Improving Decision Effectiveness of Artificial Neural Networks: A Modified Genetic Algorithm Approach

Decision Sciences

Volumn 34, Issue 3, 2003, Pages 421-442

  Sexton, Randall S ^a   Sriram, Ram S ^b   Etheridge, Harlan ^c  

^a MISSOURI STATE UNIVERSITY   (United States)

^b GEORGIA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF LOUISIANA AT LAFAYETTE   (United States)

Author keywords

Artificial Intelligence; Decision Support; Financial Distress; Genetic Algorithm

Indexed keywords

EID: 1642491872     PISSN: 00117315     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1540-5414.2003.02309.x     Document Type: Article

References (60)

1. Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
2. Altman, E., Haldeman, R., & Narayanan, P. (1977). ZETA analysis: New model to identify bankruptcy risk of corporations. Journal of Banking and Finance (June), 29-54.
3. Barr, R. S. & Siems, T. F. (1994). Predicting bank failure using DBA to quantify management quality. Federal Reserve Bank of Dallas Financial Industry Studies, 1, 1-31.
4. Baum, E. B., & Haussler, D. (1989). What size net gives valid generalization? Neural Computation, 1, 151-160.
5. Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
6. Bell, T. B., Ribar, G. S., & Verchio, J. (1990). Neural nets versus logistic regression: A comparison of each model's ability to predict commercial bank failures. In R. P. Srivastava (Ed.), Proceedings of the 1990 Deloitte and Touche/University of Kansas Symposium of Auditing Problems, Lawrence, KS, 29-58.
7. Bell, T. B., & Tabor, R. H. (1991). Empirical analysis of audit uncertainty qualifications. Journal of Accounting Research, 29(2), 350-371.
8. Boritz, J. E., & Kennedy, D. B. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9(4), 95-112.
9. Brockett, P. L., Cooper, W. W., Golden, L. L., & Pitaktong, U. (1994). A neural network method for obtaining an early warning of insurer insolvency. Journal of Risk and Insurance, 67(3), 402-424.
10. Burkitt, A. N. (1991). Optimization of the architecture of feed-forward neural nets with hidden layers by unit elimination. Complex Systems, 5, 371-380.
11. Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 22(3), 143-154.
12. Cottrell, M., Girard, B., Girard, Y., & Mangeas, M. (1993). Time series and neural network: A statistical method for weight elimination. In M. Verlysen (Ed.), European Symposium on Artificial Neural Networks. Brussels: D. Facto, 157-164.
13. Davis, L. (1991). Handbook of genetic algorithms. New York: Van Nostrand Reinhold.
14. Dilla, W. N., File, R. G., Solomon, I., & Tomassini, L. A. (1991). Predictive bankruptcy judgments by auditors: A probabilistic approach. Auditing, Advances in Behavioral Research, 1, 113-129.
15. Dopuch, N., Holthausen, R. W., & Leftwich, R. W. (1986). Abnormal stock returns associated with media disclosures of "subject to" qualified audit opinions. Journal of Accounting and Economics, 5(2), 93-117.
16. Dorsey, R. E., Johnson, J. D., & Mayer, W. J. (1994). A genetic algorithm for the training of feed-forward neural networks. In J. D. Johnson & A. B. Whinston (Eds.), Advances in artificial intelligence in economics, finance, and management. Vol. 1, Greenwich, CT: JAI Press, 93-111.
17. Dorsey, R. E., Johnson, J. D., & Van Boening, M. V. (1994). The use of artificial neural networks for estimation of decision surfaces in first price sealed bid auctions. In W. W. Cooper & A. B. Whinston (Eds.), New direction in computational economics. Amsterdam: Kluwer Academic, 19-40.
18. Dorsey, R. E., & Mayer, W. J. (1995). Genetic algorithms for estimation problems with multiple optima, non-differentiability, and other irregular features. Journal of Business and Economic Statistics, 13(1), 53-66.
19. Drucker, H., & LeCun, Y. (1992). Improving generalization performance using double backpropagation. IEEE Transactions on Neural Networks, 3, 991-997.
20. Elam, R. (1975). The effect of lease data on the predictive ability of financial ratios. Accounting Review, 50(1), 25-43.
21. Etheridge, H. L., & Sriram, R. S. (1996). A neural network approach to financial distress analysis. Advances in Accounting Information Systems, 4, 201-222.
22. Etheridge, H. L., & Sriram, R. S. (1997). A comparison of the relative costs of financial distress models: Artificial neural networks, logit and multivariate discriminant analysis. Intelligent Systems in Accounting, Finance, and Management, 6, 235-248.
23. Etheridge, H., Sriram, R. S., & Hsu, K. (2000). Artificial neural networks help auditors evaluate client financial viability. Decision Sciences, 31(2), 531-549.
24. Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. Neural Information Processing Systems, 2, 524-532.
25. Fanning, K., & Cogger, K. O. (1994). A comparative analysis of artificial neural networks using financial distress prediction. Intelligent Systems in Accounting, Finance, and Management, 3(4), 241-252.
26. FDIC Statistics on Banking. (1992). Washington, DC: Federal Deposit Insurance Corporation, Division of Research and Statistics.
27. Glorfeld, L. W., Hardgrave, B. C., & Pendley, J. (1998). Bankruptcy prediction of financially stressed firms: An extension of the use of artificial neural networks to evaluate going concern. Advances in Accounting Information Systems, 6, 163-182.
28. Graham, F. C., & Horner, J. E. (1988). Bank failure: An evaluation of the factors contributing to the failure of national banks. Issues in Bank Regulation, 12(2), 8-12.
29. Hamer, M. (1983). Failure prediction: Sensitivity of classification accuracy to alternative statistical methods and variable sets. Journal of Accounting and Public Policy, 2(4), 289-307.
30. Holmstrom, L., & Koistinen, P. (1992). Using additive noise in backpropagation training. IEEE Transactions on Neural Networks, 3, 24-38.
31. Hopwood, W., McKeown, J., & Mutchler, J. (1989). A test of the incremental explanatory power of opinions qualified for consistency and uncertainty. Accounting Review, 64(1), 28-48.
32. Jones, F. L. (1987). Current techniques in bankruptcy prediction. Journal of Accounting Literature, 6, 131-164.
33. Kamimura, R. (1993). Internal representation with minimum entropy in recurrent neural networks: Minimizing entropy through inhibitory connections. Network Computation in Neural Systems, 4, 423-440.
34. Karmin, E. D. (1990). A simple procedure for pruning back-propagation trained networks. IEEE Transactions on Neural Networks, 1, 239-242.
35. Klersey, G. F., & Dugan, M. T. (1995). Substantial doubt: Using artificial neural networks to evaluate going concern. Advances in Accounting Information Systems, 3, 137-159.
36. Koh, H. C. (1992). The sensitivity of optimal cutoff points to misclassification costs of type I and type II errors in the going-concern prediction context. Journal of Business Finance and Accounting, 19(2), 187-197.
37. Koh, H. C., & Killough, L. N. (1990). The use of multiple discriminant analysis in the assessment of the going concern status of an audit client. Journal of Business Finance and Accounting, 17(2), 179-192.
38. Koh, H. C., & Oliga, J. C. (1990). More on AUP17 and going-concern prediction models. Australian Accountant, 60(9), 67-71.
39. Kruschke, J. K. (1989). Distributed bottlenecks for improved generalization in back-propagation networks. International Journal of Neural Networks Research and Applications, 1(1), 187-193.
40. Lendaris, G. G., & Harls, I. A. (1990). Improved generalization in ANN's via use of conceptual graphs: A character recognition task as an example case. Proceedings IJCNN-90. Piscataway, NJ: IEEE, 551-556.
41. Liang, T. P., Chandler, J. S., Han, I., & Roan, J. (1992). An empirical investigation of some data effects on the classification accuracy of probit, ID3, and neural networks. Contemporary Accounting Research, 9(1), 306-328.
42. Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking and Finance, 1, 249-276.
43. Michalewicz, Z. (1992). Genetic algorithms + data structures = evolution programs. Berlin: Springer.
44. Montagno, R., Sexton, R. S., & Smith, B. N. (2002). Using neural networks for identifying organizational improvement strategies. European Journal of Operational Research, 142(2), 382-395.
45. Montana, D. J., & Davis, L. (1989). Training feed-forward neural networks using genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, 379-384.
46. Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. In Proceedings of the International Joint Conference on Neural Networks, Amesterdam, Norway, 1-23.
47. Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
48. Palmrose, Z. (1987). Litigation and independent auditors: The role of business failures and management fraud. Auditing: A Journal of Practice and Theory, 6(2), 90-103.
49. Palmrose, Z. (1988). An analysis of auditor litigation and audit service quality. Accounting Review, 63(1), 55-73.
50. Parker, J. E., & Abramowicz, K. F. (1989). Predictive abilities of three modeling procedures. Journal of the American Taxation Association, 1, 37-53.
51. Romaniuk, S. G. (1993). Pruning divide and conquer networks. Network: Computation in Neural Systems, 4, 481-494.
52. Schaffer, J. D., Whitley, D., & Eshelman, L. J. (1992). Combinations of genetic algorithms and neural networks: A survey of the state of the art, COGANN-92. Combinations of Genetic Algorithms and Neural Networks. Los Alamitos, CA: IEEE Computer Society Press, 1-37.
53. Sexton, R. S., Alidaee, B., Dorsey, R. E., & Johnson, J. D. (1998). Global optimization for artificial neural networks: A tabu search application. European Journal of Operational Research, 106, 570-584.
54. Sexton, R. S., & Dorsey, R. E. (2000). Reliable classification using neural networks: A genetic algorithm and backpropagation comparison. Decision Support Systems, 30, 11-22.
55. Sexton, R. S., Dorsey, R. E., & Johnson, J. D. (1998). Toward a global optimum for neural networks: A comparison of the genetic algorithm and backpropagation. Decision Support Systems, 22, 171-185.
56. Sexton, R. S., Dorsey, R. E., & Johnson, J. D. (1999). Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. European Journal of Operational Research, 114, 589-601.
57. Sinkey, J. F., Jr. (1975). A multivariate statistical analysis of the characteristics of problem banks. Journal of Finance, 30(1), 21-36.
58. Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38(7), 926-947.
59. Wilson, R. L., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 544-557.
60. Zmijewski, M. E. (1984). Essays on corporate bankruptcy. Doctoral dissertation. Ann Arbor, MI: University Microfilms International, 1-54.