Modelling the financial performance of construction companies using neural network via genetic algorithm
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Net profit, annual work volume, and working capital can be considered as the main financial performance indicators for any construction company. Sufficient liquidity must be properly assessed to ensure the survival of the business in both short-term and long-term bases. Large amount of working capital simply means idle funds in a form of current assets that does not gain any profit for the company. On other hand, small amount of working capital means that the company is unable to meet its liabilities and it faces complexity to participate in new project tenders, as a consequence its annual work volume might be decreased. Then, the excess or shortage of working capital affects badly the companies’ profitability. Hence, it is obvious that the construction companies’ working capital, net profit, and annual work volume constitute three interrelated financial performance indicators that have to be appropriately assessed. The present study aims to develop a model to help the construction companies’ managers to assess and forecast their companies’ financial performance indicators: working capital, net profit, and annual work volume. Through this research, the genetic algorithm technique (GA) will be integrated with the neural network technique (NN) to develop the proposed model. The developed model will be able to predict the three financial performance indicators: working capital, net profit, and annual work volume, for an upcoming year based on previously published financial statements data. A comprehensive literature review was conducted and 23 factors were identified as the most influencing factors on the construction companies’ financial indicators: working capital, net profit, and annual work volume. One hundred and sixty four Egyptian construction companies’ financial statements were gathered and analyzed to extract data regarding the identified 23 factors. The extracted data were used to develop a NN–GA hybrid and NN only models to assess the construction companies’ financial indicators. The two developed model outputs are compared to evaluate their predictive capability. This comparison showed that, the NN–GA hybrid model predictive capability is better than the NN only model predictive capability. Incorporating the GA enhances the predicting capability of the developed model by an average of 4.0%.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle