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Predicting financial distress in Norway : using logistic regression and random forest models

Zhang, Guang Na; Ye, Fan
Master thesis
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URI
https://hdl.handle.net/11250/2649614
Date
2019
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  • Master Thesis [3749]
Abstract
Financial distress can be a highly costly and disruptive event, both on the level of the firm as

well as for the society. Models to predict financial distress for this reason have been beneficial.

In this thesis, we aim to develop a similar model which is applicable to Norwegian companies.

Rather than solely focusing on bankruptcy predictions as previous research has done, we use

financial ratios and other related company information, to predict whether firms are likely to

enter financial distress within the next two years. Furthermore, we seek to identify early

warning signs of financial distress in order for the management to start financial reconstruction

in time.

A traditional and a more recent algorithm – logistic regression and random forest – were

utilized in our analysis for their complementary properties. The models were created based on

data provided by the Norwegian School of Economics where we selected a sample of 30 000

companies in the period from 2013 - 2016 after thorough cleaning of data.

We find very similar performance for both models where random forest shows slight

superiority to logistic regression. Both models yield an AUC of ~ 0.65, and from the results

obtained, it indicates that they are able to correctly predict ~ 60% of both healthy and

financially distressed companies ahead of time.

Moreover, the results indicate that our models assign high importance to some commonly used

ratios in the past, such as Size (Log of total assets), ROA, Retained earnings/Total assets,

Total debt/Total assets and Debt/Equity. We also find Cash ratio and Net profit margin as

important variables, which have been neglected previously. All these variables may contribute

as warnings signs of financial distress when making predictions.

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