The Financial Lobster Bias

5. Integration of Artificial Intelligence and Predictive Modeling

To enhance predictive accuracy, machine-learning algorithms were incorporated to classify risk behaviors and forecast financial collapse:

• K-Means Clustering was used to segment firms into three categories: sustainable growth, unsustainable expansion, and crisis/insolvency. This clustering identified behavioral archetypes across liquidity and debt variables.
• Logistic Regression Models estimated the probability of bankruptcy based on working capital, debt ratios, and liquidity perception metrics, allowing early detection of firms at hidden risk.
• Decision Trees and Random Forests were employed to determine the most influential predictors of survival or collapse, quantifying the relative importance of financial and behavioral factors.

All models were executed using Python (pandas, statsmodels, scikit-learn). Statistical significance was evaluated at p < 0.05, and robustness checks were conducted through cross-validation across temporal subsamples.

6. Methodological Insight and Expected Contributions

The combined use of econometric and AI-based approaches enables a dual perspective: quantitative validation of behavioral-finance hypotheses and predictive modeling of collapse dynamics. This methodology not only empirically tests the Financial Locust Bias but also provides a replicable analytical framework to detect liquidity illusions and overexpansion risks across economic sectors.

1.1.1. General Conclusion of Hypothesis 1

The analyses conducted demonstrate that liquidity misperceptions are a progressively intensifying phenomenon and are intrinsically linked to the dynamics of corporate treasury management. The empirical evidence reveals clear and consistent patterns that substantiate this relationship and confirm the behavioral foundation of the False Treasury Reality Hypothesis.

As illustrated in Figure 6, the evolution of the Liquidity Misperception Index (PEL) over time aligns closely with major economic crises in Spain, highlighting how periods of financial stress exacerbate perception distortions and reinforce biased decision-making. The main findings supporting this hypothesis can be summarized as follows:

• Increasing reliance on delayed payments (Average Payment Period – PMP) has become a recurrent liquidity strategy, artificially sustaining firms’ short-term solvency.
• Economic downturns amplify liquidity illusions, as firms overestimate their resilience and underestimate their structural vulnerabilities.
• A strong correlation between the PMP–PMC-based PEL and the financial-structure-based PEL confirms that liquidity misperception is not an isolated behavioral deviation, but a persistent structural bias embedded in managerial practice.
• Econometric models reveal a temporal intensification of the bias, showing that unless corrective measures are implemented, this pattern will continue to propagate in future financial cycles.

Collectively, these findings provide empirical and predictive evidence confirming Hypothesis 1: the False Treasury Reality systematically generates erroneous perceptions of liquidity, distorting financial judgment and undermining long-term sustainability. This behavioral distortion, captured quantitatively through the PEL, represents a measurable mechanism by which cognitive biases translate into macroeconomic fragility within the SME ecosystem.

4. General Conclusion

The analysis confirms that the Financial Lobster Bias is a persistent phenomenon that will continue to affect companies in the future if more sustainable liquidity management strategies are not adopted. The data projection supports Hypothesis 1, showing that the false perception of liquidity is not a temporary problem, but rather a structural trend in corporate financial decision-making.

The results indicate that companies will continue to use late payments as a source of liquidity, which will inevitably lead to further crises in the future. This practice, far from being a temporary solution, has become a cornerstone of many companies’ cash management, increasing their vulnerability to changes in access to credit or market conditions.

Furthermore, it is confirmed that the erroneous perception of liquidity will not disappear on its own, as it responds to a deep-rooted pattern in the way companies interpret and manage their financial resources. Confidence in fictitious stability fosters expansion cycles based on illusory liquidity, which increases the risk of collapse in times of economic crisis.

One of the most relevant findings is that if this pattern of behavior continues over time, it will be possible to predict which companies are at greatest risk of bankruptcy in future crises. This opens the door to the implementation of more advanced predictive models that can identify early signs of financial risk and take preventive measures before companies reach an irreversible situation.

In conclusion, the study reinforces the urgent need to design more sustainable liquidity management strategies that reduce reliance on practices that distort financial reality. The ability to anticipate and correct these patterns will not only improve the stability of individual companies but also contribute to the resilience of the financial system as a whole, avoiding recurring crises driven by false perceptions of liquidity.

Hypothesis 2: Does More Liquidity Mean Less Debt?

The objective of this analysis was to determine whether companies with greater operating liquidity tend to reduce their financial debt levels. In other words, we wanted to test whether there is an inverse relationship between the amount of cash available (working capital) and the level of financial debt companies assume.

To do so, I conducted an empirical study based on historical data, gathering information on companies’ working capital and financial debt over time. I then applied linear regression techniques to assess whether there was a significant correlation between the two variables.

First Test: Relationship Between Liquidity and Financial Debt

An econometric analysis of the relationship between liquidity and financial debt revealed a consistent and statistically significant pattern: firms with greater working capital availability tend to rely less on external sources of financing. As illustrated in Figure 7, the correlation between liquidity and total financial debt (both short- and long-term) follows a clear inverse trend, underscoring the behavioral logic that financially self-sufficient firms exhibit lower levels of leverage.

The results of the regression analysis show a negative slope coefficient of –1.9496, indicating that as working capital increases, debt levels decrease. This finding confirms the initial hypothesis that companies with stronger liquidity positions are able to finance a greater proportion of their operations internally, thereby reducing their dependency on external credit instruments such as bank loans or short-term lines of credit.

The robustness of the model is supported by a coefficient of determination (R²) of 0.8792, meaning that 87.92% of the variability in debt levels is explained by the firm’s operating liquidity. This high explanatory power reinforces the reliability of the regression model and supports the conclusion that efficient liquidity management directly enhances financial autonomy.

From a behavioral-finance perspective, this finding is particularly relevant. Firms with solid liquidity positions are less susceptible to external financial pressures, such as tightening credit conditions or interest rate fluctuations. By avoiding overreliance on debt, these companies demonstrate greater resilience to macroeconomic shocks and mitigate the risk of falling into a debt overhang trap, a condition often associated with the Financial Lobster Bias, where expansion is financed through unsustainable borrowing.

The liquidity–debt ratio graph further visualizes this inverse relationship: as working capital increases (X-axis), debt levels decline (Y-axis). The downward-sloping trend line depicted in Figure 7 confirms the existence of a negative correlation between liquidity and indebtedness, emphasizing that effective cash-flow management is fundamental to reducing financial dependence and strengthening long-term corporate stability.

Second Test: Relationship Between Liquidity and Business Creation

The second econometric test analyzed the relationship between corporate liquidity and business creation, seeking to determine whether higher levels of liquidity stimulate the emergence of new firms and entrepreneurial initiatives. As illustrated in Figure 8, the results reveal a clear positive association: when companies have greater liquidity reserves, the rate of new business creation increases accordingly.

The regression analysis produced a positive slope coefficient of 1,796.2, indicating that for each additional unit of operating liquidity, an average of 1,796 new businesses are created. This finding confirms that firms with abundant liquidity not only consolidate their internal stability but also tend to expand outward through the establishment of new entities or subsidiaries, thus contributing to market dynamism and employment generation.

The robustness of the model is reflected in a coefficient of determination (R²) of 0.6169, meaning that 61.69% of the variability in business creation can be explained by the availability of liquidity. Although this explanatory power is lower than that observed in the liquidity–debt relationship, the model remains statistically significant and highlights a strong behavioral and financial linkage between liquidity abundance and expansion-oriented activity.

From a behavioral-finance standpoint, this phenomenon can be interpreted through three complementary perspectives:

• Expansion confidence – Firms with greater liquidity demonstrate stronger confidence and a greater willingness to diversify, opening new ventures without compromising their financial balance.
• Access to internal start-up capital – Liquidity provides the capacity to finance new business creation internally, minimizing dependence on external credit and reducing borrowing costs.
• Financial optimism during expansion cycles – Periods of elevated liquidity tend to foster collective optimism, leading entrepreneurs to perceive the environment as favorable for expansion and to take on new initiatives, sometimes beyond their sustainable capacity.

Overall, these findings underscore the dual nature of liquidity: while efficient cash-flow management strengthens financial resilience, excess liquidity can fuel expansionary behavior that—if unmanaged—may evolve into the very dynamics described by the Financial Lobster Bias.

General Conclusions

After analyzing the relationship between liquidity, debt and business creation, several key conclusions can be drawn that reinforce the importance of efficient and sustainable financial management.

First, it confirms that liquidity and debt are inversely related. Companies with higher working capital rely less on bank financing, which not only reduces their exposure to financial risk but also indicates better cash management. By operating with their own resources, companies can maintain their stability without relying on credit volatility.

liquidity has been shown to boost new business creation. As working capital increases, the number of businesses established increases, suggesting that the availability of liquidity fosters business expansion and entrepreneurship. This trend can be explained both by entrepreneurs’ confidence in periods of stability and by the ease of financing new initiatives without resorting to external debt.

However, the results also highlight the role of cognitive biases in financial decision-making. There are two key biases that can distort perceptions of liquidity and affect business expansion:

• The false reality bias in cash management, which leads some companies to overestimate their liquidity and make investment decisions that are not always sustainable.
• The financial lobster bias, which explains why companies tend to expand rapidly in times of prosperity, without anticipating possible drops in their cash flow, which can lead them to unexpected crisis situations.

Practical Implications

The findings of this study have direct applications in corporate financial planning. Prudent liquidity management not only reduces the need for debt, but also helps companies make strategic expansion decisions with greater confidence.

To avoid falling into cycles of unsustainable expansion and collapse, it is essential that companies adopt financial control mechanisms based on real data, preventing erroneous perceptions of liquidity from influencing their planning. This way, they can leverage their resources to grow solidly and sustainably, without relying on strategies that only generate fictitious stability.

Hypothesis 3 based on the Unsustainable Expansion Index (IEI) and its relationship with business bankruptcies:

Analysis of the Unsustainable Expansion Index (IEI) and Its Relationship with Business Bankruptcies

The Unsustainable Expansion Index (IEI) has been key to assessing whether the growth of new companies in a sector is sustainable or, on the contrary, generates a business bubble that ends in a crisis. This indicator has been used to determine whether the pace of business creation exceeds the market’s real capacity to sustain them, eventually leading to an increase in bankruptcies and defaults.

The analysis is based on a clear hypothesis: when the IEI exceeds a critical threshold, it means the market is experiencing accelerated expansion that cannot be sustained over time. In these scenarios, it is common for a period of uncontrolled growth to be followed by a sharp increase in the number of companies that enter into crisis and disappear.

To calculate the IEI, two key variables were considered:

1 Business Growth Rate, which measures the number of new businesses formed compared to the previous year.

2 Bankruptcy Growth Rate, which reflects the increase in the number of businesses that cease operations or enter into bankruptcy.

The Unsustainable Growth Index is obtained by dividing the business creation rate by the business failure growth rate. When this value exceeds 1.5, it indicates that the number of new businesses is growing at an unsustainable rate compared to business failures. Conversely, values close to or below 1.0 reflect more stable and healthy growth.

Results: Identification of Phases of Unsustainable Expansion

Analyzing historical data, three key moments were identified when the IEI reached high values, indicating an uncontrolled expansion of the business community:

• 2005: IEI = 2.34 → Business growth well above the historical average.
• 2006: IEI = 2.87 → Peak expansion, with the highest number of company creations compared to bankruptcies.
• 2007: IEI = 1.72 → Still above the risk threshold, although beginning to decline.

These values reflect that, between 2005 and 2007, the market experienced a business boom, driven largely by a misperception of stability and economic growth. During these years, many companies were created under the assumption that favorable conditions would continue indefinitely, without considering long-term risks.

However, in 2008, the EII collapsed to 0.30, indicating that the global financial crisis led to a dramatic decline in new business creation and, at the same time, a significant increase in bankruptcies. This behavior reinforces the idea that the growth observed in the previous years was not sustainable, but was driven by a false sense of liquidity and stability.

Interpretation and Relevance of the IEI

The results derived from the Unsustainable Expansion Index (IEI) confirm that periods of uncontrolled business growth are frequently linked to episodes of excessive financial optimism and distorted risk perception. As illustrated in Figure 9, the IEI effectively captures the behavioral and structural dynamics that underlie expansionary bubbles in the corporate sector. When liquidity conditions appear favorable, firms tend to proliferate rapidly—often without a solid financial foundation—thereby becoming highly vulnerable to subsequent economic shocks.

The pronounced decline in the IEI during 2008 provides empirical validation of this mechanism: periods of seemingly stable and prosperous growth eventually give way to abrupt contractions once the underlying financial fragility is exposed. This behavioral pattern aligns closely with the Financial Lobster Bias, in which entrepreneurs, driven by a false sense of abundance, overexpand and consume available resources without recognizing that such liquidity surpluses are temporary.

These findings highlight the critical importance of monitoring the IEI as an early warning indicator of systemic financial risk. A rapid and sustained increase in the index’s values can serve as a precursor to rising insolvency rates, business closures, and broader economic instability. By identifying the onset of unsustainable expansion phases, policymakers and financial institutions can implement countercyclical measures that promote sustainable growth and prevent the destructive cycles of boom, overexpansion, and collapse characteristic of liquidity-driven behavioral biases.

Graphic Analysis: Cross Between the IEI and Bankruptcies

To further explore the predictive validity of the Unsustainable Expansion Index (IEI), a comparative graphical analysis was conducted to examine its relationship with corporate bankruptcies and payment suspensions over time. As illustrated in Figure 10, the two variables exhibit partially synchronized patterns: peaks in the IEI—indicating periods of accelerated and unsustainable business creation—are often followed by increases in bankruptcy rates, though the intensity and timing of these effects vary across economic cycles.

During the 2005–2007 expansion phase, the IEI reached unsustainable levels, reflecting a wave of new business formations driven more by perceived liquidity abundance than by solid financial fundamentals. The subsequent collapse in 2008 marked a sharp correction, confirming that the apparent stability of the pre-crisis years was largely an illusion of liquidity, consistent with the behavioral mechanisms described by the Financial Lobster Bias.

However, the regression analysis (R² = 0.0797) revealed that, in the long term, the IEI alone is not a strong predictor of corporate bankruptcies. While the number of bankruptcies declined steadily after 2010—possibly due to improved financial regulation, restricted credit access, and government support policies—the IEI remained relatively stable, suggesting a more moderate and cautious pattern of business creation in the post-crisis period.

The trend line of bankruptcies and payment suspensions presents a downward slope, confirming that corporate insolvencies have decreased over time. In contrast, the IEI trend line shows limited variation after 2008, implying that expansion dynamics have stabilized and that firms now display more conservative behavior regarding growth and leverage.

This analysis suggests that while the IEI is an effective early-warning indicator of periods of overexpansion, it should be interpreted in conjunction with complementary financial variables to enhance its predictive capacity. Factors such as interest rate trends, corporate debt levels, credit market liquidity, and business confidence indices play crucial roles in determining the transition from expansion to collapse.

In summary, the IEI captures the behavioral essence of unsustainable growth cycles, illustrating how liquidity-driven optimism can fuel expansionary waves that ultimately lead to fragility. Yet, its predictive power is partial: the relationship between business creation and bankruptcy is nonlinear and mediated by multiple macroeconomic and psychological factors. Future models integrating the IEI with structural financial indicators could therefore enable more accurate forecasting of systemic vulnerabilities and support the design of preventive policies for sustainable economic development.

Second Test for the Verification of Hypothesis 3: Unsustainable Expansion and its Relationship with Business Bankruptcies

The second test examined the relationship between unsustainable business expansion and corporate bankruptcies, with the goal of determining whether firms created during the pre-2008 expansion phase were the same ones that later collapsed due to liquidity problems. The underlying assumption was clear: if expansion had been excessive and unsupported by solid financial foundations, the subsequent depletion of liquidity should have led to a wave of bankruptcies.

To test this assumption, the relationship between working capital and the number of bankruptcies recorded in subsequent years was analyzed. The rationale was straightforward: if firms born during the expansion period relied on fictitious or fragile liquidity, the exhaustion of those resources should have resulted in operational difficulties and eventual insolvency.

The analysis was conducted on two levels. First, a comparative graph was generated to track the evolution of working capital and bankruptcies over time, in order to visually detect possible correlations. Second, a correlation coefficient was calculated to statistically measure the strength and direction of the relationship between the two variables.

As shown in Figure 11, the visual analysis revealed that while working capital fluctuated over time, there were no abrupt changes clearly associated with peaks in bankruptcies. Economic crises temporarily reduced available liquidity, but no proportional increase in business closures was observed. This preliminary evidence suggested that liquidity shortages alone were not the primary cause of corporate failures.

To quantify this finding, the correlation coefficient between working capital and bankruptcies was computed. A value near +1 would indicate a strong positive relationship (more liquidity, more bankruptcies), while a value near –1 would imply a strong negative one (less liquidity, more bankruptcies). The obtained result—r = 0.061—indicates an absence of significant correlation between liquidity levels and business failures.

This result is crucial: it challenges the assumption that liquidity crises were the main driver of corporate collapses. Although individual firms may have experienced distress due to poor cash management, the aggregate data do not support the hypothesis that a lack of working capital was the dominant causal factor behind widespread business failures.

Several alternative explanations emerge from this evidence:

• Profitability deterioration may have undermined firms’ long-term sustainability more than liquidity constraints.
• Excessive leverage and debt burden likely exerted a stronger influence on bankruptcy risk, as highly indebted firms can fail even when maintaining short-term cash flow.
• Macroeconomic shocks, such as declining demand or rising production costs, may have been the actual triggers of the corporate collapse.

These findings suggest that the relationship between unsustainable expansion and corporate failure is far more complex than initially assumed. While the first test confirmed a pre-2008 period of uncontrolled expansion, this second analysis indicates that liquidity was not the determining factor differentiating survival from failure. Instead, the data imply that over-leveraged firms were the most vulnerable, leading to a reformulation of the original hypothesis.

This discovery opens new lines of inquiry into how companies manage financial risk during crisis periods. If excessive debt, rather than liquidity shortages, is the key determinant of failure, then future preventive efforts should focus on controlling debt accumulation and promoting sustainable financing structures, rather than merely monitoring working capital. Ultimately, these results challenge conventional assumptions about financial stability and provide new insights into the behavioral and structural factors that govern corporate survival under systemic stress.

Confirmation of Hypothesis 3: Business Expansion and False Market Perception

The final stage of the analysis confirmed that a substantial proportion of firms were created under a false perception of market stability, which ultimately led to their collapse once real demand proved insufficient to sustain their operations. Through a combination of statistical correlation analysis, clustering techniques, and machine learning models, the study identified recurring patterns linking uncontrolled business expansion with subsequent crisis cycles.

The first relevant finding concerns the relationship between firm creation and market revenue. In 2020, despite a 4.4% decline in total turnover, the number of registered companies increased by 1.86%. This apparent contradiction suggests that many of these firms were established under an illusion of economic recovery, disregarding the underlying contraction in demand. In the following years, most of these entities disappeared, confirming the unsustainable nature of their foundation.

Evidence of a contraction bias became clear in the post-crisis years. During 2021 and 2022, while the total number of businesses stabilized, turnover rose by 13.14%, demonstrating that the economic recovery was driven by the performance of existing firms, not by the creation of new ones. This supports the hypothesis that many of the companies formed during 2020 were non-viable and short-lived, created in response to distorted market perceptions rather than genuine economic opportunity.

The multiple correlation analysis reinforces this conclusion. A correlation coefficient (r) of 0.415 revealed a moderate positive relationship between firm growth and turnover. However, the coefficient of determination (R² = 0.1722) indicated that only 17.22% of the variability in turnover can be explained by the number of new firms created—showing that an increase in firm count does not necessarily enhance market profitability. Even more revealing, the negative coefficient of the variable X (–0.1338) demonstrated that excessive business formation can actually erode overall market profitability, as many new firms compete for limited demand, driving margins downward.

Clustering analysis confirmed that not all aggressive expansion is harmful, but identified a subset of firms characterized by extreme growth followed by a spike in bankruptcy rates. These companies expanded rapidly without adequate market assessment, making them disproportionately vulnerable to external shocks. The positive correlation (r = 0.30) between new firm creation and bankruptcies further supports the notion that, in certain conditions, overexpansion itself becomes a systemic risk factor.

The application of machine learning models provided additional predictive insights. By analyzing the temporal relationship between firm creation peaks, liquidity availability, and subsequent bankruptcies, the models detected lagged patterns: periods of intense firm formation were consistently followed by increases in business closures. This indicates that many of the firms founded during expansionary phases lacked sustainable financial structures or stable demand, consistent with the behavioral dynamics of the False Reality Bias.

In conclusion, these results confirm Hypothesis 3: Business Expansion and False Market Perception. The evidence shows that overexpansion driven by liquidity illusions and misperceived market stability leads to structural fragility and business collapse. Moreover, the number of new firms created is not a reliable indicator of economic health; in volatile environments, it may instead signal collective overconfidence and distorted market expectations.

These findings emphasize the importance of rigorous environmental assessment before undertaking aggressive expansion strategies and the need to integrate multidimensional indicators—such as debt levels, profit margins, and credit conditions—to better anticipate systemic risk. Future research should explore how debt accumulation and profitability erosion interact with behavioral biases to determine the long-term sustainability of firms born during periods of speculative overexpansion.

Figure 12. Bankruptcy evolution chart showing the post-expansion dynamics of firm creation and collapse, illustrating the role of false market perception and liquidity misjudgment in business instability. Source: Prepared by the authors.

Figure 12. Bankruptcy evolution chart showing the post-expansion dynamics of firm creation and collapse, illustrating the role of false market perception and liquidity misjudgment in business instability. Source: Prepared by the authors.

To complement the econometric and behavioral analyses, Figure 13 displays the evolution of established and defunct companies in Spain between 2000 and 2024. This longitudinal representation allows for a visual understanding of how business creation and dissolution evolve through expansionary and contractionary economic phases, revealing the cyclical dynamics that characterize the entrepreneurial ecosystem.

The blue line, representing established companies, shows a significant upward trend between 2000 and 2007, peaking at over 140,000 new firms prior to the global financial crisis. This expansion phase reflects widespread liquidity optimism and collective overconfidence, consistent with the False Treasury Reality Bias, where entrepreneurs overestimated financial stability and market potential.

Following the 2008 financial collapse, a sharp decline in business creation is observed, with new incorporations dropping by nearly 40%. This post-crisis correction marks the behavioral realization of the Financial Lobster Bias, in which firms that expanded aggressively under perceived abundance were unable to sustain operations once liquidity tightened.

From 2010 to 2024, the number of established firms shows a gradual recovery and stabilization, reflecting a more cautious and risk-aware business environment. The temporary dip observed around 2020 coincides with the pandemic-related economic disruption, after which creation levels again trended upward toward moderate stability.

The orange line, representing defunct companies, follows a subtler but persistent upward trajectory, increasing from approximately 20,000 in 2000 to over 30,000 by 2024. Although the magnitude of extinctions is lower than that of incorporations, the slow and steady rise underscores that business mortality remains structurally embedded in the Spanish entrepreneurial landscape.

The widening gap between the two lines after 2014 suggests a structural decoupling between entrepreneurial optimism and long-term survival capacity. While more companies are being created than dissolved, the quality and resilience of these firms remain heterogeneous, with many still susceptible to cyclical shocks.

From a behavioral-finance perspective, Figure 11 encapsulates the central mechanism of the Financial Lobster Bias: expansion waves driven by perceived liquidity and imitation behavior eventually correct when the real market constraints reassert themselves. This visualization thus provides both empirical and metaphorical evidence of how collective overexpansion and liquidity illusion shape business demography over time.

Building the Machine Learning Model

The implementation of a machine learning classification model made it possible to empirically validate the hypothesis that unsustainable business expansion, driven by a misperception of liquidity, can lead to corporate collapse. To this end, a supervised model was developed using data on established and defunct firms, bankruptcies, paid-in capital, and available liquidity. The primary objective was to predict whether periods of rapid business formation would be followed by significant increases in bankruptcies in subsequent years.

The model achieved an overall accuracy of 60%, reflecting moderate predictive capacity with potential for further refinement. This level of accuracy suggests that while accelerated business formation is a relevant predictor of corporate crises, it is not the sole determinant. Other macroeconomic variables—such as credit cycles, profitability dynamics, and demand fluctuations—also play critical roles in determining whether firms survive or fail.

As shown in Figure 14, the heat map of variable interactions reveals one of the clearest patterns identified by the model: periods of accelerated business expansion combined with declining paid-in capital were systematically followed by increases in bankruptcies and insolvency proceedings. This finding reinforces the idea that overexpansion fueled by false liquidity perceptions erodes financial resilience and exposes firms to collapse once liquidity dissipates and real market demand proves insufficient.

The analysis further confirmed that firms tend to expand during periods of apparent financial abundance, relying on a perception of stability that is not always grounded in fundamentals. By overestimating their growth capacity and entering markets without assessing profitability or demand sustainability, many companies become highly vulnerable to adverse economic conditions. When perceived liquidity contracts and market reality reasserts itself, these firms—often created during phases of illusory stability—are disproportionately likely to fail.

Another critical insight derived from the model is that expansion unaccompanied by proportional growth in turnover serves as a strong indicator of impending financial distress. This finding emphasizes that an increase in the number of firms within a market does not equate to healthy economic growth. Without structural demand or sound financial models, overexpansion inevitably results in waves of bankruptcies in subsequent years.

From a behavioral-finance perspective, these results validate the conceptual foundations of the Financial Lobster Bias. Just as lobsters in their gregarious phase overconsume shared resources, firms expand aggressively when they perceive favorable conditions—without recognizing the temporary nature of those resources. When liquidity illusions fade and profitability declines, a contraction follows, confirming that growth driven by perceived abundance, rather than sustainable fundamentals, leads to cyclical fragility.

To enhance the model’s predictive precision, future research could incorporate deep learning architectures and integrate additional variables, including credit conditions, profit margins, and consumer sentiment indicators. These improvements would allow for the development of early-warning systems capable of anticipating crisis onset and informing preventive strategies for financial risk mitigation and sustainable expansion.

Correlation Heat Map and Clustering Analysis

The Heat Map analysis of correlations between financial variables was fundamental for understanding how the main factors influencing business stability and the sustainability of expansion interact. This multivariate approach revealed how business growth, operating liquidity, financial debt, and the cycles of business creation and dissolution are interconnected, allowing for the identification of structural and behavioral mechanisms underlying economic bubbles and collapse dynamics.

The main objective of this analysis was to examine the connections between key variables and determine whether specific correlation patterns could explain the impact of accelerated business expansion on market sustainability. Additionally, the role of perceptual biases was explored, particularly how false perceptions of stability or liquidity can drive firms toward unsustainable growth strategies.

As illustrated in Figure 15, the correlation heat map and K-Means clustering visually capture the relationships among the most relevant variables. The heat map highlights strong associations between business creation, the Unsustainable Expansion Index (IEI), working capital, financial debt, and firm mortality, while the K-Means clustering distinguishes groups of firms according to their expansion-risk profiles.

Key Correlations Identified

• A strong positive correlation was observed between the number of firms created and the Unsustainable Expansion Index (IEI). This finding indicates that during phases of rapid expansion, the IEI increases proportionally—implying that many new firms are founded not on genuine market opportunities but on perceived economic stability. However, this relationship displays a temporal lag, suggesting that the adverse effects of overexpansion often emerge only in later years.
• A negative correlation between working capital and financial debt confirms that firms with higher liquidity tend to rely less on external credit, reinforcing the previous conclusion that efficient liquidity management strengthens financial autonomy and reduces vulnerability to credit market fluctuations. The financial-structure-based PEL also shows a significant correlation with short- and medium-term debt, demonstrating that liquidity perception strongly influences financing behavior—often leading to excessive leverage when firms overestimate their stability.
• Cyclical relationships between growth and collapse were clearly identified: periods of high business creation were followed by an increase in firm closures in subsequent years. This lagged effect confirms that many companies established during phases of optimistic expansion lacked the structural resilience needed for long-term survival.

Behavioral Interpretation

The results reinforce the behavioral hypotheses established earlier. The positive correlation between firm creation and the IEI demonstrates that overexpansion can generate speculative economic bubbles, driven by cognitive and emotional biases rather than rational financial planning. Simultaneously, the negative correlation between liquidity and debt highlights that false liquidity perceptions distort financing decisions, prompting riskier behavior and eventual instability.

From a behavioral-finance perspective, the clustering results visualize the Financial Lobster Bias in action: firms expand aggressively when liquidity appears abundant, but once real financial constraints reemerge, many cannot sustain operations. This cyclical behavior reflects a collective misperception of abundance—a hallmark of herd-driven financial decision-making.

Future Research Directions

Further studies should integrate macroeconomic variables—such as interest rate policy, regulatory interventions, and credit supply trends—to refine predictive modeling of expansion and collapse cycles. Incorporating these factors into advanced machine learning frameworks, such as deep neural networks or dynamic Bayesian systems, would enhance the capacity to anticipate crises and guide the design of preventive financial policies promoting sustainable growth.

K-Means Clustering and Correlation Matrix Analysis

The K-Means clustering analysis identified key patterns in the relationship between false perceptions of liquidity and financial risk among firms. This method grouped companies according to their working-capital-to-revenue ratio and their financial-debt-to-equity ratio, revealing consistent trends in financial stability and vulnerability to economic shocks.

The primary objective was to identify which companies might be operating under a liquidity misperception, making them more susceptible to future financial distress. The model produced three distinct clusters, each representing a different behavioral and financial profile:

• Cluster 0 – Balanced firms: Companies with average working capital relative to turnover and moderate debt. These firms show relatively stable management practices but carry latent risk if liquidity is poorly managed or external shocks occur.
• Cluster 1 – Over-leveraged firms: Companies with high working capital relative to turnover but extremely high debt compared to equity. This indicates reliance on debt-financed operations rather than sustainable cash flows. These firms experience a false sense of liquidity—appearing solvent while actually depending on credit to maintain operations—and are therefore highly exposed to collapse if financing conditions tighten.
• Cluster 2 – Conservative firms: Companies with low working capital and low debt. They adopt prudent liquidity management and rely less on external credit. Although financially stable, limited liquidity may constrain their ability to expand.

As illustrated in Figure 16, these clusters clearly visualize how the false perception of liquidity corresponds to different risk levels. The results confirm that high debt relative to equity and overestimated liquidity are the most critical predictors of financial fragility. This behavioral pattern epitomizes the Financial Lobster Bias, in which firms expand aggressively under an illusion of stability, only to encounter crisis once liquidity contracts.

Correlation Matrix Analysis

The subsequent correlation-matrix analysis deepened the understanding of how liquidity misperceptions, indebtedness, and corporate stability interact over time. As shown in Figure 14, the heat-map correlations reveal fundamental behavioral and financial interdependencies that influence firm growth and survival.

Several critical relationships emerged:

• Negative correlation between liquidity misperceptions and business creation.
• Firms that believe they possess greater liquidity than they actually do tend to delay or limit new investments, adopting a conservative stance driven by a false sense of stability. This defensive behavior reflects a psychological bias toward protection rather than expansion when financial perception is distorted.
• Positive correlation between indebtedness and business creation.
• During expansionary periods, firms increasingly rely on external financing to sustain growth. While typical of booming economies, this dynamic becomes risky when expansion lacks a robust equity base, leading to systemic vulnerability once credit conditions deteriorate.
• Strong relationship between the Unsustainable Expansion Index (IEI) and bankruptcies.
• Years with elevated IEI values were followed by a marked increase in bankruptcies, confirming that uncontrolled expansion precipitates later collapse. This temporal lag supports the logic of the Financial Lobster Bias, where exuberant growth based on illusory stability leads to crisis once conditions normalize.
• Extremely high correlation between liquidity misperceptions and indebtedness.
• Firms that overestimate liquidity tend to borrow more aggressively, creating a cycle of debt dependence that becomes unsustainable when financing costs rise. This overconfidence fosters expansion that appears rational in the short term but escalates long-term insolvency risk.

In conclusion, the correlation-matrix results demonstrate that liquidity misperception is a central determinant of corporate stability. It shapes not only growth and strategic decision-making but also leverage dynamics and bankruptcy risk. Business expansion, therefore, must be grounded in objective financial fundamentals, not in perceived liquidity or transient market optimism.

Firms that manage liquidity prudently, balance internal and external financing, and align expansion with genuine market demand are significantly more likely to endure across economic cycles. These findings highlight the need for integrated financial-behavioral monitoring systems capable of detecting liquidity illusions early and guiding firms toward sustainable strategic decisions.