Predicting Bankruptcy at Polish Companies: A Comparison of Selected Machine Learning and Deep Learning Algorithms
DOI:
https://doi.org/10.15678/ZNUEK.2018.0978.0603Słowa kluczowe:
prognozowanie upadłości, deep learning, uczenie maszynowe, finanse przedsiębiorstwAbstrakt
Tytuł artykułu: Prognozowanie upadłości polskich przedsiębiorstw – porównanie skuteczności wybranych metod uczenia maszynowego oraz deep learningu
Poprawne przewidywanie niewypłacalności przedsiębiorstw jest niezwykle istotne z perspektywy zarządzania finansami przedsiębiorstw, gdyż ma ono kluczowe znaczenie w zarządzaniu należnościami, ocenie projektów inwestycyjnych, zarządzaniu kapitałem obrotowym, oceną zdolności do kontynuowania działania, podejmowaniu współpracy i podpisywaniu umów z innymi przedsiębiorstwami. Celem artykułu jest porównanie skuteczności wybranych algorytmów uczenia maszynowego i deep learningu, które zostały zastosowane na reprezentatywnej próbie polskich przedsiębiorstw z wykorzystaniem danych za lata 2008–2018. W artykule podjęto próbę porównania skuteczności następujących algorytmów machine learning (uczenia maszynowego): analizy dyskryminacyjnej (DA), funkcji logitowej (L), support vector machines (SVM), random forest (RF), gradient boosting decision trees (GB), sieci neuronowych z jedną warstwą ukrytą (NN), konwolucyjnych sieci neuronowych (CNN) oraz metody naïve Bayes (NB). Zgodnie z hipotezami badawczymi jeśli ma się dostęp do dużej próby firm, najskuteczniejszym algorytmem (pierwszym wyborem) w prognozie bankructwa są algorytmy: gradient boosting decision trees (H1), random forest (H2) i nierekurencyjne wielowarstwowe sieci neuronowe (H3). Wstępne hipotezy zostały sformułowane na podstawie opinii praktyków dotyczących przydatności różnych algorytmów uczenia maszynowego i algorytmów sztucznej inteligencji w prognozowaniu upadłości przedsiębiorstw. W artykule wykorzystano do uczenia algorytmów bardzo dużą (reprezentatywną) grupę przedsiębiorstw komercyjnych (dane za lata 2008–2013), a do walidacji skuteczności algorytmów również bardzo dużą populację przedsiębiorstw (dane za okres 2014–2018); obydwie populacje obejmowały zupełnie inne podmioty gospodarcze i inne okresy, co pozwoliło na rzetelne porównanie skuteczności badanych algorytmów.
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