CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection

Du, Xudong; Li, Wei; Ruan, Sumei; Li, Li

doi:10.1016/j.asoc.2020.106758

Cited by 59 publications

(38 citation statements)

References 35 publications

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“…Therefore, in order to fully understand the features of the algorithm, this study used three performance evaluation indicators: ACC, F1-measure and AUC. (Du et al 2020). The results show that the proposed CUS-LightGBM method is superior to the 9 methods listed in the comparison experiment, regardless of the technical combination.…”

Section: Evaluation Metricsmentioning

confidence: 80%

CUS-LightGBM-based financial distress prediction for small- and medium-sized enterprises with imbalanced data

Ruan

Zhang

2021

Preprint

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In this paper, by analyzing the financial data of small- and medium-sized enterprises, it is found that there is a general problem of imbalance data. Therefore, an effective financial distress prediction model based on clustering under-sampling and LightGBM model is constructed. Based on the idea of ensemble learning, this paper proposes the CUS-LightGBM (cluster-based under-sampling with LightGBM) model. First, the data are divided into minority and majority class samples. Then the K-means algorithm is used to cluster the majority class samples, and some data are selected from each cluster to form the balanced data. Finally, it is fused with the LightGBM algorithm based on decision tree to form an efficient prediction model. In addition, there are a large number of redundant features in the proposed model, which will reduce the prediction accuracy and efficiency of the model. Therefore, this paper adopts the feature selection based on ensemble strategy, and determines the main risk factors according to the principle of minority obeying majority. Finally, through the experimental analysis of real financial data, the results show that the CUS-LightGBM model can significantly improve the recognition ability of small- and medium-sized enterprises in financial distress, and the proposed model is more effective in processing financial ratio data than the benchmark model.

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Section: Evaluation Metricsmentioning

confidence: 80%

CUS-LightGBM-based financial distress prediction for small- and medium-sized enterprises with imbalanced data

Ruan

Zhang

2021

Preprint

Self Cite

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“…The effect of learners will decline with the severely unbalanced dataset [ 7 , 10 , 45 ]. It is necessary to preprocess the imbalanced train set.…”

Section: Methodsmentioning

confidence: 99%

“…The quantitative financial indicators are downloaded from the China Stock Market and Accounting Study database (CSMAR). Based on previous researches [ 5 , 10 , 12 , 24 ], 48 financial indicators are taken into account, including solvency, ratio structure, operation, profitability, cash flow, risk, development, and the index of per share. Solvency and cash flow describe a company's ability to repay short-term and long-term debts to prevent bankruptcy.…”

Section: Methodsmentioning

confidence: 99%

“…Based on these techniques, Mai et al [ 29 ] employ shallow layers of neural networks for text embedding and apply RNN for text classification. Besides, Du et al [ 10 ] apply pretrained word2vec neural networks for word embeddings and employ models based on bidirectional LSTM (Bi-LSTM) for risk prediction. However, the longer the input sequence is accepted by the RNN, the more likely the training fails to remember the previous part of the article due to gradient vanishment or gradient explosion.…”

Section: Literature Reviewmentioning

confidence: 99%

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Deep Learning Based on Hierarchical Self‐Attention for Finance Distress Prediction Incorporating Text

Ruan

Sun

Yao

et al. 2021

Computational Intelligence and Neuroscience

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To detect comprehensive clues and provide more accurate forecasting in the early stage of financial distress, in addition to financial indicators, digitalization of lengthy but indispensable textual disclosure, such as Management Discussion and Analysis (MD&A), has been emphasized by researchers. However, most studies divide the long text into words and count words to treat the text as word count vectors, bringing massive invalid information but ignoring meaningful contexts. Aiming to efficiently represent the text of large size, an end-to-end neural networks model based on hierarchical self-attention is proposed in this study after the state-of-the-art pretrained model is introduced for text embedding including contexts. The proposed model has two notable characteristics. First, the hierarchical self-attention only affords the essential content with high weights in word-level and sentence-level and automatically neglects lots of information that has no business with risk prediction, which is suitable for extracting effective parts of the large-scale text. Second, after fine-tuning, the word embedding adapts the specific contexts of samples and conveys the original text expression more accurately without excessive manual operations. Experiments confirm that the addition of text improves the accuracy of financial distress forecasting and the proposed model outperforms benchmark models better at AUC and F2-score. For visualization, the elements in the weight matrix of hierarchical self-attention act as scalers to estimate the importance of each word and sentence. In this way, the “red-flag” statement that implies financial risk is figured out and highlighted in the original text, providing effective references for decision-makers.

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“…Fan and Lu used GBDT to predict regional water evapotranspiration (ET0) [17] and pan evaporation (EP) [18]; David et al used GBDT to predict solar irradiance [19]; Amar et al used GBDT to predict interfacial tension (IFT) in the crude oil/brine system [20]; Wang et al used GBDT to predict the medium-term load of the power grid [21]; Zheng, Wang and others used GBDT to predict the short-term load of the power grid [22,23]. GBDT is also widely used in urban planning [24][25][26][27][28][29], life sciences [30][31][32][33][34][35][36][37], financial forecasting [38][39][40][41][42][43][44][45], energy forecasting and other fields. In urban planning work, some scholars used GBDT to predict the cooling load of low-energy buildings [26] and building energy consumption [46].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

et al. 2021

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As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

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CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection

Cited by 59 publications

References 35 publications

CUS-LightGBM-based financial distress prediction for small- and medium-sized enterprises with imbalanced data

CUS-LightGBM-based financial distress prediction for small- and medium-sized enterprises with imbalanced data

Deep Learning Based on Hierarchical Self‐Attention for Finance Distress Prediction Incorporating Text

Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

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