Bankruptcy prediction definition

Analysis of public companies' sustainability, in the long run, using different types of accounting ratios and statistical and advanced models

Bankruptcy prediction definition                                 

What is bankruptcy prediction?

Bankruptcy prediction estimates the possibility that a company will default and identifies businesses that will most likely face financial problems in the future. Bankruptcy prediction is performed with different theoretical, statistical, financial models and techniques. The development of machine learning algorithms makes these models even more complex but it also increases their accuracy.

Bankruptcy prediction techniques were initiated in 1932 by Paul J FitzPatrick, who published his analysis of successful and bankrupt companies. He tried to predict the company's bankruptcy through trends in the ratios included in the estimations.

Bankruptcy prediction models

If creditors and investors fail to identify a company with potential insolvency problems it could result in loss of money. That’s why one of the biggest challenges in the bankruptcy prediction is to select adequate and relevant models as well as identifying the relevant factors and indicators. If the best models are selected, then the analysis will be more accurate.

Some of the earliest studies related to bankruptcy estimation consisted of ratio analysis, also called univariate analysis. The development of univariate models set the ground for the creation of multivariate models. One such model for bankruptcy prediction is the Altman Z-score. It considers different factors and characteristics of the companies and it assigns a predefined coefficient for each of the factors used. 

Another method for estimating the bankruptcy prediction is the KMV Merton Distance to Default model. This model states the results in percentage, which represents the probability of bankruptcy.

Bankruptcy prediction limitations

Although bankruptcy prediction is highly useful for a number of parties, it comes with certain limitations. The search for the right statistical model for predicting technique can be very time-consuming. Various types of models can have different predictive power for different industries, companies or countries. Moreover, inappropriate levels of data or lack of available information are other obstacles in the prediction process. The analysis may be problematic in developing countries because of data limitations.

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