Bankruptcy prediction

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.

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)

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.

The prediction that OpenAI may go bankrupt by 2024 was reported in 2023 by Analytics India Magazine. Their analysis was based on OpenAI's growing financial losses, declining user base, inability to generate consistent and meaningful revenue, and declining quality of star products. ChatGPT's running costs were $700,000 daily, and its user base in July 2023 was down 12% from the previous month. OpenAI has been looking for new and better ways to monetize the GPT-4 LLM. However, it has yet to turn a profit; since the development of ChatGPT, its losses have reached $540 million. Sam Altman himself admitted in December 2022 that OpenAI would have to monetize the AI model. With Elon Musk's stated intention to create a competing chatbot called TruthGPT, which is not as prejudiced or hallucinatory as OpenAI's ChatGPT, and the emergence of competitors such as LLaMA2, competition is intensifying. Also, it seems that talented people are switching to other companies. It was argued that there is also a lack of enterprise-level GPUs in the market, which is a hurdle for learning models.[6]

• FitzPatrick 1932
• Beaver 1966. Financial ratios predictors of failure. Journal of Accounting Research, 4 (Supplement), p. 71-111.
• Beaver 1968
• Altman, Edward I (1968). "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy". Journal of Finance. 23 (4): 589–609. doi:10.1111/j.1540-6261.1968.tb00843.x. S2CID 154437292.
• Ohlson, James. 1980.
• Balcaen, Sofie; Ooghe, Hubert (2006). "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems". British Accounting Review. 38: 63–93. doi:10.1016/j.bar.2005.09.001.
• Zmijewski, Mark E. 1984. "Methodological issues related to the estimation of financial distress prediction models". Journal of Accounting Research 22 (Supplement), p. 59-86.
• Jackson, Richard; 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.
• Danilov, Konstantin (2014). Corporate Bankruptcy: Assessment, Analysis and Prediction of Financial Distress, Insolvency, and Failure. SSRN Working Paper Series (Thesis). Elsevier BV. doi:10.2139/ssrn.2467580. hdl:1721.1/90237. ISSN 1556-5068.

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