CatBoost model and artificial intelligence techniques for corporate failure prediction

Jabeur, Sami Ben; Gharib, Cheima; Mefteh‐Wali, Salma; Arfi, Wissal Ben

doi:10.1016/j.techfore.2021.120658

Cited by 180 publications

(99 citation statements)

References 73 publications

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“…We computed the AUC indicator, which is the area under the ROC curve, to deepen the analysis. According to Ben Jabeur et al (2021) , the AUC indicator is a common metric for assessing a model's overall discriminatory power. Measuring a model's performance based on classification accuracy may be deceptive because oil price crash is such an uncommon occurrence.…”

Section: Resultsmentioning

confidence: 99%

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

Jabeur¹,

Khalfaoui

Arfi

2021

Journal of Environmental Management

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This study aims to predict oil prices during the 2019 novel coronavirus (COVID-19) pandemic by looking into green energy resources, global environmental indexes (ESG), and stock markets. The study employs advanced machine learning, such as the LightGBM, CatBoost, XGBoost, Random Forest (RF), and neural network models. An accurate forecasting framework can effectively capture the trend of the changes in oil prices and reduce the impact of the COVID-19 pandemic on such prices. Additionally, a large dataset with different asset classes was used to investigate the crash period. The research also introduced SHapely Additive exPlanations (SHAP) values for model analysis and interpretability. The empirical results indicate the superiority of the RF and LightGBM over traditional models. Moreover, this new framework provides favorable explanations of the model performance using the efficient SHAP algorithm. It also highlights the core features of predicting oil prices. The study found that high values of GER and ESG lead to lower crude oil prices. Our results are crucial for investors and policymakers in promoting climate change mitigation and sustained economic prosperity through green energy resources.

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Section: Resultsmentioning

confidence: 99%

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

Jabeur¹,

Khalfaoui

Arfi

2021

Journal of Environmental Management

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“…Lastly, CatBoost builds the combination of classification features through greedy strategy, and takes the mentioned combinations as additional features, which makes it easier for the model to capture high-order dependencies and improve the prediction accuracy more significantly. Furthermore, CatBoost selects the forgetting decision tree as the basic prediction period, thereby reducing the possibility of overfitting and increasing the execution speed of the model [25]- [29].…”

Section: ) Gbdt Algorithmmentioning

confidence: 99%

Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model

Zhang

Chen

Zhang³

et al. 2021

IEEE Access

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To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model. Based on data feature extraction, this method adopted DBSO (Difference-mutation Brain Storm Optimization) algorithm to optimize CatBoost model and diagnose faults. First, for data preprocessing, the ratio method was introduced to add features to the original data, the SHAP (Shapley Additive Explanations) method was applied for feature extraction, and the KPCA (Kernel Principal Component Analysis) algorithm was employed to reduce the dimension of data. Subsequently, the preprocessed data were inputted into the CatBoost model for training, and the DBSO algorithm was adopted to optimize the parameters of the CatBoost model to yield the optimal model. Lastly, the DBSO-CatBoost model was exploited to diagnose the transformer fault and output the fault type. As indicated from the example results, the accuracy of the transformer fault diagnosis based on DBSO-Catboost model could be 93.71%, 3.958% higher than that of CatBoost model and significantly exceeding that of some common models. Furthermore, compared with other preprocessing methods, the accuracy of fault diagnosis by employing the data preprocessing method proposed in this study was significantly improved.

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“…The primary purpose is to improve the overall predictive accuracy. Jabeur et al [58] explain that the ensemble method is a suitable tool to predict financial distress in the effective warming system. The results show that classification performance is better than other advanced approaches.…”

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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CatBoost model and artificial intelligence techniques for corporate failure prediction

Cited by 180 publications

References 73 publications

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model

Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

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