Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

[1]  Ruibin Geng,et al.  Prediction of financial distress: An empirical study of listed Chinese companies using data mining , 2015, Eur. J. Oper. Res..

[2]  Hui Li,et al.  Data mining method for listed companies' financial distress prediction , 2008, Knowl. Based Syst..

[3]  Yongsheng Ding,et al.  Forecasting financial condition of Chinese listed companies based on support vector machine , 2008, Expert Syst. Appl..

[4]  Deron Liang,et al.  The effect of feature selection on financial distress prediction , 2015, Knowl. Based Syst..

[5]  David A. Elizondo,et al.  Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks , 2008, Decis. Support Syst..

[6]  Petr Hájek,et al.  Mining corporate annual reports for intelligent detection of financial statement fraud - A comparative study of machine learning methods , 2017, Knowl. Based Syst..

[7]  Z. Sanusi,et al.  Earnings Management: An Analysis of Opportunistic Behaviour, Monitoring Mechanism and Financial Distress , 2015 .

[8]  Sotiris B. Kotsiantis,et al.  A Deep Dense Neural Network for Bankruptcy Prediction , 2019, EANN.

[9]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[10]  Umar Farooq,et al.  Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria , 2019, Journal of Forecasting.

[11]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[12]  Jian Ma,et al.  A comparative assessment of ensemble learning for credit scoring , 2011, Expert Syst. Appl..

[13]  Yi Jiang,et al.  Corporate Distress Prediction in China: A Machine Learning Approach , 2018, Accounting & Finance.

[14]  Kemin Wang,et al.  Pledgee competition, strategic disclosure, and future crash risk , 2019, China Journal of Accounting Research.

[15]  Xiang Yu,et al.  Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies , 2011 .

[16]  Kyung-shik Shin,et al.  Bankruptcy Prediction Modeling Using Qualitative Information Based on Big Data Analytics , 2016 .

[17]  Ling Ma,et al.  Deep learning models for bankruptcy prediction using textual disclosures , 2019, Eur. J. Oper. Res..

[18]  Philip J. Shrives,et al.  Risk reporting: A study of risk disclosures in the annual reports of UK companies , 2006 .

[19]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[20]  W. Beaver.  Financial Ratios As Predictors Of Failure , 1966 .

[21]  Jeong‐Bon Kim,et al.  Linguistic Information Quality in Customers' Forward‐Looking Disclosures and Suppliers' Investment Decisions , 2019, Contemporary Accounting Research.

[22]  M. Manzaneque,et al.  Corporate governance effect on financial distress likelihood: Evidence from Spain , 2016 .

[23]  Domingo García‐Pérez‐de‐Lema,et al.  Bootstrap Replacement to Validate the Influence of the Economic Cycle on the Structure and the Accuracy Level of Business Failure Prediction Models , 2015 .

[24]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[25]  Gang Wang,et al.  A new random subspace method incorporating sentiment and textual information for financial distress prediction , 2018, Electron. Commer. Res. Appl..

[26]  Praveen Pathak,et al.  Making words work: Using financial text as a predictor of financial events , 2010, Decis. Support Syst..

[27]  Sungbin Cho,et al.  A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction , 2010, Expert Syst. Appl..

[28]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

[29]  Qinghua Huang,et al.  Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches , 2014, Knowl. Based Syst..

[30]  Yang Li,et al.  Feature Selection for Support Vector Machine in the Study of Financial Early Warning System , 2014, Qual. Reliab. Eng. Int..

[31]  Petr Hájek,et al.  Comprehensive Assessment of Firm Financial Performance Using Financial Ratios and Linguistic Analysis of Annual Reports , 2017 .

[32]  Vineet Agarwal,et al.  Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test , 2014 .

[33]  P. Hájek,et al.  Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making , 2014 .

[34]  Sebastián Maldonado,et al.  Integrated framework for profit-based feature selection and SVM classification in credit scoring , 2017, Decis. Support Syst..

[35]  David L. Olson,et al.  Comparative analysis of data mining methods for bankruptcy prediction , 2012, Decis. Support Syst..

[36]  G. Saranya,et al.  A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars , 2011, ArXiv.

[37]  Zhongsheng Hua,et al.  Predicting corporate financial distress based on integration of support vector machine and logistic regression , 2007, Expert Syst. Appl..

[38]  Tim Loughran,et al.  Textual Analysis in Accounting and Finance: A Survey: TEXTUAL ANALYSIS IN ACCOUNTING AND FINANCE , 2016 .

[39]  Philippe du Jardin,et al.  A two-stage classification technique for bankruptcy prediction , 2016, Eur. J. Oper. Res..

[40]  Deron Liang,et al.  Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study , 2016, Eur. J. Oper. Res..

[41]  M. Borell,et al.  Do private equity owners increase risk of financial distress and bankruptcy , 2012 .

[42]  Bo Zhou,et al.  Does the rhetoric always hide bad intention: annual report’s tone and stock crash risk , 2018 .

[43]  Peter F. Wanke,et al.  Financial distress drivers in Brazilian banks: A dynamic slacks approach , 2015, Eur. J. Oper. Res..

[44]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[45]  Hui Li,et al.  Gaussian case-based reasoning for business failure prediction with empirical data in China , 2009, Inf. Sci..

[46]  Manuel Landajo,et al.  Nonparametric Quantile Regression‐Based Classifiers for Bankruptcy Forecasting , 2014 .

[47]  Stefan Feuerriegel,et al.  Decision support from financial disclosures with deep neural networks and transfer learning , 2017, Decis. Support Syst..

[48]  Petr Hájek,et al.  Evaluating Sentiment in Annual Reports for Financial Distress Prediction Using Neural Networks and Support Vector Machines , 2013, EANN.

[49]  Ning Chen,et al.  Financial credit risk assessment: a recent review , 2015, Artificial Intelligence Review.

[50]  Ligang Zhou,et al.  Predicting the listing status of Chinese listed companies with multi-class classification models , 2016, Inf. Sci..

[51]  Deron Liang,et al.  A novel classifier ensemble approach for financial distress prediction , 2018, Knowledge and Information Systems.

[52]  Kin Keung Lai,et al.  Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation , 2014, Int. J. Syst. Sci..

[53]  Chong Wu,et al.  Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method , 2019 .

[54]  Saeid Fallahpour,et al.  Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem , 2017 .