Financial Distress Prediction Using Hybrid Machine Learning Techniques
Asian Journal of Economics, Business and Accounting · pp. 1–12 · Published 13 Jul 2020
10.9734/ajeba/2020/v16i230231Abstract
The purpose of this study is to establish an effective financial distress prediction model by applying hybrid machine learning techniques. The sample set is 262 financially distressed companies and 786 non-financially distressed companies, listed on the Taiwan Stock Exchange between 2012 and 2018. This study deploys multiple machine learning techniques. The first step is to screen out important variables with stepwise regression (SR) and the least absolute shrinkage and selection operator (LASSO), followed by the construction of prediction models, as based on classification and regression trees (CART) and random forests (RF). Both financial variables and non-financial variables are incorporated. This study finds that the financial distress prediction model built with CART and variables screened by LASSO has the highest accuracy of 89.74%.
Cited by 17
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Sanjay Goswami, Jyoti Mishra, Mahendra Tiwari · Data Analytics for Management, Banking and Finance · 2023
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Yu Xia · 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) · 2025
Showing 16 of 17 known citations — external sources report more than can currently be individually listed.
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