Using Glow-worm algorithm to predict companies’ financial distress
One important research issue in the risk management area is to predict the financial distress of companies. This case has received great attention from banks, companies, managers, and investors. Although there are many studies on this case, the hybrid models (mixed feature selection and classifier...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Spanish |
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Universidad del Quindio
2022-09-01
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| Series: | Revista de Investigaciones Universidad del Quindío |
| Subjects: | |
| Online Access: | https://ojs.uniquindio.edu.co/ojs/index.php/riuq/article/view/1018 |
| _version_ | 1811258072137465856 |
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| author | Ali Mayeli Erfan Mehregan Mohsen Manna |
| author_facet | Ali Mayeli Erfan Mehregan Mohsen Manna |
| author_sort | Ali Mayeli |
| collection | DOAJ |
| description |
One important research issue in the risk management area is to predict the financial distress of companies. This case has received great attention from banks, companies, managers, and investors. Although there are many studies on this case, the hybrid models (mixed feature selection and classifier models) have been used by researchers in recent years. The main objective of this study is to propose a high-performance predictive model and compare its results with other models that are commonly used for financial distress prediction. To do this, the Glowworm optimization algorithm-based hybrid neural network model was employed. Moreover, the neural network and logistic regression model, which is one of the statistical classifier models were used. The results indicated that the glowworm optimization algorithm (also known as firefly optimization algorithm)-based hybrid neural network model had higher performance compared to the neural network and logistic regression models.
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| first_indexed | 2024-04-12T18:07:37Z |
| format | Article |
| id | doaj.art-d3d9d51d832140b6bd2f6715688764e3 |
| institution | Directory Open Access Journal |
| issn | 1794-631X 2500-5782 |
| language | Spanish |
| last_indexed | 2024-04-12T18:07:37Z |
| publishDate | 2022-09-01 |
| publisher | Universidad del Quindio |
| record_format | Article |
| series | Revista de Investigaciones Universidad del Quindío |
| spelling | doaj.art-d3d9d51d832140b6bd2f6715688764e32022-12-22T03:21:56ZspaUniversidad del QuindioRevista de Investigaciones Universidad del Quindío1794-631X2500-57822022-09-0134S310.33975/riuq.vol34nS3.1018Using Glow-worm algorithm to predict companies’ financial distressAli Mayeli0Erfan Mehregan1Mohsen Manna2Stony Brook UniversitySharif University of TechnologyUniversity of Hormozgan One important research issue in the risk management area is to predict the financial distress of companies. This case has received great attention from banks, companies, managers, and investors. Although there are many studies on this case, the hybrid models (mixed feature selection and classifier models) have been used by researchers in recent years. The main objective of this study is to propose a high-performance predictive model and compare its results with other models that are commonly used for financial distress prediction. To do this, the Glowworm optimization algorithm-based hybrid neural network model was employed. Moreover, the neural network and logistic regression model, which is one of the statistical classifier models were used. The results indicated that the glowworm optimization algorithm (also known as firefly optimization algorithm)-based hybrid neural network model had higher performance compared to the neural network and logistic regression models. https://ojs.uniquindio.edu.co/ojs/index.php/riuq/article/view/1018Glowworm AlgorithmFinancial DistressHybrid ModelsNeural Network |
| spellingShingle | Ali Mayeli Erfan Mehregan Mohsen Manna Using Glow-worm algorithm to predict companies’ financial distress Revista de Investigaciones Universidad del Quindío Glowworm Algorithm Financial Distress Hybrid Models Neural Network |
| title | Using Glow-worm algorithm to predict companies’ financial distress |
| title_full | Using Glow-worm algorithm to predict companies’ financial distress |
| title_fullStr | Using Glow-worm algorithm to predict companies’ financial distress |
| title_full_unstemmed | Using Glow-worm algorithm to predict companies’ financial distress |
| title_short | Using Glow-worm algorithm to predict companies’ financial distress |
| title_sort | using glow worm algorithm to predict companies financial distress |
| topic | Glowworm Algorithm Financial Distress Hybrid Models Neural Network |
| url | https://ojs.uniquindio.edu.co/ojs/index.php/riuq/article/view/1018 |
| work_keys_str_mv | AT alimayeli usingglowwormalgorithmtopredictcompaniesfinancialdistress AT erfanmehregan usingglowwormalgorithmtopredictcompaniesfinancialdistress AT mohsenmanna usingglowwormalgorithmtopredictcompaniesfinancialdistress |