Exploring the Key Drivers of Financial Performance in the Context of Corporate and Public Governance: Empirical Evidence

Abstract

This research focuses on analyzing the determinants of financial performance for the companies included in the Standard & Poor’s 500 index over the period from 2014 to 2023. To guide managerial decisions aimed at enhancing company performance, this study examines, as key drivers, the main financial indicators, core corporate governance characteristics, and U.S. public governance indicators. The investigation begins with a retrospective review of the specialized literature, highlighting the findings of previous studies in the field and providing the basis for selecting the variables used in the present empirical analysis. The research method employed is fixed-effects panel-data regression. The dependent variables are financial performance measures, such as the EBITDA margin, EBIT margin, net profit margin, and ROA. This study’s main results show that the price-to-book ratio, liquidity, sales growth, CEO duality, board gender diversity, ESG score, and U.S. regulatory quality exert a positive influence on financial performance. In contrast, the price-to-earnings ratio, net debt, capital intensity, R&D intensity, weighted average cost of capital, board independence, and the COVID-19 pandemic crisis have a negative impact on the financial performance of U.S. companies. The findings of this investigation could serve as benchmarks for supporting managerial decisions at the company level regarding the improvement of their financial performance.

1. Introduction

Financial performance—a key objective of financial management—functions as the compass that guides managerial decision-makers who are committed to achieving company-level goals aimed at sustainable growth and sustained profitability. Consequently, analyzing the determinants of corporate financial performance is essential to developing effective financial management strategies.

The main signals of a company’s financial health come from financial indicators that measure leverage, price multiples, liquidity, capital and R&D intensity, taxation, sales growth, firm size, and the weighted average cost of capital.

In other words, there are several factors that can all raise or lower a company’s profitability, such as a firm’s financing structure reflected in net debt, its ability to withstand bankruptcy risk, as shown by how quickly it can turn assets into cash, or the corporate tax burden. Also, sales growth rate, R&D spending and its impact on capital intensity, competitive advantages generated by firm size or price multiples, and the efficiency of capital determined by the cost of capital and the mix of funding sources can be taken into consideration.

Regarding the theoretical framework, it is worth mentioning the pecking order theory [1], according to which companies prefer internal financing over external financing. In other words, companies prefer to finance their new projects, firstly, using internal funds, namely retained earnings, and if external financing is needed, they prefer debt financing over equity financing. Thus, the managerial decisions regarding the establishment of the optimal ratio between debt and equity have an important effect on companies’ financial performance.

Corporate governance indicators—board size, board meetings, CEO duality, board independence, gender diversity, and ESG score—are additional parameters that shape business decisions. Safeguarding stakeholder rights by aligning shareholder and managerial objectives, ensuring transparency, fulfilling board responsibilities, and maintaining fair treatment among interested parties is aimed at limiting the effects of potential conflicts of interest and inefficient managerial decisions. From a theoretical standpoint, agency theory is fundamental to the analysis of corporate governance. Thus, the conflicts of interest between managers and shareholders generate agent costs that impact financial performance. According to Jensen and Meckling [2], a company that is better governed reports higher performance as a result of reduced agent costs.

Given that the external environment influences corporate financial performance, it is appropriate to incorporate U.S. public governance indicators into empirical analysis—specifically, control of corruption, government effectiveness, political stability and absence of violence/terrorism, rule of law, voice and accountability, and regulatory quality. These indicators reflect key aspects of governance, including perceptions of the extent to which public power is used for private gain (control of corruption), the quality and independence of public services and civil service from political influence (government effectiveness), and the risk of political instability or violence, including terrorism (political stability and absence of violence/terrorism). They also assess the level of trust in and adherence to societal rules (rule of law), the degree of citizen participation in governance and freedom of expression (voice and accountability), as well as the government’s ability to design and enforce effective policies and regulations that support private sector growth (regulatory quality).

The purpose of this study is to identify the influence exerted by financial indicators, corporate governance characteristics, and global public governance indicators on the financial performance of U.S. companies included in the Standard & Poor’s 500 index over the period from 2014 to 2023. The findings make a substantive contribution to the specialized literature by measuring financial performance through accounting-based indicators such as return on assets (ROA), net profit margin, EBIT margin, and EBITDA margin. In addition, this research offers a fresh insight into the impact that price multiples (P/E, P/BV), weighted average cost of capital, public governance indicators, and the COVID-19 pandemic crisis have on corporate financial performance.

This study distinguishes itself from prior research in several key aspects. Firstly, the originality lies in the approach to analyzing financial performance through the lens of three categories of explanatory factors, namely, financial indicators, corporate governance indicators, and public governance indicators. By incorporating a broader set of factors—spanning both microeconomic and macroeconomic levels—into the analysis of financial performance, this study contributes value to the existing scientific literature on the topic. Moreover, to enhance the robustness of the research, we also used indicators less frequently encountered in the literature review, such as price multiples and the weighted average cost of capital, as independent variables, as well as the EBITDA margin as a dependent variable.

Moreover, while the relationship between financial indicators and company performance has been extensively studied, and corporate governance indicators have received considerable attention in the literature, the influence of public governance variables on firm-level outcomes remains underexplored. Most existing studies have focused on macroeconomic or institutional impacts at the country level, overlooking how public governance quality may shape the operational and financial outcomes of companies. Furthermore, there is a lack of empirical evidence integrating public governance indicators into firm-level financial performance models. Thus, this research seeks to fill this gap by providing a comprehensive analysis that incorporates both corporate and public governance dimensions into the assessment of company financial performance. In this way, this study contributes to the literature by highlighting how the broader institutional environment interacts with company-specific factors in shaping financial outcomes.

This paper is organized as follows: Section 2 reviews the international literature on the determinants of corporate financial performance, Section 3 describes the database and research variables, together with descriptive statistics and a correlation matrix, Section 4 presents the estimated regression models and interprets the empirical findings, and Section 5 summarizes the results and highlights their contribution to managerial decision-making aimed at improving corporate financial performance.

2. Literature Review

Although a wide array of studies examines the determinants of corporate financial performance, their findings are varied and often contradictory. The challenges of measuring financial performance, the diversity of potential drivers, dataset particularities, and the multitude of analysis methods all expose limitations in the existing literature and underscore the need to broaden the set of variables under investigation. Financial performance is fundamental to a firm’s sustainability and financial health, serving as the foundation of managerial decision-making. Over time, corporate performance has been assessed through numerous metrics. Accounting-based indicators include return on assets (ROA), return on equity (ROE), return on invested capital (ROIC), net profit margin, and EBIT margin, while market-based indicators encompass the price-to-book ratio (P/BV), price-to-earnings ratio (P/E), Tobin’s Q, and earnings per share (EPS).

2.1. Financial Indicators and Firm Performance

Studies investigating the impact of leverage, total indebtedness, short-term indebtedness, long-term indebtedness, and net debt on financial performance overwhelmingly indicate a negative effect. Specifically, empirical evidence from Matias et al. [3], Melwani [4], Nguyen et al. [5], Apan and İslamoğlu [6], Fareed et al. [7], Vintilă and Nenu [8], Lazăr [9], Lazăr and Istrate [10], Lehenchuk et al. [11], Gharios et al. [12], and Matar and Eneizan [13] shows that total indebtedness reduces ROA and, according to Tran et al. [14], ROE. Moreover, other researchers [15,16] have demonstrated that lower financial leverage is associated with higher profitability, as measured by ROA. Additionally, other studies [14,17,18] have reported that both short- and long-term indebtedness negatively influence ROA and ROE. Regarding net debt, Vintilă [19] reports a positive effect on ROE in fixed- and random-effects panel regressions but a negative effect on ROA in pooled panel regression models. Another factor shown to influence corporate financial performance is liquidity. Empirical studies by Matar and Eneizan [13], Lazăr and Istrate [10], Apan and İslamoğlu [6], Tudose et al. [20], Karanovic [21], Vintilă [19], Nguyen et al. [5], Gharios et al. [12], and Gobbi et al. [22] have demonstrated that liquidity has a positive impact on ROA, contradicting the findings of Alarussi and Gao [15] and Vintilă and Nenu [16]. Gobbi et al. [22] likewise report a positive influence of current liquidity on ROE, in contrast to Tudose et al. [20] and Vintilă and Nenu [16]. Regarding the effect of current liquidity on net profit margin, the empirical analysis by Al-Jafari and Al Samman [23] supports a positive impact, whereas Tudose et al. [20] report the effect to be insignificant. Moreover, Zavalii et al. [24] show that current liquidity positively affects the EBIT margin in Poland and Slovakia but has an insignificant impact in the Czech Republic and Ukraine. Furthermore, the research by Lehenchuk et al. [11], Ramnoher and Seetah [25], and Khatib et al. [26] indicates that liquidity exerts an insignificant influence on financial performance.

Regarding the firm size, Mirza and Javed [17], Bala Ado et al. [27], Pantea et al. [28], Kurawa and Saidu [29], García-Gómez et al. [18], Wieczorek-Kosmala et al. [30], Gharios et al. [12], and Al-Jafari and Al Samman [23] show that larger firms achieve stronger financial performance, as measured by ROA. These findings contradict the results of Matar and Eneizan [13], Nenu et al. [31], Pitulice et al. [32], Ramnoher and Seetah [25], and Kyere and Ausloos [33]. Moreover, the research by Tudose et al. [20] and Al-Jafari and Al Samman [23] indicates that firm size has a positive influence on net profit margin, while Zavalii et al. [24] report that firm size positively affects the EBIT margin in Slovakia and Ukraine but has an insignificant impact in Poland and the Czech Republic. The interest in how capital intensity affects financial performance, as measured by ROA, is explored by Matias et al. [3], Bala Ado et al. [27], Ojeka et al. [34], Apan and İslamoğlu [6], and Vintilă and Nenu [8], all of whom report a negative relationship between the two variables, contradicting the results reported by Pantea et al. [28]. Equally notable is the study by Lehenchuk et al. [35], which concludes that capital intensity does not influence financial performance, as measured by ROA, ROE, net profit margin, and EBIT margin.

Sales growth is also another important factor that determines a company’s financial performance. Studies by Lazăr [9], García-Gómez et al. [18], and Singh et al. [36] show that firms with higher growth rates achieve higher ROA, in contrast with the results obtained by Bala Ado et al. [27] and Vintilă and Nenu [8]. Other research [4,28,31] indicates that a company’s sales growth rate does not influence its financial performance. Less frequently examined in prior research, R&D expenditure intensity represents another determinant of financial performance. According to Melwani [4], this independent variable has a negative impact on ROA. Moreover, Liu et al. [37] show that R&D expenditure intensity does not exert a statistically significant influence on ROA, ROE, and EBIT margin. Other researchers have investigated how the effective corporate tax rate (ETR) affects financial performance. Nenu et al. [31], Lazăr and Istrate [10], Khuong et al. [38], Vintilă [19], and Pitulice et al. [32] suggest that ETR has a significantly negative impact on ROA, whereas Melwani [4] and Kurawa and Saidu [29] report a statistically insignificant effect. Furthermore, the empirical analysis by Ștefănescu et al. [39] reveals that the effective tax rate has a negative influence on both the net profit margin and ROE. Another factor that affects corporate financial performance is the ESG score. Using panel data regression analysis, Che et al. [40] identify a significant positive effect, suggesting that a higher ESG score is associated with an increased ROA. By contrast, Shobhwani and Lodha [41], Wisanggeni and Rahmawati [42], and Chaabouni et al. [43] show that the ESG score has a statistically insignificant influence on ROA. Moreover, Chaabouni et al. [43] observe that a higher ESG score reduces firm performance, as measured by ROE and ROIC.

Table 1. Summary of the literature review on the influence of financial indicators on financial performance.

Authors	Sample and Period	Methods	Conclusion
Al-Jafari and Al Samman [23]	Industrial companies listed on the Muscat Stock Exchange over the period 2006–2013	Ordinary least squares	Current liquidity has a positive influence on profit margin and return on assets. Company size positively impacts profit margin and return on assets.
Vintilă [19]	466 pharmaceutical companies in Europe and the United States of America during the period 2012–2021	Regression analysis without effects, with fixed effects, and with random effects	Effective tax rate has a negative impact on return on assets and return on equity. Net debt positively influences return on equity in models with fixed effects and random effects and negatively impacts return on assets in models with no effects.
Nenu et al. [31]	Companies listed on the Bucharest Stock Exchange between 2000 and 2016	Fixed-effects regression, two-step system generalized method of moments	Effective tax rate negatively affects return on assets. Company size has a negative influence on return on assets.
Melwani [4]	Automobile companies listed on the Bombay Stock Exchange for a period of ten financial years from 2007 to 2016	Regression analysis	Research and development intensity negatively impacts return on assets. Indebtedness has a negative impact on return on assets.
Tudose et al. [20]	Companies in the automotive industry for the period 2010–2019	Regression analysis	Company size positively influences return on assets, profit margin, and economic value added. Liquidity has a positive impact on return on assets, and a negative impact on return on equity and economic value added.
Pitulice et al. [32]	Companies listed on the Bucharest Stock Exchange between 2012 and 2014	Regression analysis	Company size negatively influences return on assets and positively affects net profit margin. Effective tax rate has a negative impact on return on assets and net profit margin.
Lehenchuk et al. [35]	527 Slovak agricultural companies over the period 2015–2019	Regression analysis	Capital intensity does not influence return on assets, return on equity, net profit margin, and EBIT margin.
García-Gómez et al. [18]	313 U.S. hospitality companies for the period 2001–2018	Ordinary least squares, fixed- and random-effects regression, system generalized method of moments	Sales growth has a positive impact on return on assets.
Zavalii et al. [24]	Advertising and marketing companies across four Central and Eastern European countries during the period 2021–2023	Regression analysis	Company size has a positive impact on EBIT margin in Slovakia and Ukraine but has an insignificant impact in Poland and the Czech Republic.
Gharios et al. [12]	4257 non-financial companies listed in Europe for the period 2011–2023	Fixed- and random-effects regression, system generalized method of moments	Liquidity and company size have a positive impact on return on assets. Indebtedness negatively influences return on assets.

Source: Authors’ own processing.

presents a summary of the literature review on the impact of financial indicators on corporate financial performance.

Based on the previously reviewed literature, the following research hypotheses have been formulated regarding the impact of financial indicators on companies’ financial performance:

H1. 

Net debt has a negative influence on financial performance.

H2. 

The current ratio positively impacts financial performance.

H3. 

Company size positively affects financial performance.

H4. 

Capital intensity has a negative impact on financial performance.

H5. 

Sales growth positively influences financial performance.

H6. 

Research and development intensity negatively impacts financial performance.

H7. 

The effective tax rate has a negative influence on financial performance.

H8. 

The ESG score positively affects financial performance.

2.2. Corporate Governance Indicators and Firm Performance

Being considered one of the principal factors shaping a company’s path and management, corporate governance seeks to ensure transparency and protect shareholders. Consequently, the relationship between firm performance and corporate governance indicators has attracted the attention of researchers, policymakers, and regulatory authorities. When it comes to board size, researchers have not reached a consensus about its impact on financial performance. While Talalwa et al. [44], Khatib et al. [26], and Palaniappan [45] report that board size has a negative influence on ROA, Kyere and Ausloos [33] report a positive effect. Other empirical studies suggest that board size does not significantly affect financial performance [42,46,47]. Regarding the impact of board independence on firm performance, empirical findings are mixed. Chaabouni et al. [43] and Khatib et al. [26] show that board independence has a negative influence on ROA, contradicting the results of Kyere and Ausloos [33], Palaniappan [45], Musallam [48], and Tarighi et al. [46]. Furthermore, Vintilă and Gherghina [49] report that board independence has no significant influence on financial performance, as measured by Tobin’s Q, P/BV, ROA, ROE, and P/E. Another key corporate governance metric whose impact on financial performance has been examined is CEO duality. According to Kyere and Ausloos [33], Puni and Anlesinya [50], and Talalwa et al. [44], the effect of CEO duality on ROA is statistically insignificant. However, Pucheta-Martínez and Gallego-Álvarez [51] show that CEO duality positively influences financial performance, as measured by ROE, contradicting the results reported by Farooq and Ahmad [47]. Regarding the number of board meetings, Farooq and Ahmad [47] show that this corporate governance characteristic does not affect financial performance, as measured by ROA, ROE, and EPS. The empirical analysis by Palaniappan [45] indicates that increasing the frequency of board meetings lowers ROA, contradicting the findings of Nguyen and Huynh [52], Sahoo et al. [53], Tarighi et al. [46], and Khatib et al. [26]. Another corporate governance variable is represented by the board’s gender diversity. Nguyen and Huynh [52], Sahoo et al. [53], and Khatib et al. [26] suggest that the presence of women on boards enhances ROA. Conversely, the results reported by Talalwa et al. [44], Chaabouni et al. [43], and Tarighi et al. [46] indicate that the board’s gender diversity does not influence the company’s financial performance.

Table 2. Summary of the literature review on the influence of corporate governance indicators on financial performance.

Authors	Sample and Period	Methods	Conclusion
Khatib et al. [26]	528 non-financial companies listed on Bursa Malaysia from 2015 to 2019	Two-step system generalized method of moments	Board size has a negative influence on return on assets and return on equity. Board independence negatively impacts financial performance. Increasing board meetings leads to improved return on assets and return on equity. Board gender diversity contributes to the improvement of return on assets and return on equity.
Palaniappan [45]	275 companies listed on the National Stock Exchange of India from 2011 to 2015	Regression analysis	Board independence positively affects return on assets and negatively impacts return on equity. Board size negatively impacts return on assets and return on equity.
Kyere and Ausloos [33]	252 companies listed on the London Stock Exchange in 2014	Regression analysis	Board size and board independence have a positive influence on return on assets.
Sahoo et al. [53]	113 companies listed on the Bombay Stock Exchange from 2013 to 2020	Regression analysis	Board gender diversity contributes to the improvement of return on assets, return on equity, and return on capital employed.
Chaabouni et al. [43]	117 companies listed on the Saudi Stock Exchange in 2023	Ordinary least squares and Python	Board independence has a negative impact on return on assets, return on equity, and return on invested capital.
Tarighi et al. [46]	183 companies listed on the Tehran Stock Exchange from 2016 to 2021	Random-effects regression, two-stage least squares, generalized method of moments	Increasing board meetings leads to an increase in return on assets. Board independence positively affects return on assets.
Nguyen and Huynh [52]	52 construction and real estate companies listed on the Vietnam Stock Exchange during the period 2006–2020	Pooled OLS, fixed-effects regression, random-effects regression, feasible generalized least squares, two-step generalized method of moments	Board gender diversity contributes to an improvement in return on assets. Board meetings have a positive influence on return on assets.

Source: Authors’ own processing.

summarizes the studies identified in the literature on the impact of corporate governance indicators on companies’ financial performance.

Based on the previously reviewed literature, the following research hypotheses have been formulated regarding the impact of corporate governance indicators on companies’ financial performance:

H9. 

Board size negatively affects financial performance.

H10. 

Board independence has a negative influence on financial performance.

H11. 

Board meetings have a positive impact on financial performance.

H12. 

Board gender diversity positively influences financial performance.

3. Research Methodology

3.1. Database and Variables

This research is based on a dataset of non-financial companies included in the Standard & Poor’s 500 index over a 10-year period, from 2014 to 2023. The financial companies were excluded from the 500 companies in the S&P 500 index, resulting in a final sample of 439 companies for empirical analysis.

According to the literature presented in the state of knowledge, there are a variety of studies that analyze financial performance from the perspective of both financial indicators and corporate governance indicators. However, the weighted average cost of capital and price multiples are financial indicators whose impact on financial performance has not been identified in the analyzed studies. Moreover, the analysis of the impact of public governance indicators on companies’ financial performance represents another research gap in the literature. Additionally, financial performance measured by the EBITDA margin is a less studied indicator in the literature.

The dependent variables are represented by financial performance indicators expressed in accounting terms, namely, the EBITDA margin, EBIT margin, net profit margin, and ROA.

The independent variables are grouped into three categories.

The first category includes financial indicators reported by companies, such as indebtedness (net debt), liquidity (current ratio), and taxation (effective corporate tax rate). Other indicators include sales dynamics (growth), capital intensity, R&D intensity, and firm size (natural logarithm of total assets). It also covers key financial performance indicators like the price-to-earnings ratio, the price-to-book ratio, and the weighted average cost of capital.

The second category focuses on corporate governance characteristics of U.S. companies. These include board size, board independence, the number of board meetings, CEO duality, and board gender diversity. Executive gender diversity and the company’s environmental, social, and governance (ESG) score are also included.

The third category consists of global public governance indicators at the U.S. level. These include control of corruption, government effectiveness, political stability and the absence of violence/terrorism, rule of law, voice and accountability, and regulatory quality.

A description of the variables is provided in

Table 3. Description of the variables.

Variable Name	Symbol	Definition	Source
Dependent variables
Financial performance indicators
EBITDA margin	ebitdam	EBITDA/Sales	Thomson Reuters Eikon
EBIT margin	ebitm	EBIT/Sales	Thomson Reuters Eikon
Net profit margin	netm	Net profit/Sales	Thomson Reuters Eikon
Return on assets	roa	Net profit/Total assets	Thomson Reuters Eikon
Independent variables
Financial indicators
Net debt	netd	(Total debt − Cash and cash equivalents)/Equity	Thomson Reuters Eikon
Price-to-earnings ratio	pe	Price per share/Earnings per share	Thomson Reuters Eikon
Price-to-book ratio	pbv	Price per share/Book value per share	Thomson Reuters Eikon
Current ratio	crtr	Current assets/Current liabilities	Thomson Reuters Eikon
Capital intensity	capi	Tangible assets/Total assets	Thomson Reuters Eikon
R&D intensity	rdi	Research and development expenses/Total revenue	Thomson Reuters Eikon
Effective tax rate	etr	Tax expenses/Earnings before taxes	Thomson Reuters Eikon
Sales growth	salg	Year-over-year change in total revenue	Thomson Reuters Eikon
Firm size	size	Natural logarithm of total assets	Thomson Reuters Eikon
Weighted average cost of capital	wacc	Cost of equity × Equity/Invested capital + Cost of debt × Debt/Invested capital × (1 − ETR)	Thomson Reuters Eikon
Characteristics of corporate governance
Board size	bsize	Number of board members	Thomson Reuters Eikon
Board meetings	bmeet	Number of board meetings during the year	Thomson Reuters Eikon
CEO duality	ceod	Dummy variable: 1 if the CEO is also the Chairman, and 0 otherwise	Thomson Reuters Eikon
Board independence	bindep	Percentage of independent members on the board	Thomson Reuters Eikon
Board gender diversity	bgdiv	Percentage of female members on the board	Thomson Reuters Eikon
Executive members gender diversity	execdiv	Percentage of female executive members on the board	Thomson Reuters Eikon
ESG score	esg	Score, ranging from 0 to 100	Thomson Reuters Eikon
Global public governance indicators
Control of corruption	cc	Score, ranging from −2.5 to 2.5	World Bank Group
Government effectiveness	ge	Score, ranging from −2.5 to 2.5	World Bank Group
Political stability and absence of violence/terrorism	pv	Score, ranging from −2.5 to 2.5	World Bank Group
Rule of law	rl	Score, ranging from −2.5 to 2.5	World Bank Group
Voice and accountability	va	Score, ranging from −2.5 to 2.5	World Bank Group
Regulatory quality	rq	Score, ranging from −2.5 to 2.5	World Bank Group
COVID-19 pandemic crisis
COVID-19 pandemic crisis	covid	Dummy variable: 1 for the years 2020–2022, and 0 otherwise	-

Source: Authors’ own processing.

.

3.2. Descriptive Statistics and Correlation Matrix

Table 4. Descriptive statistics.

Variable	Mean	Median	Min	Max	SD	VIF
ebitdam	0.2631	0.2300	0.0500	0.6100	0.1508	-
ebitm	0.1808	0.1700	0.0200	0.4100	0.1051	-
netm	0.1170	0.1100	−0.0600	0.3200	0.0952	-
roa	0.0761	0.0600	−0.0200	0.2200	0.0625	-
netd	0.7226	0.5600	−1.5000	3.9350	1.2068	1.76
pe	31.5407	25.0800	9.7900	96.2800	21.3102	1.23
pbv	6.4950	3.9900	1.2800	26.9500	6.6152	1.66
crtr	1.6014	1.3500	0.5600	3.8700	0.8787	1.54
capi	0.2611	0.1500	0.0300	0.8500	0.2471	1.14
rdi	0.0383	0.0000	0.0000	0.2000	0.0625	1.67
etr	0.1935	0.2100	−0.0500	0.3800	0.1116	1.22
salg	0.0818	0.0600	−0.1500	0.4300	0.1360	1.18
size	23.6417	23.6364	21.5701	25.7377	1.1331	1.93
wacc	0.0647	0.0700	0.0200	0.1000	0.0232	1.36
bsize	10.7804	11.0000	8.0000	14.0000	1.7544	1.44
bmeet	7.6395	7.0000	4.0000	14.0000	2.7566	1.13
ceod	0.4763	0.0000	0.0000	1.0000	0.4995	1.08
bindep	0.8241	0.8600	0.4500	0.9300	0.1207	1.20
bgdiv	0.2374	0.2500	0.0000	0.4300	0.1093	1.56
execdiv	0.1667	0.1700	0.0000	0.4000	0.1239	1.23
esg	61.0548	64.3100	27.2400	84.7500	16.4821	1.71
cc	1.2150	1.2350	1.0200	1.3700	0.1310	3.58
ge	1.3890	1.4350	1.2200	1.5400	0.1097	2.51
pv	0.2400	0.1900	−0.0300	0.6600	0.2417	2.43
rl	1.4690	1.4500	1.3300	1.6100	0.1072	2.13
va	0.9690	0.9550	0.8600	1.1100	0.0978	2.39
rq	1.4060	1.4050	1.2400	1.6200	0.1315	1.26
covid	0.3000	0.0000	0.0000	1.0000	0.4583	2.99

Source: Authors’ own processing.

provides an overview of the descriptive statistics. Thus, the mean, median, minimum, maximum, and standard deviation are calculated for the variables included in the empirical analysis. Furthermore, the variance inflation factor (VIF) analysis indicates that all the independent variables have VIF values lower than five, suggesting that the empirical models are not affected by the multicollinearity phenomenon.

Based on the descriptive statistics presented in Table 4, the mean values for EBITDA margin, EBIT margin, net profit margin, and return on assets are 0.2631, 0.1808, 0.1170, and 0.0761, with minimum values of 0.0500, 0.0200, −0.0600, and −0.0200, and maximum values of 0.6100, 0.4100, 0.3200, and 0.2200.

Regarding the financial indicators reported by the companies, it is observed that there are variables, such as net debt, price-to-book ratio, research and development intensity, and sales growth, that exhibit a standard deviation that exceeds the mean, showing that these variables are more volatile than the others. In addition, the current ratio ranges from 0.5600 to 3.8700, suggesting that some companies exhibit substantial liquidity, while other firms have fewer current assets. Capital intensity varies from 0.0300 to 0.8500, meaning that some companies have more tangible assets than others.

Considering the characteristics of corporate governance, in U.S. companies, the board of directors typically consists of 8 to 14 members, and these members have 4 to 14 meetings throughout the year. The independence of the board of directors is quite pronounced, with an average of 82.41% of the members being independent. Furthermore, both the presence of women on the board of directors and among executives is very low, with an average of 23.74% of the board of directors being women and 16.67% of executive directors being women. The CEO duality variable is more volatile than the others because the standard deviation surpasses the mean value. The ESG score ranges from 27.2400 to 84.7500, with a mean value of 61.0548.

Regarding global public governance indicators, it is observed that political stability and the absence of violence/terrorism, as well as voice and accountability, are more volatile than control of corruption, government effectiveness, rule of law, and regulatory quality because the standard deviation is greater than the mean.

Moreover, since the presence of outliers has been identified, with the minimum and maximum values of the variables being quite far apart, a 90% winsorization of the data was applied.

Table 5. Correlation matrix.

Variable	(1) ebitdam	(2) ebitm	(3) netm	(4) roa	(5) netd	(6) pe	(7) pbv
(1) ebitdam	1.0000
(2) ebitm	0.8939	1.0000
(3) netm	0.7027	0.8512	1.0000
(4) roa	0.1796	0.4367	0.6324	1.0000
(5) netd	0.0808	−0.0012	−0.1618	−0.2129	1.0000
(6) pe	0.0304	−0.0615	−0.1715	−0.2274	−0.1090	1.0000
(7) pbv	0.0635	0.1918	0.2379	0.4454	0.3278	0.2728	1.0000
(8) crtr	0.0402	0.1881	0.3019	0.4126	−0.4278	0.0954	0.0342
(9) capi	0.0361	−0.0443	−0.0788	0.0013	0.1039	−0.1209	−0.0783
(10) rdi	0.1936	0.2593	0.3608	0.1845	−0.3168	0.2882	0.1683
(11) etr	−0.1208	−0.0558	−0.2335	−0.0355	0.0451	−0.0534	−0.0239
(12) salg	0.0621	0.0738	0.1213	0.1748	−0.1212	0.1889	0.1440
(13) size	0.1658	0.0116	−0.0689	−0.3424	0.1891	−0.1585	−0.2322
(14) wacc	−0.0907	−0.0147	0.0590	0.1889	−0.1795	−0.0173	0.0376
(15) bsize	−0.0069	−0.0717	−0.1315	−0.1948	0.2222	−0.1322	−0.0766
(16) bmeet	0.1059	0.0368	−0.0308	−0.1733	0.1150	−0.0711	−0.1545
(17) ceod	−0.0316	−0.0324	−0.0427	−0.0666	0.0546	−0.0509	−0.0261
(18) bindep	0.0783	0.0373	−0.0015	−0.1123	0.1195	−0.0672	−0.0269
(19) bgdiv	−0.0193	−0.0272	0.0102	0.0089	0.0364	−0.0148	0.0882
(20) execdiv	0.0327	0.0474	0.0223	0.0309	0.0860	−0.0241	0.1098
(21) esg	−0.0149	−0.0095	−0.0049	−0.0638	0.1240	−0.1125	−0.0037
(22) cc	−0.0539	−0.0506	−0.0922	−0.0301	−0.0000	−0.0693	−0.0826
(23) ge	−0.0410	−0.0449	−0.0673	−0.0133	0.0112	−0.0340	−0.0495
(24) pv	−0.0560	−0.0488	−0.0974	−0.0319	−0.0035	−0.0728	−0.0838
(25) rl	−0.0524	−0.0449	−0.0890	−0.0149	−0.0000	−0.0919	−0.0926
(26) va	−0.0539	−0.0465	−0.0941	−0.0250	−0.0012	−0.0772	−0.0876
(27) rq	−0.0029	−0.0084	0.0150	0.0241	0.0191	−0.0423	−0.0291
(28) covid	0.0374	0.0419	0.0716	0.0393	−0.0030	0.0190	0.0451
Variable	(8) crtr	(9) capi	(10) rdi	(11) etr	(12) salg	(13) size	(14) wacc
(8) crtr	1.0000
(9) capi	−0.1018	1.0000
(10) rdi	0.3454	−0.2886	1.0000
(11) etr	−0.0688	0.1271	−0.3178	1.0000
(12) salg	0.1187	−0.0258	0.1468	−0.0420	1.0000
(13) size	−0.3818	0.1143	−0.0793	−0.0654	−0.0857	1.0000
(14) wacc	0.2304	0.0274	0.1531	−0.0130	−0.0266	−0.2196	1.0000
(15) bsize	−0.2687	0.0646	−0.1916	0.0328	−0.1637	0.4912	−0.1801
(16) bmeet	−0.1489	−0.0234	−0.0234	−0.0023	0.0225	0.2097	−0.1799
(17) ceod	−0.1149	−0.0259	−0.0915	0.0177	−0.0829	0.1526	−0.0552
(18) bindep	−0.1089	−0.0581	−0.0227	−0.0397	−0.0947	0.1666	−0.0531
(19) bgdiv	−0.1561	0.0020	0.0086	−0.0952	−0.0589	0.2327	−0.1321
(20) execdiv	−0.0830	0.0145	−0.0135	−0.0247	−0.0463	0.2028	−0.0981
(21) esg	−0.1582	0.0787	0.0220	−0.1116	−0.1385	0.4636	−0.1324
(22) cc	0.0511	−0.0253	−0.0258	0.1925	−0.1302	−0.1568	0.2441
(23) ge	0.0508	−0.0239	−0.0194	0.0717	−0.0653	−0.1449	0.1749
(24) pv	0.0519	−0.0299	−0.0284	0.2452	−0.1198	−0.1545	0.1915
(25) rl	0.0540	−0.0296	−0.0285	0.1995	−0.0502	−0.1611	0.2703
(26) va	0.0501	−0.0319	−0.0300	0.2412	−0.0592	−0.1589	0.2044
(27) rq	−0.0295	0.0174	−0.0003	−0.1102	0.0034	−0.0114	0.3545
(28) covid	−0.0367	0.0128	0.0151	−0.1075	0.1798	0.1091	−0.2332
Variable	(15) bsize	(16) bmeet	(17) ceod	(18) bindep	(19) bgdiv	(20) execdiv	(21) esg
(15) bsize	1.0000
(16) bmeet	0.1147	1.0000
(17) ceod	0.0897	0.0439	1.0000
(18) bindep	0.1012	0.0786	0.1689	1.0000
(19) bgdiv	0.1492	0.0622	0.0739	0.2238	1.0000
(20) execdiv	0.1936	0.1063	0.0356	0.1526	0.3501	1.0000
(21) esg	0.3318	0.1083	0.0692	0.3397	0.4171	0.3180	1.0000
(22) cc	−0.0427	−0.0582	0.0438	−0.1260	−0.4396	−0.2165	−0.2896
(23) ge	−0.0347	−0.0391	0.0387	−0.1044	−0.4066	−0.2141	−0.2412
(24) pv	−0.0414	−0.0309	0.0412	−0.1255	−0.4403	−0.2202	−0.2940
(25) rl	−0.0410	−0.0341	0.0469	−0.1238	−0.4575	−0.2288	−0.2841
(26) va	−0.0441	−0.0313	0.0458	−0.1285	−0.4528	−0.2288	−0.2940
(27) rq	−0.0030	−0.0635	0.0123	−0.0065	−0.0167	−0.0050	−0.0033
(28) covid	0.0276	0.0777	−0.0236	0.0915	0.2873	0.1466	0.2139
Variable	(22) cc	(23) ge	(24) pv	(25) rl	(26) va	(27) rq	(28) covid
(22) cc	1.0000
(23) ge	0.8207	1.0000
(24) pv	0.9197	0.7156	1.0000
(25) rl	0.9265	0.8175	0.8478	1.0000
(26) va	0.9297	0.7240	0.9428	0.9160	1.0000
(27) rq	0.2282	0.4381	−0.0186	0.2629	0.1144	1.0000
(28) covid	−0.8044	−0.6558	−0.6753	−0.6139	−0.6811	−0.2175	1.0000

Source: Authors’ own processing.

reports the correlation matrix, highlighting the strength of the relationships among the factors.

No strong correlations are observed among the independent variables, except for the global public governance indicators—control of corruption, government effectiveness, political stability and absence of violence/terrorism, rule of law, and voice and accountability—which show strong positive links (from 0.7156 to 0.9428). Because of these high correlations, these governance indicators are used in separate regression models. Regulatory quality is the only public governance indicator that is not strongly correlated with the others; therefore, it is included in every regression model alongside each of the remaining public governance variables.

3.3. Regression Analysis

The analysis employs multiple-panel data regression with fixed effects. The general specification is as follows: ${\mathrm{Y}}_{\mathrm{it}}={\beta }_0+{\sum }_{\mathrm{k}=1}^{\mathrm{n}}{\beta }_{\mathrm{k}}{\mathrm{X}}_{\mathrm{k}\mathrm{it}}+{\omega }_{\mathrm{i}}+{\mu }_{\mathrm{t}}+{\epsilon }_{\mathrm{it}}$, where ${\mathrm{Y}}_{\mathrm{it}}$ = the dependent variable, ${\mathrm{X}}_{\mathrm{k}\mathrm{it}}$ = the independent variable, ${\omega }_{\mathrm{i}}$ = the unobserved cross-section effect, ${\mu }_{\mathrm{t}}$ = the unobserved time effect, and ${\epsilon }_{\mathrm{it}}$ = the error term. The determinants of the financial performance of S&P 500 companies are examined using Stata 18 software. To choose the most appropriate model, the Hausman test was applied; because its p-value is below 10%, fixed-effects panel regressions are preferred. For each dependent variable, several regression models are estimated (

Table 6. General form of regression models.

Models	General Form
(1), (6), (11), (16)	$\begin{array}{c}{\mathrm{ebitdam}}_{\mathrm{it}}/{\mathrm{ebitm}}_{\mathrm{it}}/{\mathrm{netm}}_{\mathrm{it}}/{\mathrm{roa}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+{\beta }_1{\mathrm{netd}}_{\mathrm{it}}+{\beta }_2{\mathrm{pe}}_{\mathrm{it}}+{\beta }_3{\mathrm{pbv}}_{\mathrm{it}}+{\beta }_4{\mathrm{crtr}}_{\mathrm{it}}+{\beta }_5{\mathrm{capi}}_{\mathrm{it}}+{\beta }_6{\mathrm{rdi}}_{\mathrm{it}}+{\beta }_7{\mathrm{etr}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_8{\mathrm{salg}}_{\mathrm{it}}+{\beta }_9{\mathrm{size}}_{\mathrm{it}}+{\beta }_{10}{\mathrm{wacc}}_{\mathrm{it}}+{\beta }_{11}{\mathrm{bsize}}_{\mathrm{it}}+{\beta }_{12}{\mathrm{bmeet}}_{\mathrm{it}}+{\beta }_{13}{\mathrm{ceod}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{14}{\mathrm{bindep}}_{\mathrm{it}}+{\beta }_{15}{\mathrm{bgdiv}}_{\mathrm{it}}+{\beta }_{16}{\mathrm{execdiv}}_{\mathrm{it}}+{\beta }_{17}{\mathrm{esg}}_{\mathrm{it}}+{\beta }_{18}{\mathrm{cc}}_{\mathrm{it}}+{\beta }_{19}{\mathrm{rq}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{20}{\mathrm{covid}}_{\mathrm{it}}+{\omega }_{\mathrm{i}}+{\mu }_{\mathrm{t}}+{\epsilon }_{\mathrm{it}}\hfill \end{array}$
(2), (7), (12), (17)	$\begin{array}{c}{\mathrm{ebitdam}}_{\mathrm{it}}/{\mathrm{ebitm}}_{\mathrm{it}}/{\mathrm{netm}}_{\mathrm{it}}/{\mathrm{roa}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+{\beta }_1{\mathrm{netd}}_{\mathrm{it}}+{\beta }_2{\mathrm{pe}}_{\mathrm{it}}+{\beta }_3{\mathrm{pbv}}_{\mathrm{it}}+{\beta }_4{\mathrm{crtr}}_{\mathrm{it}}+{\beta }_5{\mathrm{capi}}_{\mathrm{it}}+{\beta }_6{\mathrm{rdi}}_{\mathrm{it}}+{\beta }_7{\mathrm{etr}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_8{\mathrm{salg}}_{\mathrm{it}}+{\beta }_9{\mathrm{size}}_{\mathrm{it}}+{\beta }_{10}{\mathrm{wacc}}_{\mathrm{it}}+{\beta }_{11}{\mathrm{bsize}}_{\mathrm{it}}+{\beta }_{12}{\mathrm{bmeet}}_{\mathrm{it}}+{\beta }_{13}{\mathrm{ceod}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{14}{\mathrm{bindep}}_{\mathrm{it}}+{\beta }_{15}{\mathrm{bgdiv}}_{\mathrm{it}}+{\beta }_{16}{\mathrm{execdiv}}_{\mathrm{it}}+{\beta }_{17}{\mathrm{esg}}_{\mathrm{it}}+{\beta }_{18}{\mathrm{ge}}_{\mathrm{it}}+{\beta }_{19}{\mathrm{rq}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{20}{\mathrm{covid}}_{\mathrm{it}}+{\omega }_{\mathrm{i}}+{\mu }_{\mathrm{t}}+{\epsilon }_{\mathrm{it}}\hfill \end{array}$
(3), (8), (13), (18)	$\begin{array}{c}{\mathrm{ebitdam}}_{\mathrm{it}}/{\mathrm{ebitm}}_{\mathrm{it}}/{\mathrm{netm}}_{\mathrm{it}}/{\mathrm{roa}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+{\beta }_1{\mathrm{netd}}_{\mathrm{it}}+{\beta }_2{\mathrm{pe}}_{\mathrm{it}}+{\beta }_3{\mathrm{pbv}}_{\mathrm{it}}+{\beta }_4{\mathrm{crtr}}_{\mathrm{it}}+{\beta }_5{\mathrm{capi}}_{\mathrm{it}}+{\beta }_6{\mathrm{rdi}}_{\mathrm{it}}+{\beta }_7{\mathrm{etr}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_8{\mathrm{salg}}_{\mathrm{it}}+{\beta }_9{\mathrm{size}}_{\mathrm{it}}+{\beta }_{10}{\mathrm{wacc}}_{\mathrm{it}}+{\beta }_{11}{\mathrm{bsize}}_{\mathrm{it}}+{\beta }_{12}{\mathrm{bmeet}}_{\mathrm{it}}+{\beta }_{13}{\mathrm{ceod}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{14}{\mathrm{bindep}}_{\mathrm{it}}+{\beta }_{15}{\mathrm{bgdiv}}_{\mathrm{it}}+{\beta }_{16}{\mathrm{execdiv}}_{\mathrm{it}}+{\beta }_{17}{\mathrm{esg}}_{\mathrm{it}}+{\beta }_{18}{\mathrm{pv}}_{\mathrm{it}}+{\beta }_{19}{\mathrm{rq}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{20}{\mathrm{covid}}_{\mathrm{it}}+{\omega }_{\mathrm{i}}+{\mu }_{\mathrm{t}}+{\epsilon }_{\mathrm{it}}\hfill \end{array}$
(4), (9), (14), (19)	$\begin{array}{c}{\mathrm{ebitdam}}_{\mathrm{it}}/{\mathrm{ebitm}}_{\mathrm{it}}/{\mathrm{netm}}_{\mathrm{it}}/{\mathrm{roa}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+{\beta }_1{\mathrm{netd}}_{\mathrm{it}}+{\beta }_2{\mathrm{pe}}_{\mathrm{it}}+{\beta }_3{\mathrm{pbv}}_{\mathrm{it}}+{\beta }_4{\mathrm{crtr}}_{\mathrm{it}}+{\beta }_5{\mathrm{capi}}_{\mathrm{it}}+{\beta }_6{\mathrm{rdi}}_{\mathrm{it}}+{\beta }_7{\mathrm{etr}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_8{\mathrm{salg}}_{\mathrm{it}}+{\beta }_9{\mathrm{size}}_{\mathrm{it}}+{\beta }_{10}{\mathrm{wacc}}_{\mathrm{it}}+{\beta }_{11}{\mathrm{bsize}}_{\mathrm{it}}+{\beta }_{12}{\mathrm{bmeet}}_{\mathrm{it}}+{\beta }_{13}{\mathrm{ceod}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{14}{\mathrm{bindep}}_{\mathrm{it}}+{\beta }_{15}{\mathrm{bgdiv}}_{\mathrm{it}}+{\beta }_{16}{\mathrm{execdiv}}_{\mathrm{it}}+{\beta }_{17}{\mathrm{esg}}_{\mathrm{it}}+{\beta }_{18}{\mathrm{rl}}_{\mathrm{it}}+{\beta }_{19}{\mathrm{rq}}_{\mathrm{it}}+{\beta }_{20}{\mathrm{covid}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\omega }_{\mathrm{i}}+{\mu }_{\mathrm{t}}+{\epsilon }_{\mathrm{it}}\hfill \end{array}$
(5), (10), (15), (20)	$\begin{array}{c}{\mathrm{ebitdam}}_{\mathrm{it}}/{\mathrm{ebitm}}_{\mathrm{it}}/{\mathrm{netm}}_{\mathrm{it}}/{\mathrm{roa}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}={\beta }_0+{\beta }_1{\mathrm{netd}}_{\mathrm{it}}+{\beta }_2{\mathrm{pe}}_{\mathrm{it}}+{\beta }_3{\mathrm{pbv}}_{\mathrm{it}}+{\beta }_4{\mathrm{crtr}}_{\mathrm{it}}+{\beta }_5{\mathrm{capi}}_{\mathrm{it}}+{\beta }_6{\mathrm{rdi}}_{\mathrm{it}}+{\beta }_7{\mathrm{etr}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_8{\mathrm{salg}}_{\mathrm{it}}+{\beta }_9{\mathrm{size}}_{\mathrm{it}}+{\beta }_{10}{\mathrm{wacc}}_{\mathrm{it}}+{\beta }_{11}{\mathrm{bsize}}_{\mathrm{it}}+{\beta }_{12}{\mathrm{bmeet}}_{\mathrm{it}}+{\beta }_{13}{\mathrm{ceod}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{14}{\mathrm{bindep}}_{\mathrm{it}}+{\beta }_{15}{\mathrm{bgdiv}}_{\mathrm{it}}+{\beta }_{16}{\mathrm{execdiv}}_{\mathrm{it}}+{\beta }_{17}{\mathrm{esg}}_{\mathrm{it}}+{\beta }_{18}{\mathrm{va}}_{\mathrm{it}}+{\beta }_{19}{\mathrm{rq}}_{\mathrm{it}}\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\beta }_{20}{\mathrm{covid}}_{\mathrm{it}}+{\omega }_{\mathrm{i}}+{\mu }_{\mathrm{t}}+{\epsilon }_{\mathrm{it}}\hfill \end{array}$

Source: Authors’ own processing.

), with the results reported in

Table 7. Empirical estimations using the EBITDA margin as the dependent variable.

Variable	(1)	(2)	(3)	(4)	(5)
netd	−0.009 ***	−0.0092 ***	−0.009 ***	−0.009 ***	−0.009 ***
	(0.0015)	(0.0015)	(0.0015)	(0.0015)	(0.0015)
pe	−0.0008 ***	−0.0007 ***	−0.0008 ***	−0.0008 ***	−0.0008 ***
	(0)	(0)	(0)	(0)	(0)
pbv	0.0028 ***	0.0029 ***	0.0028 ***	0.0028 ***	0.0028 ***
	(0.0002)	(0.0002)	(0.0002)	(0.0002)	(0.0002)
crtr	0.0017	0.0016	0.0017	0.0018	0.0017
	(0.0017)	(0.0017)	(0.0017)	(0.0017)	(0.0017)
capi	−0.037 **	−0.0294 *	−0.0351 **	−0.0388 **	−0.0365 **
	(0.016)	(0.0158)	(0.0159)	(0.0161)	(0.016)
rdi	−0.9387 ***	−0.9273 ***	−0.9393 ***	−0.9391 ***	−0.9394 ***
	(0.0708)	(0.0708)	(0.0708)	(0.0708)	(0.0708)
etr	0.0562 ***	0.0515 ***	0.0571 ***	0.0561 ***	0.0578 ***
	(0.008)	(0.0079)	(0.008)	(0.008)	(0.0081)
salg	0.0027	0.0033	0.0028	0.0047	0.0049
	(0.0057)	(0.0057)	(0.0057)	(0.0057)	(0.0057)
size	0.0336 ***	0.0367 ***	0.0338 ***	0.0329 ***	0.0337 ***
	(0.0028)	(0.0027)	(0.0027)	(0.0029)	(0.0028)
wacc	−0.0904 **	−0.1042 ***	−0.075 *	−0.0734 *	−0.0857 **
	(0.0379)	(0.0385)	(0.0384)	(0.0385)	(0.038)
bsize	−0.0001	−0.0001	−0.0001	−0.0001	−0.0001
	(0.0006)	(0.0006)	(0.0006)	(0.0006)	(0.0006)
bmeet	−0.0002	−0.0002	−0.0002	−0.0001	−0.0002
	(0.0003)	(0.0003)	(0.0003)	(0.0003)	(0.0003)
ceod	0.0039 *	0.0038 *	0.0039 *	0.004 *	0.004 *
	(0.0021)	(0.0021)	(0.0021)	(0.0021)	(0.0021)
bindep	−0.0068	−0.0065	−0.0071	−0.007	−0.0072
	(0.0146)	(0.0147)	(0.0146)	(0.0146)	(0.0146)
bgdiv	−0.0037	0.0066	−0.0031	−0.0059	−0.0033
	(0.0118)	(0.0118)	(0.0117)	(0.012)	(0.0118)
execdiv	−0.0165 *	−0.0146	−0.0169 *	−0.0178 **	−0.017 *
	(0.0089)	(0.009)	(0.0089)	(0.0089)	(0.0089)
esg	−0.0001	−0.0001	−0.0001	−0.0001	−0.0001
	(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.0001)
cc	−0.038 ***
	(0.0117)
ge		−0.0076
		(0.0105)
pv			−0.0178 ***
			(0.0051)
rl				−0.0399 ***
				(0.0116)
va					−0.0419 ***
					(0.0127)
rq	0.0069	0.0053	−0.0017	0.0086	0.0034
	(0.0055)	(0.0065)	(0.0055)	(0.0056)	(0.0054)
covid	−0.008 ***	−0.0032 *	−0.0059 ***	−0.0044 **	−0.0056 ***
	(0.0023)	(0.0018)	(0.0019)	(0.0017)	(0.0019)
_cons	−0.4322 ***	−0.5479 ***	−0.4697 ***	−0.4075 ***	−0.4369 ***
	(0.0735)	(0.0684)	(0.0665)	(0.0765)	(0.0725)
Observations	3138	3138	3138	3138	3138
F-stat	46.9827	46.3072	47.0921	47.0611	46.9963
Within R2	0.2563	0.2536	0.2568	0.2567	0.2564
Between R2	0.0104	0.0081	0.0103	0.0112	0.0104
Overall R2	0.0072	0.0054	0.0071	0.0077	0.0072
Hausman Test (Prob.)	0.0944	0.0745	0.0221	0.0511	0.0234

Source: Authors’ own processing using Stata 18 software. Note: Significance level: *** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors are displayed in brackets.

for the EBITDA margin,

Table 8. Empirical estimations using EBIT margin as the dependent variable.

Variable	(6)	(7)	(8)	(9)	(10)
netd	−0.0116 ***	−0.0118 ***	−0.0116 ***	−0.0115 ***	−0.0115 ***
	(0.0015)	(0.0015)	(0.0015)	(0.0015)	(0.0015)
pe	−0.0011 ***	−0.0011 ***	−0.0011 ***	−0.0011 ***	−0.0011 ***
	(0)	(0)	(0)	(0)	(0)
pbv	0.0033 ***	0.0034 ***	0.0033 ***	0.0033 ***	0.0033 ***
	(0.0002)	(0.0002)	(0.0002)	(0.0002)	(0.0002)
crtr	0.0043 ***	0.0044 ***	0.0042 **	0.0045 ***	0.0043 ***
	(0.0016)	(0.0017)	(0.0016)	(0.0016)	(0.0016)
capi	−0.0204	−0.011	−0.0174	−0.024	−0.0206
	(0.0155)	(0.0154)	(0.0154)	(0.0156)	(0.0155)
rdi	−0.9094 ***	−0.8967 ***	−0.9097 ***	−0.9113 ***	−0.912 ***
	(0.0688)	(0.0689)	(0.0688)	(0.0687)	(0.0687)
etr	0.0877 ***	0.0804 ***	0.0888 ***	0.0881 ***	0.0906 ***
	(0.0078)	(0.0077)	(0.0078)	(0.0077)	(0.0078)
salg	0.0149 ***	0.0159 ***	0.0152 ***	0.018 ***	0.0183 ***
	(0.0056)	(0.0056)	(0.0056)	(0.0056)	(0.0056)
size	0.0149 ***	0.0175 ***	0.0154 ***	0.0135 ***	0.0146 ***
	(0.0027)	(0.0026)	(0.0027)	(0.0028)	(0.0027)
wacc	−0.0262	−0.0592	−0.0055	0.0005	−0.0179
	(0.0368)	(0.0374)	(0.0373)	(0.0374)	(0.0369)
bsize	0.0002	0.0003	0.0002	0.0003	0.0003
	(0.0006)	(0.0006)	(0.0006)	(0.0006)	(0.0006)
bmeet	0.0001	0.0001	0.0001	0.0001	0.0001
	(0.0003)	(0.0003)	(0.0003)	(0.0003)	(0.0003)
ceod	0.0054 ***	0.0053 **	0.0053 **	0.0055 ***	0.0054 ***
	(0.0021)	(0.0021)	(0.0021)	(0.0021)	(0.0021)
bindep	−0.0182	−0.018	−0.0187	−0.0186	−0.0189
	(0.0142)	(0.0142)	(0.0142)	(0.0142)	(0.0142)
bgdiv	−0.0019	0.005	−0.0005	−0.0065	−0.0027
	(0.0115)	(0.0114)	(0.0113)	(0.0116)	(0.0114)
execdiv	−0.0105	−0.0111	−0.011	−0.0129	−0.0117
	(0.0086)	(0.0087)	(0.0086)	(0.0087)	(0.0086)
esg	0	0.0001	0	0	0
	(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.0001)
cc	−0.0537 ***
	(0.0113)
ge		−0.0318 ***
		(0.0102)
pv			−0.0244 ***
			(0.0049)
rl				−0.0605 ***
				(0.0113)
va					−0.0641 ***
					(0.0124)
rq	0.0102 *	0.0154 **	−0.0019	0.0132 **	0.0053
	(0.0054)	(0.0064)	(0.0053)	(0.0055)	(0.0052)
covid	−0.0061 ***	−0.001	−0.0031 *	−0.0012	−0.0032 *
	(0.0022)	(0.0018)	(0.0018)	(0.0017)	(0.0018)
_cons	−0.0662	−0.163 **	−0.1235 *	−0.0147	−0.057
	(0.0714)	(0.0665)	(0.0646)	(0.0743)	(0.0704)
Observations	3138	3138	3138	3138	3138
F-stat	64.7275	63.7982	64.8672	65.1807	65.051
Within R2	0.322	0.3188	0.3225	0.3235	0.3231
Between R2	0.0299	0.0283	0.0298	0.0311	0.0304
Overall R2	0.017	0.016	0.0171	0.0174	0.0173
Hausman Test (Prob.)	0.0000	0.0000	0.0000	0.0000	0.0000

Source: Authors’ own processing using Stata 18 software. Note: Significance level: *** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors are displayed in brackets.

for the EBIT margin,

Table 9. Empirical estimations using net profit margin as the dependent variable.

Variable	(11)	(12)	(13)	(14)	(15)
netd	−0.0174 ***	−0.0176 ***	−0.0174 ***	−0.0173 ***	−0.0174 ***
	(0.0014)	(0.0014)	(0.0014)	(0.0014)	(0.0014)
pe	−0.0019 ***	−0.0019 ***	−0.0019 ***	−0.0019 ***	−0.0019 ***
	(0)	(0)	(0)	(0)	(0)
pbv	0.004 ***	0.0041 ***	0.004 ***	0.0039 ***	0.004 ***
	(0.0002)	(0.0002)	(0.0002)	(0.0002)	(0.0002)
crtr	0.0082 ***	0.0082 ***	0.0081 ***	0.0084 ***	0.0081 ***
	(0.0016)	(0.0016)	(0.0016)	(0.0016)	(0.0016)
capi	0.0109	0.0182	0.0139	0.0061	0.0121
	(0.0149)	(0.0148)	(0.0148)	(0.0149)	(0.0149)
rdi	−0.4916 ***	−0.4811 ***	−0.4899 ***	−0.4958 ***	−0.4911 ***
	(0.066)	(0.0661)	(0.066)	(0.0659)	(0.0661)
etr	−0.0544 ***	−0.0594 ***	−0.0544 ***	−0.0532 ***	−0.0533 ***
	(0.0074)	(0.0074)	(0.0075)	(0.0074)	(0.0075)
salg	0.0165 ***	0.0172 ***	0.0168 ***	0.0191 ***	0.0187 ***
	(0.0053)	(0.0054)	(0.0053)	(0.0053)	(0.0054)
size	0.0167 ***	0.0192 ***	0.0175 ***	0.0147 ***	0.0171 ***
	(0.0026)	(0.0025)	(0.0026)	(0.0027)	(0.0026)
wacc	−0.0784 **	−0.0969 ***	−0.067 *	−0.0535	−0.0748 **
	(0.0354)	(0.0359)	(0.0359)	(0.0359)	(0.0355)
bsize	0.0004	0.0004	0.0004	0.0004	0.0004
	(0.0006)	(0.0006)	(0.0006)	(0.0006)	(0.0006)
bmeet	0.0002	0.0002	0.0002	0.0002	0.0002
	(0.0003)	(0.0003)	(0.0003)	(0.0003)	(0.0003)
ceod	0.0045 **	0.0044 **	0.0044 **	0.0046 **	0.0045 **
	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
bindep	−0.0291 **	−0.0288 **	−0.0293 **	−0.0294 **	−0.0294 **
	(0.0136)	(0.0137)	(0.0136)	(0.0136)	(0.0136)
bgdiv	0.0048	0.0127	0.0075	−0.002	0.0063
	(0.011)	(0.011)	(0.0109)	(0.0111)	(0.011)
execdiv	−0.0211 **	−0.0203 **	−0.021 **	−0.0238 ***	−0.0213 **
	(0.0083)	(0.0084)	(0.0083)	(0.0083)	(0.0083)
esg	0.0001	0.0001 *	0.0001	0.0001	0.0001
	(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.0001)
cc	−0.0386 ***
	(0.0109)
ge		−0.0148
		(0.0098)
pv			−0.015 ***
			(0.0047)
rl				−0.0523 ***
				(0.0108)
va					−0.0389 ***
					(0.0119)
rq	0.0081	0.009	0.0001	0.0116 **	0.0045
	(0.0052)	(0.0061)	(0.0051)	(0.0053)	(0.005)
covid	−0.0048 **	−0.0005	−0.0022	−0.0017	−0.0022
	(0.0022)	(0.0017)	(0.0018)	(0.0016)	(0.0017)
_cons	−0.1528 **	−0.248 ***	−0.2086 ***	−0.0791	−0.1693 **
	(0.0686)	(0.0638)	(0.062)	(0.0713)	(0.0676)
Observations	3138	3138	3138	3138	3138
F-stat	153.5933	152.5021	153.3159	154.7319	153.3955
Within R2	0.5298	0.5281	0.5294	0.5317	0.5295
Between R2	0.0065	0.0062	0.0067	0.0062	0.0068
Overall R2	0.0193	0.0185	0.0185	0.0211	0.0188
Hausman Test (Prob.)	0.0000	0.0000	0.0000	0.0000	0.0000

Source: Authors’ own processing using Stata 18 software. Note: Significance level: *** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors are displayed in brackets.

for the net profit margin, and

Table 10. Empirical estimations using ROA as the dependent variable.

Variable	(16)	(17)	(18)	(19)	(20)
netd	−0.0157 ***	−0.0158 ***	−0.0157 ***	−0.0157 ***	−0.0157 ***
	(0.0011)	(0.0011)	(0.0011)	(0.0011)	(0.0011)
pe	−0.0012 ***	−0.0012 ***	−0.0012 ***	−0.0012 ***	−0.0012 ***
	(0)	(0)	(0)	(0)	(0)
pbv	0.0036 ***	0.0037 ***	0.0037 ***	0.0036 ***	0.0036 ***
	(0.0002)	(0.0002)	(0.0002)	(0.0002)	(0.0002)
crtr	0.0016	0.0017	0.0016	0.0017	0.0016
	(0.0012)	(0.0012)	(0.0012)	(0.0012)	(0.0012)
capi	−0.0008	0.0021	0.0011	−0.0031	−0.0009
	(0.0116)	(0.0114)	(0.0115)	(0.0116)	(0.0115)
rdi	−0.5353 ***	−0.5317 ***	−0.5336 ***	−0.5374 ***	−0.5362 ***
	(0.0512)	(0.0511)	(0.0512)	(0.0511)	(0.0512)
etr	−0.0228 ***	−0.0253 ***	−0.0232 ***	−0.0222 ***	−0.0218 ***
	(0.0058)	(0.0057)	(0.0058)	(0.0058)	(0.0058)
salg	0.0389 ***	0.0392 ***	0.0391 ***	0.0401 ***	0.04 ***
	(0.0041)	(0.0041)	(0.0041)	(0.0041)	(0.0042)
size	−0.0127 ***	−0.0121 ***	−0.0121 ***	−0.0137 ***	−0.0128 ***
	(0.002)	(0.002)	(0.002)	(0.0021)	(0.002)
wacc	−0.006	−0.019	−0.0026	0.0056	−0.0033
	(0.0274)	(0.0278)	(0.0278)	(0.0278)	(0.0275)
bsize	0.0005	0.0006	0.0005	0.0006	0.0005
	(0.0005)	(0.0005)	(0.0005)	(0.0005)	(0.0005)
bmeet	0	0	0	0	0
	(0.0002)	(0.0002)	(0.0002)	(0.0002)	(0.0002)
ceod	0.0063 ***	0.0063 ***	0.0063 ***	0.0064 ***	0.0063 ***
	(0.0015)	(0.0015)	(0.0015)	(0.0015)	(0.0015)
bindep	−0.0042	−0.0042	−0.0042	−0.0044	−0.0044
	(0.0106)	(0.0106)	(0.0106)	(0.0106)	(0.0106)
bgdiv	0.0208 **	0.0219 **	0.023 ***	0.0176 **	0.0205 **
	(0.0085)	(0.0085)	(0.0084)	(0.0087)	(0.0085)
execdiv	−0.0067	−0.0073	−0.0063	−0.0079	−0.007
	(0.0064)	(0.0065)	(0.0064)	(0.0064)	(0.0064)
esg	0.0001 *	0.0001 **	0.0001 *	0.0001 *	0.0001 *
	(0.0001)	(0.0001)	(0.0001)	(0.0001)	(0.0001)
cc	−0.0176 **
	(0.0084)
ge		−0.0137 *
		(0.0076)
pv			−0.0055
			(0.0037)
rl				−0.0242 ***
				(0.0084)
va					−0.021 **
					(0.0092)
rq	0.0115 ***	0.0144 ***	0.0082 **	0.0131 ***	0.0099 **
	(0.004)	(0.0047)	(0.004)	(0.0041)	(0.0039)
covid	−0.0004	0.001	0.001	0.001	0.0006
	(0.0017)	(0.0013)	(0.0014)	(0.0012)	(0.0013)
_cons	0.4195 ***	0.3982 ***	0.3866 ***	0.4547 ***	0.4227 ***
	(0.0531)	(0.0494)	(0.0481)	(0.0553)	(0.0524)
Observations	3138	3138	3138	3138	3138
F-stat	128.3361	128.2328	128.1329	128.721	128.4205
Within R2	0.485	0.4848	0.4846	0.4857	0.4851
Between R2	0.1482	0.1479	0.1459	0.1525	0.1477
Overall R2	0.2014	0.2017	0.1997	0.2048	0.201
Hausman Test (Prob.)	0.0000	0.0000	0.0000	0.0000	0.0000

Source: Authors’ own processing using Stata 18 software. Note: Significance level: *** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors are displayed in brackets.

for ROA.

4. Results and Discussions

The empirical analysis of the factors that influence the financial performance indicators of S&P 500 companies yields relevant results that explain between 25% and 53% of the variation in the financial performance variables. Specifically, 52.81–53.17% of the variation in net profit margin is explained by the results, 48.46–48.57% of the variation in return on assets is explained by the results, 31.88–32.35% of the variation in the EBIT margin is explained by the results, and 25.26–25.68% of the variation in the EBITDA margin is explained by the results.

The analysis shows that net debt is statistically significant and has a negative effect on financial performance indicators, suggesting that the firms analyzed rely heavily on debt financing. High financing costs have an unfavorable impact on profit because profit decreases as indebtedness increases. This finding is consistent with the pecking order theory, according to which internal financing is often insufficient, and firms therefore turn first to debt financing, followed by new equity financing [1]. Similar results are also highlighted in the study conducted by Vintilă [19].

The current ratio has a statistically significant positive impact on both the EBIT margin and the net profit margin. Companies with greater liquidity can benefit from both long- and short-term investment opportunities that have an impact on increasing profitability, while also having the capacity to cover all their current liabilities with their current assets, which are convertible into cash. A positive impact of liquidity on the financial performance of the companies is also obtained by Al-Jafari and Al Samman [23] and Zavalii et al. [24]. However, the EBITDA margin and ROA are not affected by the current ratio, with similar results obtained by Lehenchuk et al. [11], Ramnoher and Seetah [25], and Khatib et al. [26].

Capital intensity shows a statistically significant negative influence on the EBITDA margin. This could be explained by the reduction in the proportion of tangible assets in total assets, resulting from the high degree of automation of production capacities, which could lower production costs and, in turn, boost profitability. Capital intensity does not affect the EBIT margin, net profit margin, or ROA; these results are in line with those of Lehenchuk et al. [35].

The S&P 500 companies report low research and development expenses, with a small proportion of R&D expenses in total revenue, which explains the statistically significant negative influence of R&D intensity on financial performance indicators, including the EBITDA margin, EBIT margin, net profit margin, and ROA. Similar results were obtained by Melwani [4].

The effective tax rate (ETR) has a statistically significant negative effect on both the net profit margin and ROA. Better tax planning could cut income tax expenses, boost net profit, and thereby increase net profit, which in turn would implicitly increase financial performance, as measured by net profit margin and ROA. Similar results were obtained by Nenu et al. [31], Lazăr and Istrate [10], Khuong et al. [38], Vintilă [19], Ștefănescu et al. [39], and Pitulice et al. [32]. By contrast, the effective tax rate has a positive influence on financial performance, as measured by the EBITDA margin and EBIT margin. This can be explained by the reduction in the ETR resulting from the increase in gross profit, which is generated by the increase in sales, causing a decrease in the EBITDA margin and EBIT margin.

Sales growth exerts a statistically significant positive influence on the EBIT margin, net profit margin, and ROA. This result was expected, since faster-growing companies generate higher profits and more development opportunities. Similar results were obtained by Lazăr [9], García-Gómez et al. [18], and Singh et al. [36]. However, sales growth is not statistically significant in regressions where the dependent variable is the EBITDA margin.

Company size, as measured by total assets, has a statistically significant positive impact on the EBITDA margin, EBIT margin, and net profit margin, reflecting the ability of larger enterprises to generate higher revenue and, consequently, higher profits. Similar favorable effects related to company size are noted by Zavalii et al. [24], Tudose et al. [20], and Al-Jafari and Al Samman [23]. In contrast, company size negatively influences ROA, as reported in studies by Matar and Eneizan [13], Nenu et al. [31], Pitulice et al. [32], Ramnoher and Seetah [25], and Kyere and Ausloos [33].

Given companies’ mixed financing structures—composed of equity and debt, with financing costs exerting a significant impact on profitability—it is justified to propose and include the weighted average cost of capital (WACC) as an indicator in the empirical analysis. The regression results show a statistically significant negative influence on financial performance, as measured by the EBITDA margin and net profit margin, which can be attributed to lower financing costs that have a significantly favorable effect on profit growth.

Because the Standard & Poor’s 500 index comprises top U.S. companies listed on the New York Stock Exchange or Nasdaq Stock Market—covering roughly 80% of the market capitalization of the U.S. capital market—it is necessary to include new factor variables in the empirical analysis that capture the influence of market capitalization on these firms’ financial performance. In this context, to enhance the robustness of the research, additional market-based variables are introduced alongside the financial metrics identified in previous studies. These variables, expressed in market values and shown to be relevant for corporate financial performance, include the price-to-earnings ratio (P/E) and the price-to-book ratio (P/BV). The empirical models reveal a statistically significant positive influence of P/BV and a negative influence of P/E on all financial performance indicators.

To inform optimal managerial decisions aimed at improving corporate financial performance, this study also incorporates variables describing the corporate governance characteristics of U.S. companies, such as board size, board meetings, CEO duality, board independence, board gender diversity, executive members’ gender diversity, and ESG scores. Among the corporate governance variables analyzed, board size and the number of board meetings are statistically insignificant, a finding that is consistent with other studies [42,46,47].

An important characteristic of the board of directors is represented by the CEO duality, which has a statistically significant positive effect on all financial performance indicators. This suggests that when the CEO also serves as chairman, company financial performance improves. The effect can be explained by the fact that power and control over the company are held by the same person, which may lead to unified leadership, faster decision-making, lower coordination costs, and better information flow between the board and management, all of which favor enhanced financial performance. Board independence, however, has a statistically significant negative impact on financial performance as measured by net profit margin, possibly because independent directors, being more cautious, can face various challenges that slow or complicate the decision-making process.

Financial performance, as measured by return on assets (ROA), is statistically and positively influenced by the board’s gender diversity. Women’s participation in managerial decision-making can bolster corporate competitiveness and encourage innovation and higher professional standards at the board level; women tend to undertake safer projects because they are generally more risk-averse than men. The presence of women on boards therefore improves ROA, as shown by Nguyen and Huynh [52], Sahoo et al. [53], and Khatib et al. [26]. By contrast, gender diversity among executive members is statistically insignificant in regression models where ROA is the dependent variable.

The ESG score has a statistically significant positive effect on financial performance, as measured by net profit margin and ROA. A higher ESG score enhances a company’s image and reputation, making it easier for companies to access diverse funding sources that ultimately lift financial outcomes. Shareholders, creditors, governments, and other stakeholders alike expect extensive ESG engagement to translate to stronger financial performance. Similar findings are reported by Che et al. [40].

Because the U.S. economic and political environment shapes corporate financial performance, empirical models include public governance indicators, namely, control of corruption, government effectiveness, political stability and the absence of violence/terrorism, rule of law, voice and accountability, and regulatory quality. While the first five indicators exert statistically negative effects on financial performance, regulatory quality has a statistically positive impact on the EBITDA margin, EBIT margin, net profit margin, and ROA. This result highlights that robust government policies and regulations underpin private sector development and help raise firms’ financial performance.

The COVID-19 pandemic crisis exerted a statistically negative influence on financial performance, as measured by the EBITDA margin, EBIT margin, and net profit margin. The health crisis eroded companies’ financial performance, as measured by margin rates, because sales suffered under production activity restrictions. Conversely, the pandemic crisis is statistically insignificant in ROA models, suggesting that COVID-19 did not affect companies’ total assets.

5. Conclusions

Exploring the determinants of financial performance in the context of corporate and public governance for U.S. Standard & Poor’s 500 companies, during the 2014–2023 period, highlights relevant results that explain between 25% and 53% of the variation in financial performance indicators, namely, the EBITDA margin (25.26–25.68%), EBIT margin (31.88–32.35%), net profit margin (52.81–53.17%), and ROA (48.46–48.57%).

Indebtedness ratio, price multiples, liquidity, capital intensity, R&D intensity, taxation, sales growth, company size, and the weighted average cost of capital are factors that affect financial performance and provide a solid basis for choosing the optimal strategic direction for each company. This study shows that financial performance can be enhanced by raising the price-to-book ratio, improving current liquidity, and boosting sales. By contrast, the COVID-19 pandemic crisis, which took place from 2020 to 2022, reduced firms’ financial performance because sales were affected by restrictions on production activities. On the corporate governance side, the most influential drivers of stronger financial performance are CEO duality, board gender diversity, and ESG score. The result regarding the positive influence of CEO duality on financial performance is in contradiction with the agency theory. Thus, among the disadvantages of power concentration are reduced monitoring by the board of directors and increased agency costs. While CEO duality is often viewed critically in the corporate governance literature due to concerns over weakened oversight and increased agency costs, several contextual arguments support its potential positive impact on financial performance. Firstly, in large, complex companies, such as those in the S&P 500, the dual role can facilitate strategic coherence and unified leadership, allowing for more effective and timely decision-making. Secondly, in environments with high institutional pressure and transparency, the risks associated with CEO duality may be mitigated by strong regulatory frameworks. In such settings, the benefits of efficient decision-making may outweigh the potential governance risks. From a public governance perspective, the U.S. economic and political environment also matters; the regulatory quality indicator is particularly impactful, as sound regulations foster private sector development and thus enhance corporate financial performance.

While positive determinants of financial performance have been identified, it is equally important to address the negative factors that affect financial performance on the basis of determining which measures should be proposed for reducing or eliminating the factors that generate these unfavorable influences. Efficient managerial decisions can only be based on maintaining and amplifying factors with a favorable influence on financial performance and diminishing those with an unfavorable influence. This process will ensure sustainable growth and maximize companies’ profits.

A retrospective analysis of the results obtained shows that 10 of the 12 research hypotheses tested are validated. Regarding the impact of financial indicators on financial performance, hypotheses 1, 4, and 6 are validated, confirming the negative impact of net debt, capital intensity, and R&D intensity on financial performance. At the same time, hypotheses 2, 5, and 8 are also confirmed, according to which the current ratio, sales growth, and ESG score positively influence financial performance. Considering the positive impact of company size on the EBIT margin, EBITDA margin, and net profit margin, hypothesis 3 is also validated. Also, the negative impact generated by the effective tax rate on the net profit margin and ROA validates hypothesis 7. Regarding the hypotheses established for corporate governance indicators, the results confirm that board independence has a negative influence, but also that board gender diversity has a positive impact on financial performance, with hypotheses 10 and 12 being validated. Hypotheses 9 and 11 are rejected, as the board size and board meetings have a statistically insignificant impact on the companies’ financial performance.

Like any empirical study, this research has some limitations, but these can be considered opportunities for future research. On the one hand, the present study does not include all the factors that could determine the companies’ financial performance. Future research could include other independent variables, such as company age, gender diversity among the executive team, audit committee independence, macroeconomic variables, and others, in the analysis. On the other hand, this study was conducted only on non-financial companies included in the S&P 500 index. Future research directions could aim at expanding the sample by including companies from other indices or activity sectors in the analysis.