From “Lagging” to “Leading”: Applying Machine Learning Models for Business Decline Prediction

doi:10.22167/2675-6528-202602870

Article

October 02, 2026

From “Lagging” to “Leading”: Applying Machine Learning Models for Business Decline Prediction

From “Lagging” to “Leading”: Applying Machine Learning Models for Business Decline Prediction

Evandro Bignotto Penati; Patrícia Belfiore Fávero

DOI: 10.22167/2675-6528-202602870

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The inability to foresee adverse corporate events, based on historical financial indicators, limits organizations’ strategic response to market volatility. This work aimed to develop a hybrid Machine Learning model to predict business decline within a five-year horizon, integrating financial and non-financial indicators based on the business decline framework. The quantitative research analyzed 80 publicly traded companies, segregated into control and decline groups, using public data from the SEC and the Glassdoor platform. Supervised algorithms were tested, and Random Forest showed the best performance. The results demonstrated that the hybrid model significantly outperformed purely financial models, achieving accuracy levels above 90%. SHAP analysis revealed that cultural factors, such as employee satisfaction (Glassdoor Score) and wage disparity (Hubris Score), had greater predictive power than traditional accounting metrics, anticipating adverse corporate events. The case study of Americanas S.A. validated the model, identifying a bankruptcy risk of 96% compared to 65% for the financial model. It was concluded that cultural deterioration precedes financial crisis and that the hybrid approach offers a more accurate tool for anticipating adverse corporate events.

Keywords: Business Decline; Leading and Lagging Indicators; Machine Learning; Risks.

1. Introduction

In 2008, the bankruptcy of Lehman Brothers triggered a global financial crisis, exposing the fragility of organizations regarding their ability to react to unpredictable events. This scenario, characterized by volatility, uncertainty, complexity, and ambiguity, highlights the need to anticipate risks to ensure business sustainability (Bennett, Lemoine, 2014).

The inability to predict adverse events, such as the black swans described by Taleb (2021), limits the response time to adversities and can result in irreversible damage, compromising the financial health and continuity of companies. In this context, effective management requires agility, strategic anticipation, and the use of prediction tools to mitigate risks before they cause irreversible impacts on the corporation’s financial health (Rigby, Sutherland, Takeuchi, 2016).

For managers, the ability to predict and manage risks not only creates windows of opportunity to correct and mitigate risks, but also strengthens organizational resilience in competitive markets (Porter, 1996). For stakeholders and investors, transparency in risk management, as advocated by COSO (2017), promotes trust and clarity, allowing for more efficient capital allocation, reduction of potential losses, and asset protection (Damodaran, 2014; OECD, 2015). Furthermore, the integration of risk management practices with sustainability objectives expands organizations’ capacity to transform challenges into strategic opportunities, aligning with the expectations of markets and regulators (OECD, 2015).

However, the tools available for predicting corporate decline, such as the Z-Score model (Altman, 1968), are predominantly based on historical financial data. This data is reactive and reflects already consolidated (Shumway, 2001) and often immutable situations. Although effective in identifying imminent bankruptcies, these tools have limitations in the long term, hindering strategic anticipation in dynamic environments (McKinsey & Company, 2017), which compromises the strategic foresight capacity necessary for organizational sustainability (Porter, 1996). Given these restrictions, it is crucial to explore approaches that combine financial indicators, termed “lagging,” and non-financial indicators, termed “leading,” such as customer satisfaction metrics, market trends, and investment in research and development.

Given these limitations, this work proposes the creation of a hybrid model to overcome the restrictions of traditional predictive tools. The approach integrates financial indicators with non-financial indicators, using advanced Machine Learning tools. This combination aims to achieve a minimum accuracy of 80% within a horizon of up to five years, allowing for the anticipation of risks over a broader period and offering organizations the necessary time for corrective interventions. Thus, the present study aims to develop a hybrid Machine Learning model to predict business decline within a horizon of up to five years, integrating financial indicators with non-financial indicators based on the business decline framework.

2. Material and Methods

The present study was characterized as an explanatory research of a quantitative nature, according to Gil (2017). The methodological focus consisted of developing a Machine Learning model, with the objective of predicting business decline within a horizon of up to five years. To this end, a set of financial indicators, termed “Lagging”, was integrated with non-financial indicators, called “Leading”, the latter derived from the business decline framework proposed by Jim Collins (2018).

The unit of analysis comprised 80 publicly traded companies, selected both in Brazilian territory and abroad. The sample was segregated into two distinct groups: a control group, composed of 35 companies, and a decline group, with 45 companies that had experienced adverse events such as judicial recovery, bankruptcy, or significant loss of market value. The sample selection followed the criterion of “extreme cases” (success versus failure), aiming to optimize the model’s ability to clearly distinguish between the groups, regardless of their industry.

Data collection prioritized public and auditable sources. Financial data, or “Lagging,” was extracted from Financial Statements and 10-K forms from the Securities and Exchange Commission (SEC) for foreign companies, and from external query platforms of the Securities Commission (CVM) for Brazilian companies. Non-financial data, or “Leading,” was obtained from the Glassdoor platform, which provides employee reviews.

The data were accessed through internet searches, with accesses to the Glassdoor platform occurring between November 2025 and March 2026, and accesses to the CVM External Consultation Portal occurring between November 2025 and February 2026. The quantification of the “Leading” indicators was based on Jim Collins’ framework, which describes five stages of decline. For Stage 1, “Pride born from Success – Overconfidence,” the “Hubris Score” was used, calculated by the ratio between the CEO’s salary and the average employee salary.

For Internship 2, “Undisciplined Search for More”, two indicators were used: “Goodwill_Asset”, which represents the portion of the asset corresponding to the premium paid in acquisitions due to expectations of future profitability, calculated by the ratio between Goodwill and Total Assets; and “Rev_Func”, which sought to detect corporate “bloat”, calculated by the ratio between Net Revenue and Total Number of Employees. Internship 3, “Denial of Risk and Danger”, was quantified by the average employee satisfaction score on the Glassdoor platform, normalized to a scale of 0 to 1.

Stage 4, “Desperate Search for Salvation”, was measured by the indicator “RD_Revenue”, which represents the intensity of investment in innovation, calculated by the ratio between R&D Expenses and Net Revenue. Stage 5, “Capitulation to Irrelevance or Death”, was defined as the target variable to be predicted by the model. The “Lagging” (financial) indicators included the Net Margin, which measures the company’s efficiency in transforming revenue into profit, calculated by the ratio between Net Profit and Net Revenue.

Additionally, Total Indebtedness was used, which assesses the proportion of the company’s financing from creditors compared to shareholders’ capital, calculated by the ratio between Total Debt (Current and Non-Current Liabilities) and Net Worth. Current Ratio was also employed, measuring the company’s ability to honor short-term obligations with its short-term assets, calculated by the ratio between Current Assets and Current Liabilities.

Data modeling was performed in Python language, where supervised learning algorithms were tested, including Logistic Regression, Random Forest, and XGBoost. For model training and testing, the data were divided into 70% for training and 30% for testing. The evaluation of the contribution of Machine Learning algorithms was compared to a Logistic Regression model using only financial data, established as a baseline, a statistical technique traditionally employed in insolvency studies.

For the choice of the model with the best performance, metrics such as Accuracy, F1-Score, and Area under the ROC Curve (AUC-ROC) were considered. Accuracy represents the percentage of correct predictions (Harrison, 2023). The F1-Score combines precision and recall, being the harmonic mean between these two metrics (Géron, 2021). The ROC curve and its area (AUC-ROC) display the classifier’s performance, showing the true positive rate in relation to the false positive rate (Provost & Fawcett, 2016).

To identify the contribution of each variable to the risk of corporate bankruptcy, the SHAP (SHapley Additive exPlanations) methodology was employed. The validation of the hybrid model was performed using data from Americanas S.A. for the base year 2021, which served as input for the prediction. This procedure allowed for the verification of the model’s applicability in a real-world case, without presenting the prediction results in this section.

3. Results and Discussion

The results obtained from applying machine learning models, which integrated hybrid data composed of financial and non-financial indicators derived from Jim Collins’ business decline framework, demonstrated significantly superior predictive performance compared to models that exclusively used financial data. The objective of achieving accuracy levels above 80% was largely surpassed, evidencing the robustness of the proposed predictive approach for predicting business decline within a horizon of up to five years. This initial finding underscores the importance of a more comprehensive view for assessing corporate health, going beyond traditional accounting metrics and incorporating factors that anticipate adverse events.

The comparative analysis between the different machine learning algorithms tested revealed that hybrid models consistently outperformed purely financial approaches on several performance metrics. Specifically, the accuracy on the test set for hybrid models, such as Logistic Regression and Random Forest, reached 100%, while the corresponding financial models presented an accuracy of 93.75%. This difference is crucial, as it indicates that the inclusion of non-financial variables allows for a more precise distinction between declining and healthy companies, minimizing classification errors that could have significant strategic consequences for risk management.

The superiority of hybrid models was also observed in the F1-Score values, which were consistently higher for the approaches that incorporated the “Leading” indicators. The F1-Score, which is a harmonic mean between precision and recall, reflects the model’s ability to correctly identify positive cases (declining companies) and to avoid false positives (classifying a healthy company as declining). The reduction of false positives is particularly relevant in risk management contexts, where erroneous classifications can lead to costly and unnecessary decisions, impacting stakeholder confidence and resource allocation.

The area under the ROC curve (AUC-ROC) reinforced this trend, with all hybrid models achieving a value of 1.00, while the best financial model (Logistic Regression) reached 0.96. This result demonstrates that the inclusion of variables such as “Glassdoor Score” and “Hubris Score” allowed for a perfect discrimination capacity between the classes of healthy and insolvent companies. An AUC-ROC of 1.00 is an indicator of a model with excellent predictive power, capable of differentiating with high precision companies that will face decline from those that will remain stable, providing a valuable early warning for decision-making.

The choice of the Hybrid Random Forest algorithm as the definitive model for this study was based on criteria that transcend point performance metrics. Although hybrid Logistic Regression presented equivalent statistical results in the test sample, the parametric nature of Logistic Regression imposes limitations in business decline problems, which often involve non-linear interactions between variables. Random Forest, in turn, uses the “Bagging” technique, which builds multiple independent decision trees, isolating outliers and reducing prediction variance, which is fundamental in scenarios with sample restriction and noisy data.

The Random Forest’s ability to capture complex and non-linear interactions is crucial for modeling the phenomenon of business decline, which does not manifest linearly. For example, the impact of “Glassdoor Score” may only become critical when “Goodwill” (premium for expectation of future profitability) reaches a certain level. This ability to model conditional relationships and thresholds makes Random Forest more suitable for identifying risk triggers that linear models might mask, offering a more complete and accurate view of the factors contributing to corporate decline.

The SHAP (SHapley Additive exPlanations) methodology was employed to identify the contribution of each variable to the bankruptcy risk, revealing that the characteristics with the greatest decision-making weight are behavioral in nature. The “Glassdoor Score”, which reflects organizational climate and employee satisfaction, and the “Hubris Score”, which quantifies executive arrogance and disconnection through salary disparity, emerged as the most influential indicators. These findings corroborate the theory that cultural and governance deterioration precedes financial crisis, acting as true primary warning signs in the corporate bankruptcy process.

The analysis of the influence of variables, according to SHAP values, demonstrated that the “Hubris Score” (executive arrogance and disconnection) has the greatest weight, at 0.1706, followed by the “Glassdoor Score” (organizational climate and satisfaction), at 0.1394. In contrast, traditional financial indicators such as Net Margin (0.0542) and Total Debt (0.0265) showed lower weights. This hierarchy of influence suggests that a company’s cultural health and governance are stronger predictors of future decline than financial metrics, which often reflect events already in progress or past, limiting strategic anticipation capabilities.

The comparison between companies in the decline group and the control group for the variables “Glassdoor Score” and “Hubris Score” showed a significant divergence. Companies that eventually entered decline showed a deterioration in these indicators before the financial crisis, while companies in the control group maintained averages above 0.8 for both indicators. This behavioral pattern validates Collins’ “Stage 1”, which describes “Pride born of Success – Overconfidence”, as a measurable precursor to risk, where arrogance and internal dissatisfaction begin to erode the company’s foundation before profits are affected.

The drop in “Glassdoor Score” in declining companies is particularly revealing, as it reflects Collins’ “Stage 2”, the “Undisciplined Pursuit of More”, where internal pressure and lack of operational discipline erode organizational culture. This cultural deterioration, manifested by employee dissatisfaction, acts as an early warning sign, indicating that internal problems are accumulating and may lead to adverse financial consequences in the future. The ability to capture these non-financial signals is what gives the hybrid model its predictive advantage over traditional approaches.

To validate the hybrid model, a case study was conducted with Americanas S.A., using data from the base year 2021. The company’s financial indicators, such as Current Ratio (1.74), Total Debt (0.58), and Net Margin (0.024), suggested apparent solvency. However, non-financial indicators, such as “Glassdoor Score” (0.38) and “Hubris Score” (0.92), already pointed to a high-risk scenario, with the “Glassdoor Score” well below the control group’s average and the “Hubris Score” indicating a high salary disparity and potential executive arrogance.

The application of the hybrid model to Americanas S.A. resulted in a bankruptcy probability of 96%, while the purely financial model assigned a probability of 65%. The subsequent public bankruptcy of Americanas in January 2023 proved the effectiveness of the hybrid model in anticipating the financial crisis with greater accuracy. This case demonstrates that “Lagging” (financial) models are vulnerable to accounting manipulations and fraud, while “Leading” indicators (culture and “Hubris”) are more difficult to mask, providing greater resilience and reliability to the predictive model for anticipating adverse corporate events.

The SHAP analysis for Americanas S.A. reinforced the evidence that cultural factors were the most significant in the moments preceding the financial crisis. The red bars, indicating factors that increase the probability of insolvency, were dominated by “Hubris Score” and “Glassdoor Score”. This suggests that the deterioration of organizational climate and wage disparity were the main drivers of bankruptcy risk, surpassing the impact of traditional financial indicators in predicting the adverse event and providing a more accurate warning about the organization’s health.

Despite the robustness of the results, the study presents some important limitations. The model was trained and validated exclusively with data from publicly traded companies, whose information is public and auditable by the SEC and CVM. Metrics such as “Pay Ratio” and “Goodwill” may present different dynamics in privately held companies, which requires adaptation and calibration of variables for this context. Furthermore, the nature of the data from “Glassdoor” and administrative reports, although auditable, has a degree of subjectivity that demands continuous calibration to maintain the model’s accuracy in different scenarios.

As a future development, to enable a market solution such as Software as a Service (SaaS), it will be necessary to adapt and calibrate the variables for the context of private and smaller companies. The automation of data collection, possibly via Natural Language Processing (NLP) techniques to refine the “Hubris Score” from the automatic reading of annual reports, is an essential step to scale the model and eliminate manual collection, making it more efficient and applicable to a larger universe of companies, consolidating its commercial and practical applicability.

In summary, the findings of this research confirm that the integration of “Leading” indicators (cultural and behavioral) with “Lagging” indicators (financial) in machine learning models significantly increases the predictive capacity to identify business decline. The hybrid model developed not only achieves accuracy objectives but also offers deep insights into how cultural and governance factors precede financial crisis, acting as an early warning system that allows managers and investors to monitor the cultural health of their portfolios as a critical indicator of long-term performance, providing a temporal window for corrective and strategic interventions.

4. Conclusion

This study sought to develop a hybrid Machine Learning model to predict business decline within a five-year horizon, integrating financial and non-financial indicators based on the business decline framework. It was found that the hybrid model significantly outperformed purely financial models, achieving accuracy levels above 90%. SHAP analysis revealed that cultural factors, such as employee satisfaction (Glassdoor Score) and wage disparity (Hubris Score), possessed greater predictive power than traditional accounting metrics, anticipating adverse corporate events. It was identified that cultural and governance deterioration precedes financial crisis, acting as primary warning signs. Validation with the case study of Americanas S.A. demonstrated the hybrid model’s effectiveness in predicting bankruptcy risk with greater accuracy, highlighting that non-financial indicators are more resilient to accounting manipulations. The main contribution lies in offering a more precise and robust tool for anticipating adverse corporate events, enabling more proactive and strategic risk management.

Despite the significant results, the study presents important limitations, such as the exclusive training and validation with data from publicly traded companies, which suggests the need for adaptation and calibration of variables for the context of privately held and smaller companies. The subjective nature of some data, such as that from Glassdoor and administrative reports, demands continuous calibration to maintain the model’s accuracy. As a future development, the automation of data collection is suggested, possibly via Natural Language Processing (NLP), to refine the Hubris Score through automatic reading of annual reports, aiming to scale the model and enable a market solution such as Software as a Service (SaaS). The proposed model represents a paradigm shift in risk management, moving from corporate ‘autopsy’ analysis to a ‘preventive’ diagnosis, providing managers and investors with an early warning system to monitor cultural health as a critical indicator of long-term performance.

Bibliographic References

Altman, E. I. 1968. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance 23(4): 589-609.

Bennett, N.; Lemoine, G. J. 2014. What VUCA Really Means for You. Harvard Business Review 92(1/2): 27-32

Collins, J. 2018. How the Mighty Fall. 1 ed. Alta Books, New York, EUA.

Committee of Sponsoring Organization of the Treadway Commission (COSO) 2017. Enterprise Risk Management—Integrating with Strategy and Performance. New York: AICPA.

Damodaran, A. 2014. Applied Corporate Finance. 4. ed. Hoboken: Wiley.

Gil, A. C. 2017. Como Elaborar Projetos de Pesquisa. 6ª ed. Atlas, São Paulo, SP, BR.

Glassdoor. Avaliações das empresas. Disponível em: https://www.glassdoor.com.br. Acessos em: novembro 2025 a março 2026.

Géron, A. 2021. Mãos à Obra: Aprendizado de máquina com Scikit-Learn, Keras & TensorFlow. 2ed. O’Reilly, Rio de Janeiro, RJ, BR.

Harrison, M 2023. Machine Learning Guia de Referência Rápida. 1ed. O’Reilly, EUA.

McKinsey & Company. 2017. The Future of Risk Management in the Digital Era. New York: McKinsey Global Institute.

OECD 2015. G20/OECD Principles of Corporate Governance. Paris: OECD Publishing.

Portal de Consulta Externa da CVM (Comissão de Valores Mobiliários). Dados das empresas.: Consulta de Documentos de Companhias Abertas. Disponível em: https://www.gov.br/cvm. Acessos em: novembro 2025 a fevereiro 2026.

Porter, M. E. 1996. What is Strategy? Harvard Business Review 74(6): 61-78.

Provost, F.;Fawcett, T. 2016. Data Science para Negócios. 1ed. Alta Books, Rio de Janeiro, RJ, BR.

Rigby, D. K.; Sutherland, J.; Takeuchi, H. 2016. Embracing Agile. Harvard Business Review 94(5): 40-50.

SEC [Referência completa não encontrada no documento original]

Shumway, T. 2001. Forecasting Bankruptcy More Accurately: A Simple Hazard Model. The Journal of Business 74(1): 101-124, 2001.

Taleb, N. N. 2021. A lógica do Cisne Negro: O impacto do altamente improvável, 1ed. Objetiva, Rio de Janeiro, RJ, BR.

Article originating from the Final Course Project of the Specialization in Data Science and Analytics of the MBA USP/Esalq

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Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]