A comparison of dynamic hazard models and static models for predicting the special treatment of stocks in China with comprehensive variables

The stock exchanges in China give a stock special treatment in order to indicate its risk warning if the corresponding listed company cannot meet some requirements on financial performance. To correctly predict the special treatment of stocks is very important for the investors. The performance of the prediction models is mainly affected by the selection of explanatory variables and modelling methods. This paper makes a comparison between the multi-period hazard models and five widely used single-period static models by investigating a comprehensive category of variables including accounting variables, market variables, characteristic variables and macroeconomic variables. The empirical result shows that the performance of the models is sensitive to the choice of explanatory variables but the performance between the multi-period hazard models and the single-period static models has no significant difference.

[1]  R. Jarrow,et al.  Bankruptcy Prediction With Industry Effects , 2004 .

[2]  Ronen Israel,et al.  The Bankruptcy Decision and Debt Contract Renegotiations , 1998 .

[3]  Vineet Agarwal,et al.  Comparing the Performance of Market-Based and Accounting-Based Bankruptcy Prediction Models , 2006 .

[4]  Hui Li,et al.  Dynamic financial distress prediction using instance selection for the disposal of concept drift , 2011, Expert Syst. Appl..

[5]  Chae Woo Nam,et al.  Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies , 2008 .

[6]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

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

[8]  S. Titman,et al.  Financial Distress and Corporate Performance , 1994 .

[9]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[10]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[11]  Marjorie B. Platt,et al.  DEVELOPMENT OF A CLASS OF STABLE PREDICTIVE VARIABLES: THE CASE OF BANKRUPTCY PREDICTION , 1990 .

[12]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[13]  Kenneth Carling,et al.  Corporate credit risk modeling and the macroeconomy , 2007 .

[14]  Michael T. Dugan,et al.  The Relationship between Bankruptcy Model Predictions and Stock Market Perceptions of Bankruptcy , 1995 .

[15]  Zongjun Wang,et al.  Financial distress prediction of Chinese listed companies: a rough set methodology , 2007 .

[16]  Jia Liu,et al.  Macroeconomic determinants of corporate failures: evidence from the UK , 2004 .

[17]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

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

[19]  Kin Keung Lai,et al.  Benchmarking binary classification models on data sets with different degrees of imbalance , 2009, Frontiers of Computer Science in China.

[20]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[21]  Tyler Shumway.  Forecasting Bankruptcy More Accurately: A Simple Hazard Model , 1999 .

[22]  Kuldeep Kumar,et al.  Business failure prediction using decision trees , 2010 .

[23]  Lars Oxelheim.  Macroeconomic Variables and Corporate Performance , 2003 .

[24]  Jie Sun,et al.  SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams , 2011, Knowl. Based Syst..

[25]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[26]  Sinan Aktan,et al.  Financial failure forecast by option pricing method: a Turkish case , 2017 .

[27]  Selwyn Piramuthu,et al.  Using Feature Construction to Improve the Performance of Neural Networks , 1998 .

[28]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[29]  C. K. Chu,et al.  Predicting bankruptcy using the discrete-time semiparametric hazard model , 2010 .

[30]  Jonathan Crook,et al.  Chinese companies distress prediction: an application of data envelopment analysis , 2014, J. Oper. Res. Soc..

[31]  SunJie,et al.  Predicting business failure using multiple case-based reasoning combined with support vector machine , 2009 .

[32]  Yu Cao,et al.  Predicting Financial Distress of Chinese Listed Companies Using Rough Set Theory and Support Vector Machine , 2011, Asia Pac. J. Oper. Res..

[33]  Sanford Weisberg,et al.  An R Companion to Applied Regression , 2010 .

[34]  Rupert G. Miller,et al.  Survival Analysis , 2022, The SAGE Encyclopedia of Research Design.

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