Implementation of Random Forest Classifier for Financial Distress Prediction in Technology Sector Companies on the Indonesia Stock Exchange

Netania Pangalinan, Muhammad Rakib and Muhammad Ashdaq
Additional contact information
Netania Pangalinan: Universitas Negeri Makassar, Indonesia
Muhammad Rakib: Universitas Negeri Makassar, Indonesia
Muhammad Ashdaq: Universitas Negeri Makassar, Indonesia

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 8, 135-144

Abstract: Using the Random Forest Classifier algorithm, this study produces a financial crisis prediction model for technology companies listed on the Indonesia Stock Exchange. Historical financial statement data of eight technology companies were analyzed to generate financial indicators as prediction inputs. The results show that this model has an accuracy of 88%, with an increase in accuracy to 92% after resampling techniques. Important financial indicators used include liquidity, profitability, activity and leverage ratios. This research provides an effective predictive tool to identify companies at risk of financial distress, assisting financial management and investment decision-making.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrias/ ... -issue-8/135-144.pdf (application/pdf)
https://rsisinternational.org/journals/ijrias/arti ... esia-stock-exchange/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:9:y:2024:i:8:p:135-144

Access Statistics for this article

International Journal of Research and Innovation in Applied Science is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Innovation in Applied Science from International Journal of Research and Innovation in Applied Science (IJRIAS)
Bibliographic data for series maintained by Dr. Renu Malsaria ().