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Yu L, Li M. A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
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Imbalanced credit risk prediction based on SMOTE and multi-kernel FCM improved by particle swarm optimization. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108153] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries. MATHEMATICS 2021. [DOI: 10.3390/math9161886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.
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Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-020-01249-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Wang L, Wu C. Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.02.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Financial distress prediction: Regularized sparse-based Random Subspace with ER aggregation rule incorporating textual disclosures. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106152] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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A new perspective of performance comparison among machine learning algorithms for financial distress prediction. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105663] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine. ENERGIES 2019. [DOI: 10.3390/en12122251] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.
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