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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.&lt;\/p&gt;\n\t\t&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/qfe.2022018","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T01:59:45Z","timestamp":1657591185000},"page":"405-432","source":"Crossref","is-referenced-by-count":15,"title":["Impact of macroeconomic indicators on bankruptcy prediction models: Case of the Portuguese construction sector"],"prefix":"10.3934","volume":"6","author":[{"given":"Ana","family":"Sousa","sequence":"first","affiliation":[{"name":"Department of Production and Systems Engineering, School of Engineering, University of Minho, Braga, Portugal"}]},{"given":"Ana","family":"Braga","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Centre, University of Minho, Braga, Portugal"}]},{"given":"Jorge","family":"Cunha","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Centre, University of Minho, Braga, Portugal"}]}],"member":"2321","reference":[{"key":"key-10.3934\/QFE.2022018-1","doi-asserted-by":"publisher","unstructured":"Abdallah FDM (2018) Statistical Modelling of Categorical Outcome with More than Two Nominal Categories. <i>Am J Appl Math Stat<\/i> 6: 262\u2013265. https:\/\/doi.org\/10.12691\/ajams-6-6-7","DOI":"10.12691\/ajams-6-6-7"},{"key":"key-10.3934\/QFE.2022018-2","doi-asserted-by":"publisher","unstructured":"Acosta-Gonz\u00e1lez E, Fern\u00e1ndez-Rodr\u00edguez F (2014) Forecasting Financial Failure of Firms via Genetic Algorithms. <i>Comput Econ<\/i> 43: 133\u2013157. https:\/\/doi.org\/10.1007\/s10614-013-9392-9","DOI":"10.1007\/s10614-013-9392-9"},{"key":"key-10.3934\/QFE.2022018-3","doi-asserted-by":"publisher","unstructured":"Acosta-Gonz\u00e1lez E, Fern\u00e1ndez-Rodr\u00edguez F, Ganga H (2019) Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data. <i>Comput Econ<\/i> 53: 227\u2013257. https:\/\/doi.org\/10.1007\/s10614-017-9737-x","DOI":"10.1007\/s10614-017-9737-x"},{"key":"key-10.3934\/QFE.2022018-4","doi-asserted-by":"publisher","unstructured":"Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. <i>J Financ<\/i> 23: 589\u2013609. https:\/\/doi.org\/10.1111\/j.1540-6261.1968.tb00843.x","DOI":"10.1111\/j.1540-6261.1968.tb00843.x"},{"key":"key-10.3934\/QFE.2022018-5","doi-asserted-by":"publisher","unstructured":"Altman EI (1983) Why businesses fail. <i>J Bus Strat<\/i> 3: 15\u201321. https:\/\/doi.org\/10.1108\/eb038985","DOI":"10.1108\/eb038985"},{"key":"key-10.3934\/QFE.2022018-6","doi-asserted-by":"crossref","unstructured":"Altman EI, Hotchkiss E (2006) <i>Corporate Financial Distress and Bankruptcy<\/i> (3rd ed.), John Wiley &amp; Sons, Inc.","DOI":"10.1002\/9781118267806"},{"key":"key-10.3934\/QFE.2022018-7","doi-asserted-by":"publisher","unstructured":"Asuero AG, Sayago A, Gonz\u00e1lez AG (2006) The correlation coefficient: An overview. <i>Crit Rev Anal Chem<\/i> 36: 41\u201359. https:\/\/doi.org\/10.1080\/10408340500526766","DOI":"10.1080\/10408340500526766"},{"key":"key-10.3934\/QFE.2022018-8","doi-asserted-by":"publisher","unstructured":"Barboza F, Kimura H, Altman E (2017) Machine learning models and bankruptcy prediction. <i>Expert Syst Appl<\/i> 83: 405\u2013417. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.006","DOI":"10.1016\/j.eswa.2017.04.006"},{"key":"key-10.3934\/QFE.2022018-9","doi-asserted-by":"publisher","unstructured":"Beaver WH (1966) Financial Ratios As Predictors of Failure. <i>J Account Res<\/i> 4: 71\u2013111. https:\/\/doi.org\/10.2307\/2490171","DOI":"10.2307\/2490171"},{"key":"key-10.3934\/QFE.2022018-10","doi-asserted-by":"publisher","unstructured":"Beaver W, McNichols M, Rhie JW (2005) Have financial statements become less informative? 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