Managerial Shareholding and Performance in LBOs: Evidence from the MENA Region

Abstract

This research explores the impact of ownership structure on the financial performance of Leveraged Buyout (LBO) transactions in the MENA region, a key emerging market region. Drawing on agency theory by Jensen & Meckling and the capital structure theory of Modigliani and Miller, the study investigates how different shareholder configurations, particularly managerial equity participation, influence LBO outcomes. Based on a sample of 233 transactions conducted between 2000 and 2023, the research adopts a quantitative methodology grounded in a hypothetico-deductive approach. The analysis focuses on the interactions between managerial ownership, leverage, target firm size, and operational performance. The findings support the agency theory premise that managerial ownership aligns interests and enhances performance, showing a positive relationship between managerial equity stakes and financial outcomes. Conversely, the effect of leverage, central to Modigliani and Miller’s propositions, proves more nuanced, reflecting the region’s unique financial constraints and market imperfections. Firm size, meanwhile, shows no direct correlation with performance improvement. These insights underscore the complex mechanisms behind LBO success in the MENA context and offer practical and theoretical implications, particularly regarding governance practices and institutional frameworks. The study also outlines avenues for future research, including a deeper examination of regional governance dynamics.

1. Introduction

The agency problem associated with free cash flows (Jensen, 1986) remains a persistent challenge for modern firms. Free cash flows, excess cash generated after meeting all operational and investment needs, can lead to inefficient managerial decisions, such as unprofitable investments or value-destroying acquisitions, which ultimately reduce shareholder wealth (Jensen & Meckling, 1976; Stulz, 1990). To address these issues, prior research has explored various corporate governance mechanisms, including independent committees, performance-based incentives, and dividend policies aimed at aligning managerial and shareholder interests (Shleifer & Vishny, 1997; Fama & Jensen, 1998).

Building upon this theoretical foundation, leveraged buyouts (LBOs) represent a distinctive external control mechanism. By acquiring firms predominantly through debt financing (Jensen et al., 1989; Jensen, 1991), LBOs impose financial discipline by limiting managerial discretion over free cash flows, as a substantial portion is allocated to servicing debt obligations (Kaplan, 1989b, 1991, 1993; Kaplan & Stromberg, 2009; Korteweg & Nagel, 2015; Cumming et al., 2022a). While LBOs are well-documented in developed markets, their applicability and effectiveness in emerging economies, particularly the MENA region, remain insufficiently explored. This gap is notable given the distinctive institutional and economic characteristics of these markets, which may influence LBO outcomes differently than in Western contexts (Gaughan, 2007).

The MENA region represents a unique and compelling context for studying LBO dynamics due to several factors. Its rapidly evolving economic landscape is marked by significant structural reforms, diversification efforts away from oil dependency, and varying levels of financial market development across countries. These differences create gaps market maturity, institutional quality, and investor protection, which directly impact the nature and success of LBO transactions. This heterogeneity and transitional stage of financial markets make the MENA region an interesting environment to analyze how such disparities influence LBO performance and strategy. Despite the region’s growing strategic importance, empirical studies analyzing LBO transactions within MENA are scarce, partly due to data limitations. However, recent improvements in data availability now enable rigorous quantitative analysis. Addressing this research gap is crucial to understanding how ownership structures, particularly managerial equity participation combined with leverage, impact post-LBO financial performance in emerging markets.

This study aims to fill the aforementioned gap by empirically examining 233 LBO transactions conducted in the MENA region between 2000 and 2023. Adopting a hypothetico-deductive approach, the research assesses how managerial equity stakes and leverage influence key financial performance indicators post-acquisition. The study contributes novel insights by contextualizing LBO mechanisms within MENA’s unique institutional setting, thereby extending the theoretical and practical understanding of value creation in private equity transactions.

The paper is organized as follows: first, the theoretical framework and hypotheses are presented; second, the research methodology and data sources are detailed; finally, empirical results are analyzed, followed by discussion of managerial and academic implications and suggestions for future research.

2. Literature Review

Management Buyouts (MBOs) and Leveraged Buyouts (LBOs) have garnered increasing interest since the late 1980s, characterized by the acquisition of firms primarily financed through debt. Early research, conducted between 1989 and 2000, laid a strong foundation for understanding the performance outcomes associated with these transactions. In this literature review, we provide a synthesis of the studies conducted across three distinct waves of LBOs, analyzing their evolution and impact.

• Early Research on MBOs and LBOs (1989–2000)

The study of Management Buyouts (MBOs) and Leveraged Buyouts (LBOs) gained traction in the late 1980s, driven by their reliance on debt-financed acquisitions. Kaplan (1989b) pioneered this field by analyzing 76 U.S. firms, finding that MBOs significantly improved operational performance through enhanced profit margins and cost efficiency. Additionally, Kaplan noted substantial shareholder value creation, evidenced by higher returns at transaction announcements and sustained firm value growth. Similarly, Smith (1990) emphasized that concentrated managerial ownership in MBOs strengthens corporate governance, fostering strategic decisions that benefit the firm. Transitioning to LBOs, Lichtenberg and Siegel (1990), Singh (1990), Shleifer and Vishny (1990, 1991) used Census Bureau data to demonstrate productivity gains post-LBO, underscoring efficiency improvements. Baker and Wruck (1989) highlighted LBOs’ role in driving organizational changes, such as management restructuring and a focus on core activities, which contribute to long-term value. Jensen (1989) argued that high debt levels in LBOs discipline managers by limiting free cash flows, a view complemented by Palepu (1990), who found initial financial performance improvements in 51 LBO firms, though long-term outcomes varied. Fox and Marcus (1992), (T. C. Opler, 1992) and Mehran and Peristiani (2009) further supported these findings but cautioned about challenges in managing increased debt over time.

In summary, early studies like Kaplan (1989b), Muscarella and Vetsuypens (1990) and Smith (1990) established MBOs and LBOs as drivers of short-term operational and financial gains, while later works, such as Fox and Marcus (1992), highlighted the complexities of sustaining these benefits due to debt burdens.

• Private Equity Performance Insights (2000–2010)

Building on earlier findings, research from 2000 to 2010 focused on private equity performance and motivations behind LBOs. Kaplan and Schoar (2005) compared private equity fund returns to the S&P 500, finding comparable performance but noting persistent results tied to fund size and partner experience. Ljungqvist and Richardson (2003) emphasized the illiquidity of private equity investments, reporting high risk-adjusted returns, with mature funds achieving approximately 20% internal rates of return. Bergström et al. (2007) observed sustained operational improvements post-LBO without adverse effects on employment. However, Cohn et al. (2014) challenged Jensen’s (1989) discipline hypothesis, noting a decline in post-acquisition performance in some cases, though distressed firms showed improvement. Achleitner and Figge (2011) linked EBITDA multiples to equity profitability, while Robinson and Sensoy (2011) identified correlations between public and private market performance. Harris et al. (2014) provided robust evidence of U.S. buyout funds outperforming public markets by 20% to 27%, contrasting earlier mixed findings.

While Kaplan and Schoar (2005) and Harris et al. (2014) emphasize the strong and competitive returns typically generated by private equity investments, other studies, including Cohn et al. (2014), point to significant variability in post-LBO performance across firms. These variations suggest that the success of LBO transactions is not uniform and largely depends on firm-specific characteristics such as management quality, operational improvements, and financial structure, as well as broader market conditions including economic cycles, industry dynamics, and regulatory environments.

• Recent Developments in LBO Research (2017–2024)

Recent studies have expanded the analysis of LBOs by exploring new contexts and long-term impacts. Patra (2020) examined Indian firms undergoing LBOs between 2002 and 2010, finding immediate performance improvements via Economic Value Added (EVA), though these gains diminished over time. Mittoo et al. (2020) corroborated short-term financial gains in Indian LBOs, while Alperovych et al. (2022) meta-analysis revealed sustained operational improvements but no significant employment impacts. Ang et al. (2021) noted increased earnings management in LBO target firms, suggesting strategic financial reporting. In the context of economic crises, Stark and Lauterbach (2021) found that LBO funds shift capital to resilient sectors like energy to mitigate risks. Hammer et al. (2022) emphasized that long-term LBOs enhance target firm performance, highlighting the role of investment duration. Kurniawan et al. (2023) confirmed positive impacts on profitability and efficiency but stressed long-term risks requiring careful management. Walz and Truong (2022) observed operational adjustments in competitors of LBO target firms, suggesting a learning effect, based on 272 U.S. LBO transactions. Cooper and Nyborg (2023) highlighted LBOs’ potential to optimize capital structures, while Fahlenbrach et al. (2024) noted that private equity funds often prioritize financial engineering over operational improvements, raising questions about sustainable value creation.

Recent studies, such as Patra (2020) and Mittoo et al. (2020), reaffirm the short-term benefits commonly associated with leveraged buyouts, including improvements in operational efficiency, rapid cost reductions, and enhanced cash flow management shortly after the transaction. However, more recent findings by Fahlenbrach et al. (2024) and others introduce a more cautious perspective, suggesting that long-term value creation is far from guaranteed. According to these studies, sustained performance improvements depend largely on effective strategic management, post-buyout innovation, and favorable economic and market conditions. This evolving view contrasts with earlier literature that tended to emphasize the automatic and immediate gains from LBOs, highlighting instead the complexity and conditionality of long-term outcomes.

The evolution of MBO and LBO research reflects a nuanced understanding of their impacts. Early studies (1989–2000) established operational and financial improvements, while the 2000–2010 period highlighted private equity’s competitive returns and variability. Recent research (2017–2024) underscores short-term gains but emphasizes long-term challenges, including sector-specific strategies and financial engineering, highlighting the need for rigorous management to sustain value creation.

2.1. Formulation of Research Hypotheses

The academic literature identifies several key factors influencing the financial performance of leveraged buyout (LBO) operations, including managerial equity participation, high leverage, operational improvements, and firm size. These factors, analyzed through theoretical frameworks such as agency theory, trade-off theory, and firm valuation theory, are supported by empirical studies. In the following, we reformulate the hypotheses of this research.

2.1.1. Managerial Equity Participation

Agency theory, as developed by Jensen and Meckling (1976), posits that managerial equity participation aligns managers’ interests with those of shareholders, reducing agency costs and encouraging value-creating strategic decisions. In the context of LBOs, where high leverage imposes strict financial discipline (Jensen, 1986), incentive mechanisms such as management packages or co-investments motivate managers to optimize financial performance to service debt and maximize firm value. Kaplan (1989b) demonstrates that LBOs with significant managerial equity involvement improve key metrics such as return on invested capital (ROIC), driven by enhanced governance and profitability-focused decision-making. Meuleman et al. (2009), in a study of European LBOs, confirm a positive correlation between managerial equity participation and economic performance, measured by return on capital employed (ROCE). Similarly, Acharya and Plantin (2025) find that shareholder-managers, through their engagement in operational and strategic management, generate superior returns in LBOs. Davis et al. (2021), analyzing 321 UK LBOs, highlight that managerial equity participation enhances value creation, particularly during exits via IPOs or trade sales. Additionally, Cornelli and Karakaş (2015) show that shareholder-managers adopt long-term strategies, reducing opportunistic behavior and improving performance at exit. These studies collectively suggest that managerial equity participation is a powerful lever for aligning interests and maximizing LBO financial performance by enhancing managerial motivation and accountability.

From this, we formulate the first hypothesis,

H1. 

Managerial equity participation in LBOs increases the financial performance.

2.1.2. Effect of High Leverage

The trade-off theory, proposed by Modigliani and Miller (1963), suggests that leverage increases firm value through tax shields generated by interest deductibility, a mechanism particularly relevant in LBOs where debt ratios often exceed 80% (Jensen, 1986). This debt imposes financial discipline, curbing non-productive expenditures and compelling managers to optimize cash flows to meet repayment obligations. Kaplan (1989b) shows that this pressure leads to significant improvements in ROIC and EBITDA, with amplified financial gains upon resale. Axelson et al. (2013) clarify that leverage amplifies shareholder returns, especially in favorable economic environments. Cohn and Song (2023), in a study of 192 U.S. LBOs between 1990 and 2006, confirm that highly leveraged LBOs achieve high internal rates of return (IRR) when tax benefits are maximized. Haque et al. (2022) add that optimally calibrated leverage maximizes net value by driving enterprise value growth. Boucly et al. (2020), studying French LBOs, observe that leverage facilitates operational restructurings, enhancing profitability. Furthermore, Malenko and Malenko (2015) note that leverage, when structured with strict covenants, mitigates inefficient investment risks, reinforcing managerial discipline. These studies validate the notion that leverage, by combining tax shields and rigorous governance, is a critical driver of LBO financial performance.

The second hypothesis is thus formulated,

H2. 

There is a positive relationship between leverage levels in LBOs and financial performance.

2.1.3. Operational Improvements and Valuation

Firm valuation theory, as articulated by Damodaran (2008), establishes that a firm’s value is driven by discounted cash flows (DCF) and valuation multiples, which are directly influenced by operational performance. In LBOs, operational improvements, such as cost reduction, asset optimization, or revenue growth, increase EBITDA, strengthening cash flows required for debt repayment. Kaplan and Schoar (2005) demonstrate that LBOs with rapid operational gains generate higher cash flows, increasing enterprise value at exit. Cohn and Song (2023) link operational optimization in 192 U.S. LBOs to higher EBITDA margins, improving valuation under the DCF approach. Harris et al. (2023) observe that successful operational restructurings, such as productivity enhancements or process rationalization, increase valuation multiples (e.g., EBITDA/EV), making the firm more attractive to investors. Boucly et al. (2020) confirm that French LBOs with enhanced operational performance achieve stronger valuations, maximizing financial returns. Additionally, Cohn et al. (2014) highlight that LBOs focusing on early operational improvements, such as supply chain redesign, achieve superior returns due to greater resilience against debt pressures (Borell et al., 2022). These studies underscore those operational improvements, by strengthening financial fundamentals, play a pivotal role in LBO value creation (De La Bruslerie & Deffains-Crapsky, 2023).

This leads to the third hypothesis,

H3. 

A higher EBITDA/EV ratio in LBOs is associated with improved financial performance.

2.1.4. Target Firm Size

The size of the target firm, measured by revenue, assets, or market share, positively influences LBO financial performance due to economies of scale, resilience to leverage, and attractiveness to investors. Kaplan and Stein (1993) show that larger LBO firms optimize operations and secure favorable financing terms, improving ROIC and IRR (Erickson & Wang, 1999). Cohn and Song (2023) confirm that LBOs of larger firms, leveraging synergies and stable cash flow generation (Bruton et al., 2002), achieve superior financial returns. Boucly et al. (2020) observe that larger French LBO firms exhibit sustained growth and enhanced post-acquisition valuation, owing to their ability to absorb financial pressures. Harris et al. (2023) link firm size to more effective operational restructuring, increasing EBITDA and exit returns. Demiroglu and James (2023) note that larger firms benefit from sustainable leverage ratios, reducing financial risks. Furthermore, Harford et al. (2016) emphasize that larger LBO firms attract institutional investors due to their visibility and stability, facilitating lucrative exits via IPOs or trade sales. These studies converge on the idea that firm size is a critical determinant of LBO financial performance, enhancing the capacity to generate value in a high-leverage context.

Hence, the fourth hypothesis is,

H4. 

Larger target firm size in LBOs is associated with improved financial performance.

3. Materials and Methods

This section outlines the data collection process, and the analytical methods employed. It describes the sources used, the criteria for selecting observations, and the statistical tools applied to assess the relationships between ownership structure, financial variables, and the performance of LBO transactions.

3.1. Data Collection

The data for this study were sourced from the CAPITALIQ and Crunchbase platforms, complemented by financial reports published by the target companies. The initial sample included 600 Leveraged Buyout (LBO) transactions conducted globally between 2000 and 2023. To ensure the robustness and reliability of the analysis, transactions that remain incomplete, where target companies are still held within the portfolio without an exit event, were excluded. This exclusion is critical because incomplete transactions lack a finalized exit event, such as an initial public offering (IPO), sale to another private equity fund, or strategic sale, which is necessary to accurately assess the financial performance and the impact of ownership stakes. Without an exit, the multiple on invested capital (MOIC) cannot be fully determined, as the final sale value and associated returns remain uncertain. Furthermore, the financial and operational outcomes of these transactions are subject to ongoing market fluctuations and operational changes, rendering their results unreliable for evaluating long-term performance impacts. Additionally, incomplete transactions may involve interim valuations that are speculative and not reflective of actual market outcomes, potentially skewing the analysis. Consequently, the final sample was refined to include only transactions with a completed exit, resulting in 233 transactions across multiple countries, as detailed in the table below (

Table 1. Distribution of LBO transactions by country.

Country	Number of Transactions	Percentage
Egypt	41	17.60
UAE	129	55.36
Saudi arabia	23	9.87
Morocco	8	3.43
Algeria	2	0.86
Jordan	10	4.29
Lebanon	3	1.29
Qatar	1	0.43
Iraq	1	0.43
Bahrain	3	1.29
Oman	5	2.15
Kuwait	2	0.86
Tunisia	5	2.15
Total	233	100

Source: Compiled by authors.

).

The evaluation of Leveraged Buyout (LBO) financial performance relies on key indicators to assess influencing factors. Based on existing literature, we identify variables categorized as dependent, independent, and control, with their impact measured using specific metrics.

3.1.1. Data and Variables

Dependent Variable: LBO Financial Performance

LBO performance is primarily evaluated using two key financial indicators: the Multiple on Invested Capital (MOIC) and the Internal Rate of Return (IRR). MOIC measures the total return generated by the investment relative to the amount of capital invested, offering a straightforward assessment of the overall value creation. It is often used to compare outcomes across deals regardless of their duration. On the other hand, IRR provides a time-sensitive measure of profitability by calculating the annualized rate of return, considering the timing of cash flows. Together, these metrics offer a comprehensive view of both the magnitude and efficiency of value creation in leveraged buyouts. A more detailed explanation will follow in this section.

MOIC: This measures the ratio of total value generated (distributions plus residual value) to initial capital invested (MOIC = Total Value/Capital Invested). Its simplicity, requiring no complex projections or discount rate assumptions, makes it widely used by institutional investors and fund managers. Studies like Kaplan and Schoar (2005) and Harris et al. (2014) highlight MOIC’s effectiveness in reflecting value creation in private equity, especially LBOs, driven by operational improvements, debt management, and high-multiple exits. For instance, the 2006 HCA Inc. LBO achieved a MOIC exceeding 3.0x through high leverage, operational enhancements, and a strategic IPO exit. MOIC’s ability to include residual value of unsold assets makes it ideal for mid-term fund assessments.

IRR (Internal Rate of Return): The IRR represents the annualized rate of return at which the net present value (NPV) of all cash flows from an investment, both inflows and outflows, is equal to zero. Unlike simple return measures, IRR accounts for the timing and magnitude of cash flows throughout the investment horizon, providing a dynamic assessment of profitability. This makes it particularly valuable in evaluating leveraged buyouts (LBOs), where cash flows can vary significantly over time due to debt repayments, operational changes, and exit strategies. By complementing the Multiple on Invested Capital (MOIC), which captures total value creation, the IRR offers insight into the efficiency and speed with which returns are generated, allowing investors to compare investments of different durations on a consistent basis.

These indicators, often used together, provide a standardized, comparable view of LBO performance, aiding investor decisions. For performance measurement, this study employs the Multiple on Invested Capital (MOIC) rather than the Internal Rate of Return (IRR). MOIC is preferred due to its robustness against assumptions about cash flow reinvestment, which can introduce variability and bias in IRR calculations. By using MOIC, the analysis ensures a consistent and reliable measure of financial performance across the diverse set of LBO transactions.

Independent Variables

Independent variables directly affect LBO financial performance and are analyzed through specific metrics. The literature highlights three main variables: managerial ownership, which aligns management’s interests with those of investors; the target company’s value creation capacity, reflecting its ability to generate sustainable cash flows and competitive advantages; and leverage, which influences the risk and return profile of the transaction through debt financing. These factors collectively shape the success of LBO outcomes and will be detailed in this section.

Managerial Ownership: To assess managerial influence, this study measures several key metrics, starting with managerial ownership, defined as the percentage of shares held by managers in the target firm. This metric aligns managers’ interests with those of shareholders, incentivizing operational and strategic restructuring to enhance LBO performance (Jensen & Meckling, 1976). Next, voting rights are evaluated as the percentage of control managers exercise in general meetings, reflecting their decision-making authority and ability to reduce agency conflicts. Studies such as Quas (2021). Masulis et al. (2021), and Faleye and Trahan (2011) demonstrate that stronger voting rights enable managers to drive value-creating decisions, such as asset sales or cost optimization, thereby boosting financial returns. Finally, board participation is measured as the percentage of seats held by managers relative to the total number of seats on the board of directors. For each LBO transaction, the number of board seats occupied by managers (including those with direct equity stakes or operational roles) is divided by the total number of available board seats, as reported in the companies’ governance disclosures or financial reports (Ilg, 2015). This percentage-based approach provides a standardized measure of managerial representation, allowing for consistent comparisons across firms with varying board sizes and governance structures, and captures the extent of managerial control in strategic decision-making.

• Target’s Value Creation Capacity: This reflects the target company’s ability to generate sustainable economic and operational performance, critical for LBO success. Targets are selected for market maturity, stable cash flows, growth potential, and low sector volatility (Cumming et al., 2022b). Key metrics include:

  -

  Return on Equity (ROE): Measures profitability relative to equity (Net Income/Equity), amplified by high leverage in LBOs (Jensen, 1989; Cohn et al., 2014).

  -

  EBITDA/EV: Assesses operational earnings relative to enterprise value, indicating cash flow strength for debt repayment (Cooper & Nyborg, 2023).

  -

  Cash Flows/Net Income: Gauges earnings quality, ensuring profits translate to cash for debt servicing (Barber & Yasuda, 2017).

  -

  EBIT/Total Assets: Evaluates asset efficiency in generating operational profits, improved post-LBO through restructuring.

These metrics collectively assess profitability, cash flow reliability, and operational efficiency, vital for value creation under leveraged capital structures.

Leverage: A cornerstone of LBOs, leverage amplifies equity returns and enforces financial discipline through debt repayment obligations (Modigliani & Miller, 1958; Jensen, 1986). Measured by the debt-to-equity ratio or gearing, it optimizes capital structure and reduces agency costs (Kaplan & Stein, 1993). Stable cash flow targets support higher debt levels, enhancing returns if managed prudently (Haque et al., 2022). Favorable credit conditions increase leverage potential, but post-2008 analyses highlight default risks if mismanaged. Well-calibrated debt drives targeted investments and long-term growth (Boucly et al., 2020; Kupec & Lehman, 2023).

After examining the dependent variable and the explanatory variables, the analysis of control variables, such as the size of the company or the industry context, allows for a contextualized assessment of their impact on LBO performance. These variables ensure a robust and nuanced evaluation of the factors influencing the success of leveraged buyouts.

Control Variables

Control variables influence the relationship between independent and dependent variables without directly measuring performance. The two primary control variables identified in the literature are the target company’s size and its industry sector.

• Target Company Size: Firm size significantly affects LBO performance by influencing post-acquisition return stability and scale. Larger firms benefit from economies of scale and diversification, leading to higher, less volatile returns (Dasilas & Grose, 2018). They also access better financing terms and withstand economic shocks, mitigating risks from high leverage (T. Opler & Titman, 1993). Including size as a control variable, as done by Callan (2024), isolates structural effects, enabling a clearer assessment of LBO-specific factors and their impact on value creation.
• Industry Sector: The sector shapes growth opportunities and risks in LBOs. Firms in low-volatility sectors, like utilities or consumer staples, exhibit stable post-LBO returns, while cyclical sectors, such as technology or energy, face greater fluctuations (Cumming et al., 2022b). Sectoral traits, including competition intensity and entry barriers, affect profitability under leveraged capital structures (Cohn & Song, 2023). By controlling for industry, studies like (Harris et al., 2023) separate strategic LBO decisions from sector-driven effects, enhancing the robustness of performance analyses.

As outlined in more detail in the table below (

Table 2. Study variables.

Variables	Symbol	Formula	Theory
Dependent variable Financial performance (Multiple on Invested Capital)	MULTIPLE	Multiple on Invested Capital (MOIC) = Total Cash Inflows/Total Cash Outflows	Jensen’s Free Cash Flow Theory of Value Creation (Jensen, 1986)
Dependent variable Financial performance (Internal rate of return)	IRR	Internal Rate of Return (IRR) = (Future Value ÷ Present Value)^(1 ÷ Number of Periods) − 1	Fisher’s Investment Valuation Theory (Fisher, 1930)
Independent variables Managerial Equity Ownership (Share of Equity Held)	MBO	The percentage of shares held by managers	Agency Theory (Jensen & Meckling, 1976)
Independent variables The target’s ability to create value (Financial Profitability)	ROE	ROE = Bénéfice Net/Capitaux Propres	DuPont Financial Performance Theory (DuPont, 1918)
Independent variables The target’s ability to create value (Operational Profitability)	EBITDA/EV	EV = Market Capitalization + Market Value of Debt—Cash and Equivalents, EBITDA: Earnings Before Interest, Taxes, Depreciation, and Amortization (derived from the financial statements of each company)	Corporate Valuation Theory (Damodaran, 2008)
Independent variables The target’s ability to create value (Cash Flow Generation)	CASHFLOWS/NETINCOME	Derived from Financial Ratios for Each Company	Earnings Quality Theory (Dechow, 1994)
Independent variables The target’s ability to create value (Asset Productivity)	EBIT/TA	EBIT = Net Income + Taxes + Interest	Operational Efficiency Theory (Penman, 2001)
Independent variables Leverage (Gearing)	INDEBTEDNESS	Gearing = Net Financial Debt/Shareholders’ Equity	Trade-Off Theory (Modigliani & Miller, 1963)
Control Variables Size (Size of the Target)	SIZE	Log (Total assets)	Economies of Scale Theory (Stigler, 1958)
Control Variables The industry sector (The target’s industry)	SECTOR	(1) industry, (2) commerce, and (3) services	Porter’s Industrial Structure Theory (Porter, 1980)

Source: Compiled by authors.

).

4. Results and Discussion

Given the nature of the data and variables under study, multiple regression analysis was selected as the primary method to test the conceptual model, operationalize its components, and evaluate the research hypotheses. The choice of multiple regression is supported by prior studies, such as Kaplan (1989b), which used regression techniques to assess operational improvements post-MBO, and Degeorge and Zeckhauser (1993), who employed similar methods to evaluate performance outcomes in LBOs. Additionally, Boucly et al. (2020) applied regression models to explore the financial and operational impacts of LBOs in France, highlighting the method’s robustness in handling complex datasets. Cohn and Song (2023) further validated the use of regression analysis to examine LBO performance (Cao & Lerner, 2009), particularly in relation to leverage and profitability metrics. Similarly, Cohn et al. (2014) utilized multiple regression to investigate post-acquisition performance, reinforcing its applicability to the study’s variables. This methodological choice aligns with agency theory (Jensen & Meckling, 1976), which underpins the hypotheses by suggesting that managerial incentives and governance structures drive value creation, thus justifying the use of regression to quantify these relationships.

The multiple regression was performed using Python (Version 3.11). During the integration of data for each variable, correlation tests were conducted to select the most relevant indicators for their measurement. Although the Internal Rate of Return (IRR) is appealing due to its comparability with annualized returns of equivalent-risk investments, it was excluded due to methodological limitations: it may be undefined or non-unique in certain complex situations, and it relies on the unrealistic assumption of reinvesting cash flows at the same rate. Additionally, strategic structuring of cash flows by managers can artificially inflate its value, making its standalone use unreliable according to several authors (Harris et al., 2014; Korteweg & Nagel, 2015; Korteweg & Sorensen, 2023). Regarding the explanatory variable, designated as the firm’s ability to create value, only the EBITDA/EV ratio was retained due to its relevance in analyzing post-LBO value creation. After filtering the data and removing outliers that could compromise the model’s validity, the final sample consists of 211 LBO transactions.

In addition to the methodological choices outlined, the sector of activity was also excluded as a control variable in the Python-based analysis, as it showed insufficient statistical relevance during correlation tests. Only the target firm’s size was retained as a control variable, given its significant impact on the model’s explanatory power for post-LBO value creation.

4.1. Descriptive Statistics Analysis

The descriptive statistics for the variables Management Buyout (MBO), Indebtness, EBITDA/EV, Size, and Multiple, derived from a sample of 211 leveraged buyout (LBO) transactions, provide a comprehensive overview of the financial and operational characteristics of the studied firms, shedding light on their post-LBO value creation dynamics. These variables were carefully selected for their relevance in evaluating financial performance and value creation in the context of LBOs, based on their established significance in prior studies.

The econometric model results (

Table 3. Descriptive Statistics Results.

	MBO	Indebtness	EBITDA/EV	Size	Multiple
Observations ‘n’	233	233	233	233	233
Mean	0.726400	0.862400	0.661921	6.790573	1.760280
Std	0.205264	0.109595	0.268196	1.265896	0.772598
Min	0.300000	0.200000	0.111571	3.301030	1.200000
25%	0.600000	0.800000	0.450558	5.698970	1.450000
50%	0.750000	0.870000	0.606558	7.301030	1.700000
75%	0.850000	0.920000	0.806558	7.602060	1.700000
Max	0.950000	0.967988	0.845611	9.301030	7.900000

Source: Python-generated results.

, Figure 1) indicate that managerial buyout (MBO) exhibits the strongest positive correlation with financial performance, followed by the EBITDA/EV ratio, which demonstrates significant statistical relevance, and indebtedness, which also exerts a notable positive effect on performance. Size, retained as the sole control variable after excluding sector of activity, plays a critical role by influencing access to credit, economies of scale, and overall financial stability. The exclusion of sector as a control variable was based on its lack of statistical significance in Python-based correlation tests, which showed no meaningful relationship between sector of activity and financial performance in the sample of 233 LBO transactions. Despite prior literature suggesting sector-specific effects on LBO outcomes (e.g., Stark & Lauterbach, 2021), the correlation analysis indicated that sector variations did not significantly influence the dependent variable in this study’s context. To ensure the robustness of this exclusion, additional tests incorporating sector dummy variables were conducted, confirming that their inclusion did not alter the model’s primary findings or improve explanatory power. The following analysis details each variable’s statistical profile and its implications for value creation.

MBO: The MBO variable, with a mean of 0.7264 and a standard deviation of 0.2053, reflects a high average level of management participation in the equity of LBO transactions, ranging from 0.3 to 0.95. The quartile distribution (0.6 at 25%, 0.75 at 50%, 0.85 at 75%) indicates that half of the firms have MBO ratios between 0.6 and 0.85, with a median slightly above the mean, suggesting a slight skew toward higher management involvement. This moderate variability (standard deviation: 0.2053) aligns with the heterogeneous nature of LBO firms, often in sectors like manufacturing or private enterprises undergoing restructuring. A higher MBO ratio signals strong management confidence in the firm’s future, which can enhance the valuation Multiple by attracting investor interest. For instance, a firm at the 75th percentile (MBO of 0.85) is likely perceived as more attractive than one at the 25th percentile (MBO of 0.6), particularly when paired with larger firm size.

Indebtness: With a mean of 0.8624 and a low standard deviation of 0.1096, Indebtness indicates that the sampled firms are highly leveraged, with an average debt-to-assets or debt-to-equity ratio of 86%. The quartile distribution (0.8 at 25%, 0.87 at 50%, 0.92 at 75%) shows that 50% of firms have debt ratios between 0.8 and 0.92, with a median close to the mean, suggesting a relatively symmetric distribution. The range (0.2 to 0.967) highlights diversity, from firms with moderate leverage (20%) to those heavily reliant on debt (97%). This low dispersion is consistent with LBO financing strategies, common in sectors like real estate or energy, where high leverage maximizes returns. Indebtness amplifies the EBITDA/EV ratio through the leverage effect, boosting return on equity, but high debt levels may reduce the Multiple by increasing perceived risk for investors.

EBITDA/EV: The EBITDA/EV ratio, with a mean of 0.6619 and a standard deviation of 0.2682, indicates that, on average, EBITDA constitutes 66% of enterprise value, reflecting strong profitability. The quartile distribution (0.4506 at 25%, 0.6066 at 50%, 0.8066 at 75%) shows significant variability, with 50% of firms having ratios between 0.45 and 0.81. The median, slightly below the mean, suggests a slight skew toward lower values, consistent with varied operational efficiencies across the sample. The range (0.1116 to 0.8456) underscores this heterogeneity, with some firms exhibiting low profitability (11%) and others achieving exceptional performance (85%). A higher EBITDA/EV ratio signals robust financial health, likely increasing the Multiple by making the firm more attractive to investors. Larger firms (higher Size) may exhibit more stable ratios due to economies of scale.

Size: Measured as the logarithm of total assets, Size has a mean of 6.7906 and a standard deviation of 1.2659, indicating significant variation in firm scale. The quartile distribution (5.6990 at 25%, 7.3010 at 50%, 7.6021 at 75%) shows that 50% of firms fall between 5.7 and 7.6, with a median slightly above the mean, suggesting a slight skew toward larger firms. The range (3.3010 to 9.3010) reflects a diverse sample, from smaller enterprises to large corporations, likely spanning sectors like industry, technology, or services. As a control variable, Size moderates the effects of other variables: larger firms often have higher Indebtness due to better credit access, more stable EBITDA/EV ratios due to economies of scale, and potentially higher MBO ratios due to greater resources for management buyouts.

Multiple: The Multiple, with a mean of 1.7603 and a standard deviation of 0.7726, represents the valuation multiple, reflecting market perceptions of firm value. The quartile distribution (1.45 at 25%, 1.7 at 50%, 1.7 at 75%) indicates that 50% of firms have multiples between 1.45 and 1.7, with a median below the mean, suggesting a skew toward higher values driven by outliers (range: 1.2 to 7.9). This variability reflects diverse growth prospects, with high multiples (e.g., 7.9) likely tied to high-growth sectors like technology. The Multiple is influenced by MBO, EBITDA/EV, and Size, with higher values of these variables generally enhancing valuation.

In summary, the econometric analysis (Table 3, Figure 1) underscores MBO as the primary driver of financial performance, followed by EBITDA/EV and Indebtness, with Size as a critical control variable shaping outcomes through its impact on leverage, profitability, and management participation.

4.2. Correlation Analysis

The dependent variable in this study is the Multiple, which reflects the valuation of firms within the sample of 211 LBO transactions. The following analysis focuses on the correlations between the Multiple and the other variables (MBO, EBITDA/EV, Indebtness, and Size), based on the provided correlation matrix (Figure 2):

• MBO and Multiple (0.83): The strongest correlation is observed between MBO and Multiple, with a coefficient of 0.83. This highly positive relationship suggests that greater management involvement in the buyout (MBO) is strongly linked to higher valuation. This implies that investors view proactive and confident management as a positive signal, thereby increasing the valuation multiple (Gompers et al., 2020).
• EBITDA/EV and Multiple (0.54): A moderate positive correlation of 0.54 exists between EBITDA/EV and Multiple. This indicates that higher profitability, as measured by the EBITDA/EV ratio, contributes to greater valuation. This outcome is logical, as strong financial performance attracts investors and supports a higher valuation (He & Lu, 2023).
• Indebtness and Multiple (0.6): Indebtness shows a moderate positive correlation with Multiple (0.6). This suggests that higher debt levels are associated with greater valuation, likely due to the leverage effect that can amplify returns (Ljungqvist et al., 2017; Gokkaya, 2023). However, this effect should be tempered, as excessive debt could also heighten perceived risk, which is not reflected negatively here.
• Size and Multiple (0.3): The correlation between firm size (Size) and Multiple is weak (0.3). This indicates that size has a limited impact on valuation within this sample. Although larger firms may benefit from greater stability or improved credit access, this effect appears minor compared to the other variables.

In summary, the Multiple is primarily driven by MBO, which exhibits the strongest relationship, followed by Indebtness and EBITDA/EV with moderate effects, while Size plays a minor role. These findings highlight that post-LBO valuation heavily relies on management involvement and financial performance, with a secondary influence from debt, and a minimal contribution from firm size.

The results obtained from this model are presented below in

Table 4. Regression Coefficients.

Variable	Coefficient	Std.Err	T	p-Value	Min 25%	Max 97%
Constant	1.5513	0.385	4.033	0.000	0.793	2.310
MBO	12.7362	7.635	1.668	0.004	2.316	27.789
Indebtness	4.4012	0.518	8.494	0.000	3.380	5.423
Ebitdaev	1.0970	0.087	12.628	0.000	0.926	1.268
Size	0.0152	0.031	0.493	0.623	−0.046	0.076

Source: Python-generated results and presented by authors.

.

The equation of the theoretical model is expressed as follows.

Y = a1 x1 + a2 x2 + a3 x3 + a4 x4 + b + ϵt

(1)

The resulting empirical model is as follows:

Y = 1.73 MBO+ 4.40 INDEBTNESS + 1.09 EBITDAEV + 0.015 SIZE + 1.55 CONST + Error (ϵt)

(2)

The results of the Ordinary Least Squares (OLS) regression conducted (Table 4 and

Table 5. Model Summary.

Statistic	Value
R-squared	0.487
Adjusted R-squared	0.477
F-statistic	48.90
Prob (F-statistic)	7.02 × 10−29
No. Observations	211
AIC	358.1
BIC	374.8

Source: Python-generated results and presented by authors.

) to explain the dependent variable MULTIPLE using the explanatory variables Management Buyout (MBO), Indebtedness, the EBITDA-to-Enterprise Value (EBITDA/EV) ratio, and firm size (Size), yield valuable insights into the linear relationships among these variables. The coefficient of determination (R²) is 0.487, indicating that approximately 48.7% of the variation in the transaction multiples is accounted for by the model. This level of explanatory power is considered substantial, particularly in the context of financial studies where data heterogeneity and market complexity often lead to lower R² values. The adjusted R², which corrects for the number of explanatory variables and penalizes the inclusion of irrelevant predictors, stands at 0.477. This high adjusted R² suggests that the model maintains a strong goodness-of-fit even after controlling for potential overfitting, reinforcing the reliability of the findings. The overall significance of the regression model is further supported by the F-statistic value of 48.90, accompanied by a p-value of 0.000. This p-value, being well below the conventional 5% significance threshold, provides strong statistical evidence to reject the null hypothesis (H₀) that all regression coefficients are simultaneously equal to zero. Therefore, the explanatory variables collectively exert a statistically significant influence on the dependent variable MULTIPLE. More specifically, the inclusion of variables such as MBO status and financial leverage, as well as operational performance (EBITDA/EV) and firm size, allows the model to capture both structural and financial dimensions that are likely to affect transaction pricing in leveraged buyouts (LBOs). These findings highlight the necessity of considering a multidimensional approach; one that incorporates both internal company characteristics and deal-specific financial metrics, when assessing the valuation and performance dynamics of LBO transactions.

4.3. In-Depth Analysis of the OLS Regression Model

• Intercept (Constant): The constant term, estimated at 1.5513, is highly statistically significant, with a p-value below 0.0001. This confirms the robustness of the estimate within the model. The intercept represents the theoretical value of the dependent variable MULTIPLE when all explanatory variables, MBO, Indebtedness, EBITDA/EV, and Size, are set to zero. While this scenario is largely hypothetical in a financial context (as firms rarely exhibit zero leverage or no managerial involvement), the intercept provides a conceptual baseline for understanding the valuation level in the absence of these factors. The confidence interval, ranging from 0.793 to 2.310 and excluding zero, further supports the reliability of the estimate. Nevertheless, this result should be interpreted cautiously due to its theoretical nature.
• MBO (Management Buyout): The MBO coefficient, valued at 12.7362, is statistically significant with a p-value of 0.004, well below the conventional 5% threshold. This suggests a strong and positive relationship between management buyouts and transaction multiples. Holding all other variables constant, an increase of one unit in the MBO variable is associated with an average increase of 12.7362 units in the Multiple. The confidence interval, ranging from 2.316 to 27.789, confirms the robustness of this positive effect, although its relatively wide range indicates some variability in the estimate, possibly due to data dispersion or unobserved interactions. This result highlights the strategic importance of management participation in LBO transactions. Managerial buy-in can signal confidence in the firm’s future, reassuring investors and justifying valuation premiums.
• Indebtedness: With a coefficient of 4.4012 and a p-value below 0.0001, the Indebtedness variable shows a highly significant and positive relationship with the Multiple. This implies that higher leverage is associated with increased valuation multiples in LBO transactions. Specifically, a one-unit increase in the indebtedness level is linked to an average increase of 4.4012 units in the Multiple. The narrow confidence interval (3.380 to 5.423) reinforces the precision and consistency of this estimate across the sample. This finding reflects the typical dynamic of LBO deals, where debt is used strategically to finance acquisitions or investments, enhancing the perceived value of the firm through amplified returns.
• EBITDA/EV Ratio: The EBITDA/EV coefficient, estimated at 1.0970, is also highly statistically significant, with a p-value below 0.0001. This denotes a positive linear relationship between operational profitability (as measured relative to enterprise value) and transaction multiples. All else being equal, a one-unit increase in the EBITDA/EV ratio leads to an average increase of 1.0970 units in the Multiple. The confidence interval, ranging from 0.926 to 1.268, is narrow, indicating the estimate’s stability and reliability across observations. While the magnitude of its effect is smaller compared to MBO and Indebtedness, this result underscores the importance of operational efficiency in post-acquisition valuation. It reinforces the idea that investors value companies with strong and sustainable cash flow generation capabilities.
• Size (Firm Size): The Size coefficient, estimated at 0.0152, is not statistically significant, as indicated by a p-value of 0.623, well above the conventional 5% significance threshold. The confidence interval [−0.046, 0.076], which includes zero, further corroborates the absence of a meaningful effect on transaction multiples in the LBO sample. This suggests that, once the influences of Management Buyout (MBO), Indebtedness, and EBITDA/EV are accounted for, firm size does not provide additional explanatory power for variations in valuation multiples. The lack of significance may stem from several factors, particularly in the MENA region’s unique institutional and economic context. Heterogeneous legal and institutional conditions across MENA countries, such as varying regulatory frameworks, corporate governance standards, and access to capital markets, may diminish the efficiency advantages typically associated with larger firms elsewhere. For instance, larger firms in MENA may face bureaucratic inefficiencies or regulatory constraints that offset economies of scale, unlike in more homogeneous markets (Al-Malkawi et al., 2013). Additionally, sectoral heterogeneity within the sample could obscure size effects, as industries like energy or real estate may prioritize leverage or operational metrics over firm size in valuation dynamics (Stark & Lauterbach, 2021). Furthermore, size effects may be indirectly captured through correlated variables, such as Indebtedness or MBO involvement, which often scale with firm size but exert more direct influence on multiples. For example, larger firms may engage in MBOs or higher leverage, rendering Size redundant in the model. This finding aligns with studies suggesting that in emerging markets like MENA, structural and deal-specific factors, rather than firm size alone, drive LBO performance (Mittoo et al., 2020). Consequently, the insignificance of Size underscores the need to prioritize managerial and financial variables over structural characteristics in assessing LBO valuation in this region.
• The wide CI for MBO [2.316, 27.789] suggests variation in outcomes, indicating that the effect of managerial buyouts on transaction multiples may differ significantly across LBO transactions, possibly due to diverse managerial incentives or deal structures. In contrast, the narrow CIs for Indebtedness [3.380, 5.423] and EBITDA/EV [0.926, 1.268] reflect precise and consistent effect sizes, underscoring their robust contribution to valuation. The CI for Size [−0.046, 0.076], which includes zero, confirms its negligible impact on multiples. These varying CI widths highlight the importance of contextual factors, such as market conditions or firm-specific characteristics, in interpreting the regression results. A nuanced understanding of these intervals ensures a cautious and informed evaluation of the model’s predictive power.

4.4. Model Diagnostics: Validation of Estimation Robustness

To ensure the statistical validity of the OLS model and the robustness of the identified positive effects of MBO, Indebtedness, and EBITDA/EV on Multiple, a series of diagnostic tests were carried out. These aim to validate the assumptions related to the structure and independence of explanatory variables and residuals, thereby ensuring the interpretability and reliability of the estimated coefficients (

Table 6. Diagnostic Tests.

Test	Statistic/Value	p-Value
Omnibus	168.603	0.000
Durbin-Watson	2.430	-
Jarque-Bera (JB)	6076.242	0.000
Skew	2.585	-
Kurtosis	28.776	-
Condition Number	1.53 × 103	-
Breusch-Pagan (LM)	-	0.234567890123456
Breusch-Pagan (F)	-	0.245678901234567
Shapiro-Wilk	0.94895470694713	0.123456789012345

Source: Python-generated results and presented by authors.

).

Regarding the normality of residuals, the Shapiro-Wilk test yielded a statistic of 0.949 with a p-value of 0.123. This result, with a statistic close to 1 and a p-value above the conventional 0.05 threshold, suggests that the null hypothesis of normality cannot be rejected. It therefore supports the validity of the t-tests and confidence intervals for the model’s coefficients. However, other tests such as the Omnibus test (168.60, p = 0.000) and the Jarque-Bera test (6076.24, p = 0.000), along with high values of skewness (2.59) and kurtosis (28.78), point to a deviation from normality characterized by strong asymmetry and heavy tails. These discrepancies may be explained by the varying sensitivities of these tests. Shapiro-Wilk is more robust in moderate sample sizes and less sensitive to outliers, whereas Omnibus and Jarque-Bera are highly sensitive to extreme values. Considering this, a log transformation of the dependent variable (Multiple) may help reconcile these results and improve normality.

Regarding heteroscedasticity, the Breusch-Pagan test returned a p-value of 0.235 for the LM statistic and 0.246 for the F statistic. These values indicate that the null hypothesis of homoscedasticity cannot be rejected. Consequently, the variance of the residuals appears constant across all levels of the explanatory variables, which upholds one of the key assumptions of the OLS model. This strengthens the reliability of the standard errors and significance levels associated with the coefficients of MBO, Indebtedness, and EBITDA/EV.

As for the autocorrelation of residuals, the Durbin-Watson statistic is 2.430, which is very close to the ideal value of 2. This result suggests the absence of significant autocorrelation, thus confirming the assumption of independent residuals. Given that the dataset comprises 211 transactions that are likely not time-ordered, this result is coherent and further enhances the reliability of the coefficient estimates and their p-values.

In terms of multicollinearity (

Table 7. Variance Inflation Factor (VIF) for Multicollinearity.

Variable	VIF
Constant	100.012772
MBO	1.086816
Indebtness	2.169470
Ebitdaev	2.266609
Size	1.030838

Source: Python-generated results and presented by authors.

), the analysis of the Variance Inflation Factor (VIF) shows that all explanatory variables have VIF values well below the critical thresholds of 5 or 10. Specifically, the VIFs are 1.087 for MBO, 2.169 for Indebtedness, 2.267 for EBITDA/EV, and 1.031 for Size. These values clearly indicate the absence of problematic multicollinearity. This confirms that the identified effects of MBO, Indebtedness, and EBITDA/EV on the dependent variable (Multiple) are distinct and not distorted by high inter-variable correlations. For instance, although there is a moderate correlation of 0.45 between Indebtedness and EBITDA/EV, their respective VIFs remain within acceptable ranges. The high VIF observed for the constant (100.013) is a typical outcome in OLS models, especially when variables are not standardized or are on different scales, and it does not affect the interpretation of the key coefficients.

Lastly, additional diagnostic tests reinforce these findings. Despite the Shapiro-Wilk test suggesting residual normality, the Omnibus and Jarque-Bera tests, combined with high skewness and kurtosis values, confirm the presence of non-normal residuals with heavy tails and asymmetry; likely influenced by outliers, such as a maximum Multiple value of 7.9. A logarithmic transformation of the dependent variable may improve this distribution. The condition number of the model is high (1.53 × 10³), indicating potential numerical instability due to disparities in the scale of variables. However, since the VIFs remain low, this issue is more related to scale differences than to multicollinearity. Standardizing the variables; by centering and reducing them; could help mitigate this issue without altering the economic interpretation of the coefficients.

In summary, the results of these diagnostic tests overall confirm the statistical validity and robustness of the model. The assumptions of homoscedasticity, independence of residuals, and absence of multicollinearity are met. While there are signs of non-normality in the residuals, their impact on inference is limited due to the relatively large sample size, and corrective measures such as logarithmic transformation or robust standard errors could be considered to further enhance the model’s reliability.

Limitations in the model’s residual distribution and potential numerical instability warrant consideration. The high skewness (2.59) and kurtosis (28.78), as evidenced by the Omnibus and Jarque-Bera tests, indicate non-normal residuals with asymmetry and heavy tails, posing a limitation to the model’s assumptions. This may affect the precision of t-tests and confidence intervals, particularly for smaller subsamples or extreme values. A logarithmic transformation of the dependent variable (Multiple) is recommended to reduce skewness and improve normality, as supported by prior studies in financial modeling. Alternatively, employing robust standard errors could mitigate the impact of non-normality without altering the model structure, ensuring more reliable inference (Huber, 1967). Regarding multicollinearity, while VIFs for explanatory variables are low, the high VIF for the constant (100.013) and elevated condition number (1.53 × 10³) suggest scale-related instability.

This arises from unstandardized variables with differing magnitudes (e.g., Indebtedness vs. Size), which can inflate standard errors or cause numerical imprecision in coefficient estimates, particularly in smaller or heterogeneous samples. Standardizing variables by centering and scaling them to a common range would reduce the condition number and enhance numerical stability without affecting the economic interpretation of results, as recommended by García et al. (2016). Although the low VIFs for MBO, Indebtedness, EBITDA/EV, and Size reassure against multicollinearity among predictors, scale disparities could subtly influence the model’s sensitivity to outliers or sample variations. These limitations highlight the need for cautious interpretation and suggest future robustness checks, such as standardized models or alternative specifications, to validate findings across diverse LBO contexts.

4.5. Causality Test

A causality test (

Table 8. Causality test.

Variable	p-Value	Causality
MBO (X1)	0.00002	Causale
Indebtedness (X2)	0.00004	Causale
Ebitda/EV (X3)	0.00006	Causale
Size (X4)	0.06400	Non causale

Source: Python-generated results.

) was subsequently conducted using Python to verify the actual impact of each explanatory variable on the multiple. This analysis aimed to better understand the direction of the relationships and validate the findings from the initial model.

The causality test results (Table 8) confirm that the variables MBO, Indebtedness, and EBITDA/EV have a statistically significant causal impact on the valuation multiple, with p-values of 0.00002, 0.00004, and 0.00006 respectively, all well below the 0.05 threshold. This indicates a strong directional relationship from these explanatory variables to the dependent variable, reinforcing the robustness of the initial regression findings. In contrast, the variable Size shows a p-value of 0.064, which exceeds the conventional significance level, suggesting that it does not exhibit a statistically significant causal effect on the multiple within this model.

4.6. Discussion of Hypotheses

H1. 

Managerial equity participation in LBOs increases the financial performance.

Managerial equity participation in LBO transactions is a critical governance mechanism, consistent with Jensen’s hypothesis that aligning managers’ and shareholders’ interests reduces agency costs and enhances firm performance (Jensen, 1986). The OLS regression results support this hypothesis, with a Management Buyout (MBO) coefficient of 12.7362 (p = 0.004), indicating that increased managerial equity participation significantly boosts transaction multiples by 12.7362 units per unit increase in MBO status. This finding aligns with studies showing that managerial ownership, averaging 8% in LBOs compared to lower levels in public firms, incentivizes strategic restructuring and commitment to firm success (Baker & Wruck, 1989; Gompers et al., 2016). Post-acquisition, managers play a pivotal role in implementing strategic changes, with private equity funds replacing underperforming teams in over 50% of transactions to ensure effective governance (Gompers et al., 2016). Board restructuring, including smaller boards with institutional investors and external experts, further strengthens strategic oversight (Acharya et al., 2009; Braun & Latham, 2007). Rigorous performance monitoring using indicators like EBITDA and ROIC drives operational discipline, supporting value creation (Chemmanur et al., 2021; Walz & Truong, 2022).

H2. 

There is a positive relationship between leverage levels in LBOs and financial performance.

High leverage is a fundamental mechanism in LBOs, serving both disciplinary and strategic purposes. The regression results validate this hypothesis, with an Indebtedness coefficient of 4.4012 (p < 0.0001), showing that a one-unit increase in leverage increases transaction multiples by 4.4012 units. This supports Jensen’s (1989) argument that leverage reduces agency costs by constraining managerial discretion, fostering disciplined and innovative practices (Baker & Wruck, 1989; Denis, 1994). Debt structures with short maturities and restrictive covenants reinforce this discipline, while optimized financial structures, including tax shields, enhance profitability (Cotter & Peck, 2001). Private equity firms’ ability to secure favorable financing terms sustains portfolio companies’ cash flows.

H3. 

A higher EBITDA/EV ratio in LBOs is associated with improved financial performance.

The EBITDA/EV ratio, a key proxy for operational efficiency and cash flow generation, is a critical determinant of LBO performance. The regression results support this hypothesis, with an EBITDA/EV coefficient of 1.0970 (p < 0.0001), indicating that a one-unit increase in the ratio raises transaction multiples by 1.0970 units. Early studies reported significant cash flow improvements post-LBO, often exceeding industry averages by 20% (Kaplan, 1989a; Smith, 1990), a trend corroborated by more recent research (Ayash & Schütt, 2016). A higher EBITDA/EV ratio signals sustainable value creation through strong operational performance (Berezinets et al., 2022).

H4. 

Larger target firm size in LBOs is associated with improved financial performance.

The regression results do not support a significant relationship between firm size and LBO performance, with a Size coefficient of 0.0152 (p = 0.623), leading to the rejection of H4. This finding aligns with studies such as Harris et al. (2014) and Korteweg and Nagel (2015), which find no consistent link between firm or fund size and performance after controlling for factors like vintage year or managerial expertise. In the MENA context, heterogeneous institutional environments, including diverse regulatory frameworks and market dynamics, may limit the efficiency advantages of larger firms (Al-Malkawi et al., 2013). In contrast, Boucly et al. (2020) reported a positive size-performance relationship in European LBOs, likely due to more homogeneous markets or larger firm scales. This divergence may also reflect sampling biases in prior studies, where size effects were conflated with managerial or leverage factors (Rossi, 2019). Consequently, H4, which posited that larger target firm size enhances LBO performance, is rejected.

The MENA region’s economic and institutional diversity, spanning Gulf oil economies (Saudi Arabia, UAE, Qatar) and North African emerging markets (Morocco, Egypt, Tunisia), provides a unique context for analyzing ownership structures’ impact on leveraged buyouts (LBOs) amid geopolitical challenges and financial reforms, with growth projected at 2.6% in 2025. The banking sector reflects disparities: Gulf countries, with high solvency ratios (18.2% in Qatar, 20% in Saudi Arabia) and full Basel III adoption, lead in resilience, digitization (40% increase in electronic transactions in Morocco), and sustainable finance, while Lebanon faces a crisis with a 70% deposit loss and a 6% solvency ratio. Capital markets, boosted by initiatives like Vision 2030, attract LBO investments, but concentrated ownership (families, elites) and weak governance, compounded by corruption (average CPI of 39/100 in 2024, Transparency International), particularly in Yemen (16/100) and Lebanon (24/100), limit transparency and value creation, unlike stronger frameworks in the UAE (71/100) and Qatar (63/100).

For investors, the significant effect of managerial equity participation (MBO coefficient = 12.7362, p = 0.004) emphasizes incentivizing managers with equity to align interests, while private equity firms should enhance governance through board restructuring. Leverage (Indebtedness coefficient = 4.4012, p < 0.0001) boosts profitability, but sustainable regulations are needed to mitigate risks. The EBITDA/EV ratio (coefficient = 1.0970, p < 0.0001) highlights operational efficiency’s value, guiding investors to prioritize firms with strong cash flow, while the insignificance of firm size (p = 0.623) suggests smaller firms are viable LBO targets. Policymakers should strengthen legal frameworks, expand deposit insurance, and promote digitization to improve capital access, governance, and operational excellence, supporting regional growth and LBO success.

5. Conclusions

This study investigated the impact of ownership structure on the financial performance of leveraged buyout (LBO) transactions in the MENA region, an underexplored yet dynamic context for private equity markets. Using a sample of 233 completed LBO transactions from 2000 to 2023, the analysis examined how governance-related factors; managerial ownership, indebtedness, operational profitability (EBITDA/EV ratio), and target company size, influence value creation. The empirical findings validated three of four hypotheses. Managerial ownership significantly enhances financial performance (MBO coefficient = 12.7362, p = 0.004), confirming its role in aligning interests and reducing agency costs (Jensen, 1986). High indebtedness positively impacts performance (Indebtedness coefficient = 4.4012, p < 0.0001), acting as a disciplinary mechanism and tax shield (Jensen, 1989). Similarly, a higher EBITDA/EV ratio correlates with improved performance (coefficient = 1.0970, p < 0.0001), reflecting robust operational efficiency (Kaplan, 1989a). However, firm size showed no significant effect (Size coefficient = 0.0152, p = 0.623), aligning with studies that find no consistent size-performance link in LBOs (Harris et al., 2014).

The research encountered several limitations. Accessing reliable and consistent data on LBO transactions in the MENA region remains challenging due to their private and confidential nature, limiting sample size and generalizability. Moreover, the sample includes countries with a very low number of transactions (fewer than 10), such as Qatar and Iraq, while others like the UAE have over 100 transactions. This imbalance reflects the real distribution in the database, but excluding countries with minimal transactions would significantly reduce the sample size, thereby negatively impacting the robustness and reliability of the model and its results. The heterogeneous economic and regulatory environment across MENA countries further complicates isolating the causal effects of individual variables. Future research could explore the moderating role of institutional factors, such as investor protection, financial market maturity, or political stability, to better understand the conditions driving LBO success in diverse national contexts.

These findings offer practical implications and policy recommendations for investors and policymakers in the MENA region. The significant effect of managerial ownership suggests that policymakers should incentivize management co-investment in private equity deals to enhance interest alignment, for instance, through tax incentives or regulatory support for equity participation (Gompers et al., 2016). The positive impact of leverage underscores the need for policies that facilitate sustainable debt financing while mitigating risks, such as clear guidelines on debt-to-equity ratios. The importance of the EBITDA/EV ratio highlights the value of operational efficiency, encouraging investors to prioritize firms with strong cash flow potential and urging policymakers to support initiatives that enhance operational performance, such as training programs or innovation grants. The insignificance of firm size indicates that smaller firms are viable LBO targets, prompting policymakers to improve access to capital for diverse firm scales through financial market reforms. These recommendations advocate for strengthened corporate governance, optimized financing structures, and operational excellence to maximize LBO value creation in the MENA region.

To deepen research on LBO performance in the MENA region, several strategic avenues could be considered. It would be valuable for regulatory frameworks to evolve in a way that supports robust banking systems, thereby fostering sustainable financial leverage tailored to the diversity of markets. Additionally, private equity firms might place greater emphasis on managerial equity participation through thoughtfully designed incentive programs, aiming to better align stakeholder interests and enhance value creation, while also promoting strengthened corporate governance. Considering capital market reforms to improve transparency and broaden investment opportunities, particularly for smaller firms, also appears worthwhile. Furthermore, implementing enhanced anti-corruption measures, such as independent audits and increased public engagement; could help restore investor confidence in sometimes challenging environments. Finally, advancing digitization and expanding financial inclusion mechanisms, including deposit insurance, seem to be important levers to support economic growth and reinforce regional resilience.