A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

Xu, Wei; Fu, Hongbo; Pan, Yingjun

doi:10.1155/2019/3085247

Cited by 9 publications

(5 citation statements)

References 42 publications

(54 reference statements)

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“…All similarly created models are rather outdated, as they use the data that were up to date before the world financial crisis. This paper shows an up-to-date and simple model (most existing studies create relatively complex hybrid models-e.g., Xu et al 2019), which can be gradually updated using new data, and thus even become more accurate (due to neural networks learning).…”

Section: Svm/nn Comparisonmentioning

confidence: 99%

Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

Horák

Vrbka

Šuleř

2020

JRFM

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Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future development of any company in the market. The objective of this paper is to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and profit and loss accounts) of industrial companies operating in the Czech Republic for the last 5 marketing years were used. For the application of classification methods, TIBCO’s Statistica software, version 13, is used. In total, 6 models were created and subsequently compared with each other, while the most successful one applicable in practice is the model determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is not applicable in the structure of correct classifications.

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Section: Svm/nn Comparisonmentioning

confidence: 99%

Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

Horák

Vrbka

Šuleř

2020

JRFM

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“…This method can obtain more data than other outlier elimination methods. Normalization was applied such that there was no difference in the level at which the features were learned [42,56,57]. In the experimental stage of this study, single-machine learning and ensemble models selected optimization parameters that derived the highest accuracy in ten learnings through 10-fold cross-validation and a Bayesian optimizer.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry

Jeong

Kim

2022

Buildings

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A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions in other industries, as an additional input variable. To compare the performances of the prediction models, single-machine learning and ensemble models’ performances were compared. The comprehensive performance comparison of these models was based on the average value of the prediction performance and the results of the Friedman test. The comparison result determined that the random subspace (RS) model exhibited the best performance in predicting the financial status of construction companies after a medium-to-long-term period. The proposed model can be effectively employed to help large-scale project stakeholders avoid damage caused by the financial distress of construction companies during the project implementation process.

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“…Financial variables. We summarise theoretical knowledge on ensemble models focusing on dependent variables according to [2,5,7,8,59,[61][62][63]65,66,70]. This research applies a wide spectrum of financial variables to estimate financial distress.…”

Section: Methodsmentioning

confidence: 99%

“…The results demonstrate that the approach significantly enhances the overall accuracy of the prediction model. Finally, Xu et al [65] supposed a novel soft ensemble model (ANSEM) to predict financial distress based on various sample sizes. The sample includes 200 companies from 2011 to 2017.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Pavličko¹,

Ďurica²,

Mazanec³

2021

Mathematics

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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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A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

Cited by 9 publications

References 42 publications

Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry

Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

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