Bankruptcy prediction

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Bankruptcy prediction is the art of predicting bankruptcy and various measures of financial distress of public firms. It is a vast area of finance and accounting research. The importance of the area is due in part to the relevance for creditors and investors in evaluating the likelihood that a firm may go bankrupt.

Contents

The quantity of research is also a function of the availability of data: for public firms which went bankrupt or did not, numerous accounting ratios that might indicate danger can be calculated, and numerous other potential explanatory variables are also available. Consequently, the area is well-suited for testing of increasingly sophisticated, data-intensive forecasting approaches.

History

The history of bankruptcy prediction includes application of numerous statistical tools which gradually became available, and involves deepening appreciation of various pitfalls in early analyses. Research is still published that suffers pitfalls that have been understood for many years.

Bankruptcy prediction has been a subject of formal analysis since at least 1932, when FitzPatrick published a study of 20 pairs of firms, one failed and one surviving, matched by date, size and industry, in The Certified Public Accountant. He did not perform statistical analysis as is now common, but he thoughtfully interpreted the ratios and trends in the ratios. His interpretation was effectively a complex, multiple variable analysis.

In 1967, William Beaver applied t-tests to evaluate the importance of individual accounting ratios within a similar pair-matched sample.

In 1968, in the first formal multiple variable analysis, Edward I. Altman applied multiple discriminant analysis within a pair-matched sample. One of the most prominent early models of bankruptcy prediction is the Altman Z-score, which is still applied today.

In 1980, James Ohlson applied logit regression in a much larger sample that did not involve pair-matching.

Modern methods

Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy (Ogachi, D.; Ndege, R.; Gaturu, P.; Zoltan, Z. (2020)

Comparison of differing approaches

The latest research within the field of Bankruptcy and Insolvency Prediction compares various differing approaches, modelling techniques, and individual models to ascertain whether any one technique is superior to its counterparts.

Jackson and Wood (2013) is one of many reviews of the literature to date, and included an empirical evaluation of 15 popular models from the existing literature. These models range from the univariate models of Beaver through the multidimensional models of Altman and Ohlson, and continuing to more recent techniques which include option valuation approaches. They find that models based on market data - such as an option valuation approach - outperform those earlier models which rely heavily on accounting numbers. [1]

Zhang, Wang, and Ji (2013) proposed a novel rule-based system to solve bankruptcy prediction problem. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features; second, a rule-based model was chosen to fit the given dataset since it can present physical meaning; third, a genetic ant colony algorithm (GACA) was introduced; the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm—fitness-scaling chaotic GACA (FSCGACA), which was used to seek the optimal parameters of the rule-based model; and finally, the stratified K-fold cross-validation technique was used to enhance the generalization of the model. [2]

There are a few sources where data can be obtained for bankruptcy prediction. Among others the UCLA-LoPucki [3] Database, which looks at Large US Company bankruptcies from Oct-97 to present and the Federal Judicial Center [4] that looks at bankruptcies from 2008. Some financial providers have started to use these datasets with machine learning models to attempt to predict future bankruptcy risks. [5] This is an emerging field and we expect that future research will look into using unstructured financial data and alternative data sources in prediction models.

Related Research Articles

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In corporate finance, capital structure refers to the mix of various forms of external funds, known as capital, used to finance a business. It consists of shareholders' equity, debt, and preferred stock, and is detailed in the company's balance sheet. The larger the debt component is in relation to the other sources of capital, the greater financial leverage the firm is said to have. Too much debt can increase the risk of the company and reduce its financial flexibility, which at some point creates concern among investors and results in a greater cost of capital. Company management is responsible for establishing a capital structure for the corporation that makes optimal use of financial leverage and holds the cost of capital as low as possible.

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<span class="mw-page-title-main">Bankruptcy in the United States</span> Overview of bankruptcy in the United States of America

In the United States, bankruptcy is largely governed by federal law, commonly referred to as the "Bankruptcy Code" ("Code"). The United States Constitution authorizes Congress to enact "uniform Laws on the subject of Bankruptcies throughout the United States". Congress has exercised this authority several times since 1801, including through adoption of the Bankruptcy Reform Act of 1978, as amended, codified in Title 11 of the United States Code and the Bankruptcy Abuse Prevention and Consumer Protection Act of 2005 (BAPCPA).

Distressed securities are securities over companies or government entities that are experiencing financial or operational distress, default, or are under bankruptcy. As far as debt securities, this is called distressed debt. Purchasing or holding such distressed-debt creates significant risk due to the possibility that bankruptcy may render such securities worthless.

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Financial distress is a term in corporate finance used to indicate a condition when promises to creditors of a company are broken or honored with difficulty. If financial distress cannot be relieved, it can lead to bankruptcy. Financial distress is usually associated with some costs to the company; these are known as costs of financial distress.

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<span class="mw-page-title-main">Financial statement analysis</span>

Financial statement analysis is the process of reviewing and analyzing a company's financial statements to make better economic decisions to earn income in future. These statements include the income statement, balance sheet, statement of cash flows, notes to accounts and a statement of changes in equity. Financial statement analysis is a method or process involving specific techniques for evaluating risks, performance, valuation, financial health, and future prospects of an organization.

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Edward I. Altman is a Professor of Finance, Emeritus, at New York University's Stern School of Business. He is best known for the development of the Altman Z-score for predicting bankruptcy which he published in 1968. Professor Altman is a leading academic on the High-Yield and Distressed Debt markets and is the pioneer in the building of models for credit risk management and bankruptcy prediction.

<span class="mw-page-title-main">Altman Z-score</span> Model for assessing likelihood of bankruptcy

The Z-score formula for predicting bankruptcy was published in 1968 by Edward I. Altman, who was, at the time, an Assistant Professor of Finance at New York University. The formula may be used to predict the probability that a firm will go into bankruptcy within two years. Z-scores are used to predict corporate defaults and an easy-to-calculate control measure for the financial distress status of companies in academic studies. The Z-score uses multiple corporate income and balance sheet values to measure the financial health of a company.

A period of financial distress occurs when the price of a company or an asset or an index of a set of assets in a market is declining with the danger of a sudden crash of value occurring, either because the company is experiencing increasing problems of cash flow or a deteriorating credit balance or because the price had become too high as a result of a speculative bubble that has now peaked.

In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis while keeping the risk of overfitting and finding spurious correlations low. The rule states that one predictive variable can be studied for every ten events. For logistic regression the number of events is given by the size of the smallest of the outcome categories, and for survival analysis it is given by the number of uncensored events.

The Ohlson O-score for predicting bankruptcy is a multi-factor financial formula postulated in 1980 by Dr. James Ohlson of the New York University Stern Accounting Department as an alternative to the Altman Z-score for predicting financial distress.

The Beneish model is a statistical model that uses financial ratios calculated with accounting data of a specific company in order to check if it is likely that the reported earnings of the company have been manipulated.

<span class="mw-page-title-main">Michael T. Dugan</span>

Michael Timothy Dugan is an accounting academic, currently serving as Professor of Accounting at Augusta University. He is noted for research contributions in the area of predictive ability and market-based archival research. Peers external to his home institution have recognized Dugan for teaching excellence.

<span class="mw-page-title-main">Yoshiko Shirata</span> Japanese accounting scholar (born 1952)

"Cindy" Yoshiko Shirata is a Japanese accounting scholar who specialized in corporate bankruptcy prediction. She is best known for her SAF2002 bankruptcy prediction model. Her bankruptcy prediction model has been used by major banks and rating companies in Japan. She is considered one of the most well-known experts to develop bankruptcy prediction models in Japan.

References

  1. ↑ Jackson, Richard H.G.; Wood, Anthony (2013). "The performance of insolvency prediction and credit risk models in the UK: A comparative study". The British Accounting Review. 45 (3): 183–202. doi:10.1016/j.bar.2013.06.009. hdl: 10871/9690 . S2CID   73518244.
  2. ↑ Zhang, Yudong; Shuihua Wang; Genlin Ji (2013). "A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm" (PDF). Mathematical Problems in Engineering. 2013: 753251. doi: 10.1155/2013/753251 . Archived (PDF) from the original on 2014-04-29. Retrieved 2014-04-29.
  3. ↑ "A window on the world of big-case bankruptcy". Archived from the original on 2021-08-30. Retrieved 2021-08-30.
  4. ↑ Bankruptcy Cases Filed, Terminated, and Pending, FY 2008-Present Archived 2022-02-09 at the Wayback Machine
  5. ↑ "Sov.ai AI Finance Research Lab". sov.ai. Archived from the original on 2021-08-30.

Ogachi, D.; Ndege, R.; Gaturu, P.; Zoltan, Z. Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya. J. Risk Financial Manag. 2020, 13, 47. https://doi.org/10.3390/jrfm13030047

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