A novel ensemble learning approach for corporate financial distress forecasting in fashion and textiles supply chains

Mathematical Problems in Engineering

Volumn 2013, Issue , 2013, Pages

  Xie, Gang ^a   Zhao, Yingxue ^b   Jiang, Mao ^a   Zhang, Ning ^a  

^a CHINESE ACADEMY OF SCIENCES   (China)

^b UNIVERSITY OF INTERNATIONAL BUSINESS AND ECONOMICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE TOOLS; BACK-PROPAGATION NEURAL NETWORKS; CHINESE LISTED COMPANIES; EMPIRICAL ANALYSIS; ENSEMBLE LEARNING APPROACH; FINANCIAL DISTRESS; FORECASTING PERFORMANCE; LOGISTIC REGRESSIONS;

LOGISTICS; NEURAL NETWORKS; SUPPLY CHAINS; SUPPORT VECTOR MACHINES; TEXTILES;

FORECASTING;

EID: 84876496216     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2013/493931     Document Type: Article

References (48)

1. Christopher M., Lowson R., Peck H., Creating agile supply chains in the fashion industry. International Journal of Retail & Distribution Management 2004 32 8 367 376 10.1108/09590550410546188
2. Bruce M., Daly L., Towers N., Lean or agile: a solution for supply chain management in the textiles and clothing industry? International Journal of Operations and Production Management 2004 24 1-2 151 170 2-s2.0-1442327301 10.1108/01443570410514867 (Pubitemid 38284939)
3. de Brito M. P., Carbone V., Blanquart C. M., Towards a sustainable fashion retail supply chain in Europe: organisation and performance. International Journal of Production Economics 2008 114 2 534 553 2-s2.0-46849106515 10.1016/j.ijpe.2007.06.012
4. Hilletofth P., Hilmola O. P., Supply chain management in fashion and textile industry. International Journal of Services Sciences 2008 1 2 127 147 10.1504/IJSSCI.2008.019608
5. Thomassey S., Sales forecasts in clothing industry: the key success factor of the supply chain management. International Journal of Production Economics 2010 128 2 470 483 2-s2.0-78049311675 10.1016/j.ijpe.2010.07.018
6. Barnes L., Lea-Greenwood G., Fast fashioning the supply chain: shaping the research agenda. Journal of Fashion Marketing and Management 2006 10 3 259 271 2-s2.0-33748296313 10.1108/13612020610679259 (Pubitemid 44323441)
7. Birtwistle G., Siddiqui N., Fiorito S. S., Quick response: perceptions of UK fashion retailers. International Journal of Retail & Distribution Management 2003 31 2 118 128 10.1108/09590550310462010
8. Hayes S. G., Jones N., Fast fashion: a financial snapshot. Journal of Fashion Marketing and Management 2006 10 3 282 300 2-s2.0-33748296311 10.1108/13612020610679277 (Pubitemid 44323443)
9. Sun Z. L., Choi T. M., Au K. F., Yu Y., Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems 2008 46 1 411 419 2-s2.0-56049098499 10.1016/j.dss.2008.07.009
10. Choi T. M., Yu Y., Au K. F., A hybrid SARIMA wavelet transform method for sales forecasting. Decision Support Systems 2011 51 1 130 140 2-s2.0-79951508907 10.1016/j.dss.2010.12.002
11. Yu Y., Choi T. M., Hui C. L., An intelligent fast sales forecasting model for fashion products. Expert Systems with Applications 2011 38 6 7373 7379 2-s2.0-79951578472 10.1016/j.eswa.2010.12.089
12. Au K. F., Choi T. M., Yu Y., Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics 2008 114 2 615 630 2-s2.0-46849097617 10.1016/j.ijpe.2007.06.013
13. Yu Y., Hui C. L., Choi T. M., An empirical study of intelligent expert systems on forecasting of fashion color trend. Expert Systems With Applications 2012 39 4383 4389 10.1016/j.eswa.2011.09.153
14. Choi T. M., Hui C. L., Ng S. F. F., Yu Y., Color trend forecasting of fashionable products with very few historical data. IEEE Transactions on Systems, Man, and Cybernetics, Part C 2012 42 1003 1010 10.1109/TSMCC.2011. 2176725
15. Hua Z., Wang Y., Xu X., Zhang B., Liang L., Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications 2007 33 2 434 440 2-s2.0-33846454686 10.1016/j.eswa.2006.05.006 (Pubitemid 46150047)
16. Beaver W., Financial ratios as predictors of failure, empirical research in accounting: selected studied. Journal of Accounting Research 1968 4 71 111
17. Altman E. L., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 1968 23 3 589 609
18. Blum M., Failing company discriminant analysis. Journal of Accounting Research 1974 12 1 1 25
19. Altman E. I., Haldeman R. G., Narayanan P., ZETATM analysis A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance 1977 1 1 29 54 2-s2.0-7944229970 10.1016/0378-4266(77)90017-6
20. Meyer P. A., Pifer H., Prediction of bank failures. The Journal of Finance 1970 25 853 868 10.1111/j.1540-6261.1970.tb00558.x
21. Laitinen E. K., Laitinen T., Bankruptcy prediction: application of the Taylor's expansion in logistic regression. International Review of Financial Analysis 2000 9 4 327 349 2-s2.0-0000407825 10.1016/S1057-5219(00)00039-9
22. Dimitras A. I., Zanakis S. H., Zopounidis C., A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research 1996 90 3 487 513 2-s2.0-0030143666 10.1016/0377-2217(95)00070-4 ZBL0907.90038 (Pubitemid 126369043)
23. Chen M.-Y., Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications 2011 38 9 11261 11272 2-s2.0-79955621501 10.1016/j.eswa.2011.02.173
24. Chen W.-S., Du Y. K., Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications 2009 36 2 4075 4086 2-s2.0-56349100536 10.1016/j.eswa.2008.03.020
25. Li H., Sun J., Ranking-order case-based reasoning for financial distress prediction. Knowledge-Based Systems 2008 21 8 868 878 2-s2.0-54949111332 10.1016/j.knosys.2008.03.047
26. Li H., Sun J., Gaussian case-based reasoning for business failure prediction with empirical data in China. Information Sciences 2009 179 1-2 89 108 2-s2.0-55249100209 10.1016/j.ins.2008.09.003
27. Kim K., Ahn H., A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research 2012 39 8 1800 1811 10.1016/j.cor.2011.06.023 MR2874395 ZBL1251.91068
28. Sun J., Li H., Financial distress prediction using support vector machines: ensemble versus individual. Applied Soft Computing 2012 12 2254 2265 10.1016/j.asoc.2012.03.028
29. Lu C.-J., Lee T.-S., Chiu C.-C., Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems 2009 47 2 115 125 2-s2.0-64949130686 10.1016/j.dss.2009.02.001
30. Xiao Z., Yang X., Pang Y., Dang X., The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowledge-Based Systems 2012 26 196 206 10.1016/j.knosys.2011.08.001
31. Sun J., Jia M. Y., Li H., AdaBoost ensemble for financial distress prediction: an empirical comparison with data from Chinese listed companies. Expert Systems with Applications 2011 38 8 9305 9312 2-s2.0-79953722212 10.1016/j.eswa.2011.01.042
32. Li H., Sun J., Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction. Expert Systems with Applications 2011 38 5 6244 6253 2-s2.0-79151479783 10.1016/j.eswa.2010.11.043
33. Hosmer D. W., Lemeshow S., Applied Logistic Regression 2000 New York, NY, USA Wiley
34. Vapnik V. N., The Nature of Statistical Learning Theory 1995 New York, NY, USA Springer xvi+188 MR1367965
35. Harrald P. G., Kamstra M., Evolving artificial neural networks to combine financial forecasts. IEEE Transactions on Evolutionary Computation 1997 1 1 40 52 2-s2.0-0031123711 (Pubitemid 127771017)
36. Hamzaçebi C., Akay D., Kutay F., Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications 2009 36 2 3839 3844 2-s2.0-56349131204 10.1016/j.eswa.2008.02.042
37. Hansen J. V., Nelson R. D., Neural networks and traditional time series methods: a synergistic combination in state economic forecasts. IEEE Transactions on Neural Networks 1997 8 4 863 873 2-s2.0-0031191950 10.1109/72.595884 (Pubitemid 127765145)
38. Leigh W., Purvis R., Ragusa J. M., Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems 2002 32 4 361 377 2-s2.0-0036498492 10.1016/S0167-9236(01)00121-X (Pubitemid 34154797)
39. Majhi B., Rout M., Majhi R., Panda G., Fleming P. J., New robust forecasting models for exchange rates prediction. Expert Systems with Applications 2012 39 16 12658 12670 10.1016/j.eswa.2012.05.017
40. Sun Z. L., Au K. F., Choi T. M., A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines. IEEE Transactions on Systems, Man, and Cybernetics B 2007 37 5 1321 1331 2-s2.0-35148882421 10.1109/TSMCB.2007.901375 (Pubitemid 47534595)
41. Premanode B., Toumazou C., Improving prediction of exchange rates using differential EMD. Expert Systems with Applications 2013 40 1 377 384 10.1016/j.eswa.2012.07.048
42. Xie G., Yue W., Liu W., Wang S., Risk based selection of cleaner products in a green supply chain. Pacific Journal of Optimization 2012 8 3 473 484
43. Xie G., Wang S., Lai K. K., Quality improvement in competing supply chains. International Journal of Production Economics 2011 134 1 262 270 10.1016/j.ijpe.2011.07.007
44. Xie G., Yue W., Wang S., Lai K. K., Quality investment and price decision in a risk-averse supply chain. European Journal of Operational Research 2011 214 2 403 410 10.1016/j.ejor.2011.04.036 MR2812908 ZBL1218.90095
45. Xie G., Wang S., Lai K. K., Optimal β k -stable interval in VPRS-based group decision-making: a further application. Expert Systems with Applications 2011 38 11 13757 13763 2-s2.0-79959945884 10.1016/j.eswa.2011.04. 177
46. Xie G., Yue W., Wang S., Lai K. K., Dynamic risk management in petroleum project investment based on a variable precision rough set model. Technological Forecasting & Social Change 2010 77 6 891 901 2-s2.0-77953871904 10.1016/j.techfore.2010.01.013
47. Xie G., Yue W., Wang S., VPRS-based group decision-making for risk response in petroleum investment. International Journal of Knowledge and Systems Sciences 2010 1 3 45 54 10.4018/jkss.2010070104
48. Xie G., Zhang J., Lai K. K., Yu L., Variable precision rough set for group decision-making: an application. International Journal of Approximate Reasoning 2008 49 2 331 343 2-s2.0-52949137301 10.1016/j.ijar.2007.04.005 ZBL1185.91077