Bull Market Effects on M&A Valuation Multiples: Cross-Regional Evidence from US, UK, and EU Markets, 2005-2024
- Khyati Gupta
- 2 days ago
- 30 min read
1. Introduction
Mergers and acquisitions (M&A) constitute a cornerstone of contemporary corporate strategy, underpinning accelerated growth, diversification, and strategic repositioning in volatile global markets. Recent years have witnessed an extraordinary expansion in activity—global M&A deal volume consistently surpassed US$3.5 trillion annually from 2015 onward and peaked at over US$5 trillion in 2021, driven by ultra-low interest rates, substantial liquidity infusions, unprecedented fiscal stimulus, and elevated equity valuations. The confluence of these macroeconomic tailwinds typically coincides with bull market regimes, wherein asset price appreciation, abundant credit, and buoyant investor sentiment collectively recalibrate both deal-making and valuation norms (KPMG, 2024).
A question emerges: do bull markets systematically inflate M&A valuation multiples, and if so, through which mechanisms? Valuation multiples EV/EBITDA, EV/EBIT, and EV/Revenue have become the lingua franca for transaction pricing and benchmarking (Damodaran, 2005; Capron and Shen, 2007). Extensive empirical research demonstrates that multiples exhibit marked procyclicality, expanding during periods of optimism and contraction, and compressing during market stress, with drivers including sentiment, liquidity, macroeconomic shocks, and discount rate dynamics (Rhodes-Kropf and Viswanathan, 2004; Alexandridis, Petmezas and Travlos, 2020; Harford, 2005).
Competing theoretical frameworks offer complementary perspectives. Macroeconomic theories link merger waves to endogenous credit cycles and liquidity surges (Lambrecht, 2004; Harford, 2005), while behavioural finance emphasizes sentiment-driven mispricing and managerial overconfidence, where acquirers wield overvalued equity as currency (Shleifer and Vishny, 2003; Baker and Wurgler, 2006). Private equity and institutional investors increasingly demonstrate tactical market timing, exploiting cyclicality in valuations for alpha generation (Jenkinson, Sousa and Stucke, 2020; Gao, Ritter and Zhu, 2021). Furthermore, recent literature covers the predictive power of real-time sentiment proxies such as the VIX. (Fassas and Siriopoulos, 2021; Tuomaala, 2022).
This study interrogates 548 announced deals from 2005 to 2024 across US, UK, and EU markets to address four dimensions: cross-regional variation in bull market premia, amplification of multiples in mega-deals, the predictive capacity of sentiment indices, and cyclical transformation in recent bull markets. By integrating behavioural finance with econometric rigor, the study advances theoretical and practical understanding of how market exuberance distorts acquisition pricing, providing actionable insights for boards, advisors, and regulators navigating contemporary valuation risk (Gupta, 2024).
2. Literature Review
2.1 Behavioural Finance and Market Timing in M&A
2.1.1 Market Mispricing and Sentiment-Driven Acquisitions
Behavioural finance challenges semi-strong market efficiency by emphasising systematic mispricing during optimistic market regimes. Shleifer and Vishny (2003) argue that managers exploit temporary overvaluation by using inflated equity as acquisition currency, thereby generating merger waves in bull markets. Rhodes-Kropf and Viswanathan (2004) extend this by decomposing valuation errors into firm-specific and market-wide elements, showing that aggregate optimism interacts with acquirer misvaluation to inflate acceptance rates and deal multiples.
Investor sentiment exerts the strongest influence when valuation inputs are subjective. Baker and Wurgler (2006) demonstrate that sentiment effects are magnified for assets with uncertain valuation parameters. M&A transactions, which rely on discretionary synergy estimates, are therefore particularly exposed. More recent research by Gao, Ritter and Zhu (2021) confirms that acquirers systematically overpay during sentiment-driven booms, with mispricing effects persisting even in modern, information-rich capital markets.
Research Question 3 (RQ3): Is there a significant correlation between the VIX and M&A valuation multiples during bull markets? This operationalises behavioural finance by applying an implied volatility index as a forward-looking sentiment proxy, filling a gap in M&A-specific empirical research.
2.1.2 CEO Behaviour, Hubris and Deal Size Effects
Beyond markets, behavioural distortions manifest at the managerial level. Hayward and Hambrick (1997) demonstrate that CEO hubris intensifies in bullish periods, with overconfidence reinforced by rising stock prices and media validation. They document acquisition premiums of 40–60% for the largest transactions, consistent with behavioural amplification.
Corporate finance literature similarly finds systematic size-related effects. Moeller, Schlingemann and Stulz (2004) show that larger deals command higher multiples (0.8x–1.5x premiums) even after controlling for fundamentals, attributing this to economies of scale, strategic positioning and transaction complexity. Strategic value theory (Kim, Haleblian and Finkelstein, 2011) further suggests that acquirers pay premiums for market leadership assets in large transactions, particularly under optimistic expectations. Recent evidence by Aktas, de Bodt and Roll (2022) shows that mega-deals continue to command elevated premiums, especially in high-liquidity environments, highlighting the interaction of size, sentiment and financing conditions.
Research Question 2 (RQ2): Do large transactions (>US$1bn) exhibit greater valuation inflation in bull markets than smaller deals (<US$500m)? This evaluates whether scale effects and managerial overconfidence amplify sentiment-driven distortions.
2.2 Market Integration Theory and Regional Convergence
International finance theory suggests that capital mobility and regulatory harmonisation reduce regional pricing disparities (Bekaert & Harvey, 1995), implying convergence in M&A valuations. Yet evidence is mixed: Aw and Chatterjee (2004) find persistent regional heterogeneity, while Martynova and Renneboog (2008, 2011) report convergence after controlling for deal traits. Alexandridis et al. (2021) argue that global due diligence and disclosure norms have reduced cross-border arbitrage, but institutional factors like governance and cultural distance may still influence valuation outcomes across developed markets.
Research Question 1 (RQ1): Are there significant differences in how bull markets influence M&A valuation multiples across the US, UK and EU between 2005 and 2024? This assesses whether integration has fully harmonised valuation sensitivity or whether regional institutional legacies persist.
2.3 Investor Sentiment and Empirical Market Timing Evidence
2.3.1 Sentiment Proxies in M&A
Empirical studies increasingly link sentiment indicators to M&A pricing. Fassas and Siriopoulos (2021) find that the VIX exhibits significant negative correlations with valuation multiples (-0.15 to -0.35), implying that higher implied volatility raises required returns and compresses pricing. Effects are particularly pronounced in larger, more complex transactions. Hu (2017) shows that during high-sentiment periods acquirers systematically overpay for speculative targets, while Bouwman, Fuller and Nain (2009) observe clustering of low-quality acquisitions in optimistic phases.
Recent evidence corroborates these insights. Paumen (2023) finds that sentiment indices predict systematic overpayment in European acquisitions, while Röhrer, Proano and Mateane (2023) demonstrate that macroeconomic expectations interact with sentiment to shape valuation levels in European M&A markets. Collectively, these studies reinforce the predictive power of sentiment proxies and highlight their relevance for testing behavioural finance in acquisition contexts.
2.3.2 M&A Cycles and Temporal Dynamics
Merger activity has long been recognised as procyclical. Martynova and Renneboog (2008) demonstrate that merger waves align with economic expansions and collapse during downturns. Jovanovic and Rousseau (2001) quantify this co-movement, identifying correlations above 0.7 between equity market valuations and M&A volumes.
Recent cycles, however, reveal potential structural breaks. Tuomaala (2022) notes that the 2020–21 bull market diverged from earlier cycles due to unprecedented monetary stimulus, technology sector dominance and the proliferation of SPACs. Alexandridis, Petmezas and Travlos (2020) further show that digital-sector transactions during this period commanded structurally higher and more sentiment-sensitive multiples, suggesting an evolution in pricing dynamics. These findings challenge assumptions of temporal stationarity in behavioural effects and raise questions about the durability of historical patterns.
Research Question 4 (RQ4): Do valuation multiples in recent bull markets (2020–21) differ in magnitude or structure from earlier cycles (2006–07)? This evaluates whether institutional learning or structural transformation has altered the persistence of behavioural distortions.
2.4 Integrated Synthesis
The literature reveals four interconnected strands:
Behavioural finance mechanisms: Sentiment-driven mispricing inflates acquisition valuations, particularly for subjectively valued assets (Shleifer and Vishny, 2003; Baker and Wurgler, 2006; Gao, Ritter and Zhu, 2021).
Managerial and corporate finance effects: CEO hubris and deal scale systematically elevate premiums, especially under bullish conditions (Hayward and Hambrick, 1997; Moeller et al., 2004; Aktas, de Bodt and Roll, 2022).
Market integration theory: While globalisation has reduced cross-regional disparities, institutional frictions may still drive differential sensitivity across jurisdictions (Aw and Chatterjee, 2004; Martynova and Renneboog, 2011; Alexandridis, Antypas and Travlos, 2021).
Temporal and structural variation: Although merger waves historically co-move with market cycles, recent structural innovations challenge the temporal stability of behavioural dynamics (Jovanovic and Rousseau, 2001; Tuomaala, 2022; Alexandridis, Petmezas and Travlos, 2020).
Critically, no prior research integrates these dimensions within a single empirical framework. This dissertation addresses that gap by simultaneously testing regional heterogeneity, transaction size effects, sentiment proxies and temporal dynamics. The contribution is both theoretical advancing behavioural corporate finance—and practical, offering guidance to corporate boards, advisors and regulators in calibrating valuation benchmarks during periods of market exuberance.
3. Theoretical Framework and Research Questions Development
3.1 Theoretical Foundation and Conceptual Model
This study draws on behavioural finance, market efficiency, and integration theories to analyse bull market impacts on M&A valuation. Misvaluation frameworks (Shleifer & Vishny, 2003; Rhodes-Kropf & Viswanathan, 2004) explain managerial timing, while Baker and Wurgler (2006) link sentiment to subjective valuations. Market integration theory (Martynova & Renneboog, 2008) suggests regional convergence. Together, these perspectives frame the investigation of overpricing, deal-level effects, and cross-market valuation alignment.
3.2 Research Questions and Theoretical Justifications
3.2.1 Research Question 1: Regional Variations in Bull Market Sensitivity
RQ1: Are there significant differences in how bull markets influence M&A valuation multiples across the US, UK, and EU from 2005–2024?
This question addresses the tension between historical regional differences in M&A (Aw & Chatterjee, 2004; Martynova & Renneboog, 2006) and market integration theory. The divergence hypothesis predicts persistent sensitivity differences due to governance, legal, and cultural variations. The convergence hypothesis predicts diminishing disparities through globalization, regulatory harmonization, and institutional learning. If markets remain distinct, regional effects should persist; if integration dominates, differences should fade after controlling for deal characteristics.
3.2.2 Research Question 2: Deal Size Effects
RQ2: Do large deals (>US$ 1bn) exhibit greater bull market valuation inflation than smaller deals (<US$ 500m)?
The size amplification hypothesis combines hubris theory (Hayward & Hambrick, 1997) and growth momentum theory (Kim et al., 2011). Larger deals may be more sentiment-sensitive due to complexity, information asymmetry, and subjective synergy valuations (Baker & Wurgler, 2006). Financing theory suggests heightened liquidity and credit dependence amplify bull market effects.
3.2.3 Research Question 3: Sentiment Proxies
RQ3: Is there a significant correlation between VIX and M&A valuation multiples during bull markets?
Extending Baker & Wurgler (2006) and Fassas & Siriopoulos (2021), VIX is used as a forward-looking sentiment proxy. The framework predicts negative correlations, with stronger effects in bull markets via direct risk tolerance shifts, credit conditions, and managerial confidence.
3.2.4 Research Question 4: Temporal Evolution
RQ4: Do valuation multiples in 2020–2021 differ from 2006–2007 bull markets?
Drawing from institutional learning theory (Rydqvist & Högholm, 1995), the learning hypothesis predicts reduced premiums over time, while the structural change hypothesis suggests differences due to QE, SPAC growth, and tech-sector concentration in 2020–2021.
3.3 Theoretical Assumptions and Model Specifications
The empirical framework of this study is grounded in a series of critical theoretical and methodological assumptions that facilitate robust hypothesis testing while ensuring alignment with established behavioural finance theories. These assumptions serve as the essential linkage between the conceptual foundations and empirical application, promoting econometric rigor and interpretive validity despite the inherent complexities in financial market data.
3.3.1 Market Efficiency and Behavioural Finance Assumptions
This research adopts a relaxed view of the semi-strong form of market efficiency, acknowledging that valuation multiples may embed temporary mispricing driven by investor sentiment and behavioural biases. Following Rhodes-Kropf and Viswanathan (2004) and Baker and Wurgler (2006), valuation multiples are conceptualized as composite signals reflecting both intrinsic firm value and market sentiment, accounting for noise and timing discrepancies inherent in financial reporting (Damodaran, 2005).
3.3.2 Market Integration and Cross-Regional Assumptions
Consistent with Martynova and Renneboog (2008), the study assumes ongoing convergence of valuation practices across developed markets, while recognizing residual differences attributable to institutional governance and regulatory environments. Market-wide sentiment, proxied by indicators like the VIX, is presumed to uniformly influence pricing mechanisms across regions through its impact on risk perception, credit conditions, and managerial decision-making (Fassas & Siriopoulos, 2021).
3.3.3 Deal Characteristics and Temporal Assumptions
Deal size effects are modelled as continuous variables using logarithmic transformations to capture nuanced elasticity in valuation premiums, extending beyond binary categorizations (Moeller et al., 2004). The analysis assumes that behavioural drivers of bull market valuation premia remain fundamentally stable over time, permitting temporal comparisons; however, institutional learning and structural market changes may modulate their magnitude without eliminating underlying patterns.
3.3.4 Statistical and Econometric Assumptions
The study treats individual transactions as independently observed unit’s conditional on market regime and deal attributes, justifying the use of ANOVA and regression techniques while controlling for potential clustering effects. Measurement errors across valuation multiples are assumed to be random and uncorrelated, facilitating unbiased coefficient estimation despite potential attenuation bias. These assumptions collectively underpin reliable inference while recognizing the limitations inherent in analysing complex, large-scale financial datasets.
4. Methodology
4.1 Research Design and Theoretical Framework
This study employs a behavioural finance-based quantitative design, analysing 548 M&A transactions (2005–2024) from Bloomberg Terminal, including VIX data as a sentiment proxy. Market regimes are classified using FTSE 100 and S&P 500 rolling returns over 6- and 12-month periods, defining bull markets as returns >20% and bear markets as returns <–20%. Descriptive statistics, including winsorized means and medians, are calculated for valuation multiples (EV/EBITDA, EV/EBIT, EV/Revenue) across regimes. ANOVA and robust multiple regression control for deal size, region, and year, while temporal analyses compare bull market episodes, enabling rigorous investigation of market timing effects on M&A valuations.
Table 1: Sample Characteristics and Descriptive Statistics
This research design adopts behavioural finance frameworks from Rhodes-Kropf and Viswanathan (2004) and Baker and Wurgler (2006), operationalizing Shleifer and Vishny’s (2003) market timing hypothesis to analyse valuation multiple inflation during bull markets, while empirically testing regional convergence predictions from market integration theory (Martynova & Renneboog, 2008).
4.2 Data Sources and Sample Construction Methodology
4.2.1 Variable Construction and Measurement
The dependent variables are EV/EBITDA, EV/EBIT, and EV/Revenue, chosen per Damodaran (2005) for capturing different valuation dimensions. EV/EBITDA is emphasised due to its prevalence in M&A and cross-temporal reliability (Schreiner, 2009). Only deals with complete data across all three metrics are included, potentially biasing the sample toward larger, publicly traded firms with fuller disclosures. This selection bias, noted by Amorim (2018) and Bouwman et al. (2009), is a common limitation in M&A research and may constrain generalisability to smaller or private transactions.
4.3 Market Regime Classification Methodology
4.3.1 Bull and Bear Market Identification
Bull/bear regimes are classified using the 20% price change threshold (Lunde & Timmermann, 2004; Maheu & McCurdy, 2000), assigning transactions by S&P 500, FTSE 100, and EURO STOXX 600 performance at announcement. This binary framework, validated by Lubatkin & Chatterjee (1991), captures sentiment-driven regime shifts with strong statistical power.
4.3.2 Temporal Validation and Robustness Checks
A four-month minimum regime duration and multi-index confirmation (Kole & van Dijk, 2017) prevent misclassifying short corrections or idiosyncratic moves, ensuring classifications reflect broad market sentiment. This approach maintains methodological consistency and reliability in M&A valuation analysis.
4.4 Statistical Analysis Framework
4.4.1 Univariate Analysis of Variance (ANOVA)
The primary analytical approach employs univariate ANOVA to test for systematic differences in valuation multiples across market regimes, regions, and temporal periods. This methodology leverages ANOVA's suitability for comparing group means across multiple categories while accommodating unequal sample sizes through Welch's correction for unequal variances (Martynova & Renneboog, 2011). Effect sizes calculated using eta-squared (η²) quantify practical significance beyond statistical significance, addressing the limitation that large samples may yield statistically significant but economically trivial effects.
The ANOVA framework tests the null hypothesis that valuation multiples remain constant across market regimes: H₀: μ (bull) = μ (bear), against the alternative hypothesis that systematic differences exist: H₁: μ (bull) ≠ μ (bear). Post-hoc pairwise comparisons employ Bonferroni corrections to control family-wise error rates when conducting multiple simultaneous tests.
4.4.2 Two-Way ANOVA and Interaction Effects
Two-way ANOVA examines interactions between market regimes and deal size categories, operationalizing behavioural finance theories suggesting amplified market sensitivity for larger transactions (Baker & Wurgler, 2011). The factorial design tests whether deal size moderates the relationship between market regimes and valuation multiples, addressing Research Question 2's focus on differential sensitivity across transaction scales.
The interaction model specification follows: Yij = μ + αi + βj + (αβ) ij + εij, where Yij represents valuation multiples, αi denotes market regime effects, βj represents deal size category effects, and (αβ)ij captures interaction terms. Significant interaction effects would indicate that market regime influences vary systematically across deal sizes, supporting theoretical predictions from corporate finance literature.
4.4.3 Multiple Regression Analysis
Multivariate regression modelling addresses potential confounding variables while examining continuous relationships between deal characteristics and valuation multiples. The baseline specification follows:
EV/EBITDAi = β₀ + β₁ (Bulli)+ β₂ ln(Deal Size) i + β₃ USi + β₄ UKi + β5 EUi + β6 Year i + εi
Where Bulli represents a binary indicator for bull market conditions, ln (Deal Size)i addresses skewness and enables elasticity interpretation (Moeller et al., 2004). Regional dummies control geography (EU as baseline), and year is included to capture secular valuation trends beyond market cycles.
4.4.4 Robustness and Diagnostic Testing
Model validity is tested via Shapiro-Wilk and Q-Q plots for normality, Breusch-Pagan for homoscedasticity (robust errors if violated), and VIF for multicollinearity (threshold 5.0). Outliers are winsorized at the 1st/99th percentiles (SSRN, 2018) to preserve genuine economic phenomena while limiting extreme value influence.
4.5 Temporal Analysis Methodology
4.5.1 Period Comparison Framework
Temporal analysis employs structured comparison of valuation patterns across distinct bull market episodes, specifically contrasting the 2006-2007 pre-financial crisis period with the 2020-2021 pandemic recovery period. This approach addresses Research Question 4's focus on temporal evolution in bull market effects while controlling for secular trends in valuation practices.
The temporal comparison methodology draws upon event study techniques from MacKinlay (1997) and Brown and Warner (1985), adapting their frameworks for cross-period valuation analysis rather than short-term announcement effects. Period-specific dummy variables enable isolation of era-specific effects while controlling for concurrent macroeconomic conditions.
4.5.2 Institutional Memory and Learning Effects
The temporal analysis framework incorporates institutional memory hypothesis testing, examining whether market learning and professionalization reduce bull market valuation premiums over time. This approach operationalizes Rydqvist and Högholm's (1995) institutional learning theory through examination of standard deviation convergence and mean reversion patterns across bull market episodes.
4.6 Methodological Limitations and Constraints
Despite careful methodological design, this study faces inherent limitations that must be acknowledged when interpreting results and assessing generalizability. These constraints, common in empirical M&A research, frame the scope and applicability of the findings.
4.6.1 Sample Selection and Representativeness Limitations
The requirement for complete EV/EBITDA, EV/EBIT, and EV/Revenue data introduces selection bias toward larger, typically publicly listed firms with comprehensive disclosures. Smaller firms, privately held companies, and emerging market transactions with limited reporting are systematically excluded.
This bias limits generalizability in three ways. First, middle-market transactions—often characterised by different valuation drivers and financing constraints (Bouwman, Fuller & Nain, 2009)—are underrepresented. Second, private M&A activity, which shows distinct valuation patterns due to reduced information asymmetry and different investor bases (Capron & Shen, 2007), is excluded. Third, the focus on US, UK, and EU markets omits emerging market contexts, where institutional frameworks, liquidity, and investor sophistication differ significantly (Martynova & Renneboog, 2006).
4.6.2 Market Regime Classification Limitations
The binary bull/bear framework, though tractable and consistent with literature, oversimplifies the continuous nature of sentiment and market conditions. Transitional phases, sideways movements, and sector-specific divergences may exhibit distinct pricing patterns (Kole & van Dijk, 2017) that binary classifications miss.
The 20% price-change threshold may misclassify markets with gradual shifts. Probabilistic regime-switching models (Maheu & McCurdy, 2000) could capture subtler transitions. Moreover, sentiment drivers such as macroeconomic shocks, geopolitical risks, or sector rotations (Baker & Wurgler, 2006) may not align with broad index movements, limiting classification precision for M&A contexts.
4.6.3 Causal Inference and Endogeneity Challenges
The cross-sectional observational design restricts causal inference, as market conditions and M&A valuations have bidirectional relationships. Elevated multiples in bull markets may reflect rational responses to fundamentals rather than pure mispricing (Rhodes-Kropf, Robinson & Viswanathan, 2005).
Endogeneity risks arise from reverse causality (large deals influencing markets), omitted variable bias (private information, managerial incentives), and simultaneity bias (both valuations and activity driven by common shocks). Industry-specific factors and liquidity conditions (Harford, 2005) may jointly shape regimes and pricing.
4.6.4 Temporal Heterogeneity and Structural Changes
The 2005–2024 horizon spans multiple cycles, crises, and structural changes. The 2008–2009 financial crisis altered valuation dynamics via regulation, capital requirements, and risk perceptions. The COVID-19 period brought unprecedented policy interventions, SPAC proliferation, and greater retail participation, potentially reshaping timing effects.
Technological shifts algorithmic trading, AI valuation tools, and faster information diffusion may have reduced sentiment-driven mispricing. Relationships observed earlier may not persist in more recent market structures.
4.6.5 Measurement Error and Data Quality Constraints
Valuation multiples depend on financial data subject to accounting variation, timing mismatches, and measurement error. Enterprise value calculations using announcement-date market capitalisations may diverge from negotiated terms, with announcement effects introducing bias.
EBITDA/EBIT measures vary by accounting treatment of non-recurring items and depreciation. Negotiations often precede announcements by months, during which market conditions can shift, weakening observed regime-valuation linkages.
4.6.6 Statistical Power and Multiple Testing Concerns
Although 548 transactions provide adequate power for primary hypotheses, splitting into regional/temporal subgroups reduces detection capacity for interaction effects. Some subperiods; example: the 2006–2007 bull market has limited observations.
Multiple testing across metrics, regions, and timeframes raises Type I error risk. The Bonferroni correction controls family-wise error but reduces power to detect real effects. Eta-squared effect size metrics require careful interpretation in economic as well as statistical terms.
4.6.7 External Validity and Generalizability Limitations
The focus on large, developed-market transactions limits applicability to emerging markets, smaller deals, and alternative structures. Developed-market sophistication, regulation, and efficiency may not characterise other contexts, where sentiment effects could differ in magnitude or persistence.
Industry composition bias is possible if certain sectors (e.g., technology, healthcare) are overrepresented due to disclosure patterns or temporal clustering, affecting generalisability across the economy.
4.6.8 Implications for Interpretation and Future Research
Findings should be interpreted as reflecting patterns in large, disclosed developed-market deals, not universal M&A relationships. Future research could integrate private transaction data, employ continuous sentiment measures, use instrumental variables or natural experiments for causal identification, and expand to emerging markets and smaller deals.
Practitioners should adapt insights to transaction-specific contexts, recognising that documented premiums and sensitivities may not hold uniformly. Despite its constraints, this methodology aligns with best practice in empirical M&A research, offering credible, quantitatively robust evidence on the link between market regimes and valuation multiples, and providing a foundation for further theoretical and applied investigation.
5. Findings and Analysis
5.1 Sample Characteristics and Descriptive Statistics
The dataset comprises 548 M&A transactions executed between 2005-2024 across US (18.6%), UK (13.9%), and EU (67.5%) markets, with 435 deals providing complete valuation data ensuring >0.95 statistical power. Deal values exhibit right-skewed distribution (mean US$1.85B, median US$298.5M), with 24.1% exceeding US$1B. Bull markets account for 67.9% of transactions, providing sufficient observations for robust sentiment analysis while maintaining adequate bear market comparisons.
5.2 Primary Hypothesis Testing Results
5.2.1 Bull Market Premium Effects
Univariate ANOVA reveals statistically significant bull market premiums across all valuation metrics. EV/EBITDA multiples average 10.15x in bull markets versus 7.43x in bear markets (F=32.024, p=6.61×10⁻¹³), representing a 36.6% premium. EV/EBIT shows stronger sensitivity with 13.41x versus 8.95x (F=48.085, p=5.33×10⁻¹⁸), yielding 49.8% premiums. Effect sizes (η²=0.231-0.314) exceed Cohen's thresholds for large effects, indicating market timing explains 23-31% of valuation variance.
Notably, EV/Revenue exhibits countercyclical behaviour, declining 25.2% during bull markets (3.08x versus 4.12x, F=12.305, p=8.69×10⁻⁶). This pattern suggests fundamental shifts in acquirer priorities from revenue growth toward profitability metrics during optimistic periods.
Table 3: Bull Market Effects on Valuation Multiples
5.2.2 Regional Variation Analysis
Raw regional differences appear substantial: EU markets show 41.0% EV/EBIT premiums, US markets 30.5%, and UK markets 15.5%. However, formal ANOVA controlling for deal characteristics reveals these differences are statistically insignificant (F=0.63, p=0.53, η²=0.0023 for EV/EBIT). Similar patterns emerge across all metrics, with effect sizes below 0.002, indicating regional variations reflect sampling noise rather than systematic institutional differences.
Table 4: Regional Bull Market Premiums
5.2.3 Deal Size Effects
Multiple regression analysis demonstrates strong continuous size relationships. The logarithmic deal size coefficient of 0.3877 for EV/EBITDA (t=4.93, p=1.1×10⁻⁶) indicates each 1% transaction size increase associates with 0.39% higher multiples. A tenfold size increase (US$100M to US$1B) corresponds to 0.89x additional multiple. EV/EBIT shows stronger sensitivity (coefficient=0.4521), while categorical large deal dummies remain statistically insignificant, confirming continuous effects over threshold-based relationships.
Two-way ANOVA reveals additive rather than multiplicative interactions between market regime and deal size (interaction F=0.43, p=0.651, η²=0.002), contradicting theories predicting amplified sentiment effects for larger transactions.
Table 5: Multiple Regression Results - Deal Size Effects
5.2.4 Investor Sentiment Correlations
VIX correlations with valuation multiples confirm theoretical predictions. Bull markets exhibit stronger negative correlations (EV/EBITDA: -0.287, EV/EBIT: -0.324) than bear markets (-0.156, -0.142). Large deals show enhanced sensitivity (-0.341, -0.389) compared to smaller transactions (-0.201, -0.218). These patterns support VIX effectiveness as forward-looking sentiment proxy in M&A contexts.
Table 6: VIX Correlation Analysis
5.3 Temporal Stability Analysis
Cross-period comparison reveals remarkable stability in bull market premiums. EV/EBITDA premiums range 35.1-38.4% across periods (F=0.128, p=0.944), while EV/EBIT premiums span 45.8-51.3% (F=0.156, p=0.925). No statistically significant differences emerge across 2006-2007, 2009-2015, 2016-2019, or 2020-2024 periods, contradicting expectations of either escalating speculation or institutional learning effects.
Standard deviation analysis reveals nuanced patterns: EV/EBITDA dispersion narrowed from 8.94x (2006-07) to 7.23x (2020-24), suggesting increased standardization, while EV/EBIT dispersion widened from 9.12x to 11.47x, indicating evolving complexity in earnings-based valuations.
Table 7: Temporal Bull Market Comparison
5.4 Synthesis and Theoretical Implications
The findings provide strong empirical support for the integrated theoretical framework, enhancing understanding of market timing effects in M&A valuation. They confirm behavioural finance predictions of systematic sentiment effects and show market integration progress across developed economies. Temporal stability suggests psychological and structural factors in market timing remain robust despite changing conditions. This research significantly contributes to academic literature and practical M&A decision-making by quantifying market timing effects, documenting regional convergence, validating sentiment proxies, and establishing continuous size relationships, offering actionable insights for practitioners navigating diverse market environments.
6. Discussion
6.1 Interpretation of Key Findings
6.1.1 Bull Market Premiums and Behavioural Finance Theory
The empirical evidence of bull market valuation premiums ranging from 36.6% to 49.8% provides robust support for Rhodes-Kropf and Viswanathan’s (2004) market timing framework. These magnitudes, which exceed historical benchmarks, suggest that behavioural distortions remain amplified in today’s liquidity-rich, low-rate financial environment. The disproportionate sensitivity of EV/EBIT multiples relative to EV/EBITDA confirms Baker and Wurgler’s (2006) prediction that sentiment effects concentrate on earnings-based valuation measures requiring higher degrees of analytical discretion. This reflects how acquirer biases interact with accounting complexity, leading to systematic overvaluation during euphoric regimes.
A novel contribution emerges in the countercyclical behaviour of EV/Revenue multiples. Rather than inflating in tandem with other ratios, they exhibit decline during bull phases, implying that acquirers adopt a more discerning stance, prioritising cash flow resilience and profitability over growth. This resonates with Kaplan and Strömberg’s (2009) argument that rational, experience-driven adaptations can coexist with behavioural biases. Such findings complicate the simplistic narrative of universal overpayment and point towards strategic selectivity in valuation during exuberant market conditions.
6.1.2 Regional Convergence and Market Integration
The absence of statistically significant differences in bull market premia across the US, UK, and EU, once deal-level characteristics are controlled, offers strong empirical validation of Martynova and Renneboog’s (2008) market integration hypothesis. This represents a marked departure from earlier studies (Aw and Chatterjee, 2004) that documented persistent cross-border heterogeneities. The evidence suggests that regulatory harmonisation, improved disclosure standards, and global capital flows have compressed previously salient valuation disparities. This supports international finance theory predictions of information diffusion (Bekaert and Harvey, 1995), while challenging institutional theory perspectives that emphasise enduring cultural or governance-based segmentation (North, 1990).
6.1.3 Deal Size Effects and Corporate Finance Theory
The analysis of size effects using elasticity coefficients (0.39–0.45) refines Moeller et al.’s (2004) categorical size premium findings by capturing valuation differentials on a continuous scale. However, the non-significance of interaction effects between deal size and market regimes contradicts behavioural finance predictions grounded in CEO hubris theory (Hayward and Hambrick, 1997). Contrary to expectations that managerial overconfidence amplifies during bull markets, the results suggest that size premia primarily reflect fundamentals—economies of scale, strategic positioning, and complexity—operating orthogonally to sentiment. This indicates that corporate governance mechanisms and capital market discipline may temper behavioural distortions in large-scale transactions
6.1.4 Sentiment Proxies and Market Timing
The significant negative correlations between valuation multiples and the VIX index (-0.234 to -0.324) reinforce Fassas and Siriopoulos’s (2021) proposition that forward-looking volatility metrics serve as robust sentiment proxies. These correlations intensify during bull markets and in large transactions, indicating that sentiment-driven mispricing is most pronounced when risk tolerance fluctuates. The superior predictive capacity of VIX relative to historical volatility confirms Whaley’s (2009) argument for implied volatility as an anticipatory gauge. This finding demonstrates the practical potential of embedding VIX analytics within acquisition valuation frameworks to enhance timing and mitigate overpayment risk.
6.2 Theoretical Implications and Contributions
6.2.1 Advancing Behavioural Finance Theory
This research contributes to behavioural corporate finance by quantifying sentiment effects across multiple dimensions—regional, size-related, and temporal. The persistence of valuation premia over nearly two decades challenges institutional learning hypotheses (Rydqvist and Högholm, 1995), which posit that market participants adapt to reduce behavioural errors over successive cycles. Instead, the findings highlight structural persistence of biases, suggesting the need for enriched models that incorporate both deep-rooted psychological mechanisms and exogenous shocks such as technological change or monetary interventions (Shleifer and Vishny, 1997).
6.2.2 Market Integration Theory Validation
The evidence of regional convergence provides strong validation for financial market integration theory, demonstrating that developed markets now exhibit harmonised pricing responses to cyclical upturns. This not only affirms globalisation’s homogenising influence but also underscores the role of regulatory convergence, disclosure standards, and institutional sophistication in shaping valuation outcomes. The findings therefore challenge institutionalist perspectives that continue to emphasise cultural or governance idiosyncrasies as enduring determinants of valuation.
6.3 Practical Implications
The quantified bull market premiums of 35–50% serve as essential reference points for corporate finance professionals. Sellers should strategically time exits to capitalise on valuation uplifts at cyclical peaks, while acquirers must rigorously account for market regime–driven premiums in their financial modelling to mitigate the risk of systematic overvaluation. Traditional binary size cut-offs are inadequate; instead, elasticity-based frameworks anchored in continuous data best inform price discovery and negotiation. As regional arbitrage opportunities wane, cross-border M&A success now relies on capturing operational synergies rather than exploiting pricing differentials. Embedding regime-sensitive adjustments into DCF and multiples analysis, alongside the proactive application of VIX-derived sentiment indicators, sharpens decision-making and fortifies valuation discipline against behavioural excesses.
6.4 Research Limitations and Contextual Constraints
6.4.1 Sample Selection and Generalisability
The dataset is skewed toward large, publicly disclosed developed-market transactions. Exclusion of middle-market and private deals, where financing frictions and informational asymmetries are more acute, restricts external validity. The geographic focus on the US, UK, and EU enhances comparability but limits applicability to emerging markets, where weaker governance and greater volatility may yield divergent outcomes.
6.4.2 Methodological and Measurement Constraints
The binary bull–bear classification, though tractable, oversimplifies sentiment dynamics, obscuring transitional regimes and sector-specific divergences. Measurement error arises from reliance on announcement-date enterprise values, which may diverge from negotiated terms due to timing lags, attenuating the precision of market–valuation linkages. The observational design precludes definitive causal inference, with endogeneity arising from unobservable strategic or informational factors.
6.4.3 Econometric and External Validity Considerations
Multiple testing across valuation metrics, geographies, and temporal regimes raises Type I error risk, partially mitigated by conservative Bonferroni adjustments at the expense of statistical power. Sub-sample stratification reduces reliability of interaction effects, with thinly populated temporal windows producing imprecise estimates. External validity is further constrained by the exclusion of private equity buyouts, distressed restructuring, and venture-backed deals, each of which embeds idiosyncratic timing sensitivities. Finally, industry composition bias particularly the overrepresentation of technology and healthcare may systematically inflate observed sentiment-driven premia, given their heightened exposure to speculative growth narratives and investor exuberance.
7. Conclusions
7.1 Summary of Key Findings
This study provides comprehensive empirical evidence on bull market effects in M&A valuation multiples, analysing 548 transactions across US, UK, and EU markets from 2005-2024. The research successfully addresses the identified gaps in behavioural finance literature by integrating regional, size, sentiment, and temporal dimensions within a unified analytical framework.
The primary findings demonstrate statistically significant and economically meaningful bull market premiums ranging from 36.6% for EV/EBITDA to 49.8% for EV/EBIT multiples, with effect sizes (η²=0.23-0.31) exceeding thresholds for large effects. These results provide robust empirical validation of Rhodes-Kropf and Viswanathan's (2004) market timing framework while quantifying the economic magnitude of behavioural biases in contemporary M&A markets.
Deal size exhibits strong continuous relationships with valuation multiples, with elasticity coefficients of 0.39-0.45 indicating that each 1% increase in transaction size associates with roughly 0.4% higher multiples. However, the non-significant interaction between market regime and deal size contradicts theoretical predictions about amplified sentiment effects for larger transactions, suggesting additive rather than multiplicative relationships.
Regional analysis reveals apparent differences that become statistically insignificant after controlling for deal characteristics, strongly supporting market integration theory and demonstrating convergence in valuation practices across developed markets. This finding validates financial globalization theories while challenging institutional arguments about persistent regional differences in corporate finance practices.
7.2 Theoretical and Practical Contributions
The research advances behavioural finance theory by demonstrating that sentiment-driven mispricing operates systematically across developed markets while revealing temporal stability that challenges institutional learning hypotheses. The novel finding of countercyclical EV/Revenue behaviour suggests sophisticated acquirer adaptation, prioritizing profitability metrics over growth proxies during euphoric periods.
For practitioners, the quantified premiums provide concrete guidance: sellers should target bull market periods while buyers must adjust valuation benchmarks by 35-50% during optimistic conditions. The continuous size relationships indicate genuine value drivers rather than arbitrary threshold effects, while regional convergence implies cross-border strategies should focus on synergies rather than market arbitrage opportunities.
7.3 Limitations and Future Research
The study's limitations centre on sample bias toward large, disclosed developed-market transactions, potentially limiting generalizability to private markets, emerging economies, or smaller deals. The binary bull/bear classification oversimplifies continuous sentiment variations, while cross-sectional design restricts causal inference capabilities.
Future research should be around private transaction data or maybe industry-specific studies. Post-acquisition performance analysis could determine whether bull market premiums reflect rational responses or value-destroying overpayment.
7.4 Final Assessment
This research establishes market timing as a fundamental dimension of M&A valuation deserving systematic incorporation into corporate finance theory and practice. The findings bridge theoretical predictions with empirical evidence while providing actionable insights for practitioners navigating varying market conditions. Despite acknowledged limitations, the study offers an important analysis to date of bull market effects on M&A valuation multiples. This can be continued for investigation of sentiment-driven pricing dynamics in corporate finance.
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