A Dynamic Prediction Model of Financial Distress in the Financial Sharing Environment

Zhu, Jie; Zhu, Hong; Lin, Nan

doi:10.1155/2023/6259689

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…The dynamic multilayer neural network model achieved the highest classification accuracy 93.4%, the best result of recurrent neural network model was 95.2%, model based on fuzzy sets achieved the best classification accuracy of 96.2% and the highest classification accuracy of decision tree model was 93%. Zhu et al [45] determined an index system for the dynamic evaluation of financial distress with the use of analytical hierarchy process and entropy weight theory. Then they used probabilistic neural network to construct a dynamic prediction model.…”

Section: Discussionmentioning

confidence: 99%

“…Shen et al [44] proposed a new dynamic approach to financial distress prediction which combined multiple forecast results with a high latitude unbalanced data stream using the Adaptive Neighbor SMOTE-Recursive Ensemble Approach. Zhu et al [45] used the Analytical hierarchy process and Entropy weight theory to determine an index system for the dynamic assessment of financial distress. They also constructed Probabilistic neural network-based dynamic financial distress prediction model.…”

Section: Introductionmentioning

confidence: 99%

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Bankruptcy Prediction for Sustainability of Businesses: The Application of Graph Theoretical Modeling

Horváthová,

Mokrišová,

Bača

2023

Mathematics

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Various methods are used when building bankruptcy prediction models. New sophisticated methods that are already used in other scientific fields can also be applied in this area. Graph theory provides a powerful framework for analyzing and visualizing complex systems, making it a valuable tool for assessing the sustainability and financial health of businesses. The motivation for the research was the interest in the application of this method rarely applied in predicting the bankruptcy of companies. The paper aims to propose an improved dynamic bankruptcy prediction model based on graph theoretical modelling. The dynamic model considering the causality relation between financial features was built for the period 2015–2021. Financial features entering the model were selected with the use of Domain knowledge approach. When building the model, the weights of partial permanents were proposed to determine their impact on the final permanent and the algorithm for the optimalisation of these weights was established to obtain the best performing model. The outcome of the paper is the improved dynamic graph theoretical model with a good classification accuracy. The developed model is applicable in the field of bankruptcy prediction and is an equivalent sophisticated alternative to already established models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%