Predicting Corporate Failure: Lessons from Global Business Failures

Deepak Kumar Gupta

FCA (India) | ACMA (CIMA, UK) | CGMA | CPA (Australia) | RPA

Corporate Failure

Corporate failure refers to a company’s inability to continue its operations due to insolvency, illiquidity, mismanagement, fraud, or strategic collapse. However, corporate failure is rarely a sudden event; rather, it is typically preceded by a series of financial, strategic, operational, and governance-related warning signals that can often be identified well in advance.

Early identification of these signals enables management, investors, lenders, regulators, and auditors to initiate timely corrective actions before the situation becomes irreversible. These warning signals usually stem from underlying organisational weaknesses, the causes of which may vary across businesses. Broadly, the causes of corporate failure can be grouped into the following distinct categories:

Financial Failures

High Financial Leverage

A prominent Indian full-service airline, incorporated in 2003 and commencing operations in 2005, pursued aggressive expansion through debt financing. However, persistent losses, high operating costs, and weak cash flows rendered its capital structure unsustainable. By 2012, the airline was unable to meet its debt obligations and was forced to suspend operations, demonstrating how excessive leverage can lead to financial distress and, ultimately, corporate insolvency.

Macroeconomic Disruptions

A leading U.S.-based global investment bank, founded in the mid-19th century and headquartered in New York, evolved from a traditional trading business into a major financial services institution. During the 2008 global financial crisis, the bank incurred substantial losses due to its high leverage and significant exposure to subprime mortgage-backed securities. The collapse of the U.S. housing market triggered a systemic financial shock, ultimately resulting in one of the largest bankruptcies in financial history and exacerbating global financial instability.

Operational & Strategic Failures

Loss of Competitive Advantage

A leading Finnish technology company, founded in the mid-19th century, evolved from traditional manufacturing into a global leader in the mobile handset industry by the late 1990s and early 2000s. The company built its success on hardware excellence and an extensive distribution network. However, the transition to smartphones, touchscreen interfaces, and software-driven ecosystems exposed its strategic rigidity and delayed response to changing market dynamics.

Intense competition from digitally integrated platforms led to a rapid erosion of its market share, culminating in the sale of its mobile handset business in 2014 and a strategic refocus on telecommunications infrastructure, network technologies, and digital services.

Unsustainable Business Model

A U.S.-based shared-workspace company, founded in 2010 and headquartered in New York, expanded rapidly by leasing commercial properties under long-term agreements and offering flexible, short-term workspace solutions. The company pursued an aggressive growth strategy supported by substantial spending and significant fixed lease commitments.

During its planned initial public offering (IPO) in 2019, concerns arose over corporate governance, persistent operating losses, and the disconnect between its technology-style valuation and the underlying economics of its real estate business, leading to the withdrawal of the IPO. Continued financial stress, rising interest rates, and declining office occupancy following the COVID-19 pandemic ultimately resulted in bankruptcy proceedings in 2023, highlighting the risks associated with an unsustainable business model.

Strategic Drift

The concept of strategic drift was propounded by Johnson (1998). It refers to the gradual and often imperceptible failure of an organisation to adapt its strategy in response to changes in the external business environment.

A Canadian technology company, founded in the 1980s, initially emerged as a global leader in mobile communications by offering secure devices with physical keyboards and enterprise-focused features. The company dominated the market during the 2000s but was slow to respond to the industry’s shift towards touchscreen smartphones, application-based ecosystems, and evolving consumer preferences. As a result, it gradually lost significant market share to more innovative competitors and eventually shifted its strategic focus to enterprise software and cybersecurity solutions, marking the end of its leadership in the mobile handset industry.

Ethical & Governance Failures

Fraud and Financial Misreporting

A U.S.-based energy and commodities company, founded in the 1980s, grew rapidly through aggressive expansion and trading activities. However, it engaged in fraudulent accounting practices and deliberate financial misreporting, concealing liabilities and losses while overstating profits. Once these practices came to light, investor confidence deteriorated sharply, leading to rapid organisational decline and ultimately resulting in bankruptcy.

The case demonstrates how corporate fraud and financial misreporting can erode stakeholder trust, destroy corporate value, and lead to sudden corporate collapse.

Weak Corporate Governance

An Indian non-banking financial company (NBFC), established in the late 20th century and headquartered in Mumbai, grew rapidly by providing housing and retail loans to a large customer base. The company pursued aggressive expansion by relying heavily on borrowings and investor funds to finance its growth.

However, weak corporate governance, ineffective internal controls, inadequate risk management, and poor board oversight resulted in financial misreporting and mismanagement of funds. Once these irregularities came to light, investor and creditor confidence deteriorated sharply, leading to severe liquidity stress and, ultimately, insolvency.

Preventing Corporate Failure

Preventing corporate failure therefore depends largely on an organisation’s ability to detect, interpret, and respond to these early warning signs. To facilitate this process, organisations rely on structured prediction models and analytical frameworks that systematically evaluate indicators of financial distress and organisational weakness.

Corporate failure prediction models can broadly be classified into quantitative and qualitative approaches. Quantitative models rely primarily on financial data and accounting ratios to assess the probability of failure. Among the earliest and most influential is Altman’s Z-Score Model (1968), developed by Professor Edward I. Altman.

In contrast, qualitative models evaluate non-financial factors such as management quality, corporate governance, strategic decisions, organisational structure, and operational practices. One of the most widely recognised qualitative approaches is Argenti’s A-Score Model (1976), developed by John Argenti.

Altman’s Z-Score

Since there can be more than one factor causing failure, hence plain regression line can’t do much or have limited application. There is need of some strong statistical tool for analysis which can handle multiple factors in one go.

Multiple Discriminant Analysis (MDA), suggested by Ronald A. Fisher in 1936, is a statistical technique in which multiple factors can be incorporated by assigning weights to each factor to classify observations into groups.

In 1968, Edward I. Altman applied MDA to study 66 publicly traded manufacturing firms with assets over $1 million, half of which had already gone bankrupt. He identified financial ratios that strongly differentiated healthy firms from failing ones and developed a weighted formula, known as the Altman Z-Score, combining liquidity, profitability, efficiency, leverage, and productivity metrics.

Basic Model (1968) for Public Manufacturers

These ratios are combined with specific weights to calculate the Z-Score, providing a single number to gauge bankruptcy risk, with lower scores indicating higher distress.

Although the original 1968 Z-Score model was developed for publicly traded manufacturing firms, subsequent versions have extended its applicability across different industries and countries. Altman’s original study of 66 firms found that the average Z-score of the bankrupt group was -0.25, whereas the average Z-score of the non-bankrupt group was +4.48.

Z-Score Model for Private Manufacturers

Since the market value of equity is generally unavailable for private companies, Altman re-estimated the model in 1983 by replacing the market value of equity with the book value of equity. The revised model is known as the Z′-Score.

Z″-Score Model for Non-Manufacturers

In 1995, Altman further revised the Z-Score model by eliminating the sales-to-total-assets ratio (X₅) to reduce industry-specific effects. The resulting model, known as the Z″-Score, is suitable for non-manufacturing firms and service organisations operating in developed markets.

Emerging Market Score (EM-Score)

This model is specifically designed for firms operating in emerging economies such as India, Brazil, and Mexico.

Interpretation Rule

Live Example: Estimation of Z″ Score of Canadian Natural Resources Limited (CNRL) 2025

Financial Data

Source: Canadian Natural Resources Limited Annual Report (2025)

Computation of Z″ Score

Interpretation

Since the Altman Z″-Score = 3.145, which is greater than 2.60, Canadian Natural Resources Limited (CNRL) falls in the Safe Zone, indicating a low probability of financial distress. This result is consistent with CNRL’s strong profitability, substantial retained earnings, and healthy capital structure, despite a slightly negative working capital position, which is not uncommon for large energy companies.

Rationale for Selecting the Altman Z″-Score Model

Canadian Natural Resources Limited (CNRL) is listed on both the Toronto Stock Exchange (TSX) and the New York Stock Exchange (NYSE). Its principal business comprises the exploration, development, and production of crude oil and natural gas, classifying it as a non-manufacturing (extractive) company.

The Original Altman Z-Score (1968) was developed for publicly listed manufacturing firms and incorporates the Sales/Total Assets (X₅) ratio. Since this ratio is highly influenced by industry characteristics, the original model may not be appropriate for extractive industries such as oil and gas.

Accordingly, the Altman Z″-Score (1995) is more suitable for CNRL, as it was specifically developed for non-manufacturing companies operating in developed economies. This model excludes the Sales/Total Assets (X₅) ratio and is therefore less affected by industry-specific differences in asset turnover.

Limitations of Altman’s Z-Score

Although Altman’s Z-score is one of the most widely used models for predicting corporate financial distress, it has certain limitations. The model was developed using historical data from relatively stable economic conditions and may therefore be less reliable during periods of economic recession, financial crises, or high market volatility.

It is primarily designed for manufacturing and non-financial companies and is generally not suitable for financial institutions, such as banks and insurance companies, owing to their distinct capital structures and regulatory frameworks.

The model also assumes that the underlying financial statements are accurate; consequently, if a company engages in fraudulent accounting practices, the resulting Z-score may be misleading.

Other Quantitative Models

The Beaver Univariate Model, developed by William Beaver in 1966, is a pioneering method for predicting corporate failure by analysing individual financial ratios (univariate) to see how well they distinguish between firms that will fail and those that won’t, finding that cash flow to total debt was a strong predictor years before bankruptcy, paving the way for more complex multivariate models like Altman’s Z-score.

Taffler and Tishaw’s Model, ZETA Model, H Score can be also be used to predict corporate failure. Taffler also came with PAS (Performance Analysis Score) which is a Multivariant model similar to Altman Z score; but Altman Z score prominently used.

Argenti’s A-Score Model

Argenti’s A-Score Model analyses corporate failure through three dimensions, namely defects, mistakes, and symptoms of failure. Each dimension is further broken down into specific negative indicators such as high gearing, autocratic leadership, weak accounting systems, etc.

Management assigns negative scores to each identified weakness. These scores are then aggregated to arrive at the overall A-Score. If the total score exceeds 25, the firm is considered to be at risk of failure, indicating the need for immediate corrective action.

Defects

Defects represent fundamental weaknesses within the organisation and include:

• Management defects such as faulty organisational structure, autocratic chief executive, and concentration of power.
• Accounting defects such as lack of budgetary control, absence of costing systems, and weak financial reporting.

Mistakes

Mistakes arise over time as a consequence of underlying defects. Defects and mistakes are therefore interrelated. For example, weak management and accounting systems inevitably lead to strategic errors such as high gearing, overtrading, or failure of major projects.

Symptoms of Failure

If mistakes persist, symptoms of failure inevitably become visible. These are outward warning signs such as:

• Deteriorating financial ratios
• Liquidity stress
• Creative accounting practices

In addition to the overall A-Score, the individual scores of each dimension are also analysed. Group-wise scores help identify the root cause of failure, thereby facilitating more effective corrective measures.

How the A-Score Works

Under Argenti’s A-Score Model, acceptable score limits are prescribed for each dimension. The Defects score should not exceed 10, and the Mistakes score should not exceed 15. In the Symptoms dimension, any score itself is an indicator of financial distress.

A firm is considered at risk of failure if any one or more of the following conditions exist:

• The overall A-Score exceeds 25, and/or
• The Defects score exceeds 10, and/or
• The Mistakes score exceeds 15, and/or
• There is any score recorded under the Symptoms category.

Example Argenti’s A-Score Model

Limitations of Qualitative Models

Although the Argenti A-Score provides valuable insights by incorporating both qualitative and quantitative factors, it has certain limitations. The model relies heavily on the subjective judgement of experts when assessing management quality, strategic decisions, and organisational weaknesses, which may introduce bias and reduce consistency across evaluations.

It also requires extensive financial and non-financial information, making the analysis time-consuming and data-intensive. Furthermore, the reliability of the results depends significantly on the accuracy, completeness, and quality of the underlying information; incomplete, outdated, or unreliable data may lead to misleading conclusions regarding a company’s risk of failure.

Conclusion