Prediction of financial distress: Analyzing the industry performance in stock exchange market using data mining

Harjani Rezkya Putri, Arian Dhini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The challenges and competition in the investment world recently became a great focus to be studied as it is greatly linked to profitability. It has been agreed that substantive negative earning of profitability of firms greatly linked to financial distress, a condition which a firm has difficulty fulfilling its financial obligations. Previous research developed financial distress prediction model using conventional statistical methods that suffer from disadvantages as it depends largely on some restrictive assumptions. This research used data mining methods as its superiority with less restrictive assumptions to predict financial distress, with both financial and non-financial variables examined. Focused in finance and infrastructure listed firms in Indonesia Stock Exchange (IDX) for 4 years period, logistic regression and C4.5 decision tree along with ensemble classifier are developed and evaluated. The decision tree with boosting model demonstrated the best performing prediction model, as it is in line with the previous research that boosting outperformed other methods, with the smallest error rates and highest accuracy, with overall accuracy, sensitivity and specificity are 94.61%, 94.6%, and 94.5% respectively. The result of this research also offer several inferences such as return on asset being the most significant or important predictive variable to determine financially distressed firms.

Original languageEnglish
Title of host publication2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119410
DOIs
Publication statusPublished - Jul 2019
Event16th International Conference on Service Systems and Service Management, ICSSSM 2019 - Shenzhen, China
Duration: 13 Jul 201915 Jul 2019

Publication series

Name2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019

Conference

Conference16th International Conference on Service Systems and Service Management, ICSSSM 2019
Country/TerritoryChina
CityShenzhen
Period13/07/1915/07/19

Keywords

  • C4.5 decision tree
  • Data mining
  • Ensemble classifier
  • Financial distress
  • Financial distress prediction
  • Indonesia Stock Exchange (IDX)
  • Logistic regression

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