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Predicting financial performance for listed companies in Thailand during the transition period A class-based approach using logistic regression and random forest algorithm
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Document Title
Predicting financial performance for listed companies in Thailand during the transition period A class-based approach using logistic regression and random forest algorithm
Author
Supsermpol P. Huynh V.N. Thajchayapong S. Chiadamrong N.
Affiliations
School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathum Thani12120 Thailand; School of Knowledge Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan; National Electronics and Computer Technology Center (NECTEC) NSTDA Pathum Thani12120 Thailand
Type
Article
Source Title
Journal of Open Innovation: Technology Market and Complexity
ISSN
21998531
Year
2023
Volume
9
Issue
3
Open Access
All Open Access Gold
Publisher
Elsevier B.V.
DOI
10.1016/j.joitmc.2023.100130
Abstract
This study presents a class-based approach developed to evaluate the financial performance of companies that have undergone public listing on the stock market. By employing both statistical analysis and machine learning methods the models consider two important determinants which are the company s internal capability determinants prior to going public and the amount of funds raised through the Initial Public Offering (IPO) to predict the company s financial performance after joining the stock market. The study demonstrates that the machine learning method (random forest algorithm) outperforms the statistical method (logistic regression) in predicting financial performance. The findings also reveal that certain determinants significantly influence the predicted financial performance in a specific period. Furthermore the study examines the impact of IPO funds on financial performance and observes that while the first year after listing does not exhibit a significant effect a subsequent positive correlation emerges in the subsequent two to three years up to a certain threshold with excessive funds potentially leading to adverse effects. Overall the predictive models provide valuable insights for companies enabling them to prioritize resources towards significant determinants in a specific relative year make informed decisions and enhance their long-term success in the stock market. ? 2023 The Authors
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
License
CC BY-NC-ND
Rights
Authors
Publication Source
WOS