A three-state early warning system for the European Union

Papadopoulos, Savas; Stavroulias, Pantelis; Sager, Thomas W.

doi:10.21314/jor.2018.392

Cited by 3 publications

(1 citation statement)

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“…(2) BP NN Artificial NN is a network model, whose operation principle is to operate by simulating the human brain's nerves, and distribute problems to each neuron in parallel [13]. In the process of network training, problems are input as variables to continuously improve the setting of neuron nodes in each layer of the network, so that the accuracy of the model can reach the preset index value, and finally solve the problem of data classification learning [14,15].…”

Section: Definition Of Relevant Conceptsmentioning

confidence: 99%

Enterprise Financial Risk Early Warning Based on Deep Neural Network

2021

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The occurrence of financial risk will have a great impact on the continuous operation of enterprises, which may lead to significant losses of enterprises and even cause bankruptcy of enterprises. With the increasing downward pressure on the domestic economy, economic transformation and upgrading, and the black swan event of the novel coronavirus epidemic, the operational pressure on listed companies has continued to increase, and the delisting risk of listed companies has gradually emerged. This paper mainly studies the enterprise financial risk early warning based on deep NN(NN). This paper first defines the concept of financial early warning and BP NN, and uses BP NN to construct the financial risk early warning model of listed companies. The model is trained and empirically analyzed. The results of empirical analysis show that the research of this paper is conducive to the development of indicators and methods for the prediction of financial risks of listed enterprises, which is conducive to the reference of regulators and investors.

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Section: Definition Of Relevant Conceptsmentioning

confidence: 99%

Enterprise Financial Risk Early Warning Based on Deep Neural Network

2021

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Multi‐class financial distress prediction based on stacking ensemble method

Chen,

Wu,

Zhang

et al. 2024

Int. J Fin Econ

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The motivation of this article is to help financial soundness companies understand their specific financial status so that they can take timely measures to avoid financial distress. Existing multi‐class financial distress prediction (FDP) studies have mainly segmented financial crisis status, with less attention paid to financial soundness companies. To fill this gap, we propose a new multi‐class definition of FDP from the perspective of financial soundness enterprises. The financial states are defined as financial soundness, moderate financial soundness, mild financial soundness and financial distress. We propose a stacking ensemble model for multi‐class FDP. First, deep neural network, multinomial logit regression (MNLogit) and multivariate discriminant analysis models are used as basic classifiers to obtain preliminary prediction results. Second, MNLogit is used to integrate the results from the previous step. To increase the effective information, stock information is then added into the model. The proposed model was trained using data from 2007 to 2019 for Chinese listed companies and tested using data from 2020. The results show that the MacroR‐Pre, MacroR‐Rec, MacroR‐F1 and MacroR‐AUC of the proposed model are better compared with the benchmark model, including individuals and ensembles, with 87.05%, 90.68%, 88.70% and 88.20%.The addition of stock information and non‐financial indicators can improve the accuracy of the multi‐class FDP model by about 8%. The innovativeness of this paper is twofold. First, it proposes a new multi‐class definition of enterprise financial status. Second, a multi‐class FDP based on stacking is constructed, which provides a new method for solving the multi‐class FDP problem. The study shows that the proposed multi‐class definition and stacking model are suitable for analysing financial soundness enterprises, which can help managers effectively grasp the specific financial status and have strong practical significance.

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