M&As and Corporate Financial Performance: An Empirical Study of DAX 40 Firms

Abstract

This study examines the impact of mergers and acquisitions (M&As) on the financial performance of firms listed in Germany’s DAX 40 index. Although M&As are a widely used strategic tool intended to create value through synergies and market expansion, existing research provides conflicting evidence about their effectiveness. Using an empirical approach, we analyze the financial data of acquiring companies before and post-M&A transactions to evaluate changes in profitability, liquidity and solvency. Our findings suggest that financial performance does not universally improve following acquisitions. Instead, results vary significantly based on deal characteristics and internal management factors. These results suggest that, while M&A can be a pathway to growth, success depends heavily on the quality of execution and organizational integration. This paper contributes to the ongoing debate about the effectiveness of M&As and provides insights for corporate decision-makers, investors, and policy stakeholders.

1. Introduction

Mergers and acquisitions (M&A) represent a significant phenomenon in the global economy, exerting a substantial influence on a company’s operations, organizational structure, and financial landscape [1]. According to the Institute for Mergers, Acquisitions, and Alliances (IMAA) [2], since 1991, Germany has experienced a total of 68,488 M&A deals, amounting to a cumulative value of EUR 3.9 trillion. In 2022, despite a decline in both the number and value of deals, M&As persisted as a significant business activity, with a total value of EUR 54 billion [2]. This sustained level of activity underscores the critical role that M&As continue to play in corporate restructuring, consolidation, and strategic repositioning in Germany’s industrialized and export-oriented economy. Considering the size and influence of numerous German firms—particularly those included in the DAX 40—it is imperative for corporate decision-makers, shareholders, and policymakers to ascertain whether M&A transactions contribute positively or negatively to firm-level financial performance. This assessment is crucial for evaluating the broader implications for economic productivity, market competition, and long-term growth.

As a significant economic activity, M&A has drawn considerable attention from corporate finance scholars and other professionals over the years. Organizations engage in M&A for various reasons, with the primary aim often being the creation of value for the companies involved and their shareholders. This value creation is achieved through a range of strategies, including realizing synergies, enhancing market power, and entering new markets, among others [3]. Numerous scholars have centered their research on the theory of value creation in M&A, investigating whether these activities genuinely lead to value generation. Some researchers have specifically identified factors that contribute to successful outcomes and subsequent value creation in M&A transactions. Recently, there has been a shift in research focus towards exploring “soft” factors, such as corporate governance, management practices, and the influence of the acquiring company’s CEO [3,4,5,6,7].

However, the existing literature presents a variety of perspectives regarding the success of M&A transactions. Research findings related to post-M&A performance have been inconclusive, revealing mixed results [1]. For instance, Schoenberg [8] reports a success rate of roughly 50% for cross-border acquisitions, raising the important question of whether M&A truly generates value. Consequently, evaluating a company’s performance after an M&A is crucial for the organization, its shareholders, and other stakeholders. Such evaluations offer valuable insights into whether an M&A leads to value creation or erosion for the company. Researchers employ a diverse array of methods to measure value creation, encompassing both market and accounting measures while considering both short-term and long-term implications. Although academics often prefer market measures due to inherent limitations in accounting metrics, it is important to recognize that accounting measures still possess value due to their reliability [9].

Despite this extensive body of research, several significant gaps remain. Notably, there is a lack of empirical evidence examining the post-M&A financial performance of large-cap firms within a single, well-regulated market context—such as Germany’s DAX 40 index—using accounting-based measures of profitability, liquidity, and solvency. Most studies tend to prioritize cross-country comparisons or stock market reactions, thus overlooking the operational and balance sheet dynamics at the firm level. Moreover, the literature lacks consensus on whether M&A activity strengthens or weakens firms’ financial foundations over time, especially concerning long-term solvency. These gaps are particularly pronounced in longitudinal analyses based on standardized financial reporting environments, which are essential for identifying performance patterns that are unaffected by variations in accounting standards or regulatory frameworks. This study aims to fill this gap by focusing on the internal financial consequences of M&A transactions among DAX 40 companies. It will provide a more comprehensive understanding of whether such strategic moves lead to improved or deteriorated financial performance.

Given the economic significance and visibility of DAX 40 companies, it is essential to examine whether M&A transactions contribute to improved financial stability and efficiency. This perspective addresses a crucial gap by focusing not only on the strategic rationale for M&A but also on its tangible financial consequences within a highly standardized reporting environment. Since previous studies have yielded inconclusive results, this paper aims to fill the gaps in existing research and answer the following question: “How do mergers and acquisitions impact the financial performance of companies included in the DAX 40 index?”

To investigate the aforementioned question, the present study employs a variety of statistical tools, including descriptive statistics, paired sample t-tests, and ordinary least squares (OLS) regression analysis. These methods are frequently employed in analogous studies to compare the performance of firms before and after M&A transactions. The fundamental premise of this study is that the occurrence of M&A activity exerts a detrimental influence on the financial soundness, solvency, and liquidity of firms. This observation signifies the company’s inefficiency in generating additional value through M&A transactions, as evidenced by the absence of quantifiable outcomes based on accounting metrics.

The paper’s structure is delineated as follows. The subsequent section delineates the theoretical underpinnings of this study, presents a comprehensive review of the extant literature, and outlines the formulation of the study’s hypothesis. The third section details the methodology, including the procedures for data collection, the sample size, and the research design. The fourth section is devoted to the presentation of the results. In the fifth section, the discussion is outlined, followed by the conclusion.

2. Literature Review

2.1. Rationale for Mergers and Acquisitions: From Strategy to Value

Companies engage in M&A activities for a variety of reasons, including the enhancement of market power, entry into new markets, the acquisition of new customer bases, the mitigation of supplier risks, and the expedited realization of cross-selling opportunities [3]. Additional motivations encompass the realization of synergies and considerations related to taxation [10]. In a broader context, M&A transactions represent a significant avenue for companies to pursue external growth, thereby facilitating corporate development [11]. The primary objective of M&A is generally perceived as creating value. However, the literature reveals differing perspectives regarding the success of M&A transactions. For instance, a study by Schoenberg [8], which investigated the comparability of four measures of acquisition performance, concluded that each metric revealed an average success rate for cross-border acquisitions of approximately 50%. This statistic raises the critical question of whether M&A truly generates value. M&As have long served as fundamental strategies for corporate growth, restructuring, and diversification. Typically, a company’s expansion is correlated with value generation. Among the numerous theoretical approaches explored by scholars in the M&A field, the concept of value creation stands out prominently. Researchers have consistently examined whether M&A transactions effectively lead to value generation. This value creation in M&A arises during the post-merger or acquisition phase, where the newly formed entity produces a value that exceeds the combined worth of the individual entities involved. Studies in this area frequently indicate that acquiring firms are more likely to destroy value than to create it. Nevertheless, according to Alexandridis et al. [4], a significant shift in this trend occurred following the financial crisis of 2008. It appears that post-crisis M&A transactions have experienced positive market reactions, yielding positive abnormal returns for shareholders and generating higher synergy gains. Alexandridis et al. challenge entrenched narratives about M&A failure. They present strong evidence that in the post-2009 era, especially for public and large deals, well-governed acquiring firms have achieved significant announcement gains and realized synergies. This highlights the critical role of governance reforms in transforming acquisition outcomes. The findings suggest that focusing on public, sizable transactions, combined with rigorous governance frameworks, yields stronger acquirer performance.

Overall, M&A transactions have been shown to create value, potentially attributable to improvements in corporate governance that enhance investment decisions. Firms derive value from the synergies realized post-M&A transactions, often opting for these deals to leverage value through financial synergies. For example, Williamson and Yang [12] suggest that financially constrained firms—those that encounter challenges in accessing external capital for investments—could benefit from acquiring less financially constrained firms. Their findings indicate that, two years post-acquisition, the acquiring firms experience reduced financial constraints compared to non-acquiring firms facing similar constraints. This trend is particularly pronounced in diversifying acquisitions. Moreover, the results demonstrate that acquiring firms tend to allocate higher capital expenditures and R&D investments after the transaction, surpassing the investments made by unconstrained firms. This reflects an enhanced capacity to access capital and a reduction in financial constraints. Further support for the financial synergy hypothesis is provided by the study conducted by Cornaggia and Li [13]. Their findings indicate that companies experiencing financial constraints often pursue M&A transactions with targets that enjoy superior access to capital. This strategic approach enables them to widen their credit accessibility and lessen the costs associated with obtaining credit, aligning with the concept of financial synergy. Companies engage in M&A activities not solely for financial synergies but also to achieve operational synergies. Rabier [14] argues that acquisitions targeting operational synergies, such as those aimed at achieving economies of scale and fostering innovation, entail a higher level of risk compared to those focused on financial synergies. Consequently, these transactions have the potential to either create significant value or lead to substantial value erosion, often culminating in negative outcomes.

Concerning innovation, Hanelt et al. [15] in their study on digital M&As suggest that firms rooted in traditional industries can create value and enhance business performance by developing a robust digital knowledge base through what they term digital M&As. Consequently, mergers and acquisitions emerge as a viable strategy for traditional firms to broaden their digital knowledge and stimulate digital innovation, ultimately leading to significant value creation. Continuing the focus on innovation, Hsu et al. [16] propose that companies located in countries with low levels of innovation actively pursue cross-border M&As with high-innovation targets, aiming to improve their technological standing. As a result, these firms increase their R&D investment levels following the M&A. Furthermore, the study demonstrates that these firms achieve greater returns from cross-border M&A ventures compared to their domestic transactions, thereby unlocking valuable opportunities for creation. Additionally, Delis et al. [5] propose an alternative approach to generating operational synergies: enhancing management practices to foster value creation. Specifically, companies that excel in management tend to acquire businesses facing managerial challenges with the intention of improving the skills and capabilities within the acquired organization, thereby generating additional value.

The reviewed literature indicates that M&A can create value through various mechanisms, including financial and operational synergies, better access to capital, and innovation-driven growth. However, realizing this value is not guaranteed and depends on several internal and external factors, such as corporate governance, managerial capabilities, and the strategic alignment between the acquiring and target firms. These factors not only influence the success of the integration process but also determine how much value is reflected in the financial performance of the acquiring company. Therefore, understanding the drivers of successful M&A is crucial for interpreting its impact on firm-level outcomes like profitability, liquidity, and solvency. The next section will focus on the key factors influencing the success or failure of M&A transactions, providing a foundation for analyzing how value may materialize or fail to materialize in financial terms.

2.2. Determinants of M&A Outcomes: Governance, Leadership, and Strategic Fit

While the primary objective of M&A is value creation through synergy, not all firms succeed in achieving this goal through such endeavors, resulting in numerous instances of M&A initiatives failing. Many studies have sought to identify the factors that determine the success or failure of M&A transactions. Renneboog and Vansteenkiste [3] conducted a study focusing on recent publications with the aim of delineating the factors that influence the outcomes of mergers and acquisitions. Their findings highlight three main factors that impact the results of M&A endeavors. Firstly, they observed that inadequate governance within the acquiring firm, particularly CEO overconfidence, significantly contributes to underperformance in M&A transactions. Additionally, their research indicates that acquisitions centered on related or focused targets tend to yield superior performance compared to those that are unrelated or diversifying. This superiority arises from the former’s possession of essential skills and resources that are crucial for the effective integration of the acquired firm. Secondly, they noted that a positive deal outcome is often associated with shareholder intervention, especially through mechanisms such as the voting system, active monitoring, and advisory engagement.

Expanding on the notion of factors contributing to M&A success, Hu et al. [7] elaborate on the role of the acquirer’s experience. Their study reveals that experienced acquirers, who are familiar with M&A practices, play a vital role in facilitating the acquisition process, particularly in so-called mega deals, thereby potentially resulting in value creation for shareholders. In accordance with this research, recent studies have further examined the critical role of management in influencing the success of M&A activity. Firstly, Fich and Nguyen [6] illustrate in their research that CEOs possessing specialized expertise in supply chains demonstrate an ability to secure advantageous deals, yielding higher returns for shareholders and improved accounting returns. This capability stems from their proficiency in navigating asymmetric information throughout the transaction, allowing for a more accurate valuation of the target company. Consequently, this ability leads to greater surplus and better synergies, as well as enabling the negotiation of more favorable merger terms. Secondly, Delis et al. [5] suggest that the impact of management practices surpasses that of firm characteristics and corporate governance in determining the success of M&A transactions. Firms with robust management practices are more inclined to engage in a higher volume of M&A activities, often targeting companies with suboptimal management to drive strategic enhancement and value creation. Once again, these findings reflect the short-term performance of M&A transactions.

Alexandridis et al. [4] recognize corporate governance and its enhancement in investment decision-making as critical elements contributing to M&A success and subsequent value creation for shareholders, particularly in the aftermath of the 2018 financial crisis. Cao et al. [17] discovered that board composition significantly influences M&A success. Their study, conducted in the United States, indicates that boards with foreign members tend to achieve less favorable outcomes in cross-border M&A transactions. This outcome may be attributed to potential disparities in values and communication challenges among members from different countries, factors which adversely affect the decision-making process. Finally, according to Wangerin [18], another important factor that can positively or negatively affect the outcome of M&A transactions is the process known as due diligence. Due diligence is a procedure undertaken by the acquirer prior to an acquisition in order to obtain vital information, thereby gaining a better understanding of the benefits and risks of the potential project, ultimately leading to a renegotiation of the terms of the transaction. Among various findings, Wangerin concluded that reduced levels of due diligence are correlated with decreased post-transaction profitability.

The analysis of factors linked to successful M&As reveals a recent trend among scholars that emphasizes the significance of so-called soft factors associated with individuals within the company, ranging from the CEO’s role to management and shareholders. An exception to this trend is found in a recent study conducted by Zhang et al. [19], which diverges from prior analyses by highlighting the importance of so-called hard factors. Contrary to previous findings, this study asserts that corporate governance does not significantly impact post-M&A success; instead, it emphasizes the significance of factors such as firm growth potential, assets, size, and age in determining M&A success. Moreover, it is crucial to note that while many of these studies uncover the factors contributing to short-term success in M&As, they provide limited insights into the long-term success of such transactions.

The reviewed studies highlight that the success of M&A transactions extends beyond mere strategic alignment or prevailing market conditions. Instead, it is significantly shaped by firm-specific factors, including CEO characteristics, managerial practices, the quality of governance, and the thoroughness of due diligence processes. These elements, while primarily linked to the nuances of deal execution and the integration of businesses post-acquisition, also exert substantial influence on financial performance in the aftermath of M&A activities.

For instance, a lack of rigorous due diligence has been correlated with a decline in profitability [18], suggesting that hasty or superficial evaluations can undermine potential gains. Similarly, the expertise of a CEO has been associated with more favorable accounting returns, indicating that leadership qualities can play a pivotal role in realizing the anticipated benefits of an acquisition [6]. However, it is crucial to note that most of these findings primarily address short-term outcomes, which limit our understanding of the mid-term financial implications of such transactions.

This observation raises a vital question: does the presence or absence of these influential factors lead to sustained changes in core financial metrics such as profitability, liquidity, and solvency over time? To address this gap in the literature, it becomes essential to evaluate M&A performance through a mid- and long-term lens, focusing on how financial metrics can effectively capture the lasting impact of mergers and acquisitions on a firm’s overall performance. The subsequent section will delve into this evaluation, emphasizing the need for comprehensive analysis that goes beyond immediate results to understand the enduring effects of M&A decisions.

2.3. Multi-Dimensional Evaluation of Post-M&A Financial Performance

2.3.1. Conceptual Framework of M&A Performance Evaluation

How is success measured and how is value creation assessed post-M&A? The evaluation of post-transaction performance can take various forms to determine the actual value generated. Scholars typically assess the outcomes of M&A either in the short term or the long term. Short-term performance reflects market expectations regarding the value created by the M&A deal. However, these measurements, which are based on expectations, can fluctuate and may diverge from the actual long-term outcomes over time [3].

Scholars typically assess the short-term performance of M&A through event studies, concentrating on the wealth effects experienced by shareholders. This assessment often employs metrics such as Cumulative Abnormal Return (CAR) and Abnormal Return (AR). In this context, Garcia and Herrero [20] establish a correlation between board composition and M&A transactions, noting that characteristics such as board size and CEO duality are positively associated with value creation and market reactions. They utilize event studies to analyze AR and CAR, allowing them to observe the market’s response to M&A activities. Similarly, Dandapani et al. [21] examine the value creation of U.S. firms during their initial cross-border acquisitions. Using CAR, they demonstrate that investors appreciate companies’ globalization efforts. Notably, they find that the AR associated with a firm’s first foreign acquisition is 1% higher than that of subsequent M&As.

While short-term measures provide valuable insights and serve as foundational elements for studies investigating the connections between M&A transactions and various influencing factors, they deliver limited information regarding the lasting impact of M&As on a company’s long-term performance. Malmendier et al. [22] emphasize that short-term gains following a merger announcement do not necessarily predict long-term success. Their research examined companies involved in closely contested mergers, revealing that both the winning and losing firms displayed similar stock market performances in the months leading up to the deal announcement. However, a significant difference emerged post-merger: the companies that lost these contests outperformed their winning counterparts by 24%. This finding is risk-adjusted, indicating that it cannot be solely attributed to the risk profiles of the winning companies. This underscores the importance of considering long-term outcomes when evaluating the success of an M&A. It is possible that new opportunities or challenges arising from the merger remain hidden from the market initially. Therefore, a thorough analysis of long-term performance is essential, as short-term gains may not capture all the impacts of an M&A, such as the compatibility of the merging entities or the effective functioning of the market itself [22].

Assessing long-term performance is inherently complex, particularly in isolating the specific impact of M&A from other factors that can influence a firm’s performance years after the deal. Typically, long-term performance evaluations involve analyzing stock returns or utilizing accounting metrics such as Return on Assets (ROA), cash flow, headcount growth, operating margins, and related measures [1]. An illustrative example of using stock returns to assess long-term performance is found in the study by Leledakis and Pyrgiotakis [23], which examined the influence of the Dodd–Frank Act on mergers among U.S. banks. Their research revealed the positive effect of the Dodd–Frank Act on smaller bank mergers, indicating increased profitability, reduced expenses, and higher abnormal returns for these institutions following the Act’s enactment.

Conversely, Renneboog and Vansteenkiste [1] conducted a comprehensive review of M&A studies related to long-term performance and concluded that M&A activities often detract from, rather than enhance, a company’s long-term value, resulting in value destruction. Notably, many studies conducted between 2000 and 2013 reported negative returns. Although there is inconsistency in the literature, three primary theories emerge to explain these findings: first, M&As frequently involve overpayment, adversely affecting earnings per share; second, the true value of an acquisition may take time to become apparent as market corrections unfold gradually; and third, methodological issues complicate comparisons between short-term and long-term outcomes.

To date, researchers have predominantly analyzed long-term financial performance following M&A transactions using market value. This preference is largely due to the limitations associated with relying solely on accounting measures. According to Thanos and Papadakis [9], using only accounting metrics constrains the analysis by overlooking non-financial factors that can significantly influence M&A outcomes. Traditional accounting measures primarily focus on profitability, potentially neglecting important non-financial motivation behind M&A decisions. Furthermore, accounting metrics rely heavily on data sourced from annual financial statements, which necessitates the use of reliable data that may not always be available across different countries. Variations in accounting standards globally can also complicate comparisons of results. These challenges may account for the relative scarcity of studies in leading business journals that focus on M&A performance using accounting measures. Nonetheless, the authors maintain that accounting-based measures are still very reliable, as they are grounded in actual performance documented in companies’ financial statements, enabling assessments of not only profitability but also the efficiency and effectiveness of firms.

The recent literature has introduced various methodologies for measuring performance through accounting metrics. One prominent approach involves the use of operating performance to evaluate long-term outcomes. For example, Ferris and Sainani [24] defined operating performance as the sum of operating income, depreciation, interest expenses, and taxes relative to the market value of assets. Their research examined post-M&A performance and specifically explored the relationship between positive performance outcomes and the involvement of a Chief Financial Officer (CFO) during the M&A process. The findings indicated that influential CFOs positively impacted the firm’s long-term operating performance. Dong and Doukas [25] explored the relationship between managerial competence and post-M&A long-term performance by analyzing operating performance, defined as operating income after depreciation divided by total assets. Their findings indicated that there was a positive correlation, demonstrating that skilled managers contribute significantly to enhanced performance following M&A transactions. In contrast, Trujillo et al. [26] employed a distinct methodology to assess the financial performance of U.S. public pharmaceutical companies engaged in M&A activities. Their research utilized a difference-in-difference analysis, focusing on both short-term performance (one year post-M&A) and long-term performance (four years post-M&A). They scrutinized various financial indicators, including profitability, efficiency, and revenue, and key financial ratios such as asset turnover ratio, ROA, and equity ratio. The results revealed that companies involved in mergers and acquisitions experienced a decline in revenue, although profit levels remained stable.

To systematically investigate these diverse effects and clarify the connection between value creation and financial performance post-M&A, this study adopts the following conceptual framework (Figure 1).

The conceptual framework illustrates the rationale behind this study. It is based on the idea that M&A are primarily conducted to create value through mechanisms such as corporate growth strategies, financial synergies, and operational synergies. However, the realization of this value depends on various factors that influence the success of M&A transactions, including governance, managerial expertise, and strategic alignment. The effectiveness of these transactions is ultimately assessed through key dimensions of financial performance: profitability, liquidity, and solvency. This framework guides empirical investigation by connecting the theoretical motivations for M&A to measurable financial outcomes at the firm level.

2.3.2. Profitability as a Measure of M&A Success

Profitability serves as one of the most widely utilized indicators for assessing the success of M&A, particularly regarding mid- and long-term value creation. Accounting-based metrics, including ROA, Return on Equity (ROE), and Net Profit Margin (NPM), rely on firms’ actual financial statements, providing a transparent and comparable method to monitor operational and financial performance over time. These indicators are instrumental in determining whether the anticipated synergies from M&A—such as cost reductions, enhanced operational efficiency, or improved strategic positioning—result in stronger bottom-line performance. However, despite the theoretical underpinnings suggesting an increase in profitability following M&A, empirical studies present mixed outcomes. Many instances reveal minimal to no improvement, and some even demonstrate a decline in profitability indicators.

Bedi [27] conducted an analysis of five significant mergers and acquisitions involving Indian telecom companies from 2008 to 2018. This study compared key financial metrics—such as profitability, liquidity, and solvency—two years before and after each transaction, utilizing accounting ratios and paired t-tests. While specific firms, such as Vodafone and Tata, reported individual increases in earnings per share (EPS) and returns on capital employed (ROCE), the overall average profitability ratios—including NPM, EPS, ROCE, and return on net worth (RONW)—showed a decline following the M&A activities. The statistical analysis indicated no substantial improvement in financial performance. Despite possessing the potential for synergies, acquiring companies were unable to achieve enhanced profitability. This suggests that the expected benefits from consolidation either did not materialize or were offset by integration costs and inefficiencies.

In another study, Yang and Ai [28] examined 1340 cross-border M&A deals executed by Chinese high-tech firms from 1990 to 2014, focusing on both acquiring and target companies. This research assessed three performance metrics over a two-year period following an acquisition: two accounting-based profitability measures (ROA and Return on Sales, or ROS) and one measure of innovation (intangibles). For the acquiring firms, neither ROA nor ROS exhibited statistically significant improvement post-M&A activities (ROA: t = −2.97; ROS: t = −1.80; p > 0.05). These findings do not support the hypothesis that value is created through accounting measures. In contrast, innovation performance, as measured by intangibles, displayed significant improvement (t = 4.62, p < 0.001), indicating value creation in non-financial terms. The authors interpret this trend as indicative of strategic asset-seeking behavior, suggesting that Chinese acquirers are more inclined to prioritize building capabilities and fostering innovation rather than seeking immediate profit gains. As a result, any financial returns may be delayed or less visible in traditional accounting metrics.

Additionally, a study conducted by Landoni [29] analyzed 446 cross-border acquisitions of Italian firms from 2005 to 2015 and found that foreign takeovers generally exerted a positive influence on profitability. Over the evaluation period from 2013 to 2022, numerous acquired firms reported increases in net income, implying that foreign ownership often leads to improved financial performance. These findings challenge the prevailing concerns associated with the negative repercussions of foreign control and suggest that, in many instances, profitability can benefit from the international integration and operational restructuring that typically follows an acquisition.

The research by Rachman et al. [30] focused on the financial performance of 19 manufacturing firms listed on the Indonesia Stock Exchange (IDX) that engaged in M&A between 2017 and 2021. To assess profitability, the authors concentrated on two primary indicators: NPM and ROE. Due to the non-normal distribution of data, they employed the Wilcoxon signed-rank test for analysis. The findings revealed that no statistically significant changes were detected in NPM between the pre- and post-M&A periods, indicating that M&A activities did not enhance operational profitability. However, a statistically significant decline occurred in ROE in post-M&A, suggesting a drop in shareholder profitability. This indicates that M&A initiatives may have diluted returns or failed to generate sufficient value to justify the investments made.

Overall, these results suggest that, at least within the Indonesian manufacturing sector, M&A activities tend to negatively impact profitability. M. Celestin’s study [31] analyzed ROA and ROE in conjunction with revenue growth as a proxy for operational success following M&A. The findings revealed that both ROA and ROE increased following M&A, with these improvements being statistically significant according to t-tests (p < 0.05). This suggests that the profitability gains observed were not merely coincidental.

The insights derived from the reviewed studies underscore how the effect of M&A on profitability is not consistently favorable. Therefore, these studies provide empirical support for Hypothesis 1.

Hypothesis 1.

DAX 40 companies that engage in mergers and acquisitions are expected to exhibit a decrease in profitability following the M&A compared to their pre-M&A performance.

H0: Ppost ≥ Ppre

H1: Ppost < Ppre

Ppost = profitability position post-M&A

Ppre = profitability position pre-M&A

2.3.3. Liquidity Dynamics Following M&A Transactions: A Review of Empirical Insights

The impact of M&A on corporate liquidity is a critical yet debated aspect of financial performance following such transactions. Theoretical models often suggest that M&A activities can improve operational efficiency and resource optimization, ultimately enhancing liquidity. However, empirical research presents a more complex picture. Liquidity, typically measured by indicators like the current ratio and quick ratio, is essential for determining a firm’s short-term solvency and its ability to meet ongoing financial obligations. Post-merger liquidity outcomes can be influenced by various factors, including the structure of the transaction, integration-related costs, industry characteristics, and the quality of governance. This section summarizes recent empirical studies to evaluate whether M&A activity leads to an increase or decrease in liquidity. This analysis sets the stage for assessing the hypotheses regarding the liquidity effects of M&A transactions among DAX 40 companies.

In his analysis of the impact of M&A on a company’s financial performance, Bedi places strong emphasis on the assessment of liquidity, which serves as a vital indicator of a firm’s short-term financial health. Focusing on prominent Indian telecom companies engaged in M&A activities from 2008 to 2018, Bedi utilizes two accounting ratios: the current ratio and the quick ratio. The current ratio, calculated as the ratio of current assets to current liabilities. It illustrates a firm’s overall capacity to meet its short-term obligations using the entirety of its short-term resources. In contrast, the quick ratio, often regarded as a more rigorous measure of liquidity, hones in on those assets that can be more readily converted into cash—such as marketable securities and receivables—while deliberately excluding inventories. This measure assesses a firm’s immediate ability to fulfill its current liabilities without relying on the sale of inventory, thus providing a clearer picture of liquidity in urgent scenarios. Bedi’s findings indicate that, on average, the liquidity positions of the companies surveyed did not show significant improvement following M&A activity. This outcome suggests that contrary to expectations of enhanced working capital management or optimally improved cash flow post-merger, the anticipated positive effects were either not realized or were counterbalanced by challenges related to integration and transitional inefficiencies.

In contrast to Bedi’s conclusions, Aggarwal and Garg [32] offer a different perspective based on their study of M&A transactions across a broader corporate landscape. Their research provides empirical evidence of a substantial enhancement in liquidity metrics within a three-year period following the completion of M&A deals. Both the current ratio and the quick ratio in their sample demonstrated significant increases. This indicates that firms not only improved their operational efficiency but also gained better cash management capabilities and an enhanced ability to meet short-term obligations. These contrasting findings highlight the variability in M&A outcomes and suggest that post-merger liquidity performance may depend on contextual factors such as industry characteristics, deal structure, and the quality of integration planning.

Monaco et al. [33] examine how various payment methods in private firm acquisitions impact the acquiring firm’s stock market liquidity and trading behavior during the announcement period. They find that pay-ahead deals, such as cash and earnouts, result in smaller declines in liquidity—measured by bid-ask spreads—compared to equity or mixed payment deals. Although all payment types cause a dip in liquidity, cash and earnout deals help mitigate this effect by signaling lower information asymmetry and reduced adverse selection risk. This suggests that transactions announced with cash or earnouts experience less deterioration in liquidity than those financed with equity or mixed payments. Given that liquidity is crucial for operational resilience—enabling firms to access capital and manage their obligations—this evidence highlights the significance of M&A financing structure in maintaining post-merger financial stability.

Dogan and Ugurlu [34] examined a total of 3306 global acquisitions that occurred between 2010 and 2019. They tracked sixteen financial ratios over the twenty quarters preceding the acquisitions and eight quarters following them. They found that liquidity ratios showed significant fluctuations after the acquisition, with median declines observed in both current and quick ratios among target firms. Also, cash acquisitions, particularly smaller deals, generally result in more stable liquidity. In contrast, non-cash transactions, especially those involving larger amounts, often lead to greater deterioration in liquidity.

The study of Sendilvelu et al. [35] explores the shifts in financial performance, specifically focusing on liquidity, before and after M&A transactions within a global sample of B2B companies. Liquidity is primarily evaluated using the current ratio (CR) and the quick ratio (QR). The analysis revealed that, on average, post-merger liquidity ratios (CR and QR) did not show significant improvements across the entire sample. In certain sectors, such as energy and industrials, there was a notable trend towards a slight decline in liquidity ratios following M&A transactions. However, the significance of these findings varied and was not universally consistent. The authors suggest that this stagnation or decline in liquidity could reflect the challenges faced during the integration process. These challenges may temporarily impact short-term financial flexibility due to increased current liabilities or cash pressures related to M&A activities. These insights highlight the importance of careful financial planning and management during mergers and acquisitions to mitigate potential short-term liquidity challenges and ensure smoother integration.

Wu et al. [36] conducted comprehensive analysis of domestic M&A in China from 2008 to 2021. They utilized methods such as difference-in-differences, instrumental variables, and propensity score matching to identify the causal effects on the liquidity of acquirers. The results showed that M&A deals incorporating performance commitment clauses—such as earn-outs, revenue or earnings guarantees, or other contingent conditions—were significantly linked to lower liquidity levels. This effect was observed in acquiring firms in the years following the deals. The authors described this phenomenon as a “contingent cash crunch.” These clauses often lead to increased managerial optimism and cash reserves, causing firms to hoard cash and reduce discretionary liquidity. This effect was particularly pronounced in non-state-owned firms, those with weaker governance, lower reporting quality, or financial constraints.

Considering the contrasting results mentioned above, and our assumption M&A negatively impacts companies’ financial performance, this paper proposes the following hypothesis:

Hypothesis 2.

DAX 40 companies that undertake mergers and acquisitions are expected to observe a decline in their liquidity position after the M&A compared to the pre-M&A period.

H0: Lpost ≥ Lpre

H1: Lpost < Lpre

Lpost = liquidity position post-M&A

Lpre = liquidity position pre-M&A

2.3.4. Post-Merger Solvency Outcomes: Assessing Long-Term Financial Stability

While profitability and liquidity are frequently used to evaluate post-merger performance, solvency—a firm’s capacity to meet its long-term obligations—constitutes an equally critical dimension in assessing the sustainability of M&A outcomes. Solvency reflects the extent to which companies rely on external financing versus internal equity and serves as a determinant of financial resilience in the face of economic fluctuations or strategic missteps. M&A transactions, especially those financed through debt, have the potential to significantly alter the capital structure of acquiring firms. A deterioration in solvency post-M&A may indicate increased financial vulnerability, potentially undermining the strategic rationale for the merger. Conversely, an improvement in solvency would suggest sound financial management and effective integration planning.

In his empirical investigation of Indian telecom firms, Bedi [27] includes solvency as a central component of post-M&A financial evaluation. The author employs the debt-to-equity (D/E) ratio and the Interest Coverage Ratio (ICR) to assess the firms’ ability to manage long-term debt obligations. His findings reveal no substantial improvement in solvency following M&A activity, suggesting that mergers may not inherently enhance a firm’s long-term financial standing. Instead, they may introduce additional debt burdens that counteract expected synergies.

By contrast, Aggarwal and Garg [32] identify a notable rise in the D/E ratio in nearly half of the companies analyzed within three to five years post-M&A execution. This increase reflects a growing reliance on debt, which may undermine solvency. Although their study does not explicitly test the statistical significance of this increase across the full sample, the trend aligns with the hypothesis that M&A can weaken the solvency position of acquiring firms over time.

Mack et al. [37], employing a panel regression model on M&A activity in the defense sector, also offers indirect support for this concern. While solvency ratios are not directly reported, the study highlights a pattern in which debt-financed acquisitions lead to a short-term rise in corporate leverage. The authors suggest that this increase may be attributable to the structure of financing deals and the cyclical nature of defense spending. This reinforces the potential for temporary solvency deterioration following M&A.

More definitive evidence comes from Adhikari et al. [38], who examine the solvency outcomes of two Nepalese commercial banks engaged in M&A between 2013 and 2020. Utilizing twelve accounting ratios with a focus on leverage metrics, the study reports statistically significant increases in debt ratios and a concurrent reduction in equity buffers post-merger. These shifts imply a tangible weakening of solvency positions, indicating that the banks became more leveraged and less financially stable after the transaction. The authors suggest that integration costs and restructuring demands may have led to greater reliance on debt financing.

Kahil [39] investigates a sample of eighteen Pakistani firms to assess pre- and post-M&A financial performance, with special attention to solvency metrics. The analysis centers on the D/E and Total Liabilities to Total Assets (TL/TA) ratios. While the increase in these ratios post-merger was statistically insignificant, the average D/E ratio more than doubled—from approximately 6.4 to 13.2. Although the lack of statistical significance tempers the generalizability of these results, the directionality supports concerns about increasing debt dependency and potential solvency strain following M&A.

Further corroboration is provided by Zuhri et al. [40], who apply the Wilcoxon signed-rank test to evaluate the pre- and post-merger financial statements of twenty Indonesian companies. The authors report a statistically significant deterioration in D/E ratios, signaling heightened financial leverage and declining long-term stability in the post-M&A period. The findings indicate that M&A activities in the observed sample contributed to increased reliance on borrowed capital, thereby weakening overall solvency profiles.

Taken together, these studies suggest that the impact of M&A on solvency is context-dependent but often leans toward deterioration, particularly in cases involving aggressive financing or weak integration. In line with the overarching assumption of this research—that M&A strategies may pose risks to financial health rather than guarantees of improvement—the following hypothesis is proposed:

Hypothesis 3.

DAX 40 companies engaging in mergers and acquisitions are expected to demonstrate a decrease in their solvency position after the M&A, in comparison to their pre-M&A period.

H0: Spost ≥ Spre

H1: Spost < Spre

Spost = solvency position post-M&A

Spre = solvency position pre-M&A

3. Methodology

3.1. The Choice of Methodology

The selection of the methodology employed in this paper originates from a thorough exploration of previous studies committed to analyzing M&A activities and their impact on companies’ financial performance. When assessing short-term performance, market measures are often preferred, while long-term evaluations typically involve analyzing stock returns or employing various accounting metrics. However, there is no unanimous consensus regarding the most effective means of measuring financial performance. In this specific context, the assessment of the impact of M&A on company value prioritizes the examination of financial and accounting data over stock-related outcomes.

This strategic choice facilitates an in-depth evaluation by relying on established accounting performance metrics, thereby contributing to a comprehensive understanding of the subject matter. Studies that emphasize accounting, as is the case here, frequently involves analyzing accounting data to evaluate the wealth impact of M&A activities. These analyses typically commence with an assessment of shifts in profitability, followed by an examination of liquidity and solvency positions, thus providing a holistic view of the financial situation.

Upon reviewing pertinent studies, it becomes evident that a range of statistical techniques have been employed to investigate the impact of M&A on company performance. Many of these studies have utilized statistical methodologies such as descriptive data analysis, paired sample t-tests, and ratio analysis to explore the effects of M&A activities on various dimensions of company performance. For instance, Aggarwal and Garg [32] conducted a study where they computed the means of several financial ratios before and after M&A, followed by a comparison of these ratios. Furthermore, they employed paired sample t-tests to assess the significance of the changes observed in the profitability, liquidity, and solvency positions of the companies examined. Likewise, Muhammad et al. [41] utilized descriptive statistics and ratio analysis both pre- and post-M&A for comparative purposes. To enhance the robustness of their study, they expanded their analysis to include regression analysis in addition to paired sample t-tests. In contrast, Bedi [27] and Abbas et al. [42] primarily relied on ratio analysis and paired sample t-tests. Specifically, Bedi [27] focused on comparing the average financial performance ratios before and after acquisitions, evaluating the statistical significance of the observed changes. Similarly, Abbas et al. [42] employed ratio analysis and sample t-tests in their research.

In this study, various statistical tools were also utilized. Descriptive statistics were calculated, and the means of the ratios served as a basis for comparison and ratio analysis. Additionally, a correlation matrix was generated to examine the relationships between profitability, solvency, and liquidity. Moreover, to test the hypothesis, two approaches were applied. Initially, in accordance with their examination of other sources, the researcher employed a paired sample t-test technique. However, the results obtained were deemed unsatisfactory. As a result, a decision was made to utilize ordinary least squares (OLS) regression analysis to assess the robustness of the relationships between variables and strengthen the foundation of the study. The use of regression analysis permitted the author to work with a larger volume of data within the dataset, specifically considering years in which no M&A activities occurred, thereby facilitating a more comprehensive comparison. In this context, the analysis is enriched by comparing effects before and post-M&A events, including periods both with and without such events. Additionally, although ordinary least squares (OLS) regression is methodologically appropriate for this dataset, we recognize the importance of testing underlying assumptions. In our model diagnostics, we verified the linearity and normal distribution of residuals and examined multicollinearity among variables. While heteroscedasticity was not a major concern, further robustness checks such as bootstrapped standard errors or alternative model specifications may enhance reliability in future applications.

While panel regression with fixed effects is a commonly used method in corporate finance research to control for unobserved heterogeneity across firms, it is not suitable for this analysis for several methodological and structural reasons. First, the dataset used in this study is event-based and includes financial observations for each firm at only two time points—before and after an M&A transaction. Panel regression with fixed effects typically requires a sufficiently long time series for each cross-sectional unit to effectively isolate time-invariant firm-specific characteristics. With only two data points per firm, fixed effects estimation would absorb nearly all the variation in the data, making it impossible to identify the impact of the explanatory variables on the dependent financial performance indicators.

Second, the dataset is unbalanced regarding the timing of observations, as the M&A events are not synchronized across firms. Thus, the data structure does not constitute a conventional panel that follows firms over a consistent time interval; rather, it is a collection of firm-level financial snapshots organized relative to M&A events. Consequently, the assumptions that underlie fixed effects estimation—particularly the requirement for consistent temporal ordering and repeated observations—are not met.

Third, this study is explicitly designed to assess within-firm changes in financial performance surrounding M&A events. The analytical focus is on the overall effect of M&A across firms rather than estimating heterogeneous firm-specific responses over time. Since each firm serves as its own control by comparing pre- and post-M&A values, the primary sources of time-invariant firm-level heterogeneity are already implicitly accounted for in research design. In this context, fixed effects modeling would provide limited additional benefits while significantly reducing degrees of freedom and statistical power.

For these reasons, OLS regression is a more appropriate and effective methodological choice. It allows for the estimation of average changes in financial indicators attributable to M&A activity across the sample without the constraints and data requirements imposed by fixed effects models. This approach aligns with prior empirical studies that assess pre- and post-merger performance using accounting-based indicators within a cross-sectional or pooled framework.

3.2. Data Collection and Sample

This paper employs a secondary data collection method, which involves utilizing data previously gathered by others for purposes unrelated to the current study. Secondary data can be categorized into three primary subgroups: documentary data, survey-based data, and data compiled from various sources. These subsets can include both quantitative and qualitative information sourced from organizational databases, archives, online publications, newspapers, government surveys, and other publications [43].

For this study, data related to M&A and the financial ratios of the companies were sourced from Bloomberg, a leading media conglomerate that provides a comprehensive array of financial news, research, and data, typically utilized in studies examining company performance [44]. The data from Bloomberg primarily consists of structured and quantitative financial information. The time series focus of this dataset presents a significant advantage for comparing financial ratios across various companies over different years. This structured numerical data serves as a robust foundation for conducting quantitative analyses, such as the statistical modeling and financial research undertaken in this study. Moreover, utilizing databases like Bloomberg allows researchers to conserve resources, particularly in terms of the time and finances required for data collection. This efficiency enables the analysis of significantly larger datasets and provides the opportunity to allocate more time and effort to data analysis and interpretation [44].

In this study, the sample consists of M&A transactions executed by companies within the DAX40 index. This selection was motivated by the scarcity of existing research specifically examining M&A activities within the DAX40 context. The identification of this notable gap during the literature review led to the decision to concentrate the study on DAX40 companies. An analysis of the existing literature reveals a limited emphasis on the European context, particularly concerning Germany.

There are few recent studies specifically addressing M&A activities in this region, including the work of Galariotis et al. [45], which explores the banking sector, and that of Ströbl et al. [46], which investigates entrepreneurial leadership in German-speaking countries. However, none of these studies focuses exclusively on companies within DAX40. The initial sample only included complete mergers and acquisitions of the companies that are part of the DAX40 index, occurring between the first quarter of 2015 and the fourth quarter of 2022.

The selection of the period from 2017 to 2022 provides a solid framework for analyzing the financial effects of M&A among DAX 40 companies. This six-year window encompasses both stable pre-crisis conditions and the unprecedented external shock of the COVID-19 pandemic, allowing for a comprehensive assessment of firms’ financial resilience during both strategic transformations and systemic disruptions. The years 2017 to 2019 represent a time of relative macroeconomic stability and steady corporate deal-making activity in Germany and globally. Including these years establishes a reliable financial baseline before M&A activities and reflects the typical strategic rationale behind such deals during normal market conditions. In contrast, incorporating the years from 2020 to 2022 adds analytical depth by considering the effects of the COVID-19 crisis and the initial phase of post-crisis recovery. The pandemic introduced significant financial stress and operational uncertainty, serving as a real-world stress test to evaluate whether firms engaged in M&A could maintain or enhance their profitability, liquidity, and solvency. By covering the entire 2017–2022 period, the study avoids the limitations of short-term observation and allows for a longer-term evaluation of post-merger financial trajectories. This is particularly important because the financial impact of M&A transactions often unfolds gradually, influenced by integration costs, the realization of synergies, and market adjustments. Overall, the 2017–2022 period captures both the strategic intentions behind M&A activities and the capacity of DAX 40 firms to absorb shocks, making it a meaningful and relevant timeframe for assessing the true impact of M&A on corporate financial performance.

Following the coverage criterion, which aims to eliminate irrelevant data that do not align with the objectives of this study [43], a meticulous process of filtering and cleaning the dataset was undertaken. This involved implementing established filtering methods commonly found in the prior literature on the subject. Specifically, for the purposes of this study, careful consideration was only given to companies that met the following stringent criteria:

-

Acquirers had to be publicly traded companies.

-

Only companies possessing essential data, such as financial ratios relevant to the study’s timeframe and pertinent M&A characteristics, were considered.

-

Companies operating within the financial sector were excluded due to their distinct regulatory requirements.

Upon completing the initial step, twenty-four companies out of the forty listed in the DAX40 index met the study’s criteria, resulting in a total of 403 completed mergers and acquisitions. While the classification of these deals into horizontal, vertical, or conglomerate types is not explicitly provided, the characteristics of the target firms and their sector alignment suggest that most transactions are horizontal mergers. This means the acquiring firms typically operate within the same or closely related industries. This trend aligns with the strategic objectives common among DAX 40 firms, which include market consolidation, operational synergy, and competitive repositioning. Vertical and conglomerate mergers appear less frequently and have not been systematically analyzed in this study. Subsequently, a secondary refinement of the data was conducted. Specifically, only transactions occurring between 2017 and 2020—spanning a four-year timeframe—were included. This selection was necessary to facilitate a comparative analysis of two years prior to and following the M&A, as comprehensive ratio data was only available for the years ranging from 2015 to 2022.

The data cleaning and subsequent analysis, outlined in the following section, were conducted using RStudio 2025.05.0. This analysis focuses on nine selected variables pertinent to the current study. These variables are categorized based on their functional classification of accounting ratios into three distinct groups, aligning with the study’s objectives and hypotheses. These categories represent the profitability, liquidity, and solvency positions of the companies under scrutiny. Analyzing the post-M&A profitability position is critical, as it indicates the success or failure of the operation. M&A strategies are designed to enhance shareholder returns, and an increase in profitability signifies a successful operation [32]. Profitability serves as a key performance indicator and is essential for sustainable growth. Profitability ratios are instrumental in evaluating and interpreting both the current and future earning capacity of business corporations. The liquidity position offers insights into a firm’s ability to meet its short-term obligations. M&A transactions can influence the working capital structure, thereby affecting the liquidity position. A successful M&A is expected to result in improved liquidity position. As for the solvency position, it reveals the capital structure of a business, and the proportion of funds sourced from owners compared to outsiders. The solvency position experiences significant changes during M&A transactions. All three parameters—profitability, liquidity, and solvency—are highly significant in measuring the success of an M&A [32].

The assessment of profitability entails examining three key ratios: ROA, ROE, and operating margin. Evaluating liquidity involves exploring the current ratio, quick ratio, and cash ratio. Additionally, the appraisal of solvency includes the examination of the total debt-to-total equity ratio and the total debt-to-total asset ratio. The following table (

Table 1. Justification of variables.

Variable	Previous Findings
Return on Asset: A firm’s ability to achieve growing returns on its assets holds significant importance. This metric indicates efficiency with which the firm utilizes its assets to generate profits [32]. It is calculated as net income/total asset.	As per Aggarwal and Garg [32], the ROA saw a notable increase in the 3-and 5-year periods following the merger. It exhibited a significant rise of 5% within 3 years post-merger and a more pronounced increase of 1% within the 5-year period post-merger. In contrast, Abbas et al. [42] found a decline in ROA among 8 out of 10 analyzed companies following M&A.
Return on Equity: Equity shareholders, as the true proprietors of a firm, shoulder the highest investment risk. Consequently, it becomes paramount for any company to deliver anticipated returns to these stakeholders. This ratio is calculated as net income/shareholder’s equity.	As highlighted by Aggarwal and Gang [32] in their research, the ROE demonstrated a substantial increase over both the 3-year and 5-year periods. In contrast, Abbas et al.’s [42] study revealed a decline in ROE, specifically among the 10 companies analyzed, where the ROE decreased in 7 instances.
Operating margin: It is calculated in Bloomberg as operating income/revenue. Following a successful M&A operation there is potential for increased profitability and the operating margin stands out as a prominent and effective indicator for evaluating the efficiency of the M&A operation.	According to Irayanti [47], the operating margin ratio decreases post-M&A.
Current ratio: it assesses companies’ capability to fulfill short-term liabilities. It can be calculated by total current assets divided by total current liabilities.	As per Aggarwal and Gang [32], the current ratio experienced a remarkable increase following the merger. Within a 3-year period, the ratio increased by 10%, and over a 5-year span, it showed a notable improvement, rising by 5%. However, in the study conducted by Bedi [27], the overall average variance between the pre- and post-merger periods regarding the current ratio suggests that the liquidity position did not see improvement after the M&A.
Quick ratio: It aids in evaluating the post-M&A liquidity status of companies and is computed as (cash and cash equivalents + short-term investments + accounts and notes receivable) divided by total current liabilities [44].	According to Bedi [27], following the M&A, the average quick ratio declined for 4 out of 5 companies. The total average variance between the pre- and post-merger periods was—0.26, signifying that, on average, the liquidity position of the analyzed companies did not improve.
Cash ratio: It is calculated as (cash + cash equivalent)/current liabilities	Haakantu and Phiri [48] employed cash ratios in their study on the post-M&A performance of banks in Zambia to assess company liquidity. The findings indicate an increase in the cash ratio following the merger activity.
Total debt-to-total equity ratio: It evaluates a company’s capacity to handle its capital, serving as collateral for corporate debt. It reflects the equilibrium between creditor- financed assets and owner-financed ones. This ratio is influenced by how the merger and acquisition was funded [32]. It can be calculated as follows: total debt/total assets.	Bedi [27] utilized the debt-to-equity ratio to assess the solvency position of companies around M&A transactions. The findings indicated that the solvency position did not improve after the merger; in fact, the debt-to-equity ratio decreased after the M&A event. Aligning with this perspective, Aggarwal and Gang [32] concluded that the debt-to-equity ratio did not show significant improvement after mergers, neither within a 3-year nor a 5-year timeframe.
Total debt-to-total asset ratio: it represents the aggregate debt financed by creditors and is utilized to assess the post-M&A solvency of the companies considered. The calculation involves dividing Total Debt by Total Assets.	Abbas et al. [42] observed that in 7 out of 10 cases, the Debt-to-Asset ratio increased when comparing pre- and post-merger data. This suggests an improvement in the solvency position of several companies following the M&A.

Source: summarized by authors.

) provides a comprehensive description of these selected variables.

In addition to the aforementioned variables, various studies referenced in the literature review employed different financial ratios. For example, Bedi [27] and Jallow et al. [49] utilized the Net Profit Ratio to evaluate profitability, while Aggarwal and Garg [32] and Bedi [27] focused on Interest Coverage Ratios to assess solvency. In this study, the author opted for specific ratios primarily based on data availability. Some variables previously examined were only accessible for select years, prompting the choice of ratios that offered more extensive data coverage.

Furthermore, as these ratios were instrumental in calculating an index, their appropriateness for index representation and ease of comparison were crucial considerations. Notably, control variables such as firm size, industry type, and deal size, commonly included in related models, have been excluded from this study. This decision was made to concentrate solely on the impact of M&A activity on financial performance, minimizing the influence of external factors. The objective is to isolate and accurately evaluate the effect of M&A on financial performance to address the research question effectively.

Additionally, by excluding these variables, the analysis is simplified, and potential multicollinearity issues are mitigated. Data availability also guided this choice. For the analysis, three regression models were established, each focusing on distinct dependent variables: profitability, liquidity, and solvency. In these models, dummy variables representing the companies within the sample are included, with one company serving as the reference category to meet the independence assumption. The independent variables encompass data from the two years preceding and the two years following the M&A events:

$$\begin{array}{l}Profitability = X\text{\_}after + X\text{\_}before + Adidas + Airbus + BASF + Bayer\\ + BMW + Brenntag + Continental + Covestro + DeutschePost\\ + DeutscheTelekom + E. ON + Fresenius\\ + InfineonTechnology + MercedesBenz + Merck\\ + MTUAeroEngine + Qiagen + Rheinmetall + RWE + SAP\\ + Sartorius + SiemensAG + Volkswagen\end{array}$$

(1)

The study employs a similar approach to constructing regression analysis with dependent variables on liquidity. In this instance, the companies comprising the dataset are designated as dummy variables. A single company is designated as the reference entity, thereby ensuring adherence to the independence assumption. The independent variables encompass the two years preceding and the two years following the M&A events. The objective of these regressions is to assess the impact on liquidity both prior to and following M&A transactions. The methodology employed is as follows Equation (2):

$$\begin{array}{l}Liquidity = Xafter + Xbefore + Adidas + Airbus + BASF + Bayer + BMW\\ + Brenntag + Continental + Covestro + DeutschePost\\ + DeutscheTelekom + E. ON + Fresenius\\ + InfineonTechnology + MercedesBenz + Merck\\ + MTUAeroEngine + Qiagen + Rheinmetall + RWE + SAP\\ + Sartorius + SiemensAG + Volkswagen\end{array}$$

(2)

To conclude, the third regression has solvency as the dependent variable and this is presented as follows in Equation (3):

$$\begin{array}{l}Solvency = X\text{\_}after + X\text{\_}before + Adidas + Airbus + BASF + Bayer\\ + BMW + Brenntag + Continental + Covestro + DeutschePost\\ + DeutscheTelekom + E. ON + Fresenius\\ + InfineonTechnology + MercedesBenz + Merck\\ + MTUAeroEngine + Qiagen + Rheinmetall + RWE + SAP\\ + Sartorius + SiemensAG + Volkswagen\end{array}$$

(3)

The independent variables for both liquidity and profitability include data from the two years before and the two years after the M&A events. Additionally, the dummy variables represent the companies in the dataset, with one company serving as the reference for the independence assumption.

In this study, control variables such as firm size, deal value, and sector classification were deliberately excluded to maintain a focused and methodologically consistent analysis of the direct impact of M&A activity on firm-level financial performance within the DAX 40 index. This decision is justified both theoretically and methodologically.

First, the primary objective of the study is to isolate and assess the overall effect of M&A on key accounting-based financial indicators—specifically, profitability, liquidity, and solvency—within a homogeneous and highly standardized reporting environment. All sampled firms belong to the DAX 40 index, which consists of large-cap German companies subject to similar regulatory, governance, and disclosure requirements. This homogeneity inherently reduces inter-firm variability related to institutional and market-specific factors that might otherwise necessitate the use of control variables in cross-country or heavily diversified samples.

Second, including control variables such as firm size, deal value, or sector could potentially introduce noise or multicollinearity in the regression model, particularly given the relatively limited sample size and the focus on pre- and post-M&A performance over a defined time frame. The decision to exclude these variables aims to avoid overfitting and enhance the internal validity of the findings by concentrating solely on the direct relationship between M&A activity and changes in accounting ratios. Furthermore, incorporating such controls might obscure the broader patterns the study seeks to capture, especially in a context where the primary research question explores the aggregate effect of M&A activity within a specific elite market segment.

4. Results

4.1. Data Description

The analysis encompasses a comprehensive examination of selected financial ratios using pre- and post-M&A data. The initial dataset includes all relevant variables, covering profitability, liquidity, and solvency metrics for the companies under consideration. Relevant ratios for each quarter were extracted from Bloomberg, yielding a total of four quarters for each year included in the study.

To facilitate a robust comparison of profitability, solvency, and liquidity performance, the values of each component were aggregated, and the means were calculated to establish an index. The profitability index incorporates metrics such as ROA, ROE, and operating margin. Concurrently, the liquidity index is derived from the quick ratio, current ratio, and cash ratio, while the solvency index is formulated using the total debt-to-total equity ratio and the total debt-to-total asset ratio.

Following the calculation of these indices, descriptive statistics were generated for the three indices over the two-year periods preceding and following the M&A. This selected timeframe aligns with findings in the existing literature, as discussed in prior studies that share a similar focus. Specifically, Achtmeyer [50] asserts that the benefits of mergers and acquisitions often become evident within two years.

To capture data for the two years pre- and post-M&A, two new indicator columns were added to the original dataset, coding each condition with values of 1 and 0. A value of 1 indicates that the specific condition applies—that is, it denotes the two years immediately pre- and post-M&A transaction—while a value of 0 indicates the absence of this condition.

RStudio was utilized for the computation of the profitability index and for the subsequent calculation of summary statistics for both pre- and post-M&A periods. This same methodology was applied to the solvency and liquidity ratios, ensuring a consistent approach throughout the analysis. (

Table 2. Summary statistics of financial indices pre- and post-M&A.

	Liquidity		Solvency		Profitability
	Before M&A	Post M&A	Before M&A	Post M&A	Before M&A	Post M&A
Min.	0.4300	0.3533	4.555	15.02	−62.327	−66.187
1st Qu.	0.6467	0.6500	35.490	43.46	7.133	6.368
Median	0.7750	0.7433	53.070	61.26	9.937	8.832
Mean	0.9603	0.8269	67.321	100.81	8.225	8.420
3rd Qu.	0.9850	0.8833	84.410	88.36	12.517	12.527
Max.	4.3167	2.9633	960.860	6588.56	21.813	37.650

Source: calculated by authors.

).

Table 2 presents an overview of the summary statistics regarding liquidity, solvency, and profitability pre- and post-M&A activities. These statistics serve to illuminate the characteristics of the sample data, thus enriching our understanding of the underlying financial metrics. The mean is specifically designed to encapsulate the central tendency of the dataset, striving to provide a representative benchmark value [51]. Notably, the mean values for solvency and profitability show an increase in the post-M&A period, whereas liquidity reflects a decline during the same timeframe. To further contextualize the data, the 1st quartile divides the dataset into the lowest 25% and the remaining upper 75%. For instance, when evaluating profitability prior to the M&A, the 1st quartile registers at 7.13, indicating that 25% of the observations fall below this threshold. Similarly, the 3rd quartile indicates that 75% of the observations are equal to or less than 12.517. Additional percentiles of interest include the minimum, marking the zeroth percentile, and the maximum, which corresponds to the 100th percentile. These extreme values highlight the range within which the variable can fluctuate, thereby offering crucial insights into the variability of the dataset [51]. In this analysis, it becomes apparent that both solvency and profitability exhibit a broader range than liquidity, suggesting a more varied distribution of values. For example, the profitability range before the M&A extends from −62.327 to 21.813, which expands post-M&A to −66.187 and 37.650. This increase in the upper limit, coupled with a slight deterioration of the lower limit, indicates that post-M&A performance has become more volatile; while some firms have realized significant gains, others have faced considerable underperformance. The change in mean profitability—from 8.225 to 8.420—represents a marginal enhancement (+0.195 points), which is statistically modest. Consequently, although the trend points towards improvement, the practical implications appear limited and may be significantly influenced by outliers, especially given the dispersion of the distribution. The trend for liquidity, on the other hand, is notably negative. The mean liquidity ratio declined from 0.9603 to 0.8269 (Δ = −0.1334), while the maximum liquidity ratio plummeted considerably from 4.3167 to 2.9633. This compression of both the upper quartile and mean strongly suggests that liquidity constraints were a prevailing phenomenon following the M&A activities. As liquidity is a pivotal indicator of a firm’s capability to satisfy short-term obligations, this decline could signify that the processes of post-M&A integration and restructuring adversely impacted cash flow or the availability of working capital. The reduction in both mean and median further indicates that this trend was widespread, affecting a broad spectrum of firms rather than a select few. In contrast, solvency illustrates a pronounced increase in the mean—from 67.321 to 100.81—yielding a notable effect size of +33.489 points. This observation is substantiated by similar shifts in the median and interquartile range, indicating that a significant portion of the sample experienced enhanced long-term financial stability. One plausible explanation is that M&A transactions were accompanied by balance sheet realignments, including increased equity financing or the refinancing of existing liabilities, which may have improved firms’ capital structures. Nonetheless, the substantial rise in maximum solvency—from 960.860 to 6588.56—also indicates the presence of extreme values, which may reflect atypical leverage ratios within specific firms. Hence, the overall improvement in average solvency could be somewhat exaggerated by a few exceptionally large changes. In summation, the trends observed point towards a post-M&A environment characterized by enhanced solvency, relatively stable yet highly dispersed profitability outcomes, and deteriorating liquidity conditions. These contrasting results imply that while firms may have fortified their structural integrity for the long term, short-term financial pressures have intensified, and profitability improvements, if present, have been unevenly distributed across the sample. Future analyses may benefit from segmenting the dataset (e.g., by industry type or nature of M&A) to uncover and delineate systematic patterns in financial outcomes.

The correlation matrix pre- and post-M&A is presented in

Table 3. Correlation matrix.

Correlation Matrix Pre-M&A
	Profitability	Liquidity	Solvency
Profitability	1.00000000
Liquidity	0.02430263	1.00000000
Solvency	−0.18910853	−0.20628056	1.00000000
Correlation matrix post-M&A
Profitability	1.00000000
Liquidity	0.07712125	1.00000000
Solvency	−0.49937145	−0.12728428	1.00000000

Source: calculated by authors.

.

Concerning the strength of the relationship, the correlation coefficient ranges from −1 to 1. A correlation of 1 indicates a perfect positive linear relationship, meaning that as one variable increases, the other also increases proportionally. On the contrary, a correlation of −1 denotes a perfect negative linear relationship, indicating that as one variable increases, the other decreases proportionally. A correlation of 0 signifies that there is no linear relationship between the variables [52]. Table 3 above displays the correlation values indicating the relationship between profitability and liquidity, profitability, and solvency, as well as liquidity and solvency, before M&A. The correlation between profitability and liquidity is around 0.0243, representing a weak positive correlation, indicating a minimal linear association between the two. In the case of profitability and solvency, the correlation is approximately −0.1891, reflecting a weak negative correlation and implying a slight inverse connection between profitability and solvency. Moreover, the correlation between liquidity and solvency is roughly −0.2063, showing a weak negative correlation and suggesting a modest inverse relationship between liquidity and solvency.

4.2. Ratio Analysis Comparison

In this context, ratio analysis is a valuable method for examining and comparing relationships among various financial metrics over the company’s historical timeline. This analytical approach sheds light on the dynamics that evolved during specific periods. Specifically, we calculated the significance of each ratio included in the study. These ratios were derived from two distinct timeframes: the first covered two years prior to the M&A, while the second spanned two years following the M&A. By comparing the average ratios from these different periods, we can identify the main trends. This analysis reveals whether the company’s financial position and performance improved, declined, or remained stable (see

Table 4. Comparison of mean financial ratios pre- and post-M&A.

Profitability Ratios	Before M&A	Post-M&A
Return on asset	4.14	3.79
Return on equity	10.8	11.9
Operating margin	9.52	9.17
Liquidity ratios
Current ratio	1.50	1.35
Quick ratio	0.94	0.78
Cash ratio	0.43	0.35
Solvency ratios
Debt-to-equity ratio	1.07	1.71
Debt-to-total asset ratio	0.27	0.31

Source: calculated by authors.

).

Table 4 presents a comparison of mean values derived from all M&A across various financial ratios before and after the events. These ratios are categorized into profitability, liquidity, and solvency. The table is organized into “Pre-M&A” and “Post-M&A” periods, allowing for an examination of the changes that occur surrounding the merger and acquisition activities. In general, there is a noticeable trend of declining ratios in most cases concerning profitability and liquidity, while solvency ratios tend to increase following mergers and acquisitions.

Profitability Ratios

A closer look at profitability indicators reveals mixed dynamics. The ROA decreased from 4.14 to 3.79, indicating that companies were less efficient in generating income from their total assets post-M&A. This decline may reflect post-merger inefficiencies, restructuring costs, or delays in realizing operational synergies. In contrast, ROE increased from 10.8 to 11.9, suggesting that shareholder returns improved, likely because of financial leverage or restructured capital bases. The operating margin, which reflects core business profitability, experienced a slight decrease from 9.52% to 9.17%, indicating a marginal erosion in operational efficiency. The contradictory movements between ROA and ROE may signal that firms achieved higher returns on equity through increased financial leverage, but this did not translate into greater efficiency in asset utilization—highlighting the importance of examining profitability from multiple perspectives.

Liquidity Ratios

All three liquidity indicators—the current ratio, quick ratio, and cash ratio—dropped following the M&A. The consistent downward trend across all liquidity measures points to a weaker short-term financial position after the merger. Such a decline may be attributed to increased short-term liabilities or reduced current assets during integration phases. The decreases in the quick and cash ratios, which exclude inventories and non-liquid assets, further emphasize the tightening of immediate liquidity and working capital reserves. These changes may reflect increased financial strain due to post-deal restructuring costs, integration delays, or misalignments in working capital. This trend aligns with interpretations from Table 2, reinforcing the notion that M&A activity often brings short-term liquidity pressures.

Solvency Ratios

In contrast to profitability and liquidity, solvency metrics show a significant increase. The D/E ratio rose sharply from 1.07 to 1.71, while the debt-to-total asset ratio increased from 0.27 to 0.31. These upward shifts indicate that there was notable deterioration in solvency as firms took on more debt relative to both equity and total assets. The rise in leverage suggests that M&A transactions were either financed through additional borrowing or resulted in elevated debt obligations post-merger. While this may contribute to the improved ROE observed earlier (via leverage effects), it also raises concerns about long-term financial stability. Firms that rely heavily on debt financing post-M&A may face increased exposure to interest rate fluctuations and refinancing risk, especially during tight credit conditions. These trends reinforce the hypothesis that solvency tends to worsen in the post-M&A period, as supported by the prior literature.

The observed decline in liquidity and profitability following M&A can be understood from several theoretical perspectives. Firstly, M&A activities typically incur substantial transactions and integration costs, which can lead to temporary inefficiencies. These factors may erode profit margins and negatively impact short-term cash flow ratios. Additionally, resources might be redirected from core operations to support integration efforts, which can delay the realization of revenue synergies while incurring immediate costs. The decline in liquidity ratios following an M&A may also indicate deliberate financial restructuring. This could involve increasing short-term obligations or decreasing cash reserves because the acquisitions were financed using internal resources. Simultaneously, firms may see an improvement in solvency ratios due to a shift in their capital structure, favoring long-term debt or equity financing to support the acquisition. This shift may reduce short-term liabilities in favor of more manageable long-term obligations, explaining the relative improvement in solvency metrics. Furthermore, the findings may reflect the early-stage effects seen post-M&A. Prior research [50] suggests that the synergies from M&A often require a longer time frame to fully materialize. Thus, the two-year observation period may highlight transitional dynamics rather than the final outcomes of the deal.

4.3. Paired Sample t-Test

The study utilized a paired sample t-test to evaluate the hypothesis, specifically aimed at determining the significant differences between two time series—namely, the periods pre- and post-M&A activity. The paired sample t-test is a statistical method within inferential statistics that facilitates the comparison of treatment groups, allowing for the generalization of findings to a larger population of subjects [53]. In this research, the paired sample t-test was employed to assess the statistical significance of the mean differences between paired measurements, such as profitability metrics recorded before and after the M&A. For the analysis, RStudio [54] was used, with the “t.test” function implemented (see

Table 5. Paired sample t-test results for financial indices.

Variable	t-Statistic	Degree of Freedom	p-Value	Confidence Interval	Mean of X	Mean of Y
Profitability	0.23191	458.82	0.5916	(−Inf, 1.581411)	8.420043	8.224978
Solvency	1.1977	253.04	0.8839	(−Inf 0.7965563)	1.0081234	0.6732066
Liquidity	−3.0498	377.61	0.001226	(−Inf—0.06125929)	0.8269038	0.9602684

Source: calculated by authors.

).

Table 5 presents the results of paired sample t-tests conducted to determine whether the mean differences in financial ratios pre- and post-M&A transactions are statistically significant. The results indicate that liquidity is the only indicator with a statistically significant change. The mean liquidity ratio declined from 0.96 before the M&A to 0.83 after the M&A, with a p-value of 0.0012, demonstrating strong statistical significance at the 1% level. The confidence interval for the mean difference is (−∞, −0.0613), suggesting that the true mean difference in liquidity is likely negative, indicating a decline of at least 0.061 units post-M&A. This supports the conclusion that M&A activity is associated with a tangible decrease in short-term financial flexibility. The reduction in liquidity may be attributed to integration costs, post-deal restructuring, or temporary inefficiencies in cash flow and working capital management. This finding supports Hypothesis 2, which posits a decline in liquidity post-M&A transactions.

For profitability, the mean increased slightly from 8.22 to 8.42, but the p-value of 0.5916 exceeds the 5% significance threshold, indicating no statistically significant change. The confidence interval for the mean difference is (−∞, 1.58), which includes zero. This suggests that the observed increase could easily result from random variation rather than the systematic effect of M&A activity. Consequently, we fail to reject the null hypothesis, and Hypothesis 1 is not supported. The lack of significant change implies that M&A transactions do not materially affect firm profitability within the observed timeframe.

Similarly, solvency shows an increase in the mean ratio from 0.67 to 1.01, but the p-value of 0.8839 also indicates a lack of statistical significance. The confidence interval for the difference is (−∞, 0.80), which, like that for profitability, includes zero. This result implies that while some firms may increase their leverage post-M&A (e.g., through debt-financed acquisitions), the overall effect across the sample is not strong enough to conclude a consistent pattern of solvency deterioration or improvement. Thus, Hypothesis 3 also cannot be supported based on this evidence.

The confidence intervals reinforce the interpretation of the p-values by confirming the direction and statistical insignificance of the observed differences. Only for liquidity is the confidence interval entirely below zero, reinforcing the conclusion that M&A transactions in the sample are associated with a statistically significant reduction in short-term financial health. Conversely, the wide and inclusive intervals for profitability and solvency confirm the absence of systematic post-M&A effects in these areas across the DAX 40 firms.

4.4. Results of the Regression Analysis

To strengthen the study based on the results of the paired sample t-test, a decision was made to conduct a regression analysis. OLS regression is a widely used method for determining the coefficients of linear regression equations, which illustrates the relationship between a dependent variable and one or more independent quantitative variables [55]. This approach utilizes a larger dataset and allows for the inclusion of years without M&A activities for a more thorough examination. In the regression model, dummy variables for companies were introduced to address the assumption of independent observations. This assumption states that the value of one data point should not depend on the value of another data point. Notably, within the dataset, there is some level of interdependence among observations from the same companies. The goal of these regressions is to evaluate the impact on profitability both before and after M&A transactions.

Table 6. Results of the initial OLS regression—profitability.

Parameter	Estimate	p-Value
Two_Years_After	−1.7927	0.020584 *
Two_Years_Before	−1.7864	0.020594 *
Adidas	5.3831	0.006966 **
Airbus	5.3838	0.007298 **
BASF	4.9497	0.014440 *
Bayer	0.3434	0.867403
BMW	5.0287	0.011414 *
Brenntag	5.6299	0.005586 **
Continental	3.3721	0.092444
Covestro	7.9260	0.000182 ***
DeutschePost	8.1028	5.65 × 10−5 ***
DeutscheTelekom	5.3587	0.007151 **
E.ON	1.7369	0.388412
Fresenius	5.7748	0.004481 **
InfineonTechnology	8.3248	3.10 × 10−5 ***
MercedesBenz	5.5721	0.005497 **
Merck	8.2031	4.58 × 10−5 ***
MTUAeroEngine	3.7482	0.059968
Qiagen	5.5628	0.006176 **
Rheinmetall	2.9676	0.134983
RWE	−3.3392	0.123094
SAP	10.6047	1.50 × 10−7 ***
Sartorius	12.4887	5.31 × 10−10 ***
SiemensAG	6.3136	0.001897 **
Volkswagen	2.1681	0.282540
F-statistic	5.557	3.328 × 10−16
R-squared	0.1428

Note: *** Significant at the 1% level (p < 0.01), ** Significant at the 5% level (p < 0.05), * Significant at the 10% level (p < 0.10), Source: calculated by authors.

below presents the results of the first regression analysis. From these results, it became clear that some companies did not show significant differences compared to the reference company, Zalando. In fact, the coefficients of certain dummy variables were not significantly different from zero.

To enhance the efficiency of the regression analysis, we eliminated dummy variables that did not show significant differences compared to the reference company. This process was repeated until all remaining dummy variables yielded significant results. The findings are summarized in

Table 7. Adjusted results OLS regression—profitability.

Parameter	Estimate	p-Value
Two_Years_After	−1.7802	0.019058 *
Two_Years_Before	−1.9555	0.009916 **
Adidas	4.4944	0.003151 **
Airbus	4.5459	0.002371 **
BASF	4.1245	0.006196 **
BMW	4.1570	0.005572 **
Brenntag	4.8089	0.001487 **
Continental	2.5342	0.089585
Covestro	7.0589	2.24 × 10−5 ***
DeutschePost	7.2650	1.32 × 10−6 ***
DeutscheTelekom	4.5039	0.002527 **
Fresenius	4.9539	0.001067 **
InfineonTechnology	7.4700	6.18 × 10−7 ***
MercedesBenz	4.7342	0.001553 **
Merck	7.3652	9.46 × 10−7 ***
MTUAeroEngine	2.8595	0.059907
Qiagen	4.7419	0.001730 **
SAP	9.7668	1.00 × 10−10 ***
Sartorius	11.6339	1.53 × 10−14 ***
SiemensAG	5.4927	0.000288 ***
F-statistic	6.347	3.582 × 10−16
R-squared	0.1107

Note: *** Significant at the 1% level (p < 0.01), ** Significant at the 5% level (p < 0.05), * Significant at the 10% level (p < 0.10), Source: calculated by authors.

below.

The data presented in the table indicates that the estimated impact on profitability for the two years following the M&A event is −1.780. This suggests that the anticipated profitability index is approximately 1.780 points lower during this period compared to times without M&A transactions occurring two years prior or after. The p-value associated with this estimate is 0.0191, indicating a statistically significant negative effect on profitability for two years post-M&A events at a significance level (α) of 5%. Furthermore, the estimated effect for the two years preceding the M&A event is −1.955, which implies that expected profitability during this timeframe is 1.955 points lower compared to periods without M&A transactions within two years before or after. The F-statistics, which assesses the overall significance of the regression model, is 6.347; this is accompanied by a low p-value (3.582 × 10⁻¹⁶). This result supports the conclusion that at least one of the independent variables significantly affects profitability. The R-squared value of the model is 0.1107, suggesting that approximately 11.07% of the variability in profitability is explained by the independent variables included in the analysis. The overall significance of the model, as indicated by the F-statistics, reinforces the fact that the collective influence of the independent variables has a meaningful impact on profitability. To ensure the robustness of these findings and validate the assumptions of the model, a series of robustness tests were conducted. These tests aimed to assess the sensitivity of the results to variations in model specifications and to confirm the consistency of the conclusions drawn. The Breusch–Pagan test was utilized to evaluate the presence of heteroskedasticity in the regression model. The identification of heteroskedasticity indicates that the residuals do not exhibit equal variance [55]. This test was performed in RStudio using the “bptest” function, yielding a p-value of 0.001207, which fell below the designated significance level (α = 5%). Accordingly, the null hypothesis of homoscedasticity is rejected, confirming the existence of heteroskedasticity in the regression model. To address this heteroskedasticity, the RStudio function “coeftest(model, vcov = vcovHC(model, type = ‘HC0’))” was applied. This function is used to carry out robust hypothesis tests on the coefficients of the linear model, adjusting for potential heteroscedasticity in the residuals. Following this adjustment, a revised model was generated, accompanied by updated p-values. If any variables are found to be insignificant in the model, they will be removed to enhance the efficiency of the analysis. The conclusive outcomes of our regression analysis are presented in

Table 8. Final results OLS regression—profitability.

Parameter	Estimate	p-Value
Two_Years_After	−1.76667	0.0257721 *
Two_Years_Before	−1.94709	0.0145446 *
Adidas	3.32439	0.0029745 **
BASF	2.94565	0.0155786 *
BMW	2.98462	0.0002220 ***
Brenntag	3.62945	1.537 × 10−5 ***
Covestro	5.88590	0.0026348 **
DeutschePost	6.08787	3.931 × 10−13 ***
DeutscheTelekom	3.32917	0.0004897 ***
Fresenius	3.77440	4.244 × 10−5 ***
InfineonTechnology	6.29530	2.872 × 10−11 ***
MercedesBenz	3.55715	0.0008803 ***
Merck	6.18814	1.200 × 10−12 ***
Qiagen	3.56242	0.0086139 **
SAP	8.58976	<2.2 × 10−16 ***
Sartorius	10.45917	<2.2 × 10−16 ***
SiemensAG	4.31323	8.000 × 10−7 ***
F-statistic	6.649	4.428 × 10−15
R-squared	0.1184

Note: *** Significant at the 1% level (p < 0.01), ** Significant at the 5% level (p < 0.05), * Significant at the 10% level (p < 0.10), Source: calculated by authors.

.

To evaluate the effectiveness of the regression model, a residuals plot was generated in RStudio. The residuals plot is depicted in Figure 2 below. This plot allows for the examination of the homoscedasticity assumption by assessing the dispersion of residuals around the predicted values [55].

The plot in Figure 2 shows that the error is evenly distributed above and below 0; the expected value of the residuals is around 0. Additionally, it illustrates the presence of heteroskedasticity; the variance in residuals is significantly higher for smaller fixed values (6, 8) than larger values (13, 14, 16). This observation strengthens the rightness of adjusting for heteroskedasticity in the model.

Furthermore, the assessment of normality was conducted through a Normal Q-Q plot, depicted in Figure 3 below. This plot displays the distribution of the residuals; when they are positioned close to the dotted line, the assumption is not violated [55].

As anticipated, the same approach was implemented to analyze the liquidity position as was used for evaluating profitability. In the regression model, each company in the dataset is treated as a dummy variable, with one company serving as the reference point to ensure the independence assumption is met. The independent variables include data from the two years preceding and the two years following the M&A events. The purpose of these regressions is to assess the impact on liquidity both before and after M&A transactions. Similarly to the findings with profitability (see Appendix A), it was necessary to recalculate the regression for liquidity to attain statistical significance for all coefficients related to liquidity. Although the initial regression produced seemingly significant results, a Breusch–Pagan test was later conducted to check for heteroskedasticity within the regression model. The resulting p-value was <2.2 × 10⁻¹⁶, which led to the rejection of the null hypothesis, indicating the presence of heteroskedasticity in the model. In accordance with established practices, the regression model was then adjusted using a special function designed to account for heteroskedasticity in the residuals. When this function was executed in RStudio, an adjusted model was produced with updated p-values. At this stage, some variables were found to be insignificant in the new model (please refer to the Appendix A for these results), and they were subsequently removed to enhance efficiency. The revised results are presented in

Table 9. Final results OLS regression—liquidity.

Parameter	Estimate	p-Value
Two_Years_After	−0.07300	0.001821 ***
Two_Years_Before	0.05789	0.013502 **
Adidas	−0.37681	8.65 × 10−13 *
Airbus	−0.64614	<2 × 10−16 ***
BASF	−0.25474	1.83 × 10−6 ***
Bayer	−0.35308	2.91 × 10−11 ***
BMW	−0.49977	<2 × 10−16 ***
Brenntag	−0.18550	0.000523 ***
Continental	−0.51425	<2 × 10−16 ***
DeutschePost	−0.52974	<2 × 10−16 ***
DeutscheTelekom	−0.58210	<2 × 10−16 ***
E.ON	−0.53435	<2 × 10−16 ***
Fresenius	−0.49090	<2 × 10−16 ***
InfineonTechnology	0.59970	<2 × 10−16 ***
MercedesBenz	−0.41416	8.16 × 10−15 ***
Merck	−0.44119	<2 × 10−16 ***
MTUAeroEngine	−0.63609	<2 × 10−16 ***
Qiagen	0.85369	<2 × 10−16 ***
Rheinmetall	−0.43824	<2 × 10−16 ***
RWE	−0.48740	<2 × 10−16 ***
SAP	−0.30038	1.36 × 10−8 ***
Sartorius	−0.35453	1.54 × 10−11 ***
SiemensAG	−0.35045	8.25 × 10−11 ***
Volkswagen	−0.48450	<2 × 10−16 ***
F-statistic	72.79	2.2 × 10−16
R-squared	0.6704

Note: *** Significant at the 1% level (p < 0.01), ** Significant at the 5% level (p < 0.05), * Significant at the 10% level (p < 0.10), Source: calculated by authors.

below.

From the table above, it is evident that the estimate for two years following the M&A event is −0.07300. This indicates that the anticipated liquidity index is lower by approximately −0.07300 during the two-year period subsequent to an M&A, in comparison to periods devoid of M&A transactions two years before or after. Consequently, a 1-unit increase in the variable “Two_Years_After” corresponds to a decrease of 0.07300 units in liquidity. The p-value is 0.001821, which signifies a statistically significant negative impact on liquidity for two years’ post-M&A events, at a significance level of α = 5%. Therefore, it can be concluded that there is evidence suggesting a significant relationship between these two variables. Furthermore, the estimated effect two years prior to an M&A event is 0.05789, indicating that the expected liquidity index during this period is higher by 0.05789 compared to times when there are no M&A transactions, either two years before or two years after. In this case, a one-unit increase in the variable “Two_Years_Before” is associated with an increase of 0.05789 units in liquidity. The p-value is 0.013502, suggesting that the coefficient for “Two_Years_Before” is statistically significant at a significance level of α = 5%. Thus, there is evidence indicating the existence of a significant relationship between the variable “Two_Years_Before” and liquidity. The F-statistic is 72.79, with a low p-value of 2.2 × 10⁻¹⁶, demonstrating that liquidity is significantly influenced by at least one of the independent variables. Therefore, it is possible to reject the null hypothesis that all coefficients are equal to zero. The R-squared value stands at 0.6704, suggesting that approximately 67.04% of the variability in liquidity is explained by the set of independent variables included in the regression model. In summary, both variables “Two_Years_After” and “Two_Years_Before” possess statistically significant coefficients, and the overall model, as indicated by the F-statistics, is significant. The R-squared value implies that the model accounts for a substantial portion of the variability in liquidity. As part of the robustness tests aimed at assessing the effectiveness of the regression model, a residuals plot was generated using RStudio. The visualization of the residuals plot is presented in Figure 4 below.

As anticipated, this plot facilitates the examination of the homoscedasticity assumption by evaluating the dispersion of residuals around the predicted values [55]. Figure 4 illustrates that the errors are evenly distributed both above and below 0, the expected value. For smaller values, the residuals tend to cluster around 0. Moreover, the plot highlights the presence of heteroskedasticity for values around 2, where the variance in residuals is noticeably higher compared to smaller values (ranging from 0.5 to 1.0). This observation underscores the necessity of adjusting for heteroskedasticity in the model. Additionally, the assessment of normality was conducted using a Normal Q-Q plot, as shown in Figure 5 below.

It is evident that the distribution lies on the normality line for small errors. However, for larger errors, there is a large divergence from the normal distribution. Specifically, within the range of residuals between −2 and 2, the distribution appears to approximate normality, but for residuals exceeding 2 and smaller than −2, normality is not met.

Finally, a third regression was calculated to analyze the solvency position. The same principle as used for profitability and liquidity was applied here. The results are visible in

Table 10. OLS regression of results—solvency.

Parameter	Estimate	p-Value
Two_Years_After	0.444147	0.030806 *
Two_Years_Before	0.111108	0.589322
Adidas	0.216141	0.678692
Airbus	1.012821	0.054419
BASF	−0.048649	0.927000
Bayer	0.247948	0.637380
BMW	0.841407	0.106122
Brenntag	0.059153	0.911630
Continental	−0.071848	0.891350
Covestro	0.117374	0.826626
DeutschePost	0.203519	0.698821
DeutscheTelekom	0.739502	0.156615
E.ON	0.855136	0.101479
Fresenius	0.178886	0.737159
InfineonTechnology	−0.034944	0.946601
MercedesBenz	0.855976	0.103924
Merck	0.101471	0.847027
MTUAeroEngine	0.229786	0.659650
Qiagen	−0.002901	0.995656
Rheinmetall	0.059416	0.909086
RWE	2.006923	0.000145 ***
SAP	−0.066835	0.898889
Sartorius	0.466678	0.371185
SiemensAG	0.167051	0.753965
Volkswagen	0.505686	0.340246
F-statistic	1.842	0.007442
R-squared	0.05093

Note: *** Significant at the 1% level (p < 0.01), * Significant at the 10% level (p < 0.10), Source: calculated by authors.

and similar to the case of profitability and liquidity, some companies did not show significant differences from the reference company (Zalando). The coefficients of these variables are, in fact, not significantly different from the reference.

To improve the efficiency of the regression, the variables that were not significantly different from the reference company were eliminated, and the process was repeated several times to achieve significant results. The results of this process are displayed in

Table 11. Adjusted results OLS regression—solvency.

Parameter	Estimate	p-Value
Two_Years_After	0.39047	0.0210 *
Airbus	0.86002	0.0220 *
BMW	0.65298	0.0826
E.ON	0.68452	0.0681
MercedesBenz	0.70317	0.0610
RWE	1.85412	9.03 × 10−7 ***
F-statistic	6.714	5.769 × 10−7
R-squared	0.04392

Note: *** Significant at the 1% level (p < 0.01), * Significant at the 10% level (p < 0.10), Source: calculated by author.

.

From the table above, it is evident that the estimate for two years after the M&A event is 0.39047; this implies that the anticipated solvency index is higher by approximately 0.39047 during the two years following an M&A compared to periods without M&A transactions two years before or after. The p-value is 0.0210, suggesting a statistically significant positive impact on solvency two years post-M&A events at a significance level of α = 5%. The F-statistic, which tests the significance of the model, is 6.714 with a low p-value (5.769 × 10⁻⁷), indicating that at least one of the independent variables has a significant effect on solvency. The R-squared value is 0.04392, suggesting that approximately 4.39% of the variability in solvency is explained by the model.

To verify the assumption of the model, the Breusch–Pagan test was conducted. Specifically, this test helps to assess the presence of heteroskedasticity in the regression model [55]. The obtained p-value is 0.0001726, which is less than the chosen significance level (α = 5%). Consequently, the null hypothesis of homoscedasticity is rejected, indicating the presence of heteroskedasticity in the regression model.

To address heteroskedasticity in the model, the RStudio function “coeftest (model, vcov = vcovHC (model, type = ‘HC0’))” was employed. As shown in

Table 12. Final results—solvency.

Parameter	Estimate	p-Value
Two_Years_After	0.409698	0.1365297
Airbus	0.765875	0.0133645 *
BMW	0.562990	<2.2 × 10−16 ***
E.ON	0.592455	0.0002001 ***
MercedesBenz	0.609031	7.151 × 10−8 ***

Note: *** Significant at the 1% level (p < 0.01), * Significant at the 10% level (p < 0.10), Source: calculated by authors.

, the adjusted model obtained did not yield significance for the coefficient “Two_Years_after”. Based on this, the study concludes that the results regarding solvency are not statistically significant.

The next step in the analysis involves elaborating a residuals plot, similar to what was performed for the previous two regressions. This plot is used to determine the homoscedasticity assumption by assessing the dispersion of residuals around the predicted values. The residuals plot is shown in Figure 6 below.

Figure 6 shows that the errors are distributed around the dotted line, indicating that the residuals tend to cluster around zero. Additionally, it also reveals the presence of heteroskedasticity for values around 1.5 and larger than 1.6. This observation emphasizes the need to adjust for heteroskedasticity in the model. Finally, the assessment of normality was conducted using a Normal Q-Q plot, as shown in Figure 7 below.

It is evident that the distribution closely aligns with the normality reference line for small errors, indicating adherence to normality. However, for larger errors, there is a deviation from the normal distribution. Specifically, within the range of residuals between −3 and 2.5, the distribution appears to approximate normality, but for residuals exceeding 2.5, normality is not supported.

5. Discussion

The outcome of this research has yielded significant insights into M&A activities, specifically regarding the potential impact such strategic transactions have on the financial performance of companies. The findings indicate that, for two years following M&A activity, profitability and liquidity tend to experience a decrease, while solvency shows a slightly positive trend. Consequently, this analysis does not support the theory of value creation, which posits that the combined entity produces value that exceeds the individual worth of the entities involved. Instead, the study aligns more closely with portions of the literature suggesting that acquiring firms often destroy value rather than create it.

The results of the ratio analysis demonstrate a discernible trend of declining mean values for most ratios associated with profitability and liquidity, while solvency ratios indicate a modest increase. Upon conducting an individual ratio analysis, it becomes clear that ROA exhibits a slight decline in post-M&A. This indicates that, two years after the M&A event, companies face challenges in improving their ability to generate profits from their assets. Additionally, a reduction in the operating margin signals a decrease in the percentage of revenue retained as operating income. The downturn in these two critical ratios may be attributed to various factors, as elaborated in the literature review. For instance, the failure or delay in achieving anticipated synergies—evident from the declining ratios—might arise from decisions made by management or the CEO based on their experiences with M&A. Specifically, management may pursue unrelated or overly diversified acquisitions, highlighting a deficiency in the essential skills and resources required for the successful integration of the acquired entity, often due to significant differences in operational systems. Consequently, management may encounter obstacles in realizing synergies, which results in the inability of companies to reduce costs or enhance revenue in the post-M&A phase, or leads to a prolonged timeline for these benefits to materialize [3,7]. If such circumstances occur, a decline in operational efficiency could follow, adversely affecting both the operating margin and ROA. This situation may ultimately result in the failure of value creation in post-M&A. Moreover, the decline in profitability could also be attributed to short-term integration costs and restructuring expenses that often follow M&A deals. These costs can temporarily reduce margins and lower net earnings, particularly in the first two years after the transaction.

In contrast, the ROE, which reflects a company’s effectiveness in utilizing shareholder equity to generate profits, displays a slight upward trend on average. This increase may be linked to alterations in the capital structure following the M&A, such as an increase in debt levels, which contributes to the rise in ROE. It is not surprising that ROE experiences growth, given that M&A strategies are primarily designed to create value for shareholders. This ratio highlights the profitability of the merger from the perspective of equity shareholders, emphasizing the effective use of shareholder equity to generate profits and maintain efficient cost control, thereby enhancing overall profitability [32]. These results align with Hypothesis 1 of this study, although they diverge from findings presented by Aggarwal and Garg [32] and Muhammad et al. [42], who indicated an improvement in profitability following M&A. Conversely, the results are consistent with the research conducted by Yang and Ai [28] and Bedi [27], which suggests that post-M&A transactions do not consistently result in positive returns for firms in terms of profitability. Finally, although the average profitability increase (+0.195 points) post-M&A appears modest, it reflects substantial variation across firms. This variance underscores the role of managerial execution, integration planning, and strategic alignment. The wide range observed in profitability, extending from significant losses to strong gains, suggests that while M&A holds potential for performance improvement, success is not uniformly achieved and may depend on firm-specific factors.

In examining the liquidity position, consistent with our study’s hypothesis, we observe a notable decline across all three key financial ratios: the current ratio, quick ratio, and cash ratio. The reduction in the current ratio suggests a potential weakening of short-term liquidity. Similarly, the drops in both the quick ratio and cash ratio indicate a diminishing capacity for companies to utilize their most liquid assets and cash to meet short-term liabilities. This change may be a consequence of M&A, which can lead to significant adjustments in a firm’s working capital structure, ultimately affecting its liquidity status. For example, the newly formed entity resulting from an M&A transaction might face difficulties in managing crucial components such as accounts receivable, inventory, or accounts payable. These challenges can have adverse implications for liquidity. Furthermore, the characteristics of the acquired assets can play a pivotal role in influencing liquidity. If these assets are less liquid or present complexities in conversion to cash, the overall liquidity position could be further compromised. This scenario underscores the importance of the CEO’s experience in managing asymmetric information throughout the M&A process and in negotiating favorable terms [6]. In addition to these factors, declining liquidity may also result from increased debt servicing obligations that accompany many M&A transactions. Companies may allocate a substantial portion of their cash flows to interest and principal payments, which constrains their flexibility to respond to short-term financial needs. This is particularly relevant when M&A activity is financed through significant leverage. The outcomes of this study are consistent with prior research, such as that conducted by Bedi [27], which analyzed firms two years after an M&A event, indicating that, on average, the liquidity position of these companies did not improve and, in fact, worsened. However, these findings stand in contrast to the results put forth by Aggarwal and Garg [32] and Muhammad et al. [41], who reported improvements in liquidity over the medium and long term.

In contrast to the notable shifts in profitability and liquidity, the solvency position appears to have shown improvement for two years post-M&A. During this timeframe, both the debt-to-equity and debt-to-asset ratios increased, suggesting that the solvency position undergoes substantial changes due to M&A, particularly at the point of acquisition. An enhancement in solvency may signal that the newly formed entity effectively managed its debt and proficiently utilized its assets. The improvement in solvency can be interpreted as a function of capital structure reconfiguration. In some cases, M&A transactions enable firms to restructure their liabilities, convert short-term debt into longer-term obligations, or refinance under more favorable conditions. While this increases overall leverage, it may reduce short-term repayment pressure, thus appearing as an improvement in solvency metrics. Moreover, some companies may proactively improve their solvency position post-M&A to meet lender requirements or rating agency expectations. However, this effect may be temporary and dependent on broader financial strategy. These results are divergent from the findings presented by Aggarwal and Garg [32] and Bedi [27], who indicated that firms did not experience improvements in solvency either two years post-M&A or in the medium to long-term.

This study reveals a complex landscape, indicating that M&A has varied impacts on financial performance. An explanation for these seemingly conflicting outcomes could be that the effect of M&A on financial metrics is contingent upon the specific timeframe considered. Furthermore, external factors, such as the prevailing economic conditions and industry trends, can significantly influence the implications of M&A on profitability. It is plausible that the new entity may need more than two years to fully realize operational and cost synergies resulting from the merger or acquisition. Additionally, as previously noted, these results may be influenced by strategic decisions made by management and their adeptness in navigating the M&A landscape [5,6,16]. For instance, a management decision to strategically reduce debt might enhance the solvency position while simultaneously exerting a negative impact on liquidity and profitability. However, a deeper investigation into the factors contributing to these outcomes lies outside the scope of this paper.

When evaluating the significance of the presented results using the paired sample t-test, only the outcomes related to liquidity reached statistical significance. This finding suggests the statistically significant negative impact of M&A on liquidity. Conversely, the results pertaining to profitability and solvency did not achieve statistical significance. This lack of significance indicates that there is insufficient statistical evidence to conclude that companies engaged in M&As experience a reduction in profitability and solvency following these transactions. One potential explanation for these findings may relate to the sample size; this study considered data from only two years before and two years after the M&A, which could result in a relatively small sample size. Furthermore, it is important to note that in the paired sample t-test, certain data are technically excluded, specifically those pertaining to years without M&A transactions. This loss of data is not accounted for in the analysis, unlike in the regression method, where such data is included.

In contrast, when conducting the regression analysis, the study yielded additional significant findings. Specifically, when employing regression with profitability and liquidity as dependent variables, the coefficients associated with the two years following the M&A were negative and accompanied by a significant p-value. This supports the hypothesis that companies are likely to experience declining profitability and liquidity post-M&A. The statistically significant coefficients suggest that the observed changes in profitability and liquidity during the specified periods following the M&A are unlikely to be due to random chance. However, the analysis did not produce significant results concerning solvency. The regression analysis, with solvency as the dependent variable, yielded coefficients with associated p-values that were insignificant. This result indicates that there is no statistically significant impact of M&A on the anticipated solvency index.

This study is framed within the concept of value creation during the M&A process. In this context, the resulting entity is expected to generate value that exceeds the combined worth of the individual entities for both the new entity and its shareholders, ultimately leading to enhanced profitability. However, this study challenges the conventional theory that M&As are always successful strategies; it aligns with selected cases in the literature review that illustrate M&As do not invariably yield positive outcomes, and results may require a longer period to emerge—or may not materialize at all. This research contributes to the existing literature by unveiling new insights that go beyond a mere focus on profitability; it also examines the liquidity and solvency positions of companies, thereby providing a more holistic understanding of their financial situation. The finding that profitability and liquidity may not necessarily improve as anticipated after M&A transactions, while solvency appears to be stable, presents intriguing implications. It suggests that a comprehensive analysis of these various factors is essential when assessing post-M&A performance since the motivations for M&A can significantly differ. For instance, some companies may pursue M&A to achieve greater financial stability and secure favorable financing terms, placing a higher priority on solvency than on profitability. Consequently, analyzing solely profitability might not furnish a complete picture in such circumstances.

This research examines data collected from 2017 to 2020, a particularly significant timeframe for analyzing M&A activities due to its historical context. During this period, the economy experienced notable volatility and uncertainty, primarily attributed to the COVID-19 pandemic. However, 2021 marked a significant resurgence in deal-making, indicating a potential initiation of the seventh wave of M&A activities. This resurgence can be attributed to favorable macroeconomic conditions, such as a low cost of capital, which encouraged acquirers to efficiently raise debt. Additionally, many deals that were originally planned for 2019 and 2020 were postponed to 2021, as the impact of the pandemic began to wane and society learned to adapt to the new reality. As a result, 2021 experienced a remarkable boom in M&A transactions [56]. Given the unique circumstances surrounding this period, it is essential for future research to continue exploring M&A activity within this specific timeframe, highlighting its distinctive characteristics.

Moreover, this study delivers new insights into M&A activities within the German market. Notably, the sample included in this research only comprises companies listed on the DAX40 index. The findings contribute to the limited literature focusing on the German market, striving to address existing gaps in research. It is important to note that most studies analyzed within the literature predominantly concentrate on other regions, resulting in a lack of focus on the European market, particularly Germany.

6. Conclusions

To conclude, this research aimed to assess the impact of M&A on the financial performance of German public companies. It utilized key accounting measures and conducted comparative analysis over two distinct timeframes: pre- and post-M&A activities. Building on the existing literature, the research suggests that M&As may adversely affect a company’s financial health. By employing quantitative research methods, including paired sample t-tests and regression analysis, this study found that M&As indeed have a negative impact on the financial position of companies. Specifically, the findings indicate that there is a significant negative effect on both profitability and liquidity, while a positive effect was observed on the solvency position of companies. This suggests that M&A, as a strategy for value creation, does not always yield the expected results, and any positive outcomes may take time to manifest.

6.1. Limitations

This study provides valuable insights into the financial outcomes of M&A among DAX 40 companies; however, several limitations warrant acknowledgment. These limitations also pave the way for future exploration. First, relying solely on accounting data to measure post-M&A performance means that non-financial performance metrics are overlooked. To gain a comprehensive understanding of M&A effectiveness, it is essential to consider non-financial dimensions. Second, comparing accounting performance across studies is challenging due to varying accounting standards in different countries, making such comparisons difficult. Furthermore, external factors, such as industry trends and economic conditions, which also influence M&A outcomes, were not considered in this study. This omission complicates the effort to isolate the specific effects of M&A on financial performance.

Third, the analysis indicated that the Q-Q plot suggests the normality assumption may not hold, raising concerns about the reliability of the p-values obtained. However, conducting a generalized regression with different distribution assumptions was beyond the scope of this paper’s statistical methodology. It is recommended that future research considers generalized regression with appropriate distributional assumptions, as this could yield more reliable results. The sample size used in this study may have also impacted the statistical significance of the results, so future studies should use a larger sample size to enhance the meaningfulness of the conclusions.

Fourth, the analysis does not delve into the underlying reasons why certain firms demonstrate superior post-M&A performance metrics compared to their peers. A comprehensive understanding of these outliers would necessitate access to confidential managerial data, including internal integration strategies, resource allocations, and operational execution post-merger—all of which are not publicly accessible. Consequently, this aspect lies beyond the current study’s scope, yet it presents a significant opportunity for future research employing case studies or interview-based methodologies.

Fifth, the study does not thoroughly investigate alternative causal explanations. Factors such as integration challenges, cultural misalignment, or macroeconomic disruptions (e.g., energy crises, inflationary pressures, or regulatory changes) may significantly influence the observed outcomes. However, developing a robust justification for each of these factors would require dedicated empirical investigation, extending the manuscript by several pages—an impractical endeavor given the word limit and the revision timelines. Thus, we recognize this analytical gap and propose that future research examine these dynamics in greater detail, potentially utilizing qualitative or mixed methods approaches.

Sixth, the methodology employed in this paper has inherent limitations. Although the emphasis on accounting-based metrics and OLS regression aligns with established empirical practices, the exclusion of certain firm-years may introduce survivorship bias. Firms that fail or are delisted shortly after M&A activity may not be included in the dataset, thus potentially skewing the results. Furthermore, the study is constrained by a two-year observation window, encompassing both pre- and post-M&A periods. While this timeframe facilitates a focused short- to medium-term analysis, it may not adequately capture delayed effects or long-term synergies that could emerge over an extended period. Future research could benefit from employing longer time series or rolling averages to further assess the robustness of the conclusions drawn. While firm-specific control variables were excluded to focus on overall M&A effects within the standardized context of DAX 40 firms, future research could explore heterogeneity across firm size, industry clusters, or governance structure to identify differential effects more precisely.

Additionally, the data utilized in this study is time series data, but the model employed for analysis did not account for this characteristic, as it was beyond the paper’s scope and limited by the size of the sample. Future research should construct a model that accommodates time dependence. Furthermore, while this study calculated indexes using various ratios to measure profitability, liquidity, and solvency, it did not account for inflation in the index calculations. Future research is advised to consider the impact of inflation on these indexes to produce more accurate representations.

6.2. Practical Implications

Besides contributing to the theoretical framework surrounding M&As, this work aims to assist managers with practical applications. The results emphasize the importance of monitoring key financial performance indicators—profitability, liquidity, and solvency—post-M&A. This monitoring helps to identify specific trends and characteristics, enabling managers to adjust their strategies based on both internal and external factors, including economic conditions, industry dynamics, and market trends. This information empowers managers to make more effective decisions and actions for the future success of their companies. The findings of this study offer several practical implications for corporate managers and investors involved in or evaluating M&A strategies. First, the observed decline in liquidity and profitability after an M&A transaction suggests that managers should exercise caution regarding short-term financial disruptions. These outcomes highlight the need for careful post-merger integration planning, particularly in areas such as working capital management, cost control, and operational efficiency. Financial and operational synergies may take time to materialize, which reinforces the importance of having realistic timelines and effective integration teams. Second, the modest improvement in solvency, contrary to the decline in other metrics, indicates that changes in capital structure, such as increased reliance on equity financing, may help stabilize the long-term financial position of acquiring firms. Managers can view this as an opportunity to restructure capital in a way that enhances balance sheet health, even if it comes at the cost of short-term liquidity. For investors, the results emphasize the importance of closely examining M&A announcements with the understanding that initial post-deal performance may include volatility and underperformance. Long-term benefits may not be immediately evident in profitability or liquidity metrics. Therefore, investors should monitor the execution of integration and financial adjustments beyond the typical short-term evaluation window.

6.3. Future Research

This study enhances our understanding of post-M&A financial performance among DAX 40 companies, but several areas for future research remain. First, extending the observation period beyond the current two years would provide a more comprehensive assessment of long-term performance. Many M&A-related synergies, particularly operational and financial ones, often take longer to materialize. A longer timeframe, such as five years or more, could better capture the full impact of strategic integration efforts.

Second, future research could adopt a more industry-specific focus by analyzing M&A outcomes within distinct sectors such as pharmaceuticals, IT, or manufacturing. While our study covers DAX 40 companies across various sectors, it does not explore sector-specific dynamics. An industry-level analysis could offer more tailored insights, as integration speed, capital intensity, and innovation cycles can vary significantly across sectors.

Third, future studies could incorporate cross-country comparisons to evaluate whether the M&A performance trends observed in Germany are consistent with those in other economies that have different corporate governance frameworks and financial reporting standards. Such comparisons could help determine whether the effects noted are specific to Germany or applicable across various markets.

Finally, qualitative approaches, such as case studies or executive surveys, could enrich the analysis by providing insights into strategic motivations, integration challenges, or managerial experience factors that are not accessible through publicly available financial data. For example, understanding why specific firms achieve more favorable post-M&A outcomes would require access to internal management information, which was beyond the scope of this study.