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RETRACTED: Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia
16 references·8 citing·38 second hop
RETRACTED: Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia (2021)RETRACTED: Corporation financial distress…Deep learning models for bankruptcy prediction using textual disclosures (2018)Deep learning models for bank…Machine learning models and bankruptcy prediction (2017)Machine learning models and b…Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction (2017)Hybrid genetic algorithm and …Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study (2016)Financial ratios and corporat…Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (2016)Ensemble boosted trees with s…Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble (2016)Dynamic financial distress pr…Deep learning for stock prediction using numerical and textual information (2016)Deep learning for stock predi…A two-stage classification technique for bankruptcy prediction (2016)A two-stage classification te…A comparative study on base classifiers in ensemble methods for credit scoring (2016)A comparative study on base c…Predicting Financial Distress: A Comparison of Survival Analysis and Decision Tree Techniques (2015)Predicting Financial Distress…Deep learning applications and challenges in big data analytics (2015)Deep learning applications an…The effect of feature selection on financial distress prediction (2014)The effect of feature selecti…Prediction of financial distress: An empirical study of listed Chinese companies using data mining (2014)Prediction of financial distr…AdaBoost based bankruptcy forecasting of Korean construction companies (2014)AdaBoost based bankruptcy for…A comparative study of classifier ensembles for bankruptcy prediction (2014)A comparative study of classi…An improved boosting based on feature selection for corporate bankruptcy prediction (2013)An improved boosting based on…Financial distress prediction in emerging markets by using a machine learning approach for financial sustainability (2025)Financial distress prediction…Multi‐class financial distress prediction based on stacking ensemble method (2024)Multi‐class financial distres…Gated recurrent unit network: A promising approach to corporate default prediction (2024)Gated recurrent unit network:…Enhancing corporate governance through AI: a systematic literature review (2024)Enhancing corporate governanc…GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction (2023)GALSTM-FDP: A Time-Series Mod…Artificial neural network and decision tree-based modelling of non-prosperity of companies (2023)Artificial neural network and…A deep learning approach of financial distress recognition combining text (2023)A deep learning approach of f…Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support (2022)Examining the impact of deep …Financial Ratios As Predictors of Failure (1966)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering (2022)Neural network credit scoring models (2000)Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Using neural network ensembles for bankruptcy prediction and credit scoring (2007)Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks (2014)Ensemble classification based on supervised clustering for credit scoring (2016)Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring (2019)35 years of studies on business failure: an overview of the 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module and a prediction LSTM module (2018)Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong (2020)A Survey of Forex and Stock Price Prediction Using Deep Learning (2021)Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)A novel heterogeneous ensemble credit scoring model based on bstacking approach (2017)What's In A Name? Malay Seals As Onomastic Sources (2018)Machine learning-driven credit risk: a systemic review (2022)Operational Research and Artificial Intelligence Methods in Banking (2022)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Machine Learning in Banking Risk Management: A Literature Review (2019)Statistical and machine learning models in credit scoring: A systematic literature survey (2020)When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks (2011)Big Data technologies: A survey (2017)Machine learning on big data: Opportunities and challenges (2017)Machine Learning With Big Data: Challenges and Approaches (2017)Predicting failure in the U.S. banking sector: An extreme gradient boosting approach (2018)
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References

  • 2015Deep learning applications and challenges in big data analytics2,597 cited
  • 2017Machine learning models and bankruptcy prediction952 cited
  • 2018Deep learning models for bankruptcy prediction using textual disclosures480 cited
  • 2014Prediction of financial distress: An empirical study of listed Chinese companies using data mining479 cited
  • 2016Deep learning for stock prediction using numerical and textual information420 cited
  • 2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction419 cited
  • 2016Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study399 cited
  • 2016A comparative study on base classifiers in ensemble methods for credit scoring248 cited
  • 2014The effect of feature selection on financial distress prediction234 cited
  • 2014A comparative study of classifier ensembles for bankruptcy prediction201 cited
  • 2013An improved boosting based on feature selection for corporate bankruptcy prediction188 cited
  • 2016Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble173 cited
  • 2016A two-stage classification technique for bankruptcy prediction142 cited
  • 2015Predicting Financial Distress: A Comparison of Survival Analysis and Decision Tree Techniques

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127 cited
  • 2017Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction126 cited
  • 2014AdaBoost based bankruptcy forecasting of Korean construction companies125 cited
  • Cited by

    • 2022Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support33 cited
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    • 2023GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction19 cited
    • 2024Gated recurrent unit network: A promising approach to corporate default prediction17 cited
    • 2025Financial distress prediction in emerging markets by using a machine learning approach for financial sustainability16 cited
    • 2023A deep learning approach of financial distress recognition combining text16 cited
    • 2023Artificial neural network and decision tree-based modelling of non-prosperity of companies15 cited
    • 2024Multi‐class financial distress prediction based on stacking ensemble method15 cited