Corporate Ownership Network in the Aerospace and Defense Sector: The Ascendency of Passive Investment Funds

Corporate Ownership Network in the Aerospace and Defense Sector: The Ascendency of Passive Investment Funds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Corporate Ownership Network in the Aerospace and Defense Sector: The Ascendency of Passive Investment Funds MARCEL SENAUBAR ALVES, Mario Sacomano, Wilton Vicente Gonçalves da Cruz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7386277/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The global rise of passive investment funds is influencing corporate control structures, presenting notable challenges and opportunities, particularly in strategic sectors such as Aerospace and Defense (A&D). This paper aims to investigate who controls the corporate ownership network in the Aerospace and Defense (A&D) sector. Specifically, the objectives are to: 1) describe the corporate ownership network structure of the global A&D sector, identifying key actors and their positions; 2) analyze network properties (such as density, modularity, and centrality) to understand the distribution of influence; and 3) explore the relationship between ownership concentration, the type of dominant shareholders (e.g., passive funds, state, families), and potential implications for strategic orientation and long-term investment. Using network analysis on data from 225 publicly traded A&D companies worldwide, processed via Gephi. The findings confirm a highly concentrated, oligopolistic structure: 10 companies hold 61.9% of invested capital, with passive investment funds – BlackRock, Vanguard, State Street, and Capital Research – playing a central role, controlling over 45.7% of this capital. While further research is needed to establish direct causal links between the observed ownership concentration and its specific impacts on strategic decision-making or long-term outcomes, the concentration itself raises pertinent questions regarding alignment with sector development goals. The analysis highlights the importance of considering differences between countries and capitalism models in understanding ownership structure in the Aerospace and Defense sector. Corporate Control Corporate Ownership Network Analysis Financialization Aerospace and Defense Corporate Governance Passive Investment Funds Figures Figure 1 Introduction The potential profit has increasingly drawn individual investors into the investment market in recent years. They often invest through financial institutions (Fligstein & Shin, 2007 ; Krippner, 2012 ), or via products, such as passive investment funds (i.e., Exchange Traded Fund - ETF) designed to replicate specific market index performance (Petry et al., 2021 ). The rise of passive investments is transforming capital markets by concentrating corporate control within a small number of large financial institutions, thereby facilitating greater control over corporations and potentially entire sectors by these agents (Neto et al., 2020 ). For companies, this shift can alter corporate governance dynamics, often intensifying the focus on maximizing short-term shareholder value. This may involve management practices and financial strategies that prioritize gains through short-term financial channels to increase profits and boost return on investment (Knafo & Dutta, 2020 ; Rzepka, 2018 ), which may come at the expense of long-term productive investments(Rabinovich & Reddy, 2024 ) crucial for sustainable growth. Among manufacturing sectors (Weber et al., 2020 ), the Aerospace and Defense (A&D) sector stands out due to its strategic importance, its potential for technological growth, and its unique government regulations (Hartley, 2014 ; Lungu, 2017 ). In addition, the A&D sector often operates at the forefront of technological development, pushing the boundaries of scientific knowledge and engineering capabilities (Rao et al., 2022 ); requiring long investment horizon to meet growing demands (Su et al., 2021 ). This inherent need for sustained, long-term investment in R&D and complex projects(POPESCU, 2022 ) seemingly clashes with the potential short-term pressures associated with financialization and passive fund dominance. This tension raises critical questions about the sector's future trajectory and its capacity for sustainable development – encompassing economic viability, technological leadership, and potentially broader environmental and social considerations inherent in large-scale industrial activity. Understanding the intricate web of corporate control is paramount in such a vital and often opaque sector. The corporate ownership network, a form of corporate control, focusing on stock ownership by a small number of actors (Newman, 2010 ). Social Network Analysis (SNA) offer a robust methodology to reveal the most influential actors within these network, whether they are individuals, families, hedge funds, or other financial institutions (Dowbor, 2017 ). According to Vitali et. al ( 2011 ), approximately 40% of the control over the economic value of large global corporations is owned by less than 0.02% of the shareholders of the network. In 2015, three major asset management firms — BlackRock, Vanguard and State Street — combined, were the largest owners accounting for 88% of S&P 500 companies in the United States, even though their holdings rarely exceed 10% (Fichtner et al., 2017 ). However, a common finding of these studies was to identify that in a worldwide network, many companies are interconnected to each other through a reduced number of connections; increasing the global influence of these managers. However, identifying and assessing the actual impact of how shareholders (and fund managers) orchestrate their voting rights in such a way as to influence corporate governance decision-making (Lazonick et al., 2017 ; Lazonick & Tulum, 2011 ) remains a significant challenge, both theoretically and empirically (Mizuno et al., 2020 ). Therefore, this article undertakes an exploratory Social Network Analysis to address the fundamental question: “Who controls the corporate ownership network of the A&D sector?”. Using public data from traded companies and follow the pattern of network analysis, highlighting specific metrics used by Borgatti et al. (2006); the specific objectives are: 1) Describe the corporate ownership network structure of the global A&D sector, identifying key actors and their positions; 2) Analyze network properties (density, modularity, centrality) to understand the distribution of influence and 3) Explore the relationship between ownership concentration, the type of dominant shareholders (passive funds, state, families), and potential implications for strategic orientation and long-term investment. Inspired by the work ofFichtner et al. ( 2017 ) and Vitali et. al ( 2011 ) this research contributes a detailed mapping of the contemporary A&D ownership network. It analyzes the significant role of passive funds and discusses the potential structural implications of this ownership structure for sector governance and sustainability, addressing a recognized gap in A&D-specific network analysis (Davis, 2008 ). The study is further contextualized by salient industry characteristics that motivate this investigation: its specific challenges (Captain, 2009 ), governmental protective measures (Fernando, 2021 ), and the notable influence of financial actors within the sector (Ajdacic et al., 2021 ; Blackrock, 2017 ). By providing a granular, structural view of ownership, this research offers a critical foundation for understanding the complex dynamics shaping the A&D industry's future. Theoretical framework Corporate governance, encompassing the practices and structures guiding company direction and control, fundamentally shapes strategic and operational decisions (Kreiner & Fligstein, 1991 ). Its core relevance lies in mitigating agency conflicts – whether between owners and managers in contexts of dispersed ownership, or between majority and minority shareholders (Benton, 2016 ) – thereby aligning interests and reducing transaction costs (Hermassi et al., 2016 ). The intrinsic link between financial choices and ownership distribution is evident, as capital structure and ownership structure are deeply intertwined (Mansour et al., 2022 ). Agency conflicts between minority shareholders and controlling shareholders may influence financial decisions, requiring specific strategies for mitigation (Davis & Thompson, 2006 ). Effectively, the distribution of power and control within a company directly reflects its corporate governance and ownership structure (Stockhammer, 2004 ). However, Fligstein ( 2001 ) argues that the recent process of financialization, introduces shifts in corporate control concepts. This process is often characterized by an intensified shareholder focus on maximizing returns through stock value appreciation. Consequently, the nature of shareholders – be they families, the state, or differing types of institutional investors – exerts significant influence on corporate strategies and performance. (Fligstein, 2001 ; Knafo & Dutta, 2020 ). While control stemming from family or traditional institutional ownership typically relies on direct ownership stakes (Belloc, 2012 ; Kirkpatrick, 2005 ), state ownership often involves both ownership and direct authority (Fligstein & Dauter, 1987 ). However, the rise of specific types of institutional investors, particularly large passive funds associated with financialization, is notably linked to management practices and financial strategies prioritizing short-term gains. This heightened focus on short-term financial metrics could potentially lead to a reduction in long-term productive investments, which are often crucial for sustainable innovation. Grounded in this potential tension between short-term financial pressures and long-term strategic needs, particularly within the context of passive fund growth, the first proposition guiding this exploratory analysis is formulated: P1: The network structure of corporate ownership of the A&D sector exhibits high concentration, with passive investment funds occupying central positions. As observed by Froud (2003), corporate governance models and ownership practices are not globally uniform. They vary significantly across countries, influenced primarily by differing legal systems, institutional arrangements, and national economic policies (Lim, 2018 ; Nölke et al., 2015 ). These dynamics, which can be evaluated through the lens of the “variety of capitalism” 1 (VoC) framework, which consider the company as crucial agent whose activities and adjustments in response to technological change and international competition impact economic performance (Hall & Soskice, 2003 ). The literature is continually evolving (Ebeling, 2016 ), and generally proposes four models: liberal market economies (LME); coordinated market economies (CME); dependent market economies (DME); and state-permeated market economy (SME) (Bresser-Pereira, 2012 ; Nölke et al., 2015 ; O’Connell & Esping-Anderson, 1991 ). This inherent variation in institutional environments suggests that the influence of ownership structures on corporate strategy is likely context-dependent. Therefore, our second proposition guiding this exploratory analysis is formulated: P2: The structural characteristics and influence of ownership types within the A&D sector´s corporate network vary significantly depending on the national institutional context (VoC). Analyzing ownership networks emerges as a tool for understanding the complex relationships of control between firms, groups, and individuals. This approach allows for the measurement and evaluation of various networks properties, enabling the quantification of structural outcomes and the extraction of relevant insights into social dynamics in different contexts. Early work on ownership network focused on specific countries i.e. German companies (Kogut & Walker, 2001 ). Other authors followed, focusing on Japan (Souma et al., 2006 ), the Czech Republic (Dietzenbacher & Temurshoev, 2008 ), Europe (Pecora & Spelta, 2015 ), Italy (Bertoni & Randone, 2006 ; D’Arcangelis & Rotundo, 2015 ) and Spain (Sacristán-Navarro & Gómez-Ansón, 2007 ). Network analysis enables the evaluation of interdependencies (or lack thereof) among actors in an organized social environment, allowing researchers to describe the influence of this system on the behavior of social actors (Mizruchi, 2006 ). The understanding of the logic of social systems and the relationships between entities comprise "actors" or "nodes" 2 . The network consists of a set of “nodes" and links between these nodes (Borgatti et al., 2013 ); corresponding to the actors (corporations or individuals) and the control relationship between these actors, respectively (Vitali et al., 2011 ). This methodology provides a framework to empirically map and characterize the distribution of power and potential influence within the A&D sector's ownership network, allowing for an exploratory examination of the dynamics underpinning P1 and P2. Methods and data From a methodological standpoint, the research is exploratory, using Social Network Analysis (SNA), combining quantitative structural data with qualitative interpretation. This approach is particularly suitable for understanding complex systems where relationships and structures are not immediately apparent, aiming to map and describe the underlying architecture rather than test pre-defined hypotheses. Exploratory research often employs techniques such as case studies and observation, providing both quantitative and qualitative insights (Collis & Hussey, 2005 ). Data Collection Data were collected from the Market Screener and Tracking Sight databases (MarketScreener, 2023 ; TrackingSight, 2023 ), yielding a sample of 225 companies operating in the A&D sector. Of this sample, 130 companies were held by at least one ETF, according to data recorded in May 2023. For each company, ownership structure data were categorized. The data selection focused on the main shareholders reported by these databases; it is acknowledged that this approach does not capture all minor holdings (i.e., potentially excluding stakes below 0.01% of company value or those beyond database reporting limits – 30 shareholders for each company). The collected data for major shareholders included: 1) shareholder name; 2) shareholder type (e.g., banks, investment funds, mutual/pension funds, government; 3) the value of these shares (in USD); and 4) a normalized value of participation in dollars. A separate dataset detailing ETF holdings in these companies was also compiled. Network Construction and Metrics Two networks were constructed: one based on direct major shareholdings and another based on ETF holdings. Thus, for each company, the control measure values were processed. The control measure was defined as C i =W ij ∗v j , where W i j and the ownership share (%) of actor i in actor j and v j and the economic value of the corporation j . The analysis included all major shareholdings reported in the source databases above their respective reporting thresholds; no additional minimum percentage threshold ( W i j ) was applied by the authors for inclusion in the network construction, ensuring a comprehensive capture of the reported structure. Python, Microsoft Excel, and Gephi were utilized for organizing, building, manipulating, and visualizing the network (Bastian et al., 2009 ; Neto et al., 2020 ). The Gephi software, based on a multiscale modular framework (Lambiotte et al., 2008 ), was specifically used for network analysis. Properties of these networks, such centrality measures (Weighted Degree, Betweenness, Eigenvector) are crucial for identifying influential actors based on the value of their holdings, their bridging position, and their connection to other influential actors, respectively (Borgatti et al., 2013 ; Scott, 1988 ; Wasserman & Faust, 1994 ). Additionally, other network metrics were calculated: average degree (the average number or value of connections per node), modularity (indicating the strength of division of a network into modules, i.e., communities or clusters), density (the proportion of actual ties relative to potential ties), and the number of connected components (the number of distinct subgraphs within the network) (Barabasi, 2016 ; Blondel et al., 2008 ; Lazzarini, 2008 ; Newman, 2010 ). These metrics provide quantitative descriptions of the network's structural characteristics, essential for an exploratory SNA. Results This section explores the main results of the corporate ownership structure of the A&D sector´s corporate network worldwide. It covers a preliminary analysis of the sector, its structure, properties, groups and modularity. Analysis and Structure of the Sector Based on the collected data, while A&D companies are present on all continents, in terms of invested capital, about 88.5% is concentrated in 4 countries: the United States, France, China and the United Kingdom, encompassing 141 companies (62.7% of the companies in this analysis). Furthermore, the sector shows high economic concentration: the top 10 largest companies account for 61.9% of the total invested capital. These top firms also attract the bulk of ETF investments within the sector (72.6% of ETF value). Table 1 presents the twenty largest companies in the A&D sector in terms of market capitalization, highlighting their representation in the sector, the number of shareholders, the largest shareholder and their share in the company's structure. Subsequently, ETF data is presented, highlighting their representation in the sector, the number of ETFs the company owns, the largest ETF shareholder and its participation in the company's ETF structure, and the ratio between the average value of ETF and the average value of shares available in the market (Wetf/Ws). Table 1 The 20 largest A&D companies, values extracted in May 2023 (prepared by the author). Companies Country of Origin M. CAP (M U$S) Vol. in the sector (%) N. of shareholders Largest shareholder Participation vol. (%) ETF vol. (%) N. of ETFs Largest Shareholder Participation vol. (%) (Wetf/Ws) Raytheon United States $136.000 10.72% 13 Capital_Research 32.13% 19.57% 94 SSGA 42.55% 19.62% BOEING United States $123.000 9.70% 10 Capital_Research 26.85% 17.35% 95 SSGA 50.05% 18.40% LOCKHEED MARTIN United States $114.000 8.99% 12 SSgA Funds 51.72% 10.85% 88 Schwab 32.23% 12.39% Airbus France $106.000 8.36% 17 Government of France 27.17% 3.81% 94 BlackRock 41.54% 3.79% NORTHROP GRUMMAN United States $66.563 5.25% 11 SSgA Funds 30.79% 3.84% 89 Vanguard 42.84% 4.85% SAFRAN France $62.521 4.93% 20 Government of France 29.85% 1.77% 95 BlackRock 37.76% 3.86% GENERAL DYNAMICS United States $56.207 4.43% 10 Capital_Research 31.49% 4.52% 90 Vanguard 36.79% 7.12% TRANSDIGM United States $43.530 3.43% 8 Capital_Research 42.90% 6.02% 95 SSGA 37.98% 15.40% BAE SYSTEMS United Kingdom $35.618 2.81% 17 Capital_Research 24.79% 1.72% 91 BlackRock 30.69% 0.06% L3HARRIS United States $33.867 2.67% 9 T. Rowe Price Associates 26.54% 3.14% 92 Vanguard 38.48% 12.71% THALES France $30.149 2.38% 20 Government of France 42.07% 0.60% 98 First Trust 51.39% 1.62% HEIC United States $19.097 1.51% 17 Vanguard Group 17.09% 0.69% 93 SSGA 15.93% 3.38% HOWMET AEROSPACE United States $17.978 1.42% 12 Vanguard Group 34.10% 1.49% 97 Vanguard 36.67% 10.53% AVIC China $16.419 1.29% 9 Government of China 90.02% 0.02% 3 ChinaAMC 96.59% 0.06% ROLLS-ROYCE United Kingdom $15.353 1.21% 23 Causeway Capital 20.74% 0.67% 91 Vanguard 58.74% 0.07% AECC China $15.252 1.20% 9 Government of China 84.73% 0.09% 43 Vanguard 29.09% 0.58% Dassault France $14.506 1.14% 10 Dassault Family 79.48% 0.17% 95 Vanguard 56.32% 0.48% AXON ENTERPRISE United States $14.404 1.14% 9 SSgA Funds 27.17% 2.80% 82 BlackRock 38.55% 24.90% MTU AERO Germany $12.783 1.01% 19 Capital_Research 21.87% 0.49% 98 BlackRock 38.61% 6.87% Textron United States $12.667 1.00% 10 Vanguard Group 32.02% 1.61% 95 Vanguard 35.35% 15.64% The main shareholders, highlighted in Table 1 , indicate a possible process of financialization, particularly large asset managers operating passive funds, are the most significant shareholders across the largest A&D corporations. Considering holdings greater than 25% by a single financial agent as a proxy for significant potential influence, such agents are present in 39.1% of the sample companies, collectively influencing assets equivalent to 45.7% of the sector's total invested capital. State ownership is also relevant, present as a dominant (> 25%) shareholder in 15.6% of companies, influencing 27% of the sector's capital. Family ownership is present in 20.4% of firms but influences a much smaller portion of the sector's capital (3.5%). At the ETF level, in general, half of the sample (57 companies) has institutional management, in which the highest average ETF participation is approximately 14% (17% deviation) in the value of the shares traded. Family businesses are the second most prominent group (13% of companies in the sample), with an average share of 4% (7% deviation). The third group is the state, which despite having a larger volume of companies in the sample, it holds about 23%, and the average participation is less than 1%. While financialization is a global economic trend, each country retains unique characteristics of economic development, institutional arrangements (regulations, contracts, and defense policies), and state intervention in the economy – elements that provide the contours of each nation-state (Hahn, 2019 ; Jayadev et al., 2018 ). Furthermore, states remain major buyers in defense markets, using their purchasing power to influence the industry´s ownership, size, structure, conduct, and performance. They generally maintain a minimum demand as a "customer" role (Block, 2008 ), which can provide a degree of isolation from global market fluctuations and support growth strategies based on domestic demand (Connor, 2019 ; Mauri & Fabre, 2016 ). This independence attracts financial institutions to generate stability within their portfolios (Connor, 2019 ; Mauri & Fabre, 2016 ). Table 2 illustrates the four nations with the highest capitalization in the A&D sector, broken down by predominant type of management. It shows their capital volume representation, the average and deviation of the sample regarding the number of shareholders, and percentage of companies that have ETFs, highlighting the average and deviation of ETF participation in these companies’ structure. Table 2 Number of shareholders and presence of ETFs, by type of shareholding structure (prepared by the author). Country Type of Management Vol. in the sector (%) Shareholder n. Presence of ETFs (%) ETF Participation Average Deviation Average Deviation United States Average 56.9% 10.1 2.2 72% 15.44% 12.9% Family 0.4% 9.8 0.7 70% 8.33% 9.0% Institutional 98.5% 10.2 2.4 74% 17.17% 13.19% Other 1.1% 10.0 0.0 50% 6.69% 2.54% France Average 17.1% 11.4 6.5 50% 2.00% 1.57% Family 6.9% 8.5 4.4 25% 0.48% - Institutional 0.1% 5.0 2.0 0% 0.00% - Governmental 99.2% 20.0 0.0 100% 3.09% 1.04% Other 0.7% 10.0 0.0 100% 0.22% - China Average 9.7% 9.3 1.8 37% 0.65% 2.31% Family 9.0% 9.8 0.8 8% 0.09% - Institutional 11.5% 8.5 2.6 7% 10.43% - Governmental 66.4% 9.4 1.5 74% 0.13% 0.17% Other 13.1% 9.5 0.5 75% 0.05% 0.06% United Kingdom Average 4.6% 10.4 5.6 67% 0.23% 0.5% Family 0.7% 10.0 0.0 50% 1.54% - Institutional 98.9% 11.0 6.2 78% 0.04% 0.02% Other 0.4% 6.0 - - 0.00% - Concerning the influence of VoC framework (Bresser-Pereira, 2012 ; Hall & Soskice, 2003 ; Lim, 2018 ; Nölke et al., 2015 ), it is noteworthy that US companies such as Raytheon, Boeing, Lockheed Martin, and others are among the largest in terms of capitalization and exhibit the largest volume of ETF capitalization and participation. These companies have around 10 shareholders, with the largest generally being American financial institutions, such as Capital Research and Vanguard Group. While literature suggests that the American economy is influenced by shareholder capitalism, characteristic of liberal market economies (LMEs), with tendencies to maximize shareholder value, analyzing shareholder distribution and the presence of ETFs reveals characteristics that also align with coordinated market economies (CME). On the other hand, French companies such as Airbus, Safran, Thales and Dassault are distinguished by a larger number of shareholders, averaging 20. The most prominent shareholders are the French government and families. This presence of the government indicates an influence of coordinated capitalism (CME). Despite the high number of shareholders (indicative of LME), these companies do not have a large number of ETFs and the capitalized value is low compared to American companies. In the United Kingdom, companies such as BAE Systems and Rolls-Royce have a shareholder structure similar to French companies and exhibit a significant presence of financial agents, akin to American companies. However, the ETF holdings are close to other European companies, suggesting a mixed influence of different capitalist approaches. In China, the Chinese government is the largest shareholder of AVIC and AECC, reflecting the influence of state capitalism (SME). In this model, the government plays a central role in corporate governance and the strategic direction of the sector. At the ETF level, the capitalized value is low, except when intuitional managers are present, which makes the average compatible with American companies. Network Properties The statistics and properties of the network, for both the shareholding structure and ETF holding, are shown in Table 3 . Both networks exhibit a low density of connections, appearing sparse and lacking strong cohesion between nodes. The difference between the average degree and the weighted average degree reinforces this low density and indicates the presence of hubs with high centrality (Newman, 2010 ). This observation is further confirmed by the modularity coefficient combined with the number of connected components, suggesting that while the network is not densely connected, it does contain distinct groups or communities (measured by the modularity and community algorithm). Table 3 Statistics and properties of the network (prepared by the author). Properties Network Shareholder ETF Number of Nodes 1494 1065 Number of Edges 2101 3641 Average degree 1.406 3.419 Weighted Average degree 196561.8 51081 Density 0.002 0.006 Modularity 0.431 0.57 Connected Components 96 60 Regarding actors in the network, A&D sector companies account for 16.6% in quantity and 35.4% of the network's invested capital. Governments represent the second most prominent group of actors, controlling 2% of invested capital with only 1.1% network representation. Investment funds constitute 52.6% of agents and control over 61% of invested capital, although this capital is highly concentrated. Analyzing the data from the shareholder network to simplify the presentation of data in Table 4 , we selected the 5 most influential shareholders for each set of metrics (number of connections, weighted degree, betweenness and centrality) sorting them by the centrality of each actor. The data shows that the majority of financial institutions are central to the A&D sector. Table 4 Shareholder Network Measures for Financial Institutions (prepared by the author). Institutions Country Connections Weighted Degree Betweenness Centrality Vanguard Group United States 88 7.50% 1.00 1.00 Blackrock United States 51 10.19% 0.20 0.64 Capital Research United States 29 14.96% 0.07 036 Dimensional United States 37 0.03% 0.25 0.35 Fidelity Fund United States 24 004% 0.01 0.27 Norges Bank Norway 23 0.48% 0.09 0.26 Chinese Government China 33 0.12% 0.22 0.22 Invesco United States 23 0.63% 0.16 0.20 SSgA Funds United States 38 0.15% 0.06 0.04 Causeway Capital United States 1 3.34% - 0.02 Templeton United States 5 5.32% 0.002 0.001 The data from the shareholder network reveals that the majority of financial institutions are central to the A&D sector, where the passive investment funds: Vanguard, BlackRock and Capital Research stand out. With the highest weighted degree (32.65% of the invested capital) in the sample and a larger volume of connections, these entities acquire a prominent position of centrality. In this context, the intermediation degree of the sample indicates the Chinese government and the Dimensional investment fund as the most intermediate actors. On the other hand, Templeton and Causeway Capital show a high weighted degree, but a lower volume of connections. In this context, the institutions highlighted in Table 4 are the main shareholders in 22% of the companies in the entire network; 12.8% of shareholders hold a majority share of over 25%, with notable entities such as the Chinese government and Capital Research. In addition, BlackRock, Norges Bank and Templeton are not majority shareholders in any company in the sector. Similarly, as presented in Table 5 , the process is repeated for the ETF network data; sorting by the centrality of each actor. ETFs are a type of passive investment fund, the custody of which is tied to an institution. Vanguard, BlackRock and State Street (SSgA) stand out with the highest weighted degree (80.3% of invested capital) in the sample, combined with a higher volume of connections. Table 5 ETF Network Measures for Financial Institutions (prepared by the author). Intuitions Number of ETFs Connections (%) Weighted Degrees Betweenness (max.) Centrality (max.) Vanguard Group 65 12.8% 29.2% 0.316 0.840 Fidelity Fund 25 2.9% 0.3% 0.245 0.840 Invesco 82 7.5% 4.2% 0.486 0.812 SSgA Funds 45 6.4% 27.1% 0.934 0.800 First Trust 57 54% 2.1% 0.134 0.709 DWS 61 7.6% 1.6% 0.785 0.707 Nomura 6 0.7% 0.1% 0.668 0.647 BlackRock 142 18.2% 24.0% 0.680 0.456 Schwab 9 2.0% 5.2% 0.025 0.388 However, the weighting between the number of ETFs in the companies’ portfolio and their connections indicates different ETF management strategies, with Vanguard emerging as the most central in ETF. In terms of intermediation, State Street is indicated as the most intermediary. On the other hand, BlackRock while having a high degree of weighting, also shows a greater volume of connections, suggesting a strategy of pulverization. Notably, only DWS (Germany) and Nomura (Japan) are not U.S.-based institutions among the prominent ETF managers. These holdings demonstrate that 44.4% of the entire network is majority-owned; with 36.4% of shareholders having a predominance of more than 25%,primarily divided by BlackRock (23.1%) and Vanguard (13.3%). Fidelity Fund and Nomura are not majority shareholders in any company in the sector. Resource dependency theory(Rossetto & Rossetto, 2005 ) suggests that passive investment funds expert an indirect influence on outcomes and behavior, as affirming direct influence remains a theoretical and empirical challenge (Mizuno et al., 2020 ). Furthermore, according to Petry et al. ( 2021 ), in the current era of passive asset management, index providers (index mutual fund and exchange-traded fund– ETFs) are increasingly becoming “gatekeepers”, exercising regulatory power 3 . Therefore, they may significantly impact corporate governance(Nedzhvetskaya, 2022 ) and the economic policies of nation-states (Hahn, 2019 ; Jayadev et al., 2018 ). This influence can stem from their management support based on company performance(Soriano, 2010 ; Stockhammer, 2004 ) or by orchestrating a centralized and consistent voting strategy among the other shareholders (Neto et al., 2020 ). Community detection Visualizing the data – nodes and connections – in graphs (Fig. 1), reveals the network structure, allowing a visual assessment of the centrality and intermediation of some companies and financial institutions (investment funds, banks, financial companies). The diameter of each component represents its volume of connections and each color of the network represents a category (1. green = financial companies [47.93%]; 2. pink = individuals or families [25.17%]; 3. blue = Companies in the A&D sector [16.6%]; 4. dark green = private equity, mutual and pension fund companies [2.61%]; 5. red = public authorities, state and government [1.07%]; 6. cyan = Banks [2.07%] and 7. Other colors = insurance companies, consultancies and others [4.48%]). The highlighted circles (in red) correspond to the main groups defined from the "modularity class" algorithm present in the Gephi program (Blondel et al., 2008 ; Lambiotte et al., 2008 ). Network modularity analysis identified 95 distinct communities, but a few dominate. Table 6 shows 7 prominent groups that collectively account for 94.8% of the network's weighted degree. These communities often reflect geographic (North America, Europe, East Asia), historical, or technological ties. The network has two most influential groups: A and B; Together, they hold 72.2% of the network’s total invested capital, owning only 9.07% of the connections, demonstrating a high degree of concentration. Furthermore, only 18.1% of the companies in these groups are from the A&D sector, with the remaining companies being investment funds. Groups C and G together account for 36% of the connections in the entire network. Group C has a majority of consolidated North American companies, and group G has a profile of new entrants). Group D and E are mostly owned by European companies. Together, they have more invested capital than group C, which generally consists of direct competitors. Finally, group F has Chinese companies and the Chinese government stands out, as it holds more than 74% of influence over the group. Table 6 Communities and their properties (prepared by the author). Group Companies N° of agents A&D companies (%) Connections (% of nodes) Weighted Degree (% of network) A Raytheon, BAE, Capital Research, Blackrock 27 11.1% 6.59% 45.52% B Rolls-Royce, Rheinmetall, Harris, Massachusetts 28 7.1% 2.48% 26.68% C Boeing, Lockheed, Newport, Wellington 245 15.5% 17.85% 9.04% D Airbus, Safran, French and German governments 70 12.9% 5.69% 6.54% E Chemring group, Senior Plc, Alantra EQMC Asset Management, Threadneedle 23 17.4% 1.45% 3.22% F AVIC, AECC, Chinese Government, Fullgoal 265 20.0% 21.09% 2.49% G Hanwha, Embraer, Michael F., Dimensional 300 16.3% 19.99% 1.30% The formed communities also indicate a high concentration of ownership structure among financial agents, especially in groups A and B (Table 6 ). The A&D sector representatives in these group are often unique intermediate suppliers (engines, avionics, special alloys, etc.). This observation suggests that these financial agents may seek portfolio stability, given that most large manufacturers in the A&D sector rely on their supply and specificities. Additionallly, companies in the sector belonging to these groups have an ETF share 11% higher than the sector average, reaching 64.4%. Group C has the highest share of ETFs, while Group F has the lowest share. Discussion This study employed Social Network Analysis to map the structure of the global Aerospace and Defense (A&D) sector's corporate ownership network, identify the key players, and analyze the network properties to understand the distribution of influence. Our findings reveal a highly concentrated and oligopolistic structure, providing structural evidence consistent with our first proposition (P1). The analysis demonstrates that a mere 10 companies hold a significant 61.9% of the sector's invested capital. Passive investment funds, notably BlackRock, Vanguard, State Street, and Capital Research, occupy central positions, influencing over 45.7% of this capital. This prominence of passive funds corroborates the broader observations of Fichtner et al. ( 2017 ), regarding the "hidden power" exercised by these large asset managers in other industries. Similarly, studies like Vitali et. al ( 2011 ), replicated and discussed by Compston (2013), also have identified extreme concentration in the control of global Transnational Corporations (TNCs), by an economic "super-entity" at the core, often composed of financial institutions. Our results structurally demonstrate that the A&D sector is not an exception to this global trend of corporate ownership concentration in the hands of a few powerful actors, particularly financial institutions. Furthermore, according to Petry et al. ( 2021 ), highlight the growing private authority of index providers in the age of passive asset management, which presents a differentiation from classical stockholder ownership and is relevant to the phenomenon of financialization. While this exploratory study does not directly measure specific firm behavior or casual impacts, the observed dominance of institutions focused on broad market index performance and potentially shorter-term financial metrics raises questions about their influence on A&D firms' governance. This structural characteristic might influence strategic decisions, potentially prioritizing shareholder payouts or cost efficiencies over the long-term, high-risk R&D investments crucial for technological leadership and addressing sustainability challenges the A&D sector. Despite the oligopolistic structure and the significant influence of investment funds, the analysis of the A&D sector ownership network also highlighted the persistent importance of state actors. While the state may hold a smaller direct equity stake in percentage terms, its influence is substantial through regulation, procurement, and defense policies which could potentially mediate the typical effects of ownership structure observed in other industries as observed by of Hartley ( 2014 ), Lungu ( 2017 ) and Ajdačić et al. (2021). The stability potentially offered by state involvement may be a factor attracting passive funds seeking diversification and stable returns. This observed interplay warrants deeper analysis to determine whether concentrated financial ownership reinforces alignment with state strategic goals or creates potential conflicts between national interests and global financial market pressures. Our data provides the structural context but does not definitively resolve this question, instead highlighting it as a critical area for future investigation. The findings provide structural evidence supporting our Proposition 2 (P2), demonstrating considerable variation in ownership patterns across countries, which aligns with Variety of Capitalism (VoC) frameworks. The strong state presence in France and China contrasts sharply with the institution-dominated US landscape, suggesting that national institutional contexts (e.g. legal systems, investor protection, etc.) significantly mediate the influence of global financialization trends. The state's dual role as owner/regulator and customer remains a powerful force, potentially counterbalancing or interacting with the influence of large financial institutions in complex ways that differ by country (Kapopoulos & Lazaretou, 2009 ). This structural observation underscores that governance solutions and sustainability strategies cannot be one-size-fits-all but must account for these institutional variations. The observed concentration and financialization trends in the A&D sector's ownership network have inferred implications for its long-term sustainability. If governance priorities shift excessively towards short-term returns due to passive fund influence, this could potentially jeopardize investments in sustainable innovation (e.g., alternative fuels, lighter materials, circular economy principles in manufacturing) that are essential for the sector's future viability and reduced environmental impact. Furthermore, the concentration of power within a few financial actors raises important questions about corporate accountability and the balancing of diverse stakeholder interests (employees, communities, environment, national security) against shareholder value. Ensuring robust governance frameworks that incentivize long-term value creation and address sustainability goals becomes crucial consideration based on these structural insights. While this study highlights these structural patterns and their inferred risks, it also points to the importance of considering proactive governance strategies within firms and potentially tailored regulatory oversight that accounts for the unique characteristics of the A&D sector and the evolving nature of corporate ownership. Final thoughts This article conducted a comprehensive Social Network Analysis of the corporate ownership landscape within the global Aerospace and Defense sector. Our exploratory findings reveal a highly concentrated, oligopolistic structure significantly influenced by large passive investment funds. Central players like Vanguard, BlackRock, and Capital Research occupy pivotal positions within this network, structurally suggesting considerable potential for influence over the sector's governance and strategic direction. The analysis also confirmed the persistent importance of state actors and highlighted significant variations in ownership patterns across different national models of capitalism. Our primary contribution lies in meticulously mapping this complex ownership landscape and thereby initiating an analysis of its inferred implications for the A&D sector's sustainability trajectory. While the descriptive findings regarding the network structure are robust, the links drawn to governance impacts and sustainability challenges are, at this stage, primarily theoretical and indicative, based on the observed structure and existing literature. The concentration of power identified raises important questions about the alignment of strategic decision-making with the long-term investments required for sustainable innovation and development in this critical sector. Despite being an exploratory and descriptive study, this research provides fundamental insights into control mechanisms within the industry. However, we recognize inherent limitations in the exploratory approach. Network centrality provides a proxy for potential influence, but it does not directly capture the mechanisms through which influence is actually exerted, nor does it quantify the specific outcomes of such influence. Most importantly, this study did not empirically test the causal relationship between ownership structure and specific firm performance or sustainability outcomes. Futhermore, the differentiation noted between ETF holdings and traditional stock ownership also warrants further investigation into its implications. From an analytical point of view, future research is needed to addressing dynamic data and delving deeper into the relationship between companies and customers, which was not quantitatively analyzed here, paving the way to explore other forms of indirect control. Dynamic analysis tracking changes in ownership and firm behavior over time would be particularly valuable. Further research could also explore the specific engagement strategies of large passive funds within the A&D sector and their actual impact on corporate boards and management. Understanding these dynamics is crucial for developing effective governance frameworks that support both the economic viability and the sustainable future of the Aerospace and Defense industry. Endnotes 1 According to Hall and Soskice ( 2003 ), the "variety of capitalism" framework posits that despite operating under global capitalism, each nation or group of nations possesses specific institutional formats in various economic domains that shape their economic structure. 2 In network analysis “nodes” can represent any individual or collective entity, such as person, company, community, country, among others. 3 Index providers influence index construction in a political manner; once companies or countries are included or excluded from an index, the criteria are set by these providers. Declarations Acknowledgments: The authors would like to thank the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the financial support, without which it would not have been possible to develop this research. Conflict of interest statement: The authors declare that they have no conflicts of interest related to this research. Data availability statement: The data supporting the findings of this study are available at Market Screener under the URL: https://www.marketscreener.com/ References Ajdacic, L., Heemskerk, E. M., & Garcia-Bernardo, J. (2021). The Wealth Defence Industry: A Large-scale Study on Accountancy Firms as Profit Shifting Facilitators. New Political Economy , 26 (4), 690–706. https://doi.org/10.1080/13563467.2020.1816947 Barabasi, A. L. (2016). Network Science (1st editio). Cambridge University Press. Bastian, M., Heymann, S., & Jacomy, M. (2009). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7386277","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505277790,"identity":"38341363-eeca-4ccf-bbb4-dbe46559ee02","order_by":0,"name":"MARCEL SENAUBAR ALVES","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYLACxgYGBn4GBjYYnw2PWiQtkg0kazE4QKwW+fbeg595d9jlG99IfvbgQwWDPL/YAbbHFXi0GJw5lyzNeybZctuNNHPDGWcYDGfOTmA3PINPi0SOgTRvG7OB2Y0EMyCDIcHgdgIb0GN4HDb/jfFv3rZ6A+MZ6d+I08Jwgwdk+GEDoHVE2mJwJsfMcu6Z4wYSZ96USc44IwH0S2K7IV6HtZ8xvvF2R7UBf3v6NokPFTby/NLJxx7idRgcCCSASAkGSDQRBfgPEKlwFIyCUTAKRhwAANa+R2/+2RhdAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0658-3545","institution":"UFSCar: Universidade Federal de Sao Carlos","correspondingAuthor":true,"prefix":"","firstName":"MARCEL","middleName":"SENAUBAR","lastName":"ALVES","suffix":""},{"id":505277791,"identity":"24037b37-4ff3-406d-a097-f2cd7321edea","order_by":1,"name":"Mario Sacomano","email":"","orcid":"","institution":"UFSCar: Universidade Federal de Sao Carlos","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Sacomano","suffix":""},{"id":505277792,"identity":"ef438da2-c7d6-4b4a-93e9-78f12d0e4b09","order_by":2,"name":"Wilton Vicente Gonçalves da Cruz","email":"","orcid":"","institution":"UFSCar: Universidade Federal de Sao Carlos","correspondingAuthor":false,"prefix":"","firstName":"Wilton","middleName":"Vicente Gonçalves da","lastName":"Cruz","suffix":""},{"id":505277793,"identity":"4fb3c906-c8c2-4244-9757-86a328c3a8b8","order_by":3,"name":"Júlio Cesar Donadone","email":"","orcid":"","institution":"UFSCar: Universidade Federal de Sao Carlos","correspondingAuthor":false,"prefix":"","firstName":"Júlio","middleName":"Cesar","lastName":"Donadone","suffix":""}],"badges":[],"createdAt":"2025-08-16 08:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7386277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7386277/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90420847,"identity":"496543f6-059d-4626-97b2-565a3676fce0","added_by":"auto","created_at":"2025-09-02 13:55:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":412802,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7386277/v1/9a0bbb6a041f4e4bf66f1f22.jpg"},{"id":95220812,"identity":"8698f05f-1986-43b4-a4c0-1b4a11965539","added_by":"auto","created_at":"2025-11-05 16:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1602050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7386277/v1/9a0041ec-684f-4c5f-86c7-0617ab5ddeed.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCorporate Ownership Network in the Aerospace and Defense Sector: The Ascendency of Passive Investment Funds\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe potential profit has increasingly drawn individual investors into the investment market in recent years. They often invest through financial institutions (Fligstein \u0026amp; Shin, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Krippner, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), or via products, such as passive investment funds (i.e., Exchange Traded Fund - ETF) designed to replicate specific market index performance (Petry et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The rise of passive investments is transforming capital markets by concentrating corporate control within a small number of large financial institutions, thereby facilitating greater control over corporations and potentially entire sectors by these agents (Neto et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For companies, this shift can alter corporate governance dynamics, often intensifying the focus on maximizing short-term shareholder value. This may involve management practices and financial strategies that prioritize gains through short-term financial channels to increase profits and boost return on investment (Knafo \u0026amp; Dutta, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rzepka, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which may come at the expense of long-term productive investments(Rabinovich \u0026amp; Reddy, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) crucial for sustainable growth.\u003c/p\u003e\u003cp\u003eAmong manufacturing sectors (Weber et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the Aerospace and Defense (A\u0026amp;D) sector stands out due to its strategic importance, its potential for technological growth, and its unique government regulations (Hartley, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lungu, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, the A\u0026amp;D sector often operates at the forefront of technological development, pushing the boundaries of scientific knowledge and engineering capabilities (Rao et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); requiring long investment horizon to meet growing demands (Su et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This inherent need for sustained, long-term investment in R\u0026amp;D and complex projects(POPESCU, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) seemingly clashes with the potential short-term pressures associated with financialization and passive fund dominance. This tension raises critical questions about the sector's future trajectory and its capacity for sustainable development \u0026ndash; encompassing economic viability, technological leadership, and potentially broader environmental and social considerations inherent in large-scale industrial activity.\u003c/p\u003e\u003cp\u003eUnderstanding the intricate web of corporate control is paramount in such a vital and often opaque sector. The corporate ownership network, a form of corporate control, focusing on stock ownership by a small number of actors (Newman, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Social Network Analysis (SNA) offer a robust methodology to reveal the most influential actors within these network, whether they are individuals, families, hedge funds, or other financial institutions (Dowbor, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Vitali et. al (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), approximately 40% of the control over the economic value of large global corporations is owned by less than 0.02% of the shareholders of the network. In 2015, three major asset management firms \u0026mdash; BlackRock, Vanguard and State Street \u0026mdash; combined, were the largest owners accounting for 88% of S\u0026amp;P 500 companies in the United States, even though their holdings rarely exceed 10% (Fichtner et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, a common finding of these studies was to identify that in a worldwide network, many companies are interconnected to each other through a reduced number of connections; increasing the global influence of these managers. However, identifying and assessing the actual impact of how shareholders (and fund managers) orchestrate their voting rights in such a way as to influence corporate governance decision-making (Lazonick et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lazonick \u0026amp; Tulum, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) remains a significant challenge, both theoretically and empirically (Mizuno et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTherefore, this article undertakes an exploratory Social Network Analysis to address the fundamental question: \u0026ldquo;Who controls the corporate ownership network of the A\u0026amp;D sector?\u0026rdquo;. Using public data from traded companies and follow the pattern of network analysis, highlighting specific metrics used by Borgatti et al. (2006); the specific objectives are: 1) Describe the corporate ownership network structure of the global A\u0026amp;D sector, identifying key actors and their positions; 2) Analyze network properties (density, modularity, centrality) to understand the distribution of influence and 3) Explore the relationship between ownership concentration, the type of dominant shareholders (passive funds, state, families), and potential implications for strategic orientation and long-term investment.\u003c/p\u003e\u003cp\u003eInspired by the work ofFichtner et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Vitali et. al (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) this research contributes a detailed mapping of the contemporary A\u0026amp;D ownership network. It analyzes the significant role of passive funds and discusses the potential structural implications of this ownership structure for sector governance and sustainability, addressing a recognized gap in A\u0026amp;D-specific network analysis (Davis, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The study is further contextualized by salient industry characteristics that motivate this investigation: its specific challenges (Captain, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), governmental protective measures (Fernando, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the notable influence of financial actors within the sector (Ajdacic et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Blackrock, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By providing a granular, structural view of ownership, this research offers a critical foundation for understanding the complex dynamics shaping the A\u0026amp;D industry's future.\u003c/p\u003e"},{"header":"Theoretical framework","content":"\u003cp\u003eCorporate governance, encompassing the practices and structures guiding company direction and control, fundamentally shapes strategic and operational decisions (Kreiner \u0026amp; Fligstein, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Its core relevance lies in mitigating agency conflicts \u0026ndash; whether between owners and managers in contexts of dispersed ownership, or between majority and minority shareholders (Benton, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) \u0026ndash; thereby aligning interests and reducing transaction costs (Hermassi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The intrinsic link between financial choices and ownership distribution is evident, as capital structure and ownership structure are deeply intertwined (Mansour et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Agency conflicts between minority shareholders and controlling shareholders may influence financial decisions, requiring specific strategies for mitigation (Davis \u0026amp; Thompson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Effectively, the distribution of power and control within a company directly reflects its corporate governance and ownership structure (Stockhammer, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, Fligstein (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) argues that the recent process of financialization, introduces shifts in corporate control concepts. This process is often characterized by an intensified shareholder focus on maximizing returns through stock value appreciation. Consequently, the nature of shareholders \u0026ndash; be they families, the state, or differing types of institutional investors \u0026ndash; exerts significant influence on corporate strategies and performance. (Fligstein, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Knafo \u0026amp; Dutta, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While control stemming from family or traditional institutional ownership typically relies on direct ownership stakes (Belloc, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kirkpatrick, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), state ownership often involves both ownership and direct authority (Fligstein \u0026amp; Dauter, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). However, the rise of specific types of institutional investors, particularly large passive funds associated with financialization, is notably linked to management practices and financial strategies prioritizing short-term gains. This heightened focus on short-term financial metrics could potentially lead to a reduction in long-term productive investments, which are often crucial for sustainable innovation. Grounded in this potential tension between short-term financial pressures and long-term strategic needs, particularly within the context of passive fund growth, the first proposition guiding this exploratory analysis is formulated:\u003c/p\u003e\u003cp\u003e\u003cem\u003eP1: The network structure of corporate ownership of the A\u0026amp;D sector exhibits high concentration, with passive investment funds occupying central positions.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs observed by Froud (2003), corporate governance models and ownership practices are not globally uniform. They vary significantly across countries, influenced primarily by differing legal systems, institutional arrangements, and national economic policies (Lim, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; N\u0026ouml;lke et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These dynamics, which can be evaluated through the lens of the \u0026ldquo;variety of capitalism\u0026rdquo;\u003csup\u003e1\u003c/sup\u003e (VoC) framework, which consider the company as crucial agent whose activities and adjustments in response to technological change and international competition impact economic performance (Hall \u0026amp; Soskice, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The literature is continually evolving (Ebeling, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and generally proposes four models: liberal market economies (LME); coordinated market economies (CME); dependent market economies (DME); and state-permeated market economy (SME) (Bresser-Pereira, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; N\u0026ouml;lke et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; O\u0026rsquo;Connell \u0026amp; Esping-Anderson, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). This inherent variation in institutional environments suggests that the influence of ownership structures on corporate strategy is likely context-dependent. Therefore, our second proposition guiding this exploratory analysis is formulated:\u003c/p\u003e\u003cp\u003e\u003cem\u003eP2: The structural characteristics and influence of ownership types within the A\u0026amp;D sector\u0026acute;s corporate network vary significantly depending on the national institutional context (VoC).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAnalyzing ownership networks emerges as a tool for understanding the complex relationships of control between firms, groups, and individuals. This approach allows for the measurement and evaluation of various networks properties, enabling the quantification of structural outcomes and the extraction of relevant insights into social dynamics in different contexts. Early work on ownership network focused on specific countries i.e. German companies (Kogut \u0026amp; Walker, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Other authors followed, focusing on Japan (Souma et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the Czech Republic (Dietzenbacher \u0026amp; Temurshoev, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Europe (Pecora \u0026amp; Spelta, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Italy (Bertoni \u0026amp; Randone, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; D\u0026rsquo;Arcangelis \u0026amp; Rotundo, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Spain (Sacrist\u0026aacute;n-Navarro \u0026amp; G\u0026oacute;mez-Ans\u0026oacute;n, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNetwork analysis enables the evaluation of interdependencies (or lack thereof) among actors in an organized social environment, allowing researchers to describe the influence of this system on the behavior of social actors (Mizruchi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The understanding of the logic of social systems and the relationships between entities comprise \"actors\" or \"nodes\"\u003csup\u003e2\u003c/sup\u003e. The network consists of a set of \u0026ldquo;nodes\" and links between these nodes (Borgatti et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); corresponding to the actors (corporations or individuals) and the control relationship between these actors, respectively (Vitali et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This methodology provides a framework to empirically map and characterize the distribution of power and potential influence within the A\u0026amp;D sector's ownership network, allowing for an exploratory examination of the dynamics underpinning P1 and P2.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMethods and data\u003c/h2\u003e\u003cp\u003eFrom a methodological standpoint, the research is exploratory, using Social Network Analysis (SNA), combining quantitative structural data with qualitative interpretation. This approach is particularly suitable for understanding complex systems where relationships and structures are not immediately apparent, aiming to map and describe the underlying architecture rather than test pre-defined hypotheses. Exploratory research often employs techniques such as case studies and observation, providing both quantitative and qualitative insights (Collis \u0026amp; Hussey, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected from the Market Screener and Tracking Sight databases (MarketScreener, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; TrackingSight, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), yielding a sample of 225 companies operating in the A\u0026amp;D sector. Of this sample, 130 companies were held by at least one ETF, according to data recorded in May 2023. For each company, ownership structure data were categorized. The data selection focused on the main shareholders reported by these databases; it is acknowledged that this approach does not capture all minor holdings (i.e., potentially excluding stakes below 0.01% of company value or those beyond database reporting limits \u0026ndash; 30 shareholders for each company). The collected data for major shareholders included: 1) shareholder name; 2) shareholder type (e.g., banks, investment funds, mutual/pension funds, government; 3) the value of these shares (in USD); and 4) a normalized value of participation in dollars. A separate dataset detailing ETF holdings in these companies was also compiled.\u003c/p\u003e\n\u003ch3\u003eNetwork Construction and Metrics\u003c/h3\u003e\n\u003cp\u003eTwo networks were constructed: one based on direct major shareholdings and another based on ETF holdings. Thus, for each company, the control measure values were processed. The control measure was defined as \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e=W\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026lowast;v\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, where \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003ej\u003c/sub\u003e and the ownership share (%) of actor \u003cem\u003ei\u003c/em\u003e in actor \u003cem\u003ej\u003c/em\u003e and \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e and the economic value of the corporation \u003cem\u003ej\u003c/em\u003e. The analysis included all major shareholdings reported in the source databases above their respective reporting thresholds; no additional minimum percentage threshold (\u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003ej\u003c/sub\u003e) was applied by the authors for inclusion in the network construction, ensuring a comprehensive capture of the reported structure.\u003c/p\u003e\u003cp\u003ePython, Microsoft Excel, and Gephi were utilized for organizing, building, manipulating, and visualizing the network (Bastian et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Neto et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Gephi software, based on a multiscale modular framework (Lambiotte et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), was specifically used for network analysis. Properties of these networks, such centrality measures (Weighted Degree, Betweenness, Eigenvector) are crucial for identifying influential actors based on the value of their holdings, their bridging position, and their connection to other influential actors, respectively (Borgatti et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Scott, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Wasserman \u0026amp; Faust, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Additionally, other network metrics were calculated: average degree (the average number or value of connections per node), modularity (indicating the strength of division of a network into modules, i.e., communities or clusters), density (the proportion of actual ties relative to potential ties), and the number of connected components (the number of distinct subgraphs within the network) (Barabasi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Blondel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lazzarini, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Newman, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These metrics provide quantitative descriptions of the network's structural characteristics, essential for an exploratory SNA.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section explores the main results of the corporate ownership structure of the A\u0026amp;D sector\u0026acute;s corporate network worldwide. It covers a preliminary analysis of the sector, its structure, properties, groups and modularity.\u003c/p\u003e\n\u003ch3\u003eAnalysis and Structure of the Sector\u003c/h3\u003e\n\u003cp\u003eBased on the collected data, while A\u0026amp;D companies are present on all continents, in terms of invested capital, about 88.5% is concentrated in 4 countries: the United States, France, China and the United Kingdom, encompassing 141 companies (62.7% of the companies in this analysis). Furthermore, the sector shows high economic concentration: the top 10 largest companies account for 61.9% of the total invested capital. These top firms also attract the bulk of ETF investments within the sector (72.6% of ETF value). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the twenty largest companies in the A\u0026amp;D sector in terms of market capitalization, highlighting their representation in the sector, the number of shareholders, the largest shareholder and their share in the company's structure. Subsequently, ETF data is presented, highlighting their representation in the sector, the number of ETFs the company owns, the largest ETF shareholder and its participation in the company's ETF structure, and the ratio between the average value of ETF and the average value of shares available in the market (Wetf/Ws).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe 20 largest A\u0026amp;D companies, values extracted in May 2023 (prepared by the author).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCompanies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry of Origin\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM. CAP (M U$S)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVol. in the sector (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN. of shareholders\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLargest shareholder\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParticipation vol. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eETF vol. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN. of ETFs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLargest Shareholder\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParticipation vol. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(Wetf/Ws)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRaytheon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$136.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapital_Research\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.13%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.57%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSGA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.55%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.62%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBOEING\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$123.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapital_Research\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.85%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.35%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSGA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.05%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.40%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLOCKHEED MARTIN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$114.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.99%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSgA Funds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.85%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSchwab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.23%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.39%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAirbus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$106.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.36%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment of France\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.17%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.81%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlackRock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.54%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.79%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNORTHROP GRUMMAN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$66.563\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.25%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSgA Funds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.79%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.84%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.84%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.85%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSAFRAN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$62.521\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.93%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment of France\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.85%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.77%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlackRock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.76%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.86%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGENERAL DYNAMICS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$56.207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.43%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapital_Research\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.49%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.52%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.79%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.12%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRANSDIGM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$43.530\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.43%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapital_Research\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.02%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSGA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.98%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.40%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBAE SYSTEMS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited Kingdom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$35.618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.81%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapital_Research\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.79%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlackRock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.69%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL3HARRIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$33.867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.67%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT. Rowe Price Associates\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.54%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.14%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.48%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.71%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHALES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$30.149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.38%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment of France\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.07%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.60%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst Trust\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.39%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.62%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$19.097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.51%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard Group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSGA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.93%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.38%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHOWMET AEROSPACE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$17.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.42%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard Group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.10%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.49%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.67%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.53%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAVIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$16.419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.29%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment of China\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.02%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChinaAMC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96.59%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eROLLS-ROYCE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited Kingdom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$15.353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.21%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCauseway Capital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.74%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.67%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58.74%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAECC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$15.252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment of China\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.73%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDassault\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$14.506\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.14%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDassault Family\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79.48%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56.32%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.48%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAXON ENTERPRISE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$14.404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.14%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSgA Funds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.17%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlackRock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.55%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMTU AERO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGermany\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$12.783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.01%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapital_Research\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.87%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.49%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlackRock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.61%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.87%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTextron\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e$12.667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard Group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.02%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.61%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.35%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.64%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main shareholders, highlighted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, indicate a possible process of financialization, particularly large asset managers operating passive funds, are the most significant shareholders across the largest A\u0026amp;D corporations. Considering holdings greater than 25% by a single financial agent as a proxy for significant potential influence, such agents are present in 39.1% of the sample companies, collectively influencing assets equivalent to 45.7% of the sector's total invested capital. State ownership is also relevant, present as a dominant (\u0026gt;\u0026thinsp;25%) shareholder in 15.6% of companies, influencing 27% of the sector's capital. Family ownership is present in 20.4% of firms but influences a much smaller portion of the sector's capital (3.5%).\u003c/p\u003e\n\u003cp\u003eAt the ETF level, in general, half of the sample (57 companies) has institutional management, in which the highest average ETF participation is approximately 14% (17% deviation) in the value of the shares traded. Family businesses are the second most prominent group (13% of companies in the sample), with an average share of 4% (7% deviation). The third group is the state, which despite having a larger volume of companies in the sample, it holds about 23%, and the average participation is less than 1%.\u003c/p\u003e\n\u003cp\u003eWhile financialization is a global economic trend, each country retains unique characteristics of economic development, institutional arrangements (regulations, contracts, and defense policies), and state intervention in the economy \u0026ndash; elements that provide the contours of each nation-state (Hahn, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jayadev et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, states remain major buyers in defense markets, using their purchasing power to influence the industry\u0026acute;s ownership, size, structure, conduct, and performance. They generally maintain a minimum demand as a \"customer\" role (Block, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), which can provide a degree of isolation from global market fluctuations and support growth strategies based on domestic demand (Connor, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mauri \u0026amp; Fabre, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). This independence attracts financial institutions to generate stability within their portfolios (Connor, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mauri \u0026amp; Fabre, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the four nations with the highest capitalization in the A\u0026amp;D sector, broken down by predominant type of management. It shows their capital volume representation, the average and deviation of the sample regarding the number of shareholders, and percentage of companies that have ETFs, highlighting the average and deviation of ETF participation in these companies\u0026rsquo; structure.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNumber of shareholders and presence of ETFs, by type of shareholding structure (prepared by the author).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eType of Management\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVol. in the sector (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eShareholder n.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePresence of ETFs (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eETF Participation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDeviation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDeviation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e56.9%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e10.1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e72%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e15.44%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e12.9%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.33%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstitutional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.17%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.19%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.69%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.54%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eFrance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e17.1%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e11.4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e6.5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.00%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.57%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstitutional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernmental\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e9.7%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e9.3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e37%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.65%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.31%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstitutional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.43%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernmental\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eUnited Kingdom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e4.6%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e10.4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e5.6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e67%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.23%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.5%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.54%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInstitutional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcerning the influence of VoC framework (Bresser-Pereira, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hall \u0026amp; Soskice, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Lim, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; N\u0026ouml;lke et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), it is noteworthy that US companies such as Raytheon, Boeing, Lockheed Martin, and others are among the largest in terms of capitalization and exhibit the largest volume of ETF capitalization and participation. These companies have around 10 shareholders, with the largest generally being American financial institutions, such as Capital Research and Vanguard Group. While literature suggests that the American economy is influenced by shareholder capitalism, characteristic of liberal market economies (LMEs), with tendencies to maximize shareholder value, analyzing shareholder distribution and the presence of ETFs reveals characteristics that also align with coordinated market economies (CME). On the other hand, French companies such as Airbus, Safran, Thales and Dassault are distinguished by a larger number of shareholders, averaging 20. The most prominent shareholders are the French government and families. This presence of the government indicates an influence of coordinated capitalism (CME). Despite the high number of shareholders (indicative of LME), these companies do not have a large number of ETFs and the capitalized value is low compared to American companies.\u003c/p\u003e\n\u003cp\u003eIn the United Kingdom, companies such as BAE Systems and Rolls-Royce have a shareholder structure similar to French companies and exhibit a significant presence of financial agents, akin to American companies. However, the ETF holdings are close to other European companies, suggesting a mixed influence of different capitalist approaches. In China, the Chinese government is the largest shareholder of AVIC and AECC, reflecting the influence of state capitalism (SME). In this model, the government plays a central role in corporate governance and the strategic direction of the sector. At the ETF level, the capitalized value is low, except when intuitional managers are present, which makes the average compatible with American companies.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eNetwork Properties\u003c/h2\u003e\n\u003cp\u003eThe statistics and properties of the network, for both the shareholding structure and ETF holding, are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Both networks exhibit a low density of connections, appearing sparse and lacking strong cohesion between nodes. The difference between the average degree and the weighted average degree reinforces this low density and indicates the presence of hubs with high centrality (Newman, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). This observation is further confirmed by the modularity coefficient combined with the number of connected components, suggesting that while the network is not densely connected, it does contain distinct groups or communities (measured by the modularity and community algorithm).\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStatistics and properties of the network (prepared by the author).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eProperties\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNetwork\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eShareholder\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eETF\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of Nodes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1494\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1065\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of Edges\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3641\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage degree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.419\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeighted Average degree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196561.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDensity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModularity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConnected Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding actors in the network, A\u0026amp;D sector companies account for 16.6% in quantity and 35.4% of the network's invested capital. Governments represent the second most prominent group of actors, controlling 2% of invested capital with only 1.1% network representation. Investment funds constitute 52.6% of agents and control over 61% of invested capital, although this capital is highly concentrated.\u003c/p\u003e\n\u003cp\u003eAnalyzing the data from the shareholder network to simplify the presentation of data in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, we selected the 5 most influential shareholders for each set of metrics (number of connections, weighted degree, betweenness and centrality) sorting them by the centrality of each actor. The data shows that the majority of financial institutions are central to the A\u0026amp;D sector.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eShareholder Network Measures for Financial Institutions (prepared by the author).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInstitutions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConnections\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWeighted Degree\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBetweenness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCentrality\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard Group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.50%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlackrock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.19%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapital Research\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.96%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDimensional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFidelity Fund\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e004%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorges Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNorway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChinese Government\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInvesco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSgA Funds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCauseway Capital\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.34%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTempleton\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnited States\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.32%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data from the shareholder network reveals that the majority of financial institutions are central to the A\u0026amp;D sector, where the passive investment funds: Vanguard, BlackRock and Capital Research stand out. With the highest weighted degree (32.65% of the invested capital) in the sample and a larger volume of connections, these entities acquire a prominent position of centrality. In this context, the intermediation degree of the sample indicates the Chinese government and the Dimensional investment fund as the most intermediate actors. On the other hand, Templeton and Causeway Capital show a high weighted degree, but a lower volume of connections. In this context, the institutions highlighted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e are the main shareholders in 22% of the companies in the entire network; 12.8% of shareholders hold a majority share of over 25%, with notable entities such as the Chinese government and Capital Research. In addition, BlackRock, Norges Bank and Templeton are not majority shareholders in any company in the sector.\u003c/p\u003e\n\u003cp\u003eSimilarly, as presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the process is repeated for the ETF network data; sorting by the centrality of each actor. ETFs are a type of passive investment fund, the custody of which is tied to an institution. Vanguard, BlackRock and State Street (SSgA) stand out with the highest weighted degree (80.3% of invested capital) in the sample, combined with a higher volume of connections.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eETF Network Measures for Financial Institutions (prepared by the author).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIntuitions\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of ETFs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConnections (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWeighted Degrees\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBetweenness (max.)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCentrality (max.)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVanguard Group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.840\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFidelity Fund\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.840\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInvesco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.812\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSgA Funds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.800\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst Trust\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.709\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDWS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.707\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNomura\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.647\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlackRock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.680\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.456\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSchwab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.388\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, the weighting between the number of ETFs in the companies\u0026rsquo; portfolio and their connections indicates different ETF management strategies, with Vanguard emerging as the most central in ETF. In terms of intermediation, State Street is indicated as the most intermediary. On the other hand, BlackRock while having a high degree of weighting, also shows a greater volume of connections, suggesting a strategy of pulverization. Notably, only DWS (Germany) and Nomura (Japan) are not U.S.-based institutions among the prominent ETF managers. These holdings demonstrate that 44.4% of the entire network is majority-owned; with 36.4% of shareholders having a predominance of more than 25%,primarily divided by BlackRock (23.1%) and Vanguard (13.3%). Fidelity Fund and Nomura are not majority shareholders in any company in the sector.\u003c/p\u003e\n\u003cp\u003eResource dependency theory(Rossetto \u0026amp; Rossetto, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) suggests that passive investment funds expert an indirect influence on outcomes and behavior, as affirming direct influence remains a theoretical and empirical challenge (Mizuno et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, according to Petry et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), in the current era of passive asset management, index providers (index mutual fund and exchange-traded fund\u0026ndash; ETFs) are increasingly becoming \u0026ldquo;gatekeepers\u0026rdquo;, exercising regulatory power\u003csup\u003e3\u003c/sup\u003e. Therefore, they may significantly impact corporate governance(Nedzhvetskaya, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the economic policies of nation-states (Hahn, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jayadev et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This influence can stem from their management support based on company performance(Soriano, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Stockhammer, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) or by orchestrating a centralized and consistent voting strategy among the other shareholders (Neto et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eCommunity detection\u003c/h3\u003e\n\u003cp\u003eVisualizing the data \u0026ndash; nodes and connections \u0026ndash; in graphs (Fig.\u0026nbsp;1), reveals the network structure, allowing a visual assessment of the centrality and intermediation of some companies and financial institutions (investment funds, banks, financial companies). The diameter of each component represents its volume of connections and each color of the network represents a category (1. green\u0026thinsp;=\u0026thinsp;financial companies [47.93%]; 2. pink\u0026thinsp;=\u0026thinsp;individuals or families [25.17%]; 3. blue\u0026thinsp;=\u0026thinsp;Companies in the A\u0026amp;D sector [16.6%]; 4. dark green\u0026thinsp;=\u0026thinsp;private equity, mutual and pension fund companies [2.61%]; 5. red\u0026thinsp;=\u0026thinsp;public authorities, state and government [1.07%]; 6. cyan\u0026thinsp;=\u0026thinsp;Banks [2.07%] and 7. Other colors\u0026thinsp;=\u0026thinsp;insurance companies, consultancies and others [4.48%]). The highlighted circles (in red) correspond to the main groups defined from the \"modularity class\" algorithm present in the Gephi program (Blondel et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lambiotte et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNetwork modularity analysis identified 95 distinct communities, but a few dominate. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows 7 prominent groups that collectively account for 94.8% of the network's weighted degree. These communities often reflect geographic (North America, Europe, East Asia), historical, or technological ties. The network has two most influential groups: A and B; Together, they hold 72.2% of the network\u0026rsquo;s total invested capital, owning only 9.07% of the connections, demonstrating a high degree of concentration. Furthermore, only 18.1% of the companies in these groups are from the A\u0026amp;D sector, with the remaining companies being investment funds. Groups C and G together account for 36% of the connections in the entire network. Group C has a majority of consolidated North American companies, and group G has a profile of new entrants). Group D and E are mostly owned by European companies. Together, they have more invested capital than group C, which generally consists of direct competitors. Finally, group F has Chinese companies and the Chinese government stands out, as it holds more than 74% of influence over the group.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCommunities and their properties (prepared by the author).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGroup\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCompanies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u0026deg; of agents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eA\u0026amp;D companies (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConnections\u003c/p\u003e\n\u003cp\u003e(% of nodes)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWeighted Degree\u003c/p\u003e\n\u003cp\u003e(% of network)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRaytheon, BAE, Capital Research, Blackrock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.59%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.52%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRolls-Royce, Rheinmetall, Harris, Massachusetts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.48%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.68%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoeing, Lockheed, Newport, Wellington\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.85%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.04%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAirbus, Safran, French and German governments\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.69%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.54%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemring group, Senior Plc, Alantra EQMC Asset Management, Threadneedle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.45%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.22%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAVIC, AECC, Chinese Government, Fullgoal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e265\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.49%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHanwha, Embraer, Michael F., Dimensional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.99%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.30%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe formed communities also indicate a high concentration of ownership structure among financial agents, especially in groups A and B (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The A\u0026amp;D sector representatives in these group are often unique intermediate suppliers (engines, avionics, special alloys, etc.). This observation suggests that these financial agents may seek portfolio stability, given that most large manufacturers in the A\u0026amp;D sector rely on their supply and specificities. Additionallly, companies in the sector belonging to these groups have an ETF share 11% higher than the sector average, reaching 64.4%. Group C has the highest share of ETFs, while Group F has the lowest share.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed Social Network Analysis to map the structure of the global Aerospace and Defense (A\u0026amp;D) sector's corporate ownership network, identify the key players, and analyze the network properties to understand the distribution of influence. Our findings reveal a highly concentrated and oligopolistic structure, providing structural evidence consistent with our first proposition (P1). The analysis demonstrates that a mere 10 companies hold a significant 61.9% of the sector's invested capital. Passive investment funds, notably BlackRock, Vanguard, State Street, and Capital Research, occupy central positions, influencing over 45.7% of this capital. This prominence of passive funds corroborates the broader observations of Fichtner et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), regarding the \"hidden power\" exercised by these large asset managers in other industries. Similarly, studies like Vitali et. al (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), replicated and discussed by Compston (2013), also have identified extreme concentration in the control of global Transnational Corporations (TNCs), by an economic \"super-entity\" at the core, often composed of financial institutions. Our results structurally demonstrate that the A\u0026amp;D sector is not an exception to this global trend of corporate ownership concentration in the hands of a few powerful actors, particularly financial institutions. Furthermore, according to Petry et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), highlight the growing private authority of index providers in the age of passive asset management, which presents a differentiation from classical stockholder ownership and is relevant to the phenomenon of financialization.\u003c/p\u003e\u003cp\u003eWhile this exploratory study does not directly measure specific firm behavior or casual impacts, the observed dominance of institutions focused on broad market index performance and potentially shorter-term financial metrics raises questions about their influence on A\u0026amp;D firms' governance. This structural characteristic might influence strategic decisions, potentially prioritizing shareholder payouts or cost efficiencies over the long-term, high-risk R\u0026amp;D investments crucial for technological leadership and addressing sustainability challenges the A\u0026amp;D sector.\u003c/p\u003e\u003cp\u003eDespite the oligopolistic structure and the significant influence of investment funds, the analysis of the A\u0026amp;D sector ownership network also highlighted the persistent importance of state actors. While the state may hold a smaller direct equity stake in percentage terms, its influence is substantial through regulation, procurement, and defense policies which could potentially mediate the typical effects of ownership structure observed in other industries as observed by of Hartley (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Lungu (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Ajdačić et al. (2021). The stability potentially offered by state involvement may be a factor attracting passive funds seeking diversification and stable returns. This observed interplay warrants deeper analysis to determine whether concentrated financial ownership reinforces alignment with state strategic goals or creates potential conflicts between national interests and global financial market pressures. Our data provides the structural context but does not definitively resolve this question, instead highlighting it as a critical area for future investigation.\u003c/p\u003e\u003cp\u003eThe findings provide structural evidence supporting our Proposition 2 (P2), demonstrating considerable variation in ownership patterns across countries, which aligns with Variety of Capitalism (VoC) frameworks. The strong state presence in France and China contrasts sharply with the institution-dominated US landscape, suggesting that national institutional contexts (e.g. legal systems, investor protection, etc.) significantly mediate the influence of global financialization trends. The state's dual role as owner/regulator and customer remains a powerful force, potentially counterbalancing or interacting with the influence of large financial institutions in complex ways that differ by country (Kapopoulos \u0026amp; Lazaretou, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This structural observation underscores that governance solutions and sustainability strategies cannot be one-size-fits-all but must account for these institutional variations.\u003c/p\u003e\u003cp\u003eThe observed concentration and financialization trends in the A\u0026amp;D sector's ownership network have inferred implications for its long-term sustainability. If governance priorities shift excessively towards short-term returns due to passive fund influence, this could potentially jeopardize investments in sustainable innovation (e.g., alternative fuels, lighter materials, circular economy principles in manufacturing) that are essential for the sector's future viability and reduced environmental impact. Furthermore, the concentration of power within a few financial actors raises important questions about corporate accountability and the balancing of diverse stakeholder interests (employees, communities, environment, national security) against shareholder value. Ensuring robust governance frameworks that incentivize long-term value creation and address sustainability goals becomes crucial consideration based on these structural insights. While this study highlights these structural patterns and their inferred risks, it also points to the importance of considering proactive governance strategies within firms and potentially tailored regulatory oversight that accounts for the unique characteristics of the A\u0026amp;D sector and the evolving nature of corporate ownership.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFinal thoughts\u003c/h2\u003e\u003cp\u003eThis article conducted a comprehensive Social Network Analysis of the corporate ownership landscape within the global Aerospace and Defense sector. Our exploratory findings reveal a highly concentrated, oligopolistic structure significantly influenced by large passive investment funds. Central players like Vanguard, BlackRock, and Capital Research occupy pivotal positions within this network, structurally suggesting considerable potential for influence over the sector's governance and strategic direction. The analysis also confirmed the persistent importance of state actors and highlighted significant variations in ownership patterns across different national models of capitalism.\u003c/p\u003e\u003cp\u003eOur primary contribution lies in meticulously mapping this complex ownership landscape and thereby initiating an analysis of its inferred implications for the A\u0026amp;D sector's sustainability trajectory. While the descriptive findings regarding the network structure are robust, the links drawn to governance impacts and sustainability challenges are, at this stage, primarily theoretical and indicative, based on the observed structure and existing literature. The concentration of power identified raises important questions about the alignment of strategic decision-making with the long-term investments required for sustainable innovation and development in this critical sector. Despite being an exploratory and descriptive study, this research provides fundamental insights into control mechanisms within the industry.\u003c/p\u003e\u003cp\u003eHowever, we recognize inherent limitations in the exploratory approach. Network centrality provides a proxy for potential influence, but it does not directly capture the mechanisms through which influence is actually exerted, nor does it quantify the specific outcomes of such influence. Most importantly, this study did not empirically test the causal relationship between ownership structure and specific firm performance or sustainability outcomes. Futhermore, the differentiation noted between ETF holdings and traditional stock ownership also warrants further investigation into its implications.\u003c/p\u003e\u003cp\u003eFrom an analytical point of view, future research is needed to addressing dynamic data and delving deeper into the relationship between companies and customers, which was not quantitatively analyzed here, paving the way to explore other forms of indirect control. Dynamic analysis tracking changes in ownership and firm behavior over time would be particularly valuable. Further research could also explore the specific engagement strategies of large passive funds within the A\u0026amp;D sector and their actual impact on corporate boards and management. Understanding these dynamics is crucial for developing effective governance frameworks that support both the economic viability and the sustainable future of the Aerospace and Defense industry.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEndnotes\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e According to Hall and Soskice (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), the \"variety of capitalism\" framework posits that despite operating under global capitalism, each nation or group of nations possesses specific institutional formats in various economic domains that shape their economic structure.\u003c/p\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e In network analysis \u0026ldquo;nodes\u0026rdquo; can represent any individual or collective entity, such as person, company, community, country, among others.\u003c/p\u003e\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Index providers influence index construction in a political manner; once companies or countries are included or excluded from an index, the criteria are set by these providers.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the financial support, without which it would not have been possible to develop this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest related to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available at Market Screener under the URL: https://www.marketscreener.com/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjdacic, L., Heemskerk, E. 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(2020). \u003cem\u003eDefence trends 2020: Investing in a digital future: Vol. Annual Glo\u003c/em\u003e (Issue 23).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Corporate Control, Corporate Ownership, Network Analysis, Financialization, Aerospace and Defense, Corporate Governance, Passive Investment Funds","lastPublishedDoi":"10.21203/rs.3.rs-7386277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7386277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global rise of passive investment funds is influencing corporate control structures, presenting notable challenges and opportunities, particularly in strategic sectors such as Aerospace and Defense (A\u0026amp;D). This paper aims to investigate who controls the corporate ownership network in the Aerospace and Defense (A\u0026amp;D) sector. Specifically, the objectives are to: 1) describe the corporate ownership network structure of the global A\u0026amp;D sector, identifying key actors and their positions; 2) analyze network properties (such as density, modularity, and centrality) to understand the distribution of influence; and 3) explore the relationship between ownership concentration, the type of dominant shareholders (e.g., passive funds, state, families), and potential implications for strategic orientation and long-term investment. Using network analysis on data from 225 publicly traded A\u0026amp;D companies worldwide, processed via Gephi. The findings confirm a highly concentrated, oligopolistic structure: 10 companies hold 61.9% of invested capital, with passive investment funds \u0026ndash; BlackRock, Vanguard, State Street, and Capital Research \u0026ndash; playing a central role, controlling over 45.7% of this capital. While further research is needed to establish direct causal links between the observed ownership concentration and its specific impacts on strategic decision-making or long-term outcomes, the concentration itself raises pertinent questions regarding alignment with sector development goals. The analysis highlights the importance of considering differences between countries and capitalism models in understanding ownership structure in the Aerospace and Defense sector.\u003c/p\u003e","manuscriptTitle":"Corporate Ownership Network in the Aerospace and Defense Sector: The Ascendency of Passive Investment Funds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 13:55:54","doi":"10.21203/rs.3.rs-7386277/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c29d5494-bbb9-4cae-98ed-955ea4ac6b45","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T07:50:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 13:55:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7386277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7386277","identity":"rs-7386277","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}