Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress
The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict finan...
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| Format: | Article |
| Language: | English |
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Sciendo
2023-07-01
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| Series: | Croatian Review of Economic, Business and Social Statistics |
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| Online Access: | https://doi.org/10.2478/crebss-2023-0002 |
| _version_ | 1797330626738651136 |
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| author | Sabek Amine |
| author_facet | Sabek Amine |
| author_sort | Sabek Amine |
| collection | DOAJ |
| description | The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict financial distress using two types of Artificial Neural Networks (ANN) compared to the Logistic Regression (LR), and this will be done by relying on the data of 12 Algerian companies for the period 2015-2019. The reason for choosing these two types of networks in particular, is attributed to the fact that Elman Neural Network (ENN) is commonly used network, in contrast to the Feed-forward Distributed Time Delay Neural Network (FFDTDNN). Regarding the choice of these companies as a study sample, can be attributed to the similarity in the temporal range covered by their financial statements, coupled with their approximate parity in terms of asset size. This study concluded that the ENN model outperformed the LR model in predicting financial distress with a classification accuracy of 100%. On the other hand, the LR model outperformed the FFDTDNN with a classification accuracy of 83.33%. Therefore, it can be asserted that ANNs cannot be regarded as superior to Logistic Regression (LR) in all statuses. Instead, it is accurate to affirm that specific types of ANNs exhibit greater efficacy than LR in predicting financial distress, while other types demonstrate relatively diminished effectiveness. |
| first_indexed | 2024-03-08T07:22:29Z |
| format | Article |
| id | doaj.art-884ae9026ff1450ab11136cd0f35aaa1 |
| institution | Directory Open Access Journal |
| issn | 2459-5616 |
| language | English |
| last_indexed | 2024-03-08T07:22:29Z |
| publishDate | 2023-07-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Croatian Review of Economic, Business and Social Statistics |
| spelling | doaj.art-884ae9026ff1450ab11136cd0f35aaa12024-02-02T23:02:12ZengSciendoCroatian Review of Economic, Business and Social Statistics2459-56162023-07-0191163210.2478/crebss-2023-0002Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distressSabek Amine01Investment bets and sustainable development stakes in border areas, University of Tamanghasset, B.P 10034 Tamanghasset Airport road, AlgeriaThe prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict financial distress using two types of Artificial Neural Networks (ANN) compared to the Logistic Regression (LR), and this will be done by relying on the data of 12 Algerian companies for the period 2015-2019. The reason for choosing these two types of networks in particular, is attributed to the fact that Elman Neural Network (ENN) is commonly used network, in contrast to the Feed-forward Distributed Time Delay Neural Network (FFDTDNN). Regarding the choice of these companies as a study sample, can be attributed to the similarity in the temporal range covered by their financial statements, coupled with their approximate parity in terms of asset size. This study concluded that the ENN model outperformed the LR model in predicting financial distress with a classification accuracy of 100%. On the other hand, the LR model outperformed the FFDTDNN with a classification accuracy of 83.33%. Therefore, it can be asserted that ANNs cannot be regarded as superior to Logistic Regression (LR) in all statuses. Instead, it is accurate to affirm that specific types of ANNs exhibit greater efficacy than LR in predicting financial distress, while other types demonstrate relatively diminished effectiveness.https://doi.org/10.2478/crebss-2023-0002artificial neural networkfinancial distresslogistic regressionpredictionc45c53g17g33 |
| spellingShingle | Sabek Amine Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress Croatian Review of Economic, Business and Social Statistics artificial neural network financial distress logistic regression prediction c45 c53 g17 g33 |
| title | Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress |
| title_full | Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress |
| title_fullStr | Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress |
| title_full_unstemmed | Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress |
| title_short | Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress |
| title_sort | unveiling the diverse efficacy of artificial neural networks and logistic regression a comparative analysis in predicting financial distress |
| topic | artificial neural network financial distress logistic regression prediction c45 c53 g17 g33 |
| url | https://doi.org/10.2478/crebss-2023-0002 |
| work_keys_str_mv | AT sabekamine unveilingthediverseefficacyofartificialneuralnetworksandlogisticregressionacomparativeanalysisinpredictingfinancialdistress |