The Rule of 40 in European Companies: An Empirical Analysis of the Balance Between Growth and Profitability in the SaaS Industry Axel Lindskog & Axel Löf Supervisor: Ted Lindblom Master’s thesis in Accounting and Financial Management, Spring 2025 Graduate School, School of Business, Economics and Law, University of Gothenburg, Sweden Abstract This study examines the relevance of the Rule of 40, a benchmark that combines revenue growth and profitability, in the valuation of publicly traded European SaaS companies between 2019 and 2023. Using panel data regression with fixed effects, the analysis explores whether firms that meet or exceed the Rule of 40 receive higher valuation multiples compared to those that do not. The results show that from 2021 onward, companies that complied with the Rule of 40 consistently achieved valuation premiums. Revenue growth is found to have a strong and statistically significant positive effect on enterprise value relative to revenue, while most profitability measures show weaker or inconsistent associations. Among the profitability indicators, only gross margin demonstrates a stable and significant link to valuation. These findings suggest that investors in the European SaaS sector continue to emphasize growth. The increasing relevance of profitability may indicate a gradual shift in investor focus as the industry evolves and matures. Acknowledgements We would like to express our sincere gratitude to our supervisor, Ted Lindblom, whose support, guidance and insightful feedback have been invaluable throughout the writing of this thesis. His advice and encouragement played a crucial role in helping us navigate challenges and complete this work. We would also like to extend our appreciation to our fellow students for their thoughtful feedback and engaging discussions along the way. Axel Löf Gothenburg 22/05 2025 Axel Lindskog Gothenburg 22/05 2025 1. Introduction........................................................................................................................................ 1 1.1 Background..................................................................................................................................1 1.2 Problem Area...............................................................................................................................2 1.3 Purpose of the Study....................................................................................................................3 1.4 Research question....................................................................................................... 3 2. Theoretical Framework..................................................................................................................... 4 2.1 Software-as-a-Service..................................................................................................................4 2.2 Technological Companies Life Cycles........................................................................................ 5 2.3 Valuation Techniques...................................................................................................................7 2.4 Efficient Market Hypothesis........................................................................................................8 2.5 Rule of 40.................................................................................................................................... 9 2.5.1 Profitability...................................................................................................................... 11 2.5.1.1 Gross Profit Margin.......................................................................................................11 2.5.1.2 EBITDA........................................................................................................................ 12 2.5.1.3 Unlevered Free Cash Flow............................................................................................13 2.5.2 Revenue Growth............................................................................................................. 13 2.6 Metrics used in Rule of 40.........................................................................................................14 2.7 Hypothesis Development:..........................................................................................................16 3. Methodology..................................................................................................................................... 17 3.1 Research Strategy Overview..................................................................................................... 17 3.2 Research Design........................................................................................................................ 17 3.3 Sample Selection and Data Collection...................................................................................... 18 3.3.1 Descriptive Statistics and Correlation Matrix..................................................................19 3.3.2 Rule of 40 Classification and Group Comparison........................................................... 20 3.3.3 Panel Data Structure and Fixed Effects Model................................................................20 3.3.4 Data Preparation and Robustness Checks........................................................................21 3.4 Regression Model......................................................................................................................22 3.5 Statistical Significance.............................................................................................................. 24 3.6 Challenges and Limitations....................................................................................................... 24 3.7 Use of Artificial Intelligence (AI)............................................................................................. 24 4. Results............................................................................................................................................... 25 4.1 Descriptive Overview of Key Financial Metrics (2019–2023)................................................. 25 4.2 Correlation Matrix..................................................................................................................... 26 4.3 Valuation Effects of Meeting the Rule of 40 (Hypothesis 1).................................................... 27 4.4 Regression results (Hypothesis 2)............................................................................................. 27 4.5 Robustness Check: Excluding 2020.......................................................................................... 30 5. Analysis............................................................................................................................................. 32 5.1 Valuation Effects of Meeting the Rule of 40: Evaluating Hypothesis 1..................................32 5.2 Growth vs. Profitability in SaaS Valuation (Hypothesis 2)....................................................... 34 6. Conclusion.........................................................................................................................................39 6.1 Limitations.................................................................................................................................40 6.2 Future Research......................................................................................................................... 40 Reference list.........................................................................................................................................42 Appendix Appendix A: Dummy Variable 47 Appendix B: Descriptive Statistics 48 Appendix C: Hausman Test 51 Appendix D: Correlation Matrix 53 Appendix E: Panel data Regression 54 1. Introduction 1.1 Background Software-as-a-Service (SaaS) has changed the way software is developed, delivered and sold. Instead of buying a license and installing software locally, customers today access applications through the internet, usually by paying a recurring subscription fee (Roche, Schneider & Shah, 2020; Mäkilä et al., 2010). This shift began in the late 1990s and has allowed companies to lower upfront costs, improve scalability and simplify maintenance through automatic updates (Cespedes & van der Kooij, 2023; Roche et al. 2020). Over the past two decades, SaaS has grown rapidly and become the dominant business model for enterprise software, driven by the rise of cloud computing and changing customer expectations (Mäkilä et al., 2010). SaaS companies differ from traditional software firms not only in how they deliver their products but also in how they generate revenue. Instead of relying on one-time license sales, SaaS businesses focus on recurring income streams, with revenue typically earned monthly or annually (Laatikainen & Ojala, 2014). This creates a more predictable revenue base, but it also requires high upfront investments in customer acquisition, often leading to lower short-term profitability. These operational differences also translate into financial distinctions. Measuring the success of SaaS companies requires a different approach than for traditional firms. In the SaaS industry, high revenue growth is often considered a sign of strong market potential, even if profitability is delayed (Mäkilä et al., 2010; Kittlaus & Clough, 2009). This has led to the development of a specific performance benchmark commonly applied to SaaS firms. One of the most widely used and industry recognized is the Rule of 40, a simple benchmark that suggests that a company’s revenue growth rate and profitability margin should add up to at least 40% (Latka, 2024.; Hottenhuis, 2020). Although the Rule of 40 is often used as a quick health check for SaaS companies, its actual effectiveness as a predictor of valuation or long-term success remains a topic of debate. As the SaaS industry continues to evolve, with companies moving from pure growth to more mature operational models, it becomes important to examine whether the benchmark Rule of 40 still holds up, especially relevant when applied to other market contexts. Since the Rule of 40 originated in the U.S., where it was shaped by local investor preferences and growth 1 dynamics (Feld, 2015), it is important to examine whether the benchmark holds in different settings such as the European SaaS market. 1.2 Problem Area The valuation of SaaS companies has been widely debated in both academic literature and industry practice. McCoy (2022) argues that these characteristics complicate valuation and challenge the applicability of conventional models such as the Capital Asset Pricing Model (CAPM) and Fama-French Three-Factor Model. Lee (2024) states that SaaS firms often prioritize growth over short-term profitability. In response, investors and analysts have developed an alternative benchmark, the Rule of 40. Historically, investors have placed a stronger emphasis on growth over profitability. They have rewarded high-growth companies with higher valuation multiples, even if those companies had negative earnings (Algkvist Nordfors & Hansson, 2023). Research on Nordic B2B SaaS firms found that growth had a strong positive correlation with valuation multiples, whereas profitability metrics (EBITDA, EBIT) showed a negative relationship with valuation (Algkvist Nordfors & Hansson, 2023). This indicates that investors prioritized growth-oriented companies, even at the expense of profitability which contradicts the equal weighting implied by the Rule of 40. Recent industry data suggests a potential shift in investor sentiment. The Monterro Nordic B2B SaaS Benchmark Report 2024 indicates that while growth remains the most important metric, profitability has gained prominence among investors (Monterro, 2024). The report highlights that 50% of surveyed companies now rank profitability as a top priority, up from 38% in 2021. It also notes that firms with more than €10 million in revenue consider growth and profitability equally important valuation drivers. This suggests that the market may no longer favor high-growth loss-making SaaS companies to the same extent as before. This study investigates the validity of the Rule of 40 in a European context. Previous research has primarily concentrated on Nordic SaaS firms or those based in the US, leaving a gap in understanding how publicly traded SaaS companies in broader European markets navigate distinct financial, regulatory, and competitive environments. This study will specifically analyze publicly traded European SaaS companies over a five-year period (2019–2023). Because of this, it is important to study European SaaS firms separately to determine whether 2 the same valuation ideas still apply. This study aims to contribute new insights by examining the European SaaS market, a region that has received limited attention in prior research. 1.3 Purpose of the Study The purpose of this study is to evaluate the applicability and effectiveness of the Rule of 40 in publicly traded European SaaS companies. Given evolving investor sentiment, this study examines whether the historical emphasis on growth over profitability still holds or has shifted toward a more balanced valuation approach. By conducting an empirical analysis, the aim is to clarify whether firms that meet or exceed the Rule of 40 are valued differently from those that do not and whether investors still prioritize growth over profitability or now assign them equal importance. 1.4 Research question How does the Rule of 40 influence the valuation of publicly traded European SaaS companies and has the relative importance of growth and profitability changed during the years 2019-2023? 3 2. Theoretical Framework 2.1 Software-as-a-Service Mäkilä et al. (2010) describe SaaS as a technology as well as a business model. The authors explain that instead of selling software through traditional licenses and installations, SaaS companies provide their applications over the internet. This allows users to access the software through a subscription, which removes the need for expensive upfront costs and reduces maintenance efforts (Mäkilä et al., 2010). Using cloud computing and providing numerous clients with a shared system (multi-tenancy) that is easily scalable and capable of providing daily updates are two of the key characteristics of SaaS companies. One important takeaway is that SaaS is not just about the technology itself but also about how the software is sold and how companies make money from it. Mäkilä et al. (2010) highlights that SaaS businesses mainly earn through ongoing subscriptions rather than one-time sales. SaaS companies usually generate revenue by examining the relationship between software architecture and pricing strategies. Laatikainen and Ojala (2014) explain that most SaaS businesses rely on subscription-based models, where customers pay recurring fees monthly or annually for access to the software. In some cases, usage-based pricing is also implemented, where customers are charged according to their consumption of resources or specific features. SaaS companies focus on customer needs, providing flexible pricing, quick setup and easy integration with other cloud tools. Mäkilä et al. (2010) and Laatikainen and Ojala (2014) highlight several challenges faced by SaaS companies and the industry, including concerns about data security, service reliability and the high costs of attracting customers. To summarize, Mäkilä et al. (2010) list five common characteristics of SaaS companies. 1. Web-based access: SaaS applications are typically delivered over the internet and accessed through a web browser, eliminating the need for local installations on user devices. 2. Multi-tenancy: A single instance of the software serves multiple customers, meaning that the software is not tailored made for a specific client, allowing shared resources while maintaining data separation and security. 3. Scalability: SaaS solutions are designed to scale efficiently, enabling providers to accommodate growing numbers of users without major architectural changes. 4 4. Automated management: Software updates, maintenance and infrastructure management are handled by the provider, ensuring seamless upgrades and minimal downtime for users. 5. Pay-per-use pricing: SaaS companies typically employ a subscription-based or usage-based pricing model, where customers pay according to usage levels rather than a one-time license fee. Understanding the unique characteristics of the SaaS business model is crucial given the industry's relative youth and rapid growth over the past two decades. These distinctions not only affect how such firms are managed and grow, but also have important implications for how they are valued by investors and therefore are relevant for the thesis. 2.2 Technological Companies Life Cycles The concept of company life cycles, as discussed by Miller and Friesen (1984), suggests that organizations evolve through a series of distinct stages, each characterized by unique strategies, structures and decision-making approaches. Miller and Friesen (1984) identify five commonly recognized stages in the corporate life cycle: birth, growth, maturity, revival and decline. 1. Birth Stage: This initial phase is marked by the emergence of a new firm striving to establish itself as a viable entity. Typically these organizations are small, owner dominated and characterized by informal structures. Innovation is high as firms focus on niche strategies to differentiate themselves. 2. Growth Stage: As firms gain a foothold, they enter the growth stage, which is characterized by rapid sales expansion and the development of a more formalized structure. Control is increasingly delegated and innovation continues at a more moderate level. The focus is on broadening product-market scope and establishing functional structures to manage increased complexity. 3. Maturity Stage: In this phase, growth stabilizes and the organizational focus shifts towards efficiency and smooth operations. Bureaucratic structures become prevalent, and innovation slows. Companies concentrate on optimizing processes and maintaining market position rather than pursuing aggressive growth. 4. Revival Stage: Some organizations manage to renew themselves by offering new products and entering new markets. This stage involves adopting divisional structures 5 to handle greater complexity and emphasizes strategic planning and sophisticated control systems. High levels of innovation return as firms seek to re-establish growth momentum. 5. Decline Stage: Eventually, many firms experience stagnation as markets contract and product lines become outdated. The decline stage is marked by reduced profitability, limited innovation and often a focus on cost-cutting and survival strategies. Organizations at this stage typically exhibit conservative decision-making and centralized structures. Damodaran (n.d) has recently discussed a business life cycle model that is more effective for analyzing technology companies and their unique stages of development shown in figure 1. Figure 1: The Company Life cycle (Damodaran, n.d, p. 2). In the start-up stage, companies are small and often struggle to turn a profit. They usually rely on investments to survive because their expenses are higher than their revenues. The main challenge here is to prove that their business model works and attract enough customers but also investors. If the company manages to survive, it moves into the growth stage, similarly as Miller and Friesen discuss (1984). At this point, managing fast growth becomes the biggest challenge. The company needs more funding to expand and might even go public. Nevertheless, despite growing sales, profits can still be low because most of the money is reinvested to fuel growth (Damodaran, n.d). 6 Next is the mature growth stage which is similar to what Miller and Friesen explain (1984), where the company’s growth starts to slow down. Hopefully it now has a solid customer base and stable profits. The focus shifts to becoming more efficient and defending its market position from competitors. As growth continues to slow, the company reaches the maturity stage. At this stage, the market is often full and there are few new opportunities. The company generates a great deal of cash but does not have many profitable ways to reinvest it. As a result, it often returns money to shareholders through dividends or stock buybacks. Eventually, if a company fails to adapt or innovate, it enters the decline stage. Sales and profits shrink as products or services become outdated. At this point, the company might focus on cutting costs, selling parts of the business, or even merging with others to survive (Damodaran, n.d). Given their high growth rates and reinvestment strategies, most SaaS companies are likely positioned within Damodaran’s “young growth” or “high growth” stages. 2.3 Valuation Techniques Ali et al. (2010) argue that the conventional DCF framework is fundamentally deterministic and fails to account for the uncertainty that characterizes internet and tech-based companies like SaaS firms. In their view, compressing future possibilities into a single expected value oversimplifies the reality of strategic, high-risk firms. Damodaran (2010) highlights several fundamental limitations of the DCF model in these contexts. First, the model relies on long-term cash flow projections that require a high degree of certainty, which is rarely available in rapidly evolving markets like the market were SaaS firms operate in. In particular, estimating reliable revenue growth, margins, reinvestment needs and terminal values becomes speculative when a firm is not yet profitable or has an unproven business model. Second, DCF assumes a stable cost of capital, yet this too is questionable for young firms whose risk profiles change dramatically as they grow and scale (Damodaran, 2010). Cohen and Neubert (2018) emphasize challenges and methods involved in valuing SaaS companies similar to previously mentioned articles. SaaS firms like Salesforce, which adopt subscription-based and cloud-based software delivery models, require valuation techniques that diverge from traditional metrics due to their non traditional business models with rapid growth, high initial investments and reliance on recurring revenue. In addition, the authors highlight that revenue growth, rather than profitability, is the primary determinant of valuation in SaaS companies. Their case study on Salesforce.com demonstrates that while 7 DCF methods provided valuations consistent with market prices, traditional relative valuation methods underestimated the company's value due to its high growth phase. Mccoy (2022) also examines how traditional investment theories struggle to explain the valuation of SaaS companies, which as discussed earlier has seen rapid growth in recent years. Traditional models like the Capital Asset Pricing Model (CAPM) and Fama-French's Three-Factor Model fail to recognize the unique aspects of SaaS business models like Cohen and Neubert (2018) discuss. These companies show traits like predictable revenues, strong growth and high margins that are not effectively captured by conventional financial metrics. The study proposes alternative frameworks, such as the use of metrics like Enterprise Value relative to revenue (EV/Revenue). McCoy (2022) explain that EV/Revenue is a valuation multiple commonly used to assess the market value of a company relative to its revenue. It is calculated by dividing the company’s Enterprise Value (EV), which includes market capitalization, debt, and minority interest minus cash, by its total revenue. McCoy (2022) also highlights the importance of industry-specific factors, such as the subscription model’s customer retention advantages and the ability to scale globally with minimal additional capital. Newton and Schlecht (2016) provide an important extension to SaaS company valuation models by analyzing the dynamic relationship between revenue growth, profitability and valuation multiples. Their research shows that historically, investors and the equity market have prioritized revenue growth over profitability when valuing SaaS firms, particularly during periods of economic stability. However, during times of market stress, the importance of profitability metrics such as EBITDA margins increases, suggesting a more balanced view from investors. Their findings support the idea that while growth remains the dominant driver of SaaS valuations, profitability is gaining importance, especially as the market matures. This evolving emphasis aligns closely with the rationale behind the Rule of 40, which seeks to capture a balanced view of company health by considering both growth and profitability simultaneously. 2.4 Efficient Market Hypothesis The Efficient Market Hypothesis (EMH), first introduced by Fama (1970), asserts that financial markets are highly efficient in processing information, ensuring that stock prices at any given moment fully reflect all available data. As a result, investors cannot consistently 8 achieve excess returns beyond what is justified by the level of risk undertaken. According to Fama (1970), markets function as information-processing mechanisms where prices adjust rapidly and correctly to incorporate new information, eliminating opportunities for systematic arbitrage. This concept has since become a fundamental principle in modern financial economics, influencing theories of asset pricing, portfolio management and investment strategies. Fama (1970) categorizes market efficiency into three distinct forms: weak-form, semi-strong form and strong-form efficiency. Weak-form efficiency suggests that stock prices fully incorporate all historical trading data, including past prices and trading volumes. Consequently, technical analysis is ineffective, as patterns in historical prices provide no predictive power over future movements. This finding aligns with early research on random walk theory, where stock prices are observed to move unpredictably and independently of past values. Expanding on this, semi-strong form efficiency posits that stock prices not only reflect past trading information but also adjust instantaneously to all publicly available information, such as corporate earnings reports, macroeconomic indicators, and financial disclosures. Fama et al. (1969) provided empirical support for this by conducting an event study in which they analyzed how stock prices react to new public information. Their study demonstrated that stock prices respond almost immediately to announcements such as dividend changes, preventing investors from systematically achieving above-market returns through fundamental analysis. The strong-form efficiency hypothesis, the most comprehensive of the three, suggests that stock prices embed all information, including both public and private (insider) data. If markets were truly strong-form efficient, even corporate executives and institutional investors with privileged access to proprietary information would be unable to consistently earn abnormal returns. Fama (1970) acknowledges that achieving perfect efficiency under this framework would require an unrealistic assumption that no informational asymmetries exist. 2.5 Rule of 40 The Rule of 40 is a benchmark widely used in the SaaS industry to assess a company's performance by balancing growth and profitability. According to the Rule of 40, a SaaS company´s combined revenue growth rate and profit margin should meet or exceed 40%. 9 Algkvist Nordfors and Hansson (2023) and Lee (2024) describe this benchmark as a quick indicator of a company's financial health, noting that if a firm is growing rapidly, it can afford lower profit margins and vice versa (see Figure 2 for an illustration of the Rule of 40). However, empirical studies suggest that revenue growth tends to have a stronger correlation with valuation multiples than profitability, indicating that investors may prioritize growth over short-term margins when assessing SaaS firms (Hottenhuis, 2020; Lee, 2024). It is important to note that while the Rule of 40 provides a useful framework, it should not replace comprehensive financial analysis. Companies may achieve the 40% threshold through various combinations of growth and profitability and the specific context, such as company maturity and market conditions, should be considered when applying this rule. Research indicates that “Free Cash Flow Margin + Revenue Growth” is one of the most significant predictors of valuation multiples, but it should be used alongside other key financial metrics rather than as a standalone valuation method (Hottenhuis, 2020; Lee, 2024). The Rule of 40 serves as a valuable tool for evaluating SaaS companies, offering a straightforward way to assess whether a company is effectively balancing its growth ambitions with profitability to achieve long-term success (Algkvist Nordfors & Hansson, 2023; Hottenhuis, 2020; Lee, 2024). Although the Rule of 40 is a simplified benchmark, it continues to be widely referenced by investors, analysts and industry reports. That's why it makes it important to study how well it actually works, especially as markets and investor focus change. Since it is widely known and often used in real-life decisions, it is useful to test how relevant it still is today. Estrada (2012) challenges the approach by showing that high levels of economic growth do not automatically lead to strong investment returns. His research highlights the risk of overvaluing companies based solely on their growth potential, especially when investors ignore the importance of profitability and pay too much for future expectations. This perspective is highly relevant for SaaS firms, which often prioritize rapid revenue expansion over short-term earnings. 10 Figure 2: Illustration of the Rule of 40 2.5.1 Profitability Damodaran (2010) defines profitability as a fundamental measure of a firm's financial health and operational efficiency, reflecting its ability to generate earnings relative to its costs and expenses. Profitability also plays a crucial role in firm valuation, as investors and stakeholders use related metrics to assess financial viability. Profitability margin is a broad term encompassing various financial indicators, including cash flow, net income, operating profit margin and return on assets. Latka (2024) identifies EBITDA as a commonly used measure of profitability for SaaS companies. 2.5.1.1 Gross Profit Margin Novy-Marx (2013) describe gross profit as a fundamental accounting metric that represents the revenue a company retains after subtracting the cost of goods sold (COGS). It serves as a primary indicator of a firm’s operational efficiency, measuring how effectively a business transforms top-line sales into retained earnings before accounting for operating expenses, taxes, or financing costs. While gross profit refers to the absolute monetary value a company retains after subtracting the COGS from its revenue, gross profit margin expresses this amount as a percentage of total revenue. Because it isolates the direct profitability of a company's core products or services, gross profit can offer a more transparent view of underlying economic productivity than net income, which is often affected by discretionary accounting choices such as R&D capitalization or marketing spend. Novy-Marx (2013) demonstrates that gross profitability, defined as gross profits divided by total assets, is a powerful predictor of future stock returns, comparable to or even exceeding traditional value 11 indicators such as book-to-market ratios. The author argues that firms with strong gross margins tend to possess a durable competitive advantage, reflected in higher productivity and market valuation over time. This insight is particularly relevant for growth-oriented firms or industries where conventional profit metrics may understate long-term value creation. In the context of SaaS, gross profit plays an equally important role, especially due to the low marginal costs of delivering cloud-based services. Hottenhuis (2020) emphasizes that gross profit provides a clearer picture than revenue alone when assessing a firm’s scalability and unit-level economics. In SaaS firms, gross profit is typically calculated by subtracting costs such as server infrastructure, third-party licenses, and customer support from revenue. While revenue growth is often the primary performance signal for investors, gross profit offers a necessary check on whether that growth is sustainable and economically sound. 2.5.1.2 EBITDA Hottenhuis (2020) highlights EBITDA as a widely used indicator of financial performance, particularly in capital-intensive and high-growth industries such as SaaS. Damodaran (2010) explain that EBITDA is calculated by adding back interest, taxes, depreciation, and amortization to a company's net income. This metric provides a clearer view of a firm's operating performance by removing the impact of financing decisions, tax policies, and non-cash accounting charges. Since EBITDA standardizes earnings across firms, it is frequently used for comparing businesses with different capital structures, especially in valuation multiples like EV/EBITDA, which assess enterprise value relative to a firm’s EBITDA. One of the primary advantages of EBITDA is that it isolates a company's core operational profitability, making it a useful tool for assessing a firm's ability to generate cash flow from its main business activities. This is particularly relevant for SaaS firms, which often prioritize customer acquisition and revenue growth over short-term profitability (McCoy, 2022; Hottenhuis, 2020). Damodaran (2010) argues that despite its limitations, EBITDA remains one of the most commonly used profitability measures in corporate finance. Its ability to offer a standardized comparison of earnings makes it a valuable tool for investors, particularly in high-growth industries where firms operate at low or negative net profitability in their early stages. It 12 should be said though that it is more effective when used alongside other financial indicators, ensuring that capital expenditures and cash flow sustainability are properly considered in investment decisions. 2.5.1.3 Unlevered Free Cash Flow Free Cash Flow (FCF) is a key financial metric that indicates how much cash a company generates through its operations that is available to be distributed to stakeholders or reinvested in the business. Agrawal (2023) presents it as a strong indicator of a company's financial health and value creation capacity. For investors and analysts, FCF is particularly important because it reflects actual cash generated, rather than accounting profits, which can be influenced by non-cash items. One common variant is Unlevered Free Cash Flow (UFCF), which represents the cash flow available to all capital providers, both equity and debt holders, before interest payments are made. Agrawal (2023) explain that the calculation starts with net income after tax, then adds back non-cash expenses such as depreciation, adjusts for after-tax interest, and subtracts capital expenditures and changes in working capital. UFCF is widely used in valuation models because it gives a cleaner view of the firm’s core cash-generating ability without the effects of financing structure. A simplified formula often used for UFCF is: UFCF = Net Income + After-Tax Interest + Depreciation & Amortization – Capital Expenditures – Change in Working Capital In this thesis, UFCF will be used as an independent variable in effort to capture the company's core operations since it excludes debt payments. 2.5.2 Revenue Growth In the context of SaaS company valuation, growth is commonly understood as the rate at which a company increases its revenue year over year. McCoy (2022) defines growth specifically as annual revenue growth and uses this metric in his empirical analysis of 13 publicly traded SaaS firms. This measure reflects how quickly a company is expanding its customer base, scaling its operations, or increasing usage of its services. Revenue growth is a fundamental financial indicator used to assess a firm’s expansion over time. It reflects the company’s ability to increase its sales and market presence, which is crucial for attracting investors and sustaining long-term performance. Ghosh et al. (2005) show that firms exhibiting consistent revenue growth tend to experience higher earnings quality and improved future operational outcomes. Revenue, as a top-line measure, can offer a clearer picture of market traction and customer demand, especially when profit margins are temporarily suppressed due to reinvestment in growth. It shows that firms demonstrating sustained growth in both earnings and revenues tend to exhibit higher earnings quality, characterized by greater persistence, lower earnings management and stronger future operational performance. Their study highlights that it is not merely growth in revenue or earnings alone, but their combination over time that signals underlying business strength and improves the credibility of reported financial results. McCoy (2022) emphasizes that growth plays a particularly important role in the early stages of a SaaS company’s lifecycle. At this point, firms often prioritize expanding revenue over maintaining profitability, as aggressive investment in sales, marketing and product development is seen as a strategy for long-term success. In this sense, revenue growth is not only a performance indicator but also a strategic choice that signals market potential and an appeal to investors. 2.6 Metrics used in Rule of 40 Although the Rule of 40 is a widely recognized benchmark for evaluating SaaS company performance, its exact formulation varies considerably across studies. As shown in Table 1, different scholars and institutions emphasize distinct combinations of growth and profitability metrics when applying the Rule of 40. 14 Study ARR Sales Revenue Operating Gross FCF % of EBITDA Multiple used Growth Growth Growth Profit % profit Revenue Margin % of revenue Growth Feld (2015) x x - Latka x x x x x - (2024) Algkvist x x EV/S Nordfors and Hansson (2023 Monterro x x - (2024) x x x x EV/Revenue Hottenhuis, M. (2020) EV/Gross Profit x x Price-To-Sales Lee (2024) Ratio Table 1: Rule of 40 combinations The most commonly cited foundation comes from Feld (2015), which defines the Rule of 40 as the sum of ARR growth and EBITDA margin. Feld (2015) also recognizes that not all public companies report ARR. As a result, many practitioners use revenue growth as a proxy. Other studies offer broader or alternative interpretations. For instance, Latka (2024) proposes a more comprehensive formulation that includes ARR growth, revenue growth, gross profit growth, FCF margin and EBITDA margin, thus providing a more holistic view of both top-line expansion and cash flow generation. The study by Algkvist Nordfors and Hansson (2023) narrows the focus to operating profit margin and FCF, aligning more with internal profitability and cash efficiency and utilizes EV/Sales (EV/S) as the valuation multiple. Meanwhile, Monterro (2024) applies a growth-oriented lens, combining ARR growth and FCF margin, without referencing any specific valuation multiple. This variation underlines the flexibility of the Rule of 40 framework depending on analytical purpose and data availability. To benchmark the 15 methodological choices, this study also includes the original work by Hottenhuis (2020), who evaluates multiple configurations including gross profit growth and uses both EV/Revenue and EV/Gross Profit as valuation outcomes. This aligns with a broader industry trend where investors increasingly rely on gross margin-adjusted performance metrics. Taken together, Table 1 illustrates the diversity in how growth and profitability are operationalized within the Rule of 40 framework, reinforcing the need for a careful and transparent methodological design in empirical research. This study extends the existing literature by empirically comparing the explanatory power of various combinations in predicting valuation multiples across European SaaS companies. 2.7 Hypothesis Development: Building on the theoretical and empirical foundations outlined in the previous sections, this study introduces two hypotheses to explore the relationship between the Rule of 40 and company valuation in the European SaaS industry. These hypotheses aim to evaluate both the relevance of the Rule of 40 as a performance benchmark and the individual roles of growth and profitability in influencing valuation multiples. Through this analysis, the study seeks to determine whether investors assign higher valuations to companies that meet the Rule of 40 and whether one component, growth or profitability, plays a more dominant role in shaping these valuations. H1: SaaS companies that meet or exceed the Rule of 40 achieve higher valuation multiples (e.g., EV/Revenue) compared to those that do not. - This hypothesis tests whether investors systematically reward companies that surpass the 40% threshold in revenue growth and profitability with premium valuations. H2: The impact of the Rule of 40 on valuation multiples is primarily driven by revenue growth rather than profitability. - This hypothesis tests whether investors place more weight on revenue growth over profitability when applying the Rule of 40, suggesting that high-growth SaaS companies are valued more favorably even if they have lower profit margins. 16 3. Methodology 3.1 Research Strategy Overview This study aims to examine how the Rule of 40 relates to the valuation of publicly traded European SaaS companies. The goal is to understand whether meeting or exceeding the Rule of 40 leads to higher valuation multiples and to explore whether growth or profitability is more important from an investor perspective. To investigate this, a quantitative research approach is used based on financial data from real companies. The study is designed around three main parts. First to analyze how companies are valued in public markets, looking specifically at the valuation multiple EV/Revenue. Secondly test whether and how the Rule of 40 explain company valuations by comparing firms that meet the rule to those that don’t. Finally to test how different financial metrics like growth and profitability relate to these valuation multiples. This structure allows us to look at both the individual effects of financial metrics and the broader usefulness of the Rule of 40. The time period 2019–2023 is chosen for two main reasons. First, although the Rule of 40 was introduced in 2015 (Feld, 2015), it appears to have gained broader recognition in recent years. Using earlier data may therefore capture firms that had not yet adjusted their strategies or reporting practices in line with the metric. Second, the selected period includes major macroeconomic developments such as the COVID-19 pandemic and rising interest rates, providing a relevant context for analyzing shifts in investor preferences regarding growth and profitability. 3.2 Research Design This study adopts a deductive research approach, in which theoretical assumptions drawn from existing literature are tested using empirical data. According to Saunders et al. (2023), a deductive approach involves formulating hypotheses based on established theory and then designing a research strategy to confirm or reject these hypotheses through systematic observation and analysis. This design is well suited for the present study, which aims to assess the applicability of the Rule of 40 within a new empirical setting: publicly traded European SaaS companies. The study is driven by hypothesis and explanatory in nature. Two main hypotheses guide the analysis. Both hypotheses are grounded in prior research (e.g., Hottenhuis, 2020; Lee, 2024; 17 Algkvist Nordfors & Hansson, 2023), which suggests that while investor preferences may be shifting, particularly during macroeconomic uncertainty such as the COVID-19 pandemic, growth remains a dominant factor in valuation. However, this study tests these assumptions in a new geographical context, focusing on publicly traded European SaaS firms, where market behavior and financial reporting standards may differ from those in the U.S. markets. To test these hypotheses, the study uses a quantitative methodology with panel data regression models, capturing firm level observations between 2019 and 2023. This longitudinal structure enables the identification of patterns over time and allows for the assessment of temporal shifts in valuation logic, particularly in response to market changes such as rising interest rates or changing investor sentiment. 3.3 Sample Selection and Data Collection This study employs a quantitative research approach to examine the relationship between key financial and operational metrics and the valuation of publicly traded software companies in Europe, with a particular focus on SaaS firms. The analysis aims to identify the most influential financial indicators in explaining valuation multiples, such as enterprise value-to-revenue. A correlation analysis and multiple regression-based methodology will be used to assess the strength and significance of these relationships. The sample consists of publicly traded software companies in Europe, categorized under industries which are application software, systems software, and internet services and infrastructure as presented in table 2. The selection criteria require that companies include software in their business description but exclude those that include hardware in the description. The exclusion of hardware-related companies is based on their structural and financial differences from SaaS firms. Hardware businesses typically involve large upfront capital investments and one-time product sales, whereas SaaS companies operate with lower capital intensity and rely on recurring, cloud-based revenue (Kittlaus & Clough, 2009). Including mixed-model firms would reduce comparability and obscure whether valuations are driven by SaaS operations or hardware sales. Focusing solely on pure SaaS firms ensures a more consistent sample and clearer conclusions about the role of recurring revenue and growth. As Mäkilä et al. (2010) note, SaaS is not only a delivery model but a distinct business model emphasizing scalability, multi-tenancy, and subscription-based pricing. These features 18 directly shape financial performance and valuation, making SaaS firms especially relevant for analyzing the Rule of 40. Filtering for companies with a market cap greater than zero ensures that only active, publicly traded businesses are included. This removes delisted or bankrupt companies but also avoids errors in the data and makes the analysis more reliable overall. The final sample consists of 106 publicly listed European SaaS companies that meet the criterias (see Table 2). It needs however to be said that the panel is unbalanced, as not all firms are present in every year. Some companies entered the sample in later periods due to IPOs and some companies can be delisted This structure is typical in financial datasets and does not pose a problem for estimation. Table 2: Data criteria retrieved from Capital IQ Pro The financial data is collected from multiple reputable sources. Capital IQ Pro will serve as the primary data provider for financial metrics such as revenue growth, EBITDA margins, and valuation multiples, including enterprise value-to-revenue. 3.3.1 Descriptive Statistics and Correlation Matrix The analysis begins with descriptive statistics to provide an overview of the key variables across the sample. This step includes measures of central tendency and dispersion for 19 valuation multiples, revenue growth, profitability margins, and firm age over the five-year period from 2019 to 2023. This is presented in section 4.1. As part of the descriptive statistics, a pairwise correlation matrix was calculated to examine the linear relationships between the key variables in the dataset. This step was used to assess potential multicollinearity issues and to better understand the associations among revenue growth, various profitability metrics, and valuation multiples. The matrix also informed the model specification choices in Chapter 4, including the exclusion of EBIT margin in the full model due to its high correlation with EBITDA margin. The results of the correlation matrix are presented and discussed in Section 4.2. 3.3.2 Rule of 40 Classification and Group Comparison To evaluate the effect of the Rule of 40 as a benchmark, a dummy variable approach is first employed. For each year and profitability metric, firms are classified into two groups: those that meet or exceed the Rule of 40, and those that do not. A binary variable distinguishes between these groups, enabling a comparison of their average EV/Revenue multiples. This comparative analysis is conducted separately for each profitability metric across all five years, resulting in a total of twenty models and is presented in section 4.3. 3.3.3 Panel Data Structure and Fixed Effects Model This study employs an unbalanced panel data regression, a widely used econometric technique for analyzing firm-level data across both time and entities. Panel data makes it possible to control for unobserved heterogeneity, meaning firm-specific characteristics that are constant over time but may systematically influence the dependent variable. Failing to account for such heterogeneity can result in biased and inconsistent estimates, particularly in financial research where structural differences between firms are common (Baltagi, 1999). As Wooldridge (2013) explain, fixed effects models remain valid and consistent with unbalanced panels, provided the absence is not systematically related to the error term. The fixed effects estimator uses the available time periods for each firm to capture within-firm variation while controlling for all time-invariant firm-specific characteristics. As presented in table 4, total observations for the regression is 407. The fixed effects estimator eliminates time-invariant heterogeneity by focusing solely on within-entity variation, making it well suited for panel data settings where factors such as firm culture, management quality, or strategic orientation may influence both profitability and valuation (Baltagi, 1999; 20 Wooldridge, 2013). To formally assess the suitability of this model over a random effects specification, a Hausman test was conducted. The test evaluates whether the individual-specific effects are correlated with the independent variables, if so, the random effects estimator becomes inconsistent and fixed effects should be preferred. A statistically significant chi-squared statistic indicates systematic differences between the two models, supporting the use of fixed effects.The test results showed a statistically significant difference between the estimators (p < 0.001), thereby confirming that the fixed effects model is the more appropriate choice for this analysis (Hausman, 1978). The formal statistical justification, including the Hausman test output, is presented in section 4.4. 3.3.4 Data Preparation and Robustness Checks In this study the dependent variable, EV/Revenue, is transformed using the natural logarithm to reduce skewness and mitigate the influence of extreme outliers. Since the dependent variable is strictly positive (EV/Revenue can not be negative) and the analysis focuses on relative changes, applying a logarithmic transformation to the dependent variable is appropriate and ease interpretation in percentage terms for the study (Wooldridge, 2013). The independent variables are kept in level form. This results in a ln-linear regression model, where the estimated coefficients are interpreted as semi-elasticities which means that when a one-unit increase in an independent variable, it corresponds to an approximate percentage change in EV/Revenue equal to 100⋅β% (Wooldridge, 2013). This interpretation is important in the context of SaaS company valuation, where relative changes in valuation multiples are meaningful to investors and a common way to measure in economics & finance. By expressing effects in percentage terms, the model provides a more intuitive understanding of how financial performance metrics influence valuation outcomes across firms of different sizes and maturity levels (Wooldridge, 2013). The independent variables used in all regressions are instead of using the natural logarithm, winsorized at the first and 99th percentiles.This is because the independent variables could be negative and because of that problematic to use ln. Winsorization replaces extreme values with the nearest value within the specified percentile threshold, helping to preserve the overall structure of the data while reducing the distortion that a few influential observations might introduce (Lien & Balakrishnan, 2021). This is important in SaaS company data, where certain firms may exhibit high growth rates or valuation multiples due to one-off events, acquisitions or short-term market sentiment. Winsorization improves the robustness of coefficient estimates 21 and reduces the risk of heteroscedasticity and non-normal residuals, without having to delete valuable data. As part of the descriptive statistical analysis, kurtosis is calculated to evaluate the shape of the distribution of key financial variables. Kurtosis is based on the fourth standardized moment of a distribution and reflects the weight of the tails relative to a normal distribution. Higher kurtosis values indicate heavier tails, meaning that extreme values occur more frequently than in a normal distribution. This is particularly relevant in financial datasets, where outliers may distort model estimations and violate the assumption of normality. Identifying variables with high kurtosis can therefore justify the application of transformations or robust regression techniques to improve model reliability (Wooldridge, 2013). All transformations, including natural logarithm conversion of the dependent variable and winsorization of independent variables, were applied only for the regression analysis. Descriptive statistics are based on untransformed raw data to maintain transparency regarding the underlying distributions but also to show the importance to deal with the skewness and outliers in the data. To ensure valid inference in the presence of within-firm correlation over time, all regressions employ clustered standard errors at the firm level. This adjustment accounts for potential serial correlation and heteroskedasticity within each firm across the five year period. With over 80 firm-level clusters in the dataset, this approach provides reliable standard error estimates and is considered appropriate in panel data settings (Cameron & Miller, 2015). Lastly, given the extraordinary impact of the COVID-19 pandemic on financial performance and market valuations in 2020, we conduct a robustness test by re-estimating our fixed effects panel regressions while excluding all observations from the year 2020. This allows us to examine whether the core relationships observed in the full sample are influenced by pandemic-related distortions. The results of this robustness check are reported and discussed in the results section 4.5. 3.4 Regression Model The regression analysis in this study aims to examine how individual profitability metrics influence SaaS company valuations, while systematically accounting for the role of revenue growth and firm maturity. The dependent variable is EV/Revenue, and the key independent 22 variables are drawn from the growth and profitability components relevant to the Rule of 40 framework. To ensure consistency and comparability across models, revenue growth and firm age are included as baseline control variables in all regression specifications. Revenue growth is included due to its central role in both SaaS valuation and the Rule of 40, where it is often the primary signal of firm performance (Lee, 2024; Cohen & Neubert, 2018). Firm age was initially included to control for differences in lifecycle stage which theory suggests may influence valuation. While statistically excluded due to collinearity with time effects, it remains conceptually relevant and was accounted for through year and firm fixed effects. Each regression model includes only one profitability metric at a time, either EBITDA margin, EBIT margin, gross margin, or UFCF margin, to avoid multicollinearity between overlapping measures of financial performance. This approach isolates the effect of each profitability measure and allows for a more precise assessment of its individual contribution to valuation. The general form of the panel data regression model is as follows: where: ln(EV/Revenue ) is the natural logarithm of enterprise value to revenue for firm i at time t. RevenueGrowth is the annual revenue growth rate. ProfitabilityMetric is one of the four profitability indicators (included separately per model). μ i represents firm fixed effects, λ t represents year fixed effects, δ s represents industry fixed effects, and ε it is the error term. In addition to the individual model specifications, a full model including revenue growth and multiple profitability metrics (excluding EBIT) is estimated and presented in table 23, appendix E. This serves as a robustness check and reflects how investors may evaluate companies based on a broader set of financial indicators. EBIT is excluded from the full model due to its high correlation with EBITDA and weaker performance in the standalone regressions. 23 3.5 Statistical Significance To evaluate the strength and reliability of the relationships between growth, profitability and valuation multiples, this study makes use of statistical significance testing. The interpretation of p-values at the 1%, 5% and 10% levels allows for a structured assessment of whether observed effects are likely to have occurred by chance or reflect genuine underlying associations within the population of European SaaS firms. A significance level of 5% is applied, meaning that a result is considered statistically significant if the probability of it occurring under the null hypothesis is less than 5%. Statistical significance is a widely used criterion in quantitative research for hypothesis testing, providing an indication of how confident one can be in drawing inferences from sample data. As Saunders et al. (2023) emphasize, statistical significance helps researchers determine the probability that a given result could have arisen under the null hypothesis, thereby enabling more rigorous conclusions. 3.6 Challenges and Limitations Since ARR was not consistently available for all firms in the sample, revenue growth was used as a proxy for growth. This is considered appropriate for pure SaaS companies, where subscription-based business models typically ensure that ARR and revenue growth track closely. The substitution is also consistent with prior studies and industry practice, where revenue growth is commonly used when ARR data is unavailable (SaaS Metrics Standards Board, 2023; Feld, 2015) 3.7 Use of Artificial Intelligence (AI) During the preparation of this thesis, the generative AI tool ChatGPT from OpenAI, was used selectively to support the writing and editing process. AI was employed primarily for language refinement, structural suggestions and clarity improvements in accordance with academic integrity guidelines. All analytical reasoning, empirical analysis, and interpretation of results were conducted by the authors. Importantly, AI was not used to generate original content, fabricate data, or bypass any methodological work. Its role was limited to improve formulation and coherence of the text based on drafts written by the authors. 24 4. Results This section presents the findings in four parts: a descriptive overview of the dataset (4.1), a correlation matrix (4.2), group comparisons based on Rule of 40 compliance to evaluate Hypothesis 1 (4.3), and panel regression results assessing the individual impact of growth and profitability on valuation to evaluate Hypothesis 2 (4.4) 4.5 presents a robustness check in which the year 2020 is excluded to test whether the main results are sensitive to potential distortions caused by the COVID-19 pandemic. 4.1 Descriptive Overview of Key Financial Metrics (2019–2023) Before presenting the regression results, it is valuable to begin with a descriptive analysis of the sample in order to better understand the broader financial trends and contextual shifts within the European SaaS industry between 2019 and 2023. Table 3 summarizes the raw data for EV/Revenue, revenue growth, EBITDA margin, EBIT margin, UFCF margin, gross margin and lastly firm age. Each table of descriptive statistics is presented in appendix B, table 10-17. This approach preserves the natural distribution of each variable, including outliers and supports the rationale for data treatment applied in the regression analysis later. Table 3: Table of Descriptive Statistics The EV/Revenue multiple has a mean of 4.63 and a median of 2.73, with values ranging from 0.13 to 77.83. Revenue growth averages 26%, with a minimum of -81% and a maximum of 570%, indicating a wide variation across firms and time periods. EBITDA margin and EBIT margin both exhibit negative averages with -9% and -16% respectively, with large standard deviations and minimum values below -800%, reflecting the presence of highly unprofitable firms in the dataset. Gross margin averages 43% and is more stable but still a large spread ranging between -190% to 97%. The UFCF margin also shows high variability, with a mean of -5% and a maximum of 79%. Firm age has a mean of 18.86 years and ranges from 0 to 84. 25 Skewness and kurtosis values indicate that most variables are highly skewed and exhibit heavy tails. Table 3 provides support for the data treatment choices outlined in the methodology. The wide range and skewed distribution of EV/Revenue, along with the presence of extreme values in both growth and profitability metrics, highlight the need to address outliers and distributional irregularities. These characteristics motivated the ln transformation of EV/Revenue and the winsorization of all independent variables at the 1st and 99th percentiles. These steps were taken to improve the robustness of the regression analysis by reducing the influence of extreme observations, and are directly supported by the patterns observed in the underlying data. To show what natural logarithm transformation does to dependent variable EV/Revenue, table 11 is presented in appendix B, where skewness is reduced to 0,146 and kurtosis to 2,904. 4.2 Correlation Matrix The pairwise correlation matrix is presented in Appendix D, table 22, includes the main variables used in the regression analysis. Revenue growth shows a positive correlation with EV/Revenue multiples (r = 0.23, p < 0.01). EBITDA margin and EBIT margin are highly correlated with each other (r = 0.99), confirming the need to exclude one in regression analysis to avoid multicollinearity. Gross margin displays moderate positive correlations with both EBITDA margin (r = 0.47) and EBIT margin (r = 0.49). UFCF margin has weaker correlations with the other profitability metrics. Firm age is negatively correlated with EV/Revenue, while revenue growth and profitability metrics show only weak correlations with each other. The correlation matrix was used to inform model specification and variable selection in the regression analysis, particularly the decision to exclude EBIT margin in the full model due to multicollinearity. 26 4.3 Valuation Effects of Meeting the Rule of 40 (Hypothesis 1) SaaS companies that meet or exceed the Rule of 40 achieve higher valuation multiples (e.g., EV/Revenue) compared to those that do not. The appendix A includes a series of five tables (table 5-9) that illustrate how SaaS companies performed from 2019 to 2023 in relation to the Rule of 40. The companies are divided into two groups, those falling below the Rule of 40, and those meeting or exceeding it. For each year, the tables show four different profitability metrics: EBITDA, EBIT, Gross Margin, and Unlevered Free Cash Flow. Each table also presents the number of observations in each group, the average EV/Revenue multiples, the difference in valuation between the groups, and the corresponding p-values. In the first two years, 2019 and 2020, the data reveals that there is no consistent pattern in how valuation multiples differ between the groups. In some cases, the average EV/Revenue multiple is even slightly lower for companies that meet or exceed the Rule of 40. The p-values are high during these years, indicating that the differences in valuation are not statistically significant. From 2021 onward, however, a more distinct trend begins to take shape. For all four metrics, companies that meet or exceed the Rule of 40 show higher average valuation multiples than those that fall below. These differences become increasingly pronounced each year. In 2023, the gap between the two groups is especially notecable. For instance, companies meeting the Rule of 40 based on EBIT have an average EV/Revenue multiple of 7.71, while those below the threshold average just 2.49 (Appendix A, Table 9). Importantly, the p-values for these differences from 2021 to 2023 are statistically significant across several of the metrics. This suggests that the observed valuation premiums for companies that meet or exceed the Rule of 40 during these years are unlikely to be due to chance. Taken together, the figures provide clear, year-by-year insight into how company valuations have evolved in relation to the Rule of 40. 4.4 Regression results (Hypothesis 2) The impact of the Rule of 40 on valuation multiples is primarily driven by revenue growth 27 rather than profitability. This section presents the results of the panel regression analysis conducted to examine the relationship between revenue growth, various profitability metrics, and EV/Revenue multiples for publicly listed European SaaS companies from 2019 to 2023. To start off, it was needed to confirm the suitability of using fixed effects in the panel regressions and Hausman tests were conducted for each model specification (Appendix C, table 18-21). In all four cases, corresponding to the EBITDA, EBIT, gross margin, and UFCF margin models, the test rejected the null hypothesis that the differences in coefficients between the fixed effects and random effects models are not systematic. The chi-squared statistics ranged from 13.81 to 40.35, with p-values below 0.01 in all models. These results indicate that the fixed effects estimator is more appropriate, as it accounts for unobserved firm-level heterogeneity that is likely correlated with the explanatory variables. Consequently all regressions in this analysis are estimated using firm fixed effects. In all four model specifications, revenue growth is statistically significant at the 1% level as presented in table 4. The coefficient estimates range from 0.144 in the model including gross margin to 0.170 in the model with UFCF margin. A coefficient of 0.170 on revenue growth implies that a one percentage point increase in revenue growth is associated with a 17% increase in the EV/Revenue multiple. The profitability metrics show mixed results. EBITDA margin, EBIT margin and UFCF margin do not show statistical significance at the 5% level. Gross margin is statistically significant at the 1% level with a coefficient of 0.658 in its respective model. In the main panel regressions, firm age was included as an independent variable to investigate its potential influence on EV/Revenue multiples. However, due to the inclusion of both firm and year fixed effects, the variable was automatically excluded from the estimation by the regression command. This occurs because firm age increases deterministically over time for each firm and is therefore perfectly collinear with the year fixed effects. As a result, its effect cannot be separately identified within a fixed effects framework that controls for both firm-level and year-specific unobserved heterogeneity. The adjusted R-squared values across the models range from 0.769 to 0.778 are only slightly lower than the corresponding R-squared values, indicating that the included variables meaningfully contribute to explaining valuation. This suggests a strong model fit with no 28 signs of overfitting. The F-statistics across all models range from 4.475 to 10.590 as presented in table 4, indicating that the regressions are mutually statistically significant. This means that the independent variables, taken together, contribute meaningfully to explaining variation in valuation multiples (ln_ev_revenue). Model (3), which includes gross margin, shows the strongest significance with the highest F-value. Table 4: Panel Regression Results Note: Standard errors clustered at the firm level in parentheses. Figure 3 presents the predicted EV/Revenue multiples by year, based on the fitted values from the log-linear fixed effects regressions. Each line represents the average predicted valuation for a given year under a specific model specification, depending on which profitability metric was included. The values have been exponentiated from their log-transformed form to reflect actual EV/Revenue multiples. The purpose of the figure is to visualize how predicted valuation levels vary over time across models, using different profitability measures. The figure shows that predicted valuation multiples rose from 2019 to a peak in 2021, followed by a decline in 2023. This trend aligns with broader market 29 developments, including strong investor sentiment during the COVID-19 recovery period and a following correction as financial conditions tightened. Across all years, the model including gross margin consistently predicts the highest valuation multiples, supporting the regression finding that gross margin is the most strongly associated profitability metric with high valuation. The differences between models are more pronounced during the high-valuation period of 2021 and 2022. Figure 3: Predicted EV/Revenue by year and model. Based on fixed effects panel regressions including year dummies. Note: The regression models use ln(EV/Revenue) as the dependent variable. The predicted values shown in Figure 3 have been exponentiated to reflect actual EV/Revenue multiples for clearer interpretation. 4.5 Robustness Check: Excluding 2020 To assess the stability of our results, data from the year 2020 were excluded and re-estimate the fixed effects regressions. The year 2020 was marked by pandemic-related volatility, which may have introduced noise or structural shifts in growth, profitability, or valuation. 30 As shown in appendix E, Table 24, revenue growth remains statistically significant across all model specifications, with slightly higher coefficients compared to the full-sample model. This indicates that the growth-valuation relationship is not driven by pandemic effects and is robust to the exclusion of 2020. Gross margin also remains significant, reinforcing its importance as a valuation driver. Model fit (Adjusted R-squared) and explanatory power (F-stat) remain strong, and the consistency of these metrics supports the overall robustness of the findings. 31 5. Analysis This chapter analyzes the result in relation to the theoretical framework discussed in Chapter two. It assesses the validity of the Rule of 40 as a valuation benchmark and evaluates whether growth or profitability has been the dominant driver of enterprise value among European SaaS companies between 2019 and 2023. 5.1 Valuation Effects of Meeting the Rule of 40: Evaluating Hypothesis 1 To evaluate Hypothesis 1, which states that firms meeting or exceeding the Rule of 40 achieve higher valuation multiples than those that do not, yearly comparisons were conducted of the EV/Revenue multiples for the two groups from 2019 to 2023. Appendix A presents the average multiples for compliant and non-compliant firms based on four profitability metrics: EBITDA, EBIT, Gross Margin, and UFCF margin. In the years 2021 through 2023, a clear pattern emerges (Appendix A, Table 7-9). Across all profitability definitions, Rule of 40-compliant firms display higher average EV/Revenue multiples than non-compliant firms. Most of these differences are statistically significant at the 5% level or lower, particularly when EBIT and EBITDA margins are used. For example, in 2023, companies that met the Rule of 40 based on EBIT margin had an average EV/Revenue multiple of 7.71, compared to 2.49 for those that did not, with a p-value below 0.01 (Appendix A, Table 9). Based on the statistically significant differences observed from 2021 onward, Hypothesis 1 cannot be rejected for those years. These results are consistent with the theoretical literature, where the Rule of 40 is frequently cited as a practical performance benchmark for SaaS companies (Feld, 2015; Hottenhuis, 2020; Latka, 2024). Several sources suggest that adhering to the Rule of 40 sends a positive signal to investors, combining growth with a baseline of financial discipline (Feld, 2015; Latka, 2024; Hottenhuis, 2020; Monterro, 2024). The metric has been increasingly adopted as a proxy for sustainable SaaS performance. The rising importance of Rule of 40 compliance in the post-2020 period aligns with these views. In contrast, during 2019 and 2020, no consistent valuation premium is observed for Rule of 40-compliant firms. These years show no statistical significance and even slightly higher valuation multiples for non-compliant firms (Appendix A, Table 5-7). One plausible 32 explanation is the prevailing market sentiment at the time. Investor behavior was more growth-focused during this period, likely encouraged by low interest rates and abundant access to capital, particularly in the tech sector (Algkvist Nordfors & Hansson, 2023; Monterro, 2024). The emergence of the COVID-19 pandemic in 2020 may also have temporarily disrupted valuation logic, with investors favoring aggressive expansion strategies despite profitability concerns. The variation in significance between earlier and later years is difficult to fully explain, especially given that macroeconomic uncertainty persisted throughout the sample period. While Newton and Schlecht (2016) argue that such uncertainty can shift investor focus toward profitability, the Rule of 40 does not specify how the balance between growth and profit is weighted. As such, the absence of valuation premiums in 2019–2020 likely reflects a broader lack of investor emphasis on this benchmark at the time, rather than a simple shift in focus from growth to profitability. The Rule of 40 appears to signal financial discipline to investors. Firms that achieve strong growth while maintaining acceptable profitability may be perceived as more sustainable in the long term, particularly in environments where access to capital is limited and investors demand clearer paths to sustainable performance. From 2021 onward, rising interest rates and a tighter funding climate may have increased investor sensitivity to capital efficiency, thereby reinforcing the Rule of 40 perceived importance. According to Monterro (2024), investors in the 2020s have placed greater emphasis on growth while increasingly rewarding companies that combine solid revenue expansion with signs of profitability. While growth remains the top metric for most companies, profitability has clearly increased in importance. Between 2021 and 2023, Monterro (2024) highlights that the share of companies identifying profitability as a top metric rose from 38% to 50%, indicating a shift toward greater emphasis on profitability. This shift aligns with the observed valuation premium for Rule of 40-compliant firms in the post-2020 period, suggesting that investors now favor firms that demonstrate both strong growth and financial discipline. According to Fama (1970), the EMH proposes that markets incorporate all publicly available information into asset prices. Fama et al. (1969) provide empirical evidence of this mechanism, showing that stock prices react almost immediately to new public information. In this context, the emergence of a valuation premium from 2021 onward may indicate that 33 markets efficiently process performance signals such as Rule of 40 compliance. In this context, the observed valuation premium for Rule of 40 compliant firms in recent years can be interpreted as evidence that investors efficiently process such information and reward companies that demonstrate strong fundamentals Overall, the results confirm Hypothesis 1 for the years 2021 to 2023, but not for 2019 and 2020. The hypothesis can therefore be partially confirmed across the full sample period. While no valuation premium is evident in the earlier part of the sample period, the Rule of 40 becomes increasingly relevant from 2021 onward. This suggests that investor attention to a combined growth and profitability benchmark has grown over time, potentially in response to evolving market conditions. 5.2 Growth vs. Profitability in SaaS Valuation (Hypothesis 2) This section analyzes the regression results presented in Section 4.4 to evaluate Hypothesis 2: whether the influence of the Rule of 40 on valuation is primarily driven by revenue growth rather than profitability. Table 4 presents that the fixed effects regression models showed a consistent and statistically significant relationship between revenue growth and valuation multiples (EV/Revenue). In contrast, most profitability metrics failed to demonstrate a similarly strong or consistent effect, with the exception of gross margin. As emphasized by Lee (2024) and Hottenhuis (2020), while the Rule of 40 is theoretically structured to give equal weight to growth and profitability, investor behavior often deviates from this balance. Empirical evidence suggests that growth tends to be the dominant driver of valuation, particularly in industries like SaaS where recurring revenue and high scalability define firm economics. The present study confirms this: revenue growth, not profitability, is the key determinant of valuation outcomes for European SaaS firms during the observed period. The results indicate a strong investor emphasis on revenue growth relative to profitability metrics. This valuation pattern aligns with fundamental characteristics of SaaS business models described by Mäkilä et al. (2010) and Laatikainen and Ojala (2014). Both sources highlight that SaaS companies operate with recurring revenue streams, scalable delivery strategies and subscription-based pricing models, all of which are essential factors in 34 achieving sustained revenue expansion. While these authors do not directly discuss strategic growth prioritization, their characterization of SaaS as naturally scalable and subscription focused helps explain investor willingness to value growth highly, anticipating profitability to follow at scale. This observed investor preference can be further understood through company life-cycle frameworks. Miller and Friesen (1984) broadly characterize the early stages of companies as periods dominated by strategic investment aimed at market penetration, often accompanied by limited profitability. However it could be argued that Damodaran’s (n.d.) model is more relevant for technology-driven companies like those in the SaaS sector. According to Damodaran, firms in the “Young Growth” and “High Growth” stages specifically focus on aggressive customer acquisition, market presence, and scalability. These are strategies that typically could intentionally suppress margins to facilitate rapid expansion. This framework helps clarify why revenue growth rather than immediate profitability, is prioritized by investors evaluating SaaS firms in the sample. Moreover, while most firms have not yet reached Damodaran’s profitability inflection point (Stage 4), sustained revenue growth may signal future value creation potential to investors. This perspective aligns with Ghosh et al. (2005), who found that companies showing consistent long-term growth in revenue and earnings generally exhibit higher earnings quality. Overall, the results reflect investor expectations that profitability will eventually materialize with scale, reinforcing Damodaran’s (n.d) life-cycle perspective in interpreting valuation dynamics in the SaaS sector. The panel regression also revealed that only gross margin had a significant and positive relationship with EV/Revenue among the profitability indicators. This finding is particularly noteworthy given Novy-Marx (2013)’s argument that gross profitability is a reliable predictor of future returns. Unlike EBITDA or EBIT, which can be influenced by accounting decisions and strategic reinvestments, gross margin offers a clearer view of a company’s core economic efficiency which investors seem to value. This is especially relevant in SaaS firms, where gross margins are relatively stable (most stable metric over the time-period based on the data set, see table 3) and indicative of scalable delivery models with low marginal costs. This pattern is also visually reinforced by Figure 3, gross margin consistently aligns closely with the overall valuation trend observed across the years, whereas models based on EBITDA margin and UFCF margin diverge more substantially. This visual consistency further supports 35 the regression result that gross margin provides a stable and significant contribution to explaining SaaS valuations, distinct from other profitability measures. The tendency of investors to prioritize revenue growth over profitability in valuing SaaS companies is consistent with much of the valuation literature. Cohen and Neubert (2018) and McCoy (2022) argue that traditional valuation models often undervalue high-growth SaaS firms because they fail to account for critical features such as recurring revenues and high customer retention. Relying only on cash flow based valuation techniques may overlook the critical role that growth plays in investor decision making, particularly in the context of SaaS firms as highlighted by McCoy (2022) and Cohen & Neubert (2018) which are seen in our results. These characteristics support the use of valuation multiples like EV/Revenue instead of earnings-based ratios, and help justify why unprofitable companies can still attract premium valuations. As demonstrated in Table 4 of this study, revenue growth emerges as the most consistent and statistically significant predictor of valuation multiples, reinforcing the idea that investors interpret high growth as a proxy for future scalability and earnings potential. Estrada cautions that excessive focus on growth can be risky for investors (2012). When growth is not supported by profitability or sound reinvestment, it may lead to overvaluation and the destruction of shareholder value. Over the five-year period examined in this study, investor behavior appears to continue favoring growth. Revenue growth consistently showed a significant and positive relationship with valuation multiples, while most profitability metrics did not as mentioned earlier. This pattern suggests that investors may still view growth as a strong signal of future scalability and long-term potential, even if short-term profitability is limited. While these findings do not directly contradict Estrada’s (2012) concerns, they highlight a potential separation between theoretical awareness and actual market behavior in the context of SaaS firms. This prioritization of growth over profitability can also be interpreted within the framework of the Efficient Market Hypothesis. According to Fama (1970), in a semi-strong efficient market, stock prices reflect all publicly available information. If investors perceive revenue growth as a stronger signal of future performance than current profitability, then this preference will be reflected in valuation multiples which is shown in the results. The consistent significance of revenue growth across models may therefore reflect market efficiency in incorporating the most forward-looking financial indicators. 36 Figure 3 in Section 4.4 displays the predicted EV/Revenue valuations across time, showing a peak in 2022 and a decline in 2023. Across all model specifications, revenue growth was the only consistently significant driver of valuation, while EBITDA, EBIT and unlevered free cash flow margins were not statistically significant. Only gross margin showed a strong and consistent relationship with valuation multiples. While Newton and Schlecht (2016) suggest that investor attention to profitability increases during periods of market stress, our results show no such shift during the 2019–2023 period, including the COVID-19 years which it could be argued is a prime example of market stress period. Instead, growth continued to dominate investor valuations, with only gross margin contributing additional explanatory power, possibly because it signals scalability and operational efficiency. The results suggest that profitability metrics remain secondary to top-line expansion in the European SaaS context. The relatively stronger performance of the gross margin model supports the conclusions of previously mentioned Novy-Marx (2013), who emphasizes that gross profitability is a transparent and effective indicator of value, particularly when other profit measures are influenced by discretionary strategic investments. Together these observations suggest that revenue growth remains the primary driver of valuation. Incorporating certain profitability metrics such as gross margin can add explanatory power, particularly in stressful market conditions, but not metrics like EBIT, EBITDA or UFCF margin which do not provide the same explanatory power. The insignificant role of UFCF margin in the regressions contrasts with the argument by Agrawal (2023) that free cash flow is a key indicator of a firm’s financial health. However, this gap may be explained by the context of high-growth SaaS firms, where cash flows are often deliberately negative due to aggressive reinvestment strategies. This finding is consistent with the arguments of Damodaran (2010) and Ali et al. (2010), who contend that traditional cash flow valuation methods such as those based on UFCF may misrepresent firm value in high-growth and uncertain environments. These models often reduce uncertainty to rigid forecasts and fail to capture the strategic flexibility or optionality that investors may include in their valuations. The empirical findings allow us to reject the null hypothesis in favor of Hypothesis 2, which states that the Rule of 40’s influence on valuation is primarily driven by revenue growth rather than profitability. It should be said that gross margin stands out as an exception. It demonstrates a significant positive relationship with valuation, indicating that operational 37 efficiency and scalability also contribute to how investors assess value. These findings suggest that the influence of the Rule of 40 on valuation is not evenly distributed between growth and profitability. Instead, revenue growth appears to be the primary factor driving investor decisions, while certain elements of profitability, gross margin, play a complementary role. This supports the conclusion that during the period from 2019 to 2023, investors in European SaaS companies continued to prioritize growth, although they also acknowledged profitability in the form of gross margin. 38 6. Conclusion This study set out to evaluate the applicability of the Rule of 40 in the valuation of publicly traded European SaaS companies between 2019 and 2023, with a focus on understanding the relative importance of growth and profitability. Our findings reveal that firms meeting or exceeding the Rule of 40 received higher valuation multiples from 2021 onward, suggesting a growing investor appreciation for Rule of 40 compliance in the European SaaS sector. However, the absence of a statistically significant valuation premium during 2019–2020 highlights that the rule’s effectiveness is not consistent across all market conditions and may be dependent on context. Regression analysis confirms that revenue growth is the dominant driver of valuation among European SaaS companies. Profitability metrics showed weaker or inconsistent relationships with valuation, except for gross margin, which emerged as a stable and significant predictor. This indicates that while investors continue to prioritize growth, they are increasingly attentive to underlying operational efficiency. These findings suggest that the Rule of 40, while useful as an analytical tool, may oversimplify the complex dynamics of SaaS firm valuation. Its static 40% threshold does not account for variations in firm maturity, industry subsegments or potential macroeconomic shifts. Managers and investors alike should therefore treat the Rule as a flexible guideline rather than a rigid benchmark. SaaS executives may find it strategically beneficial to emphasize growth, but a strong gross margin may serve as an important signal of long-term sustainability and scalable economics. Importantly, the SaaS industry’s relative newness and high-growth orientation may partly explain the dominant investor emphasis on revenue expansion over profitability. As the sector matures and firms transition into later life-cycle stages, investor priorities may shift accordingly in the future. In such a context, profitability measures could gain prominence as indicators of sustainability and cash-generating capacity. This life-cycle perspective suggests that the Rule of 40’s components may not hold equal weight indefinitely but rather reflect the evolving balance between expansion and efficiency over time. Emphasis on gross margin alongside growth hints at a maturing sector where efficiency and scalability are increasingly rewarded. This could reflect a broader evolution in capital market expectations, especially among rising interest rates and tighter financing conditions. This study contributes to the understanding of 39 SaaS valuation by applying the Rule of 40 in a European setting, an area underexplored in prior research. It demonstrates that the benchmark’s relevance is both dependent on time and metric specific. This study shows that investors place more weight on growth than on most profitability metrics, with gross margin emerging as a key indicator of achieving higher valuation from investors. 6.1 Limitations The results of this study should be interpreted with some limitations in mind. First, only publicly traded European SaaS companies are included in the analysis, excluding private companies and perhaps limiting the data' wider application. Due to the geographical focus on Europe, variations in investor behavior, market structures and regulatory frameworks in other locations are also overlooked, which may have an impact on the outcome. Second, despite being current, the study looks at a five-year period (2019–2023) that might not accurately represent longer-term valuation trends in the SaaS sector, especially in light of the COVID-19 pandemic and changing macroeconomic conditions. Third, although the Rule of 40 serves as a useful benchmark for balancing growth and profitability, it simplifies complex business realities and may not capture all relevant factors influencing valuations. Similarly, the use of EV/Revenue as the primary valuation multiple, while standard in SaaS analysis, does not incorporate profitability or risk directly. These limitations suggest that while the study provides meaningful insights, its conclusions should be interpreted with appropriate caution. 6.2 Future Research While this study provides valuable insights into the relevance of the Rule of 40 for publicly traded European SaaS companies, several important areas remain open for future exploration. One promising avenue for future research involves a deeper examination of ARR. ARR is a fundamental metric in the SaaS industry because it captures the recurring and predictable nature of a company's revenue stream, which is a critical component of long-term value creation. However, this study primarily focused on total revenue growth due to limitations in consistent ARR reporting across firms. Future studies could specifically analyze how ARR growth, as opposed to overall revenue growth, affects valuation multiples such as EV/Revenue or EV/ARR. Understanding whether investors place a valuation premium on 40 recurring revenue relative to traditional revenue streams would provide a more nuanced view of SaaS company valuation dynamics. Additionally, the evolving importance of profitability metrics in relation to ARR growth could be further explored. For instance, future research could investigate whether companies that combine high ARR growth with strong free cash flow margins or EBITDA margins receive systematically higher valuation multiples compared to companies that focus predominantly on top-line growth without achieving operating efficiency. Another potential research direction would involve examining the quality of ARR. 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It takes the value 1 if the sum of revenue growth and a selected profitability metric (e.g., EBITDA, EBIT, gross margin, or UFCF margin) is greater than or equal to 40%, and 0 otherwise. This classification is used to compare valuation multiples between firms that meet the Rule of 40 and those that do not.x Table 5: Comparison of valuation multiples 2019 between companies meeting and not meeting the Rule of 40 Table 6: Comparison of valuation multiples 2020 between companies meeting and not meeting the Rule of 40 Table 7: Comparison of valuation multiples 2021 between companies meeting and not meeting the Rule of 40 Table 8: Comparison of valuation multiples 2022 between companies meeting and not meeting the Rule of 40 46 Table 9: Comparison of valuation multiples 2023 between companies meeting and not meeting the Rule of 40 Appendix B: Descriptive Statistics Table 10: Descriptive Statistics EV/Revenue Table 11: Descriptive Statistics lnEV/Revenue 47 Table 12: Descriptive Statistics Revenue Growth Table 13: Descriptive Statistics ebitda margin Table 14: Descriptive Statistics ebit margin 48 Table 15: Descriptive Statistics Gross Margin Table 16: Descriptive Statistics ufcf margin Table 17: Descriptive Statistics firm age 49 Appendix C: Hausman Test The Hausman test is used to determine whether a fixed effects or random effects model is more appropriate for panel data analysis. The null hypothesis assumes that the random effects model is suitable, meaning that firm-specific effects are uncorrelated with the explanatory variables. The alternative hypothesis supports the use of fixed effects, suggesting that these unobserved firm-specific effects are correlated with the regressors and must be controlled to obtain unbiased estimates. Table 18: Hausman Test Revenue Growth, Gross Margin and Firm age Table 19: Hausman Test Revenue Growth, UFCF Margin and Firm age 50 Table 20: Hausman Test Revenue Growth, Ebit Margin and Firm age Table 21: Hausman Test Revenue Growth, Ebitda and Firm age 51 Appendix D: Correlation Matrix Table 22: Correlation Matrix 52 Appendix E: Panel data Regression Table 23: Full Panel Regression Model Note: Clustered standard errors in parentheses Table 24: Panel Regression Result excluded 2020 Note: Clustered standard errors in parentheses, not robust 53 54