DOI QR코드

DOI QR Code

LSTM-based Deep Learning for Time Series Forecasting: The Case of Corporate Credit Score Prediction

시계열 예측을 위한 LSTM 기반 딥러닝: 기업 신용평점 예측 사례

  • Received : 2020.02.20
  • Accepted : 2020.03.16
  • Published : 2020.03.31

Abstract

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

References

  1. 김성진, 안현철, "기업신용등급 예측을 위한 랜덤 포레스트의 응용," 산업혁신연구, 32권, 1호, 2016, pp. 187-211.
  2. 김영태, 김명환, "인공신경망을 활용한 부실기업 예측모형에 관한 연구," 회계연구, 6권, 1호, 2001, pp. 275-294.
  3. 김진성, "무디스 '한국, 무더기 신용강등' 경고," 한국경제, https://www.hankyung.com/finance/article/2019111986551, 2019
  4. 김태정, 문남희, 문성주, "회사채 신용등급 예측에 관한 연구," 회계정보연구, 21권, 2003, pp. 25-59.
  5. 나영, 진동민, "IMF 이후 신용등급예측에 있어서 재무정보의 유용성," 회계정보연구, 21권, 2003, pp. 211-235.
  6. 노태협, 유명환, 한인구, "러프집합이론과 사례 기반추론을 결합한 기업신용평가 모형," 한국정보시스템학회, 정보시스템연구, 14권, 1호, 2005, pp. 41-65.
  7. 박형권, 강준영, 허성욱, 유동현, "국내 회사채 신용 등급 예측 모형의 비교 연구," 응용통계연구, 31권, 3호, 2018, pp. 367-382.
  8. 이륜경, 정남호, 홍태호, "딥러닝을 이용한 온라인 리뷰 기반 다속성별 추천 모형 개발," 28권, 1호, 2019, pp. 97-114.
  9. 이성효, "M&A 대상기업의 재무 상태에 관한 실증적 연구," 경영경제연구, 1997.
  10. 이영찬, "DEA와 Worst Practice DEA를 이용한 정보통신기업의 신용위험평가," 한국정보시스템학회, 2005년 한국정보시스 템학회 추계 학술발표논문집, 2005, pp. 334-346.
  11. 전성일, 이기세, "보수주의 회계처리가 KIS 신용평점에 미치는 영향," 국제회계연구, 30권, 2010, pp. 245-263.
  12. 조현우, 박연희, 송혁준, "사외이사의 특성과 감사품질이 KIS 신용평점에 미치는 영향" 한국회계학회 학술연구발표회 논문집, 2005, pp. 489-515.
  13. 최병설, 김남규, "감정 딥러닝 필터를 활용한 토픽 모델링 방법론," 28권, 4호, 2019, pp. 271-291.
  14. 홍태호, 신택수, "부도확률맵과 AHP를 이용한 기업 신용등급 산출모형의 개발," 한국정보시스템학회, 정보시스템연구, 16권, 3호, pp. 1-20.
  15. Aggarwal, C. C., and Zhai, C., "Mining text data," Springer Science & Business Media, 2012.
  16. Altman, E. I., "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, Vol. 23, No.4, 1968, pp. 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
  17. Barboza, F., Kimura, H., and Altman, E., "Machine learning models and bankruptcy prediction," Expert Systems with Applications, Vol. 83, 2017, pp. 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
  18. Breiman, L., "Random forests," Machine learning, Vol. 45, No. 1, 2001, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  19. Bridle, J. S., "Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition," Neurocomputing Springer, 1990, pp. 227-236.
  20. Chaudhuri, A., and De, K., "Fuzzy support vector machine for bankruptcy prediction," Applied Soft Computing, Vol. 11, No. 2, 2011, pp. 2472-2486. https://doi.org/10.1016/j.asoc.2010.10.003
  21. Chen, S.-Y., Chen, C.-N., Chen, Y.-R., Yang, C.-W., Lin, W.-C., and Wei, C.-P., "Will Your Project Get the Green Light? Predicting the Success of Crowdfunding Campaigns," Paper presented at the PACIS, 2015.
  22. Chen, X., Wei, L., and Xu, J., "House Price Prediction Using LSTM," arXiv preprint arXiv:1709.08432, 2017.
  23. Chen, Y., Lin, Z., Zhao, X., Wang, G., and Gu, Y., "Deep learning-based classification of hyperspectral data," IEEE Journal of Selected topics in applied earth observations and remote sensing, Vol. 7, No. 6, 2014, pp. 2094-2107. https://doi.org/10.1109/JSTARS.2014.2329330
  24. Chou, C.-H., Hsieh, S.-C., and Qiu, C.-J., "Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction," Applied Soft Computing, Vol. 56, 2017, pp. 298-316. https://doi.org/10.1016/j.asoc.2017.03.014
  25. Claesen, M., and De Moor, B., "Hyperparameter search in machine learning," arXiv preprint arXiv: 1502.02127, 2015.
  26. Corbett, P., and Boyle, J., "Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings," Database, 2018.
  27. Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., and Darrell, T., "Long-term recurrent convolutional networks for visual recognition and description," Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.
  28. Duffie, D., Saita, L., and Wang, K., "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Vol. 83, No. 3, 2007, pp. 635-665. https://doi.org/10.1016/j.jfineco.2005.10.011
  29. Flagg, J. C., Giroux, G. A., and Wiggins Jr, C. E., "Predicting corporate bankruptcy using failing firms," Review of financial Economics, Vol. 1, No. 1, 1991, pp. 67-75. https://doi.org/10.1002/j.1873-5924.1991.tb00543.x
  30. Frank, R. J., Davey, N., and Hunt, S. P., "Time series prediction and neural networks," Journal of intelligent and robotic systems, Vol. 31, No. 1-3, 2001, pp. 91-103. https://doi.org/10.1023/A:1012074215150
  31. Glorot, X., Bordes, A., and Bengio, Y., "Domain adaptation for large-scale sentiment classification: A deep learning approach," Paper presented at the Proceedings of the 28th international conference on machine learning (ICML-11), 2011.
  32. Guo, X., Zhu, Z., and Shi, J., "A corporate credit rating model using support vector domain combined with fuzzy clustering algorithm," Mathematical Problems in Engineering, 2012.
  33. Hajek, P., and Michalak, K., "Feature selection in corporate credit rating prediction," Knowledge-Based Systems, Vol. 51, 2013, pp. 72-84. https://doi.org/10.1016/j.knosys.2013.07.008
  34. Hall, M. A., and Smith, L. A., "Feature subset selection: a correlation based filter approach," 1997.
  35. Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., and Scholkopf, B., "Support vector machines," IEEE Intelligent Systems and their applications, Vol. 13, No. 4, 1998, pp. 18-28.
  36. Ho, S.-L., Xie, M., and Goh, T. N., "A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction," Computers & Industrial Engineering, Vol. 42, No. 2-4, 2002, pp. 371-375. https://doi.org/10.1016/S0360-8352(02)00036-0
  37. Hochreiter, S., and Schmidhuber, J., "Long short-term memory," Neural computation, Vol. 9, No. 8, 1997, pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  38. Huang, S.-C., "Integrating nonlinear graph based dimensionality reduction schemes with SVMs for credit rating forecasting," Expert Systems with Applications, Vol. 36, No. 4, 2009, pp. 7515-7518. https://doi.org/10.1016/j.eswa.2008.09.047
  39. Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., and Wu, S., "Credit rating analysis with support vector machines and neural networks: a market comparative study," Decision Support Systems, Vol. 37, No. 4, 2004, pp. 543-558. https://doi.org/10.1016/S0167-9236(03)00086-1
  40. Kim, K.-j., and Ahn, H., "A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach." Computers & Operations Research, Vol. 39, No. 8, 2012, pp. 1800-1811. https://doi.org/10.1016/j.cor.2011.06.023
  41. KISVALUE, "KISVALUE Homepage," https://www.kisvalue.com/web/index.jsp, 2019.
  42. KISVALUE, "KISVALUE MANUAL," 2019.
  43. Koc, C. K., "Analysis of sliding window techniques for exponentiation," Computers & Mathematics with Applications, Vol. 30, No. 10, 1995, pp. 17-24.
  44. Kotsiantis, S. B., Zaharakis, I., and Pintelas, P., "Supervised machine learning: A review of classification techniques," Emerging artificial intelligence applications in computer engineering, Vol. 160, 2007, pp. 3-24.
  45. Kumar, K., and Bhattacharya, S., "Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances," Review of Accounting and Finance, Vol. 5, No. 3, 2006, pp. 216-227. https://doi.org/10.1108/14757700610686426
  46. LeCun, Y., Bengio, Y., and Hinton, G., "Deep learning," Nature, Vol. 521 No. 7553, 2015, pp. 436. https://doi.org/10.1038/nature14539
  47. LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., and Jackel, L. D., "Handwritten digit recognition with a back-propagation network," Paper presented at the Advances in neural information processing systems, 1990.
  48. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., "Gradient-based learning applied to document recognition," Proceedings of the Ieee, Vol. 86, No. 11, 1998, pp. 2278-2324. https://doi.org/10.1109/5.726791
  49. Lee, Y.-C., "Application of support vector machines to corporate credit rating prediction," Expert Systems with Applications, Vol. 33, No. 1, 2007, pp. 67-74. https://doi.org/10.1016/j.eswa.2006.04.018
  50. Li, Y., Rakesh, V., and Reddy, C. K., "Project success prediction in crowdfunding environments" Paper presented at the Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016.
  51. Liu, Y., Huang, X., An, A., and Yu, X., "ARSA: a sentiment-aware model for predicting sales performance using blogs" Paper presented at the Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007.
  52. Liu, Y., Zheng, H., Feng, X., and Chen, Z., "Short-term traffic flow prediction with Conv-LSTM," Paper presented at the 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), 2017.
  53. Mohammadi, K., Shamshirband, S., Tong, C. W., Arif, M., Petković, D., and Ch, S., "A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation," Energy Conversion and Management, Vol. 92, 2015, pp. 162-171. https://doi.org/10.1016/j.enconman.2014.12.050
  54. Pineda, F. J., "Generalization of backpropagation to recurrent neural networks," Physical Review Letters, Vol. 59, No. 19, 1987, pp. 2229. https://doi.org/10.1103/PhysRevLett.59.2229
  55. Price, D., Knerr, S., Personnaz, L., and Dreyfus, G., "Pairwise neural network classifiers with probabilistic outputs," Paper presented at the Advances in neural information processing systems, 1995.
  56. Shin, K.-s., and Han, I., "Case-based reasoning supported by genetic algorithms for corporate bond rating," Expert Systems with Applications, Vol. 16, No. 2, 1999, pp. 85-95. https://doi.org/10.1016/S0957-4174(98)00063-3
  57. Sunasra, Mohammed., "Performance Metrics for Classification problems in Machine Learning," THALUS AI in medium (online publishing platform), https://medium.com/thalus-ai/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b, 2015.
  58. Wu, H.-C., et al., "Two-stage credit rating prediction using machine learning techniques," Kybernetes, Vol. 43, No. 7, 2014, pp. 1098-1113. https://doi.org/10.1108/K-10-2013-0218
  59. Yang, Z., Platt, M. B., and Platt, H. D., "Probabilistic neural networks in bankruptcy prediction," Journal of Business Research, Vol. 44, No. 2, 1999, pp. 67-74. https://doi.org/10.1016/S0148-2963(97)00242-7
  60. Ye, Y., Liu, S., and Li, J., "A multiclass machine learning approach to credit rating prediction," Paper presented at the 2008 International Symposiums on Information Processing, 2008.
  61. Yeh, C.-C., Chi, D.-J., and Hsu, M.-F., "A hybrid approach of DEA, rough set and support vector machines for business failure prediction," Expert Systems with Applications, Vol. 37, No. 2, 2010, pp. 1535-1541. https://doi.org/10.1016/j.eswa.2009.06.088