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Predicting Corporate Financial Sustainability Using Novel Business Analytics 원문보기

Sustainability, v.11 no.1, 2019년, pp.64 -   

Kim, Kyoung-jae ,  Lee, Kichun ,  Ahn, Hyunchul

Abstract AI-Helper 아이콘AI-Helper

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerti...

참고문헌 (66)

  1. Hu, Hui, Sathye, Milind. Predicting Financial Distress in the Hong Kong Growth Enterprises Market from the Perspective of Financial Sustainability. Sustainability, vol.7, no.2, 1186-1200.

  2. Valaskova, Katarina, Kliestik, Tomas, Svabova, Lucia, Adamko, Peter. Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis. Sustainability, vol.10, no.7, 2144-.

  3. Altman, Edward I.. FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY. The Journal of finance, vol.23, no.4, 589-609.

  4. Ohlson, James A.. Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of accounting research, vol.18, no.1, 109-.

  5. Tam, Kar Yan, Kiang, Melody Y.. Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management science, vol.38, no.7, 926-947.

  6. Jo, Hongkyu, Han, Ingoo. Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction. Expert systems with applications, vol.11, no.4, 415-422.

  7. Ahn, Hyunchul, Kim, Kyoung-jae. Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied soft computing, vol.9, no.2, 599-607.

  8. Shin, Kyung-Shik, Lee, Taik Soo, Kim, Hyun-jung. An application of support vector machines in bankruptcy prediction model. Expert systems with applications, vol.28, no.1, 127-135.

  9. Min, Jae H., Lee, Young-Chan. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert systems with applications, vol.28, no.4, 603-614.

  10. Min, Sung-Hwan, Lee, Jumin, Han, Ingoo. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert systems with applications, vol.31, no.3, 652-660.

  11. Hua, Zhongsheng, Wang, Yu, Xu, Xiaoyan, Zhang, Bin, Liang, Liang. Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert systems with applications, vol.33, no.2, 434-440.

  12. Ding, Y., Song, X., Zen, Y.. Forecasting financial condition of Chinese listed companies based on support vector machine. Expert systems with applications, vol.34, no.4, 3081-3089.

  13. Chaudhuri, Arindam, De, Kajal. Fuzzy Support Vector Machine for bankruptcy prediction. Applied soft computing, vol.11, no.2, 2472-2486.

  14. Li, H., Sun, J.. Predicting business failure using support vector machines with straightforward wrapper: A re-sampling study. Expert systems with applications, vol.38, no.10, 12747-12756.

  15. Lin, Fengyi, Yeh, Ching-Chiang, Lee, Meng-Yuan. The use of hybrid manifold learning and support vector machines in the prediction of business failure. Knowledge-based systems, vol.24, no.1, 95-101.

  16. Yang, Z., You, W., Ji, G.. Using partial least squares and support vector machines for bankruptcy prediction. Expert systems with applications, vol.38, no.7, 8336-8342.

  17. Tsai, Chih-Fong, Cheng, Kai-Chun. Simple instance selection for bankruptcy prediction. Knowledge-based systems, vol.27, 333-342.

  18. Huang, Cheng-Lung, Wang, Chieh-Jen. A GA-based feature selection and parameters optimizationfor support vector machines. Expert systems with applications, vol.31, no.2, 231-240.

  19. Chen, Liang-Hsuan, Hsiao, Huey-Der. Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study. Expert systems with applications, vol.35, no.3, 1145-1155.

  20. Kim, Kyoung-jae, Ahn, Hyunchul. Simultaneous optimization of artificial neural networks for financial forecasting. Applied intelligence, vol.36, no.4, 887-898.

  21. Li, Lihua, Tang, Hong, Wu, Zuobao, Gong, Jianli, Gruidl, Michael, Zou, Jun, Tockman, Melvyn, Clark, Robert A.. Data mining techniques for cancer detection using serum proteomic profiling. Artificial intelligence in medicine, vol.32, no.2, 71-83.

  22. Ahn Global optimization of support vector machines using genetic algorithms for bankruptcy prediction Neural Information Processing 2006 Volume 4234 420 

  23. Falbo, P.. Credit-scoring by enlarged discriminant models. Omega, vol.19, no.4, 275-289.

  24. Tam, K.. Neural network models and the prediction of bank bankruptcy. Omega, vol.19, no.5, 429-445.

  25. Malhotra, Rashmi, Malhotra, D.K. Evaluating consumer loans using neural networks. Omega, vol.31, no.2, 83-96.

  26. Vapnik Statistical Learning Theory 1998 

  27. Pai, Ping-Feng, Hong, Wei-Chiang. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric power systems research, vol.74, no.3, 417-425.

  28. Gunn Support Vector Machines for Classification and Regression 1998 

  29. Drucker, H., Wu, Donghui, Vapnik, V.N.. Support vector machines for spam categorization. IEEE transactions on neural networks, vol.10, no.5, 1048-1054.

  30. Witten Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations 2000 

  31. Tay, Francis E.H, Cao, Lijuan. Application of support vector machines in financial time series forecasting. Omega, vol.29, no.4, 309-317.

  32. Kim, Kyoung-jae. Financial time series forecasting using support vector machines. Neurocomputing, vol.55, no.1, 307-319.

  33. Huang, Cheng-Lung, Chen, Mu-Chen, Wang, Chieh-Jen. Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, vol.33, no.4, 847-856.

  34. Wu, C.H., Tzeng, G.H., Goo, Y.J., Fang, W.C.. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert systems with applications, vol.32, no.2, 397-408.

  35. Zhou, Ligang, Lai, Kin Keung, Yu, Lean. Credit scoring using support vector machines with direct search for parameters selection. Soft computing : a fusion of foundations, methodologies and applications, vol.13, no.2, 149-155.

  36. Chung, Hyejung, Shin, Kyung-shik. Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction. Sustainability, vol.10, no.10, 3765-.

  37. Fu GA based CBR approach in Q&A system Expert Syst. Appl. 2004 10.1016/S0957-4174(03)00117-9 26 167 

  38. Han Datamining: Concepts and Techniques 2001 

  39. Gu, J., Zhu, M., Jiang, L.. Housing price forecasting based on genetic algorithm and support vector machine. Expert systems with applications, vol.38, no.4, 3383-3386.

  40. Howley, Tom, Madden, Michael G.. The Genetic Kernel Support Vector Machine: Description and Evaluation. The Artificial intelligence review, vol.24, no.3, 379-395.

  41. Pai, P.F., Hsu, M.F., Wang, M.C.. A support vector machine-based model for detecting top management fraud. Knowledge-based systems, vol.24, no.2, 314-321.

  42. Lee A new face authentication system for memory-constrained devices IEEE Trans. Consum. Electron. 2003 10.1109/TCE.2003.1261219 49 1214 

  43. Sun, Z., Bebis, G., Miller, R.. Object detection using feature subset selection. Pattern recognition, vol.37, no.11, 2165-2176.

  44. Samanta, B.. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical systems and signal processing, vol.18, no.3, 625-644.

  45. Yu, Enzhe, Cho, Sungzoon. Keystroke dynamics identity verification—its problems and practical solutions. Computers & security, vol.23, no.5, 428-440.

  46. Yu, Enzhe, Cho, Sungzoon. Constructing response model using ensemble based on feature subset selection. Expert systems with applications, vol.30, no.2, 352-360.

  47. Yu Mining stock market tendency using GA-based support vector machines International Workshop on Internet and Network Economics 2005 Volume 3828 336 

  48. FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS. Mechanical systems and signal processing, vol.16, no.2, 373-390.

  49. Zhao, Xing-Ming, Cheung, Yiu-Ming, Huang, De-Shuang. A novel approach to extracting features from motif content and protein composition for protein sequence classification. Neural networks : the official journal of the International Neural Network Society, vol.18, no.8, 1019-1028.

  50. Reeves Selection of training sets for neural networks by a genetic algorithm Parallel Problem-Solving from Nature 1998 

  51. Babu, T.Ravindra, Murty, M.Narasimha. Comparison of genetic algorithm based prototype selection schemes. Pattern recognition, vol.34, no.2, 523-525.

  52. Kim, Kyoung-jae. Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting. Applied intelligence, vol.21, no.3, 239-249.

  53. Kim, Kyoung-jae. Artificial neural networks with evolutionary instance selection for financial forecasting. Expert systems with applications, vol.30, no.3, 519-526.

  54. Chang, Chih-Chung, Lin, Chih-Jen. LIBSVM : A library for support vector machines. ACM transactions on intelligent systems and technology, vol.2, no.3, 1-27.

  55. Harnett Statistical Methods for Business and Economics 1991 

  56. Diebold Comparing predictive accuracy J. Bus. Econom. Stat. 1995 13 134 

  57. Derrac, J., Garcia, S., Molina, D., Herrera, F.. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and evolutionary computation, vol.1, no.1, 3-18.

  58. Fan, Guo-Feng, Peng, Li-Ling, Hong, Wei-Chiang. Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model. Applied energy, vol.224, 13-33.

  59. Iotti, Mattia, Bonazzi, Giuseppe. Analysis of the Risk of Bankruptcy of Tomato Processing Companies Operating in the Inter-Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”. Sustainability, vol.10, no.4, 947-.

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