	−0.124*** (0.039)	−0.050(0.043)	−0.161*** (0.039)	−0.076(0.048)	−0.069* (0.038)	−0.154*** (0.053)
β₃ + β₄* Bank Con._t				−0.113*** (0.033)	−0.091** (0.039)	−0.125** (0.049)	−0.114*** (0.042)	−0.142** (0.062)	−0.124*** (0.046)	−0.113* (0.062)
β₁ + β₅* B. Gray.IO_t			0.210*** (0.028)	0.227*** (0.030)	0.221*** (0.036)	0.163*** (0.041)	0.274*** (0.038)	0.152*** (0.044)	0.186*** (0.034)	0.231*** (0.049)
β₂ + β₆ * B. Gray.IO_t				−0.153*** (0.035)	−0.165*** (0.042)	−0.048(0.048)	−0.215*** (0.043)	−0.058(0.051)	−0.105*** (0.040)	−0.156*** (0.057)
Β₅ + β₆* Bank Con._t				0.140(0.112)	−0.099(0.163)	0.140(0.149)	−0.019(0.132)	0.312(0.200)	0.285* (0.147)	0.037(0.193)
Trade off effects Indep IO				0.0324 ** (0.013)	0.045 *** (0.013)	−0.0015(0.019)	0.042 *** (0.018)	0.013(0.018)	0.038 ** (0.015)	0.009(0.023)
Trade off effects Gray IO				0.006(0.004)	0.011 *** (0.004)	0.00006(0.007)	−0.003(0.004)	0.028 *** (0.009)	0.007(0.005)	0.008(0.009)

p < .1. **p < .05. ***p < .01.

Several results are worth mentioning. First, Columns 2 and 3 of Table 4 show a negative coefficient on the interaction $C a s h F l o w_{i, t} \times B a n k C o n ._{c, t}$ , consistent with H1. These columns also indicate that independent institutional blockholders reduce financial constraints: the interaction $C a s h F l o w_{i, t} \times B . I n d . I O_{i, t}$ is negative and statistically significant, with coefficients of $β_{3} = - 0.124 a n d - 0.646$ , respectively. Moreover, Column 3 shows that the triple interaction that measures the trade-off effect $C a s h f l o w_{i, c, t} x B a n k_{c, t} x I n d I O_{i, c, t}$ is positive and significant, suggesting that while independent institutions improve information quality, their monitoring attenuates banks’ incentives to establish relationship lending, weakening the role of banking concentration in easing financial constraints. By contrast, gray institutional investors do not exhibit a robust moderating effect.

Columns 4–9 of Table 4 report cross-sample estimations. Consistent with Table 3, the moderating effect of banking concentration ( $C a s h f l o w_{i, t} x B a n k C o n ._{c, t}$ ), is negative and significant for smaller firms (Column 4), firms operating in industries with low external financial dependence (Column 6), and particularly pronounced for firms with below-median implied credit ratings (Column 9). These findings support the IH and underscore the importance of relationship lending in mitigating financial constraints. Similarly, the interaction $C a s h f l o w_{i, t} x B . I n d . I O_{i, t}$ confirms that independent institutional blockholders help reduce financial constraints, consistent with their active monitoring role.

The trade-off effect between institutional ownership and banking concentration on firms’ ICFS is stronger for independent investors among small firms (0.045 vs. 0.011; Column 4), with marginal effects that are statistically significant for both investor types. In the subsamples based on industry financial dependence, we observe asymmetric results. For firms with low financial dependence, the trade-off effect is driven by independent investors, with an estimated value of 0.042 (Column 6). In contrast, for firms with high financial dependence, gray investors drive the trade-off effect, with an estimated value of 0.028 (Column 7). Finally, among firms with high credit ratings, the trade-off effect is driven by independent investors (Column 8). Overall, these outcomes are consistent with Hypotheses H3, H4a, and H4c, and provide partial support for H4b.

Control variables behave consistently across specifications. Tobin’s Q is positively associated with investment in all regressions, highlighting its role as a proxy for growth opportunities. Other sources of funds also contribute to investment financing, though to a lesser extent than internal cash flows, in line with the pecking order theory. Debt ratios are negatively related to investment, consistent with prior evidence that leverage disciplines investment behavior, while firm size is negatively associated with investment, reflecting fewer growth opportunities among larger firms. All control variables remain statistically significant at the 1% level, confirming their robustness.⁶

Additional Estimates and Extensions

We conduct several extensions to explore the heterogeneity of our estimated results. First, we examine the effect of institutional investor blockholders’ horizons by distinguishing between short-term portfolio holdings and long-term equity positions. Second, we provide an additional analysis by considering the pandemic years (2020–2022) as an external shock, assessing its impact on firms’ ICFS. Third, we estimate country-level regressions to investigate macro-level financing channels, focusing on bank concentration, the aggregate level of institutional ownership as a percentage of GDP, and its moderating effect.

Investor Horizon

The corporate finance literature highlights the importance of institutional investors’ horizons in shaping corporate policies and firm performance (Burns et al., 2010; Derrien et al., 2013; Harford et al., 2018). Short-term investors are often associated with managerial myopia, such as underinvestment in R&D (Bushee, 1998; Schain & Stiebale, 2021). In contrast, long-term independent institutional investors tend to emphasize monitoring over short-term trading gains. Evidence from US mergers and acquisitions shows that ownership by long-term independent institutions improves post-merger outcomes (X. Chen et al., 2007), while more recent studies confirm that investor heterogeneity, particularly the presence of long-term investors, enhances deal quality (Zhu et al., 2024).

Prior research also documents that long-term-oriented institutional investors promote value-enhancing investment decisions in both the United States (Harford et al., 2018) and emerging markets (Alvarez et al., 2018). Foreign long-term institutional investors help prevent suboptimal investment decisions (Z. Wang et al., 2024). Consistent with the monitoring role, long-term institutional ownership is positively associated with firm performance measures such as return-on-assets, Tobin’s Q, and earnings yield (Yin et al., 2018), as well as firm market value through network effects (Bajo et al., 2020).

Table 5 examines the role of investor horizons by distinguishing between independent and gray institutional investors. Columns 2 and 3 report results for the full sample, while Columns 4–9 present subsample analyses. Long-term institutional blockholders are defined as investors that maintain a blockholder position for at least two consecutive years, whereas short-term investors hold such positions for only 1 year.

Table 5.

Blockholder’s Institutional Investor Horizon, Banking Concentration, and Financial Constraints.

		Dependent variable: $I n v e s t m e n t_{t}$
		Total sample			Size		Industry financial dependence		Implied rating (median criteria)
		Total sample			Lowp < 50	Highp > 50	Lowp < 50	Highp > 50	Unconstrainedp > 50	Constrainedp < 50
Variables		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Cash Flow_i,t	β₁	0.092*** (0.005)	0.212*** (0.028)	0.233*** (0.031)	0.233*** (0.037)	0.162*** (0.041)	0.274*** (0.039)	0.175*** (0.044)	0.190*** (0.036)	0.248*** (0.050)
Cash Flow_i,t × Bank Con._c,t	β₂		−0.136*** (0.032)	−0.161*** (0.036)	−0.180*** (0.043)	−0.047(0.048)	−0.216*** (0.044)	−0.088* (0.051)	−0.114*** (0.042)	−0.175*** (0.059)
Cash Flow_i,t × LT.B. Ind.IO_i,t	β₃		−0.111** (0.043)	−0.661** (0.278)	−0.895*** (0.335)	−0.005(0.396)	−0.856** (0.335)	−0.108(0.536)	−0.604* (0.354)	−0.656(0.522)
Cash Flow_i,t × Bank Con._c,t × LT.B. Ind.IO_i,t	β₄			0.668* (0.347)	0.999** (0.411)	−0.201(0.496)	0.924** (0.417)	−0.063(0.661)	0.558(0.440)	0.721(0.632)
Cash Flow_i,t × ST.B. Ind.IO_i,t	β₅		−0.158*** (0.054)	−0.393(0.348)	−0.981** (0.459)	0.082(0.433)	−0.412(0.383)	−0.517(0.625)	−0.675* (0.403)	0.409(0.605)
Cash Flow_i,t × Bank Con._c,t × ST.B. Ind.IO_i,t	β₆			0.292(0.425)	0.973* (0.553)	−0.083(0.586)	0.307(0.464)	0.440(0.775)	0.722(0.503)	−0.731(0.746)
Cash Flow_i,t × LT.B.Gray.IO_i,t	β₇		0.458*** (0.164)	−0.802(0.775)	−2.556** (1.028)	0.360(1.417)	0.949(0.795)	−6.501*** (2.098)	−1.549* (0.915)	−0.936(2.016)
Cash Flow_i,t × Bank Con._c,t × LT.B.Gray.IO_i,t	β₈			1.418(0.969)	3.302*** (1.267)	−0.059(1.712)	−1.058(0.987)	8.451*** (2.492)	2.422** (1.164)	1.453(2.450)
Cash Flow_i,t × ST.B. Gray.IO_i,t	β₉		−0.377(0.366)	−1.882(2.215)	−0.524(2.906)	−2.468(3.327)	−2.709(2.809)	−0.690(3.110)	−3.444(2.783)	1.596(4.639)
Cash Flow_i,t × Bank Con._c,t × ST.B.Gray.IO_i,t	β₁₀			1.680(2.538)	−0.210(3.330)	2.582(3.822)	2.413(3.198)	0.467(3.711)	4.024(3.296)	−3.581(5.238)
LT.B. Ind.IO_i,t	β₁₁	0.009* (0.005)	0.016*** (0.006)	0.047(0.038)	0.057(0.057)	−0.004(0.050)	0.085* (0.043)	−0.040(0.064)	0.087(0.054)	−0.007(0.054)
LT.M. Ind.IO_i,t	β₁₂	0.063*** (0.012)	0.062*** (0.012)	0.062*** (0.012)	0.096*** (0.021)	0.044*** (0.012)	0.052*** (0.013)	0.088*** (0.021)	0.057*** (0.016)	0.092*** (0.019)
ST.B. Ind.IO_i,t	β₁₃	0.015** (0.006)	0.026*** (0.007)	0.016(0.043)	0.018(0.063)	0.007(0.064)	0.061(0.043)	−0.062(0.080)	0.069(0.054)	−0.008(0.060)
ST.M. Ind.IO_i,t	β₁₄	0.046*** (0.009)	0.046*** (0.008)	0.047*** (0.008)	0.079*** (0.015)	0.034*** (0.009)	0.026*** (0.010)	0.090*** (0.016)	0.045*** (0.011)	0.055*** (0.014)
LT.B. Gray.IO_i,t	β₁₅	−0.029* (0.017)	−0.065*** (0.020)	−0.093(0.108)	−0.205(0.170)	−0.054(0.168)	−0.069(0.107)	0.050(0.199)	−0.041(0.137)	−0.047(0.207)
LT.M. Gray.IO_i,t	β₁₆	0.036(0.039)	0.033(0.040)	0.035(0.040)	0.029(0.066)	0.049(0.051)	0.045(0.042)	0.018(0.084)	0.028(0.050)	−0.082(0.069)
ST.B. Gray.IO_i,t	β₁₇	−0.013(0.041)	0.014(0.055)	−0.064(0.350)	0.886* (0.487)	−0.423(0.453)	0.546(0.510)	−0.795* (0.474)	−0.050(0.482)	−0.087(0.449)
ST.M. Gray.IO_i,t	β₁₈	0.033(0.038)	0.032(0.038)	0.034(0.038)	0.060(0.068)	0.040(0.038)	0.057(0.042)	0.004(0.067)	−0.025(0.047)	0.056(0.058)
Bank Con._c,t × LT.B. Ind.IO_i,t	β₁₉			−0.038(0.046)	−0.047(0.067)	0.022(0.062)	−0.086* (0.052)	0.057(0.078)	−0.075(0.066)	0.009(0.064)
Bank Con._c,t × ST.B. Ind.IO_i,t	β₂₀			0.012(0.054)	0.020(0.076)	−0.008(0.086)	−0.044(0.055)	0.102(0.100)	−0.068(0.068)	0.053(0.077)
Bank Con._c,t × LT.B.Gray.IO_i,t	β₂₁			0.030(0.126)	0.125(0.202)	0.038(0.186)	0.058(0.130)	−0.158(0.229)	−0.039(0.164)	0.027(0.238)
Bank Con._c,t × ST.B.Gray.IO_i,t	β₂₃			0.089(0.389)	−0.861(0.539)	0.456(0.512)	−0.505(0.557)	0.801(0.563)	0.043(0.547)	0.164(0.514)
O. Sources_i,t	β₂₄	0.065*** (0.004)	0.065*** (0.004)	0.065*** (0.004)	0.063*** (0.004)	0.060*** (0.006)	0.065*** (0.006)	0.062*** (0.005)	0.063*** (0.006)	0.064*** (0.005)
Tobin’s Q_i_,t–1	β₂₅	0.009*** (0.001)	0.009*** (0.001)	0.009*** (0.001)	0.008*** (0.001)	0.011*** (0.001)	0.008*** (0.001)	0.012*** (0.001)	0.008*** (0.001)	0.013*** (0.001)
Size_i,t–1	β₂₆	−0.010*** (0.001)	−0.010*** (0.001)	−0.010*** (0.001)	−0.007*** (0.001)	−0.011*** (0.001)	−0.010*** (0.001)	−0.008*** (0.001)	−0.011*** (0.001)	−0.010*** (0.001)
Total Debt/Assets_i,t–1	β₂₇	−0.071*** (0.005)	−0.071*** (0.005)	−0.071*** (0.005)	−0.072*** (0.006)	−0.062*** (0.006)	−0.078*** (0.006)	−0.058*** (0.007)	−0.070*** (0.007)	−0.082*** (0.007)
LT. Debt/Total Debt_i,t–1	β₂₈	0.024*** (0.005)	0.024*** (0.005)	0.024*** (0.005)	0.020*** (0.006)	0.019*** (0.007)	0.032*** (0.007)	0.011(0.007)	0.016** (0.008)	0.019*** (0.007)
Observations		56,291	56,291	56,291	27,612	28,524	33,461	22,770	33,892	21,569
R ²		0.555	0.556	0.556	0.571	0.591	0.596	0.537	0.603	0.615
Adj. R²		0.509	0.510	0.510	0.516	0.545	0.552	0.476	0.541	0.529
F statistic		104.3	79.66	56.96	32.03	35.50	39.90	23.86	28.84	22.33
Country-year FE		YES	YES	YES	YES	YES	YES	YES	YES	YES
Firm FE		YES	YES	YES	YES	YES	YES	YES	YES	YES
Trade off effects LT. Indep IO				0.024* (0.012)	0.032** (0.013)	−0.008(0.020)	0.037** (0.017)	−0.002(0.020)	0.020(0.016)	0.027(0.023)
Trade off effects ST. Indep IO				0.004(0.006)	0.012* (0.007)	−0.002(0.011)	0.006(0.009)	0.005(0.010)	0.011(0.008)	−0.012(0.012)
Trade off effects LT. Gray IO				0.003(0.005)	0.009*** (0.003)	−0.000(0.009)	−0.004(0.004)	0.031*** (0.009)	0.009** (0.004)	0.006(0.010)
Trade off effects ST. Gray IO				0.001(0.0015)	−0.000(0.002)	0.002(0.003)	0.002(0.002)	0.000(0.002)	0.002(0.002)	−0.002(0.003)

Note: This table presents the baseline OLS investment regressions (equation (1)), estimated using weighted least squares (WLS) with country-year weights. The dependent variable is Investment, defined as the sum of capital expenditures, acquisitions, and R&D expenses, minus sales of PPE, scaled by lagged total assets. Institutional ownership variables are classified by investment horizon (Long-Term, LT; Short-Term, ST) and investor type (independent or gray). LT.B. Ind.IO denotes long-term independent blockholder ownership (⩾5% equity rights), while LT.M. Ind.IO and ST.M. Ind.IO refer to minority independent ownership (<5%) by long- and short-term investors, respectively. LT.B.Gray.IO and ST.B.Gray.IO indicate gray blockholder ownership, and LT.M.Gray.IO and ST.M.Gray.IO denote gray minority ownership. Cash Flow is the ratio of operating cash flow to lagged total assets. Remaining control variable definitions are in Appendix Table 8. Sample splits by Size and Industry Financial Dependence are based on median values of firm size (log of assets) and industry-level financial dependence (CAPEX minus cash flow). Credit Rating splits use the Smart Ratios Implied Rating (Refinitiv Eikon) by country-year median. Standard errors are clustered at the country-year level. Overall marginal effects appear at the bottom of the table.

p < .1. **p < .05. ***p < .01.

The results indicate that long-term independent institutional blockholders reduce firms’ ICFS, as reflected in the negative and statistically significant interaction between cash flow and long-term independent ownership in Columns 2 and 3. This monitoring effect is more pronounced among smaller firms (Column 4), firms in less financially dependent industries (Column 6), and firms with relatively higher credit quality (Column 8).

Regarding the trade-off between institutional monitoring and banking concentration, the evidence suggests that this effect is driven primarily by long-term investors. For the small-firm subsample (Column 4), long-term independent institutional investors exhibit a trade-off effect of 0.032, evaluated at the sample means of banking concentration and long-term institutional ownership. Economically, this implies that although banking concentration reduces ICFS from 0.233 (β₁) to 0.083 (β₁ + β₂ × $\bar{B a n k C o n} .$ ), this reduction is partially offset by long-term institutional ownership by 0.043, yielding a net ICFS of 0.114.⁷ A similar trade-off effect is observed for firms operating in industries with low financial dependence, where the magnitude equals 0.037 (Column 6).

Similarly, short-term independent institutional blockholders display the expected sign, and the interaction between cash flow and short-term ownership is statistically significant for the subsample of small firms. However, the economic magnitude is substantially smaller, amounting to 0.012 (Column 4), when evaluated at the sample means of banking concentration and short-term institutional ownership. Short-term holdings by gray institutional blockholders do not exhibit a substituting effect in the relationship between banking concentration and firms’ ICFS. Long-term gray institutional investors, in turn, display positive trade-off effects only in highly financially dependent industries (Column 7) and among unconstrained firms with credit ratings (Column 8), with effect sizes of 0.031 and 0.009, respectively, again evaluated at the corresponding sample means. Hence, these findings are consistent with those reported in the previous section and provide support for Hypotheses H3, H4a, and H4c, while offering a broader perspective on informational trade-offs by accounting for heterogeneity in institutional investor types and equity holding horizons.

COVID Effect on Financial Constraints

We extend the empirical analysis by examining the heterogeneous effects of the COVID-19 crisis on the relationship between banking concentration, institutional investors, and firms’ ICFS. The results, reported in Table 6, show that ICFS declined by 3% during the pandemic (Column 2) when the moderating effect of banking concentration is not included.

Table 6.

Institutional Investors Blockholders Horizon, Banking Concentration, and COVID Effect on Financial Constraints—Fixed-effects.

		Dependent variable: $I n v e s t m e n t_{t}$
		Total sample				Size		Industry financial dependence		Implied rating (median criteria)
		Total sample				Lowp < 50	Highp > 50	Lowp < 50	Highp > 50	Unconstrainedp > 50	Constrainedp < 50
Variables		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Cash Flow_t	β₁	0.204*** (0.028)	0.102*** (0.006)	0.208*** (0.028)	0.238*** (0.033)	0.241*** (0.038)	0.157*** (0.041)	0.280*** (0.041)	0.175*** (0.045)	0.196*** (0.037)	0.244*** (0.051)
Cash Flow_t × Bank Con._t	β₂	−0.124*** (0.033)		−0.126*** (0.033)	−0.161*** (0.037)	−0.185*** (0.044)	−0.034(0.049)	−0.215*** (0.047)	−0.086* (0.051)	−0.111*** (0.043)	−0.169*** (0.060)
Cash Flow_t × Covid_t	β₃	−0.092(0.062)	−0.030*** (0.009)	−0.067(0.065)	−0.134* (0.081)	−0.157(0.111)	−0.033(0.118)	−0.180(0.112)	−0.022(0.143)	−0.032(0.112)	−0.132(0.150)
Cash Flow_t × Bank Con._t × Covid_t	β₄	0.069(0.072)		0.044(0.074)	0.122(0.093)	0.156(0.126)	−0.009(0.138)	0.160(0.126)	0.017(0.166)	−0.012(0.129)	0.143(0.168)
Cash Flow_t × LT.B.IOwn_t	β₅		−0.050(0.049)	−0.049(0.050)	−0.947*** (0.299)	−1.218*** (0.375)	−0.415(0.431)	−0.885** (0.357)	−1.082** (0.495)	−1.056*** (0.398)	−0.957* (0.538)
Cash Flow_t × LT.B.IOwn_t × Bank Con._t	β₆				1.076*** (0.360)	1.407*** (0.446)	0.412(0.512)	0.959** (0.431)	1.295** (0.611)	1.197** (0.478)	1.153* (0.638)
Cash Flow_t × LT.B.IOwn_t × Covid_t	β₇		−0.131(0.085)	−0.141(0.086)	1.274** (0.612)	1.178(0.812)	1.318(0.857)	1.634** (0.781)	0.290(1.007)	1.128(0.844)	1.676* (0.972)
Cash Flow_t × LT.B.IOwn_t × Bank Con._t × Covid_t	β₈				−1.694** (0.749)	−1.592(1.016)	−1.726* (1.032)	−2.067** (0.959)	−0.607(1.227)	−1.536(1.018)	−2.294* (1.220)
Cash Flow_t × ST.B.IOwn_t	β₉		−0.111(0.068)	−0.124* (0.069)	−0.482(0.410)	−0.870(0.540)	−0.141(0.450)	−0.578(0.490)	−0.469(0.614)	−0.507(0.471)	0.702(0.729)
Cash Flow_t × ST.B.IOwn_t × Bank Con._t	β₁₀				0.436(0.491)	0.884(0.646)	0.126(0.578)	0.524(0.579)	0.406(0.770)	0.536(0.579)	−1.067(0.884)
Cash Flow_t × ST.B.IOwn_t × Covid_t	β₁₁		0.087(0.159)	0.085(0.161)	0.344(1.070)	0.313(1.365)	0.920(1.347)	0.621(1.262)	0.038(2.262)	−0.613(1.583)	−0.345(1.502)
Cash Flow_t × ST.B.IOwn_t × Bank Con._t × Covid_t	β₁₂				−0.315(1.257)	−0.248(1.582)	−1.145(1.711)	−0.507(1.580)	−0.076(2.549)	0.756(1.813)	0.533(1.923)
LT.B.IOwn _t	β₁₃	0.003(0.005)	0.007(0.006)	0.007(0.006)	0.100*** (0.037)	0.075(0.058)	0.092* (0.047)	0.103** (0.044)	0.074(0.058)	0.158*** (0.054)	0.030(0.053)
ST.B.IOwn_t	β₁₄	0.010(0.007)	0.019** (0.009)	0.020** (0.009)	−0.008(0.048)	−0.007(0.070)	−0.007(0.061)	0.067(0.058)	−0.088(0.070)	−0.047(0.066)	0.003(0.064)
LT.M.IOwn _t	β₁₅	0.062*** (0.012)	0.062*** (0.012)	0.062*** (0.012)	0.062*** (0.012)	0.095*** (0.020)	0.045*** (0.012)	0.059*** (0.013)	0.077*** (0.020)	0.062*** (0.016)	0.075*** (0.019)
ST.M.IOwn_t	β₁₆	0.043*** (0.009)	0.042*** (0.009)	0.042*** (0.009)	0.042*** (0.009)	0.081*** (0.015)	0.027*** (0.009)	0.028*** (0.010)	0.073*** (0.015)	0.033*** (0.011)	0.061*** (0.015)
LT.B.IOwn_t × Bank Con._t	β₁₇				−0.112** (0.043)	−0.074(0.068)	−0.105* (0.056)	−0.109** (0.051)	−0.098(0.071)	−0.171*** (0.064)	−0.048(0.064)
LT.B.IOwn_t × Covid_t	β₁₈		0.012(0.010)	0.012(0.010)	−0.043(0.081)	−0.039(0.096)	−0.029(0.115)	−0.090(0.107)	0.089(0.101)	−0.086(0.124)	0.126(0.096)
LT.B.IOwn_t × Bank Con._t × Covid_t	β₁₉				0.069(0.099)	0.053(0.120)	0.052(0.141)	0.129(0.132)	−0.087(0.124)	0.110(0.152)	−0.107(0.120)
ST.B.IOwn_t × Bank Con._t	β₂₀				0.033(0.058)	0.058(0.084)	0.010(0.078)	−0.052(0.069)	0.118(0.090)	0.073(0.080)	0.027(0.080)
ST.B.IOwn_t × Covid_t	β₂₁		−0.012(0.017)	−0.012(0.017)	0.098(0.107)	0.105(0.137)	0.040(0.159)	−0.058(0.146)	0.344(0.211)	0.312** (0.155)	0.101(0.170)
ST.B.IOwn_t × Bank Con._t × Covid_t	β₂₃				−0.128(0.128)	−0.177(0.167)	−0.037(0.201)	0.051(0.186)	−0.420* (0.243)	−0.373** (0.185)	−0.135(0.214)
Observations		56,872	56,872	56,872	56,872	27,823	28,907	33,670	23,156	34,362	21,705
R ²		0.555	0.554	0.555	0.555	0.571	0.588	0.595	0.536	0.602	0.613
Adj. R²		0.509	0.509	0.509	0.509	0.516	0.542	0.550	0.475	0.540	0.526
F statistic		117.9	89.69	81.89	57.99	34.70	34.45	38.88	22.95	31.51	22.96
Country-year FE		YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Firm FE		YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Control variables		YES	YES	YES	YES	YES	YES	YES	YES	YES	YES

Significance levels: *p < .1. **p < .05. ***p < .01.

Turning to the interaction between banking concentration and institutional investor horizons, the results provide evidence of a partial substitution effect. Long-term institutional blockholders appear to weaken banks’ incentives to collect proprietary information and maintain long-term lending relationships, as indicated by the positive and statistically significant coefficient β₆ ( $C a s h F l o w_{i, t} \times B a n k C o n ._{c, t} \times L . T . B I O w n_{i, t}$ ), for the full sample (Column 4) and for subsamples with low external financial dependence (Column 7) and lower credit quality (Column 10). However, the corresponding interaction with the COVID dummy (β₈) is negative and significant in the same specifications, suggesting that this moderating effect weakened during the pandemic.

Two main explanations may account for these findings. First, while concentrated banking systems may have enhanced stability by preserving long-term credit relationships under heightened uncertainty (Cuciniello, 2024), this mechanism is not supported by our results, as the interaction terms associated with β₄ are not statistically significant. Second, the ability of long-term institutional blockholders to alleviate financial constraints may have diminished during the COVID period. Although long-term blockholders reduce ICFS (β₅), this effect is offset when interacted with the COVID dummy (β₇). Heightened uncertainty and elevated market volatility likely shifted institutional investors’ focus toward financial resilience—encouraging liquidity preservation, dividend reductions, and delayed investment—rather than active monitoring aimed at investment efficiency (Ataullah et al., 2022). An alternative explanation is that increased uncertainty triggered ownership reductions and flight-to-quality behavior among institutional investors. Taken together, these mechanisms suggest that long-term independent institutional investors reduced their moderating role in enhancing banking discipline during the pandemic.

Macroeconomic Channels

Table 7 presents the macro-level country assessing mechanisms that enhance access to bank credit, measured as a share of GDP for real-sector and non-financial corporations. This indirect evidence complements the firm-level ICFS analysis. The results show that banking concentration positively affects aggregate credit supply: a 10% increase in concentration is associated with a 13.5% rise in credit to non-financial corporations (Columns 5–8).

Table 7.

Banking Concentration, Institutional Investors, and Credit Supply—Country-Level Regressions.

Dependent variable is		Credit to non-financial sector (%GDP)				Credit to non-financial corporations (%GDP)
Explanatory variables are in		t		t–1		t		t–1
Variables		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Bank Con.	β₁	1.671*** (0.438)	1.570*** (0.438)	1.608*** (0.400)	1.633*** (0.374)	1.349*** (0.416)	1.279*** (0.447)	1.383*** (0.365)	1.385*** (0.375)
Inst. Own mean	β₂	5.337* (2.898)	8.758*** (2.472)	4.529* (2.742)	8.395*** (2.287)	6.516*** (2.221)	7.025*** (2.364)	6.579*** (2.107)	7.389*** (2.159)
Bank Con. × Inst. Own	β₃	−7.689** (3.298)	−11.757*** (2.811)	−7.397** (3.046)	−11.514*** (2.570)	−8.424*** (2.448)	−9.127*** (2.631)	−8.693*** (2.280)	−9.643*** (2.377)
Observations		396	396	378	378	394	394	377	377
R ²		0.773	0.891	0.812	0.903	0.857	0.881	0.883	0.900
Country FE		YES	YES	YES	YES	YES	YES	YES	YES
Year FE		NO	YES	NO	YES	NO	YES	NO	YES
Adj. R²		0.760	0.878	0.800	0.890	0.848	0.866	0.876	0.888
F statistic		121.3	243	129.5	228.3	106.2	76.31	116.4	82.50
Marginal effects
β₁ + β₃* Inst. Own.		0.839*** (0.253)	0.298(0.247)	0.810*** (0.227)	0.390* (0.215)	0.440** (0.212)	0.295(0.233)	0.447** (0.185)	0.346* (0.200)

Note: This table displays country-level OLS regressions. Dependent variables are Credit to Non-Financial Sector over GDP, and Credit to Non-Financial Corporations over GDP, and was obtained from the Bank of International Settlements. Explanatory variables are bank concentration (Bank Conc.), the average country-year level institutional ownership (Inst. Own mean). Control variables were retrieved from the World Bank, and are GDP Growth, natural logarithm of GDP per capita, Market Capitalization over GDP, and BCrisis that is a dummy variable that takes value 1 for crisis in banking industry. Robust Standard Errors are reported. Overall marginal effects are reported in the bottom rows of the table.

p < .1. **p < .05. ***p < .01.

Institutional investors also positively affect credit supply, consistent with their role in alleviating financial constraints. However, in line with the substitution channel, institutional ownership weakens the link between banking concentration and credit supply, as indicated by a negative interaction term (β₃ < 0). Thus, higher institutional ownership is associated with a lower marginal effect of banking concentration on bank credit at the macro level. Accounting for this moderation, the marginal effect is $β_{1} + β_{3} * I O w n$ . In Column 5, where average institutional ownership is about 10%, the effect of banking concentration declines from 1.349 to 0.440, a reduction of roughly 900 basis points. Overall, these results reinforce the substitutive role of institutional investor funding in reducing firms’ reliance on bank credit.

Conclusion

Using a sample of listed firms from 37 European countries over 2001–2022, we examine the relationship between banking concentration, institutional investor blockholders, and financial constraints, measured by ICFS. Prior evidence, consistent with the IH, suggests that greater banking concentration strengthens banks’ incentives to acquire private information and establish long-term lending relationships, thereby alleviating firms’ financial constraints.

This informational role, however, is not exclusive to banks. Institutional investors, particularly blockholders, have both the incentives and resources to monitor firms and reduce asymmetric information (X. Chen et al., 2007; Chung & Zhang, 2011; Chung et al., 2005). Given their growing importance in European capital markets over the past 15 years as shareholders and debtholders, we hypothesize that institutional blockholders help alleviate firm-level financial constraints and partially substitute for banks in reducing information asymmetries, thereby moderating the effect of banking concentration.

Consistent with prior studies, our findings show that banking concentration reduces ICFS, supporting the role of relationship lending in easing financial constraints. This effect is heterogeneous and strongest among smaller firms, firms in industries with low external financial dependence, and firms with medium or low implied credit ratings.

We provide novel evidence of informational trade-offs between banking concentration and institutional ownership. Specifically, the moderating effect is driven by independent, long-term institutional blockholders. Greater blockholder ownership weakens the positive impact of banking concentration on alleviating financial constraints, suggesting that improved information flows from institutional investors reduce banks’ incentives to acquire firm-specific information and engage in relationship lending.

These trade-offs are more pronounced among smaller firms and firms with high credit quality. In addition, long-term gray institutional blockholders—such as bank trusts, pension funds, and insurance companies—exhibit significant informational trade-offs in firms operating in industries with high external financial dependence.

Our results are robust to additional controls and alternative estimation methods and remain heterogeneous across firm characteristics. The moderating effect also holds at the country level: while both institutional ownership and banking concentration are positively associated with aggregate credit demand, increases in institutional ownership attenuate the positive effect of banking concentration.

These findings have several implications. From a managerial perspective, institutional investors may generate positive externalities by alleviating financial constraints, allowing firms to adopt more flexible banking relationships and optimize capital structure decisions. From a regulatory perspective, promoting competition among large commercial banks (i.e., Bertrand competition) remains essential for easing financial constraints, particularly for smaller firms.

Finally, strengthening the role of institutional investors through Capital Markets Union initiatives such as MiFID II can enhance information flows, support long-term investment, and contribute to a more resilient and diversified European financing ecosystem.

Supplemental Material

sj-docx-1-brq-10.1177_23409444261421239 – Supplemental material for Informational Tradeoffs From Banking Concentration and Institutional Ownership on Firm Investment: Evidence From Europe

Supplemental material, sj-docx-1-brq-10.1177_23409444261421239 for Informational Tradeoffs From Banking Concentration and Institutional Ownership on Firm Investment: Evidence From Europe by Roberto Alvarez, Mauricio Jara, Carlos Pombo and Paulina Vargas in Business Research Quarterly

Footnotes

Appendix 1

Table 8.

Variable Definitions.

Abbreviation	Variable	Definition
Investment variables
Investment _{i, t}	Investment ratio	Total Investment ratio is capital expenditures (CAPEX) scaled up to lagged total assets (t–1).
Explanatory variables
Cash Flow	Operating cash flow	Cash Flow from operating activities of year t over total assets at the beginning of period (t–1)
Bank Con.	Bank asset concentration	(Sum of Banks Asset of Five largest banks in Bankscope)/(Sum of assets for all banks in Bankscope).
B.IOwn	Blockholder institutional ownership	Proportion of shares owned by institutional blockholder investors. BHL is an investor with at least 5% equity rights
B. Ind.IO	Blockholder independent institutional ownership	Proportion of shares owned by independent institutional blockholders. BHL is an investor with at least 5% equity rights
B. Gray.IO	Blockholder gray institutional ownership	Proportion of shares owned by gray institutional blockholders. BHL is an investor with at least 5% equity rights
M.IOwn	Minority institutional ownership	Proportion of shares owned by institutional minority investors.
M. Ind.IO	Minority independent institutional ownership	Proportion of shares owned by institutional independent minority investors.
M. Gray.IO	Minority gray institutional ownership	Proportion of shares owned by institutional gray minority investors.
LT.B. Ind.IO	Blockholder long-term independent institutional ownership	Proportion of shares owned by independent institutional blockholders with horizon greater than 2 years and holds at least 5% equity rights
LT.B. Gray.IO	Blockholder long-term gray institutional ownership	Proportion of shares owned by gray institutional blockholders with horizon greater than 2 years and holds at least 5% equity rights
ST.B. Ind.IO	Blockholder short-term independent institutional ownership	Proportion of shares owned by independent institutional blockholders with horizon less than 2 years and holds at least 5% equity rights
ST.B. Gray.IO	Blockholder short-term gray institutional ownership	Proportion of shares owned by gray institutional blockholders with horizon less than 2 years and holds at least 5% equity rights
LT.M. Ind.IO	Minority long-term independent institutional ownership	Proportion of shares owned by independent institutional minority investors with horizon greater than 2 years.
LT.M. Gray.IO	Minority long-term gray institutional ownership	Proportion of shares owned by gray institutional minority investors with horizon greater than 2 years.
ST.M. Ind.IO	Minority short-term independent institutional ownership	Proportion of shares owned by independent institutional minority investors with horizon less than 2 years.
ST.M. Gray.IO	Minority short-term gray institutional ownership	Proportion of shares owned by gray institutional minority investors with horizon less than 2 years
Firm-level financial variables
Tobin’s Q	Tobin’s Q	(Market capitalization + total debt)/total asset book value
Size	Size	Natural logarithm of total assets
Total Debt/Assets	Debt ratio	Total debt to total assets
Cash/Assets	Cash ratio	Cash and equivalents over total assets
LT. Debt/Total Debt	Long-term debt ratio	Long-term debt to total debt.
P1	Cash flow rights	Cash flow rights of the largest shareholder
O. Sources	Other sources	Equity issuance + sales PPE + debt issuance

Acknowledgements

The authors acknowledge valuable comments and suggestions from José De Gregorio, Gabriel de La Fuente, and Claudio Raddatz.

ORCID iD

Mauricio Jara

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Partial financial support for this study was funded by the Spanish Ministry of Science, Innovation and Universities (grant no. PID2024-155796NB-I00).

Declaration of conflicting interests

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

Supplemental material

Supplemental material for this article is available online.

Notes

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