Impact of macroeconomic indicators on bankruptcy prediction models: Case of the Portuguese construction sector

Abstract

The importance of macroeconomic indicators on the performance of bankruptcy prediction models has been a contentious issue, due in part to a lack of empirical evidence. Most indicators are primarily centered around a company's internal environment, overlooking the impact of the economic cycle on the status of the company. This research brings awareness about the combination of microeconomic and macroeconomic factors. To do this, a new model based on logistic regression was combined with principal component analysis to determine the indicators that best explained the variations in the dataset studied. The sample used comprised data from 1,832 Portuguese construction companies from 2009 to 2019. The empirical results demonstrated an average accuracy rate of 90% up until three years before the bankruptcy. The microeconomic indicators with statistical significance fell within the category of liquidity ratios, solvency and financial autonomy ratios. Regarding the macroeconomic indicators, the gross domestic product and birth rate of enterprises proved to increase the accuracy of bankruptcy prediction more than using only microeconomic factors. A practical implication of the results obtained is that construction companies, as well as investors, government agencies and banks, can use the suggested model as a decision-support system. Furthermore, consistent use can lead to an effective method of preventing bankruptcy by spotting early warning indicators.

Keywords

• bankruptcy
• construction sector
• logistic regression
• macroeconomic indicators
• microeconomic indicators
• principal component analysis
• roc curve