The farmers home administration and farm debt failure prediction

Journal of Accounting and Public Policy

Volumn 18, Issue 2, 1999, Pages 99-139

  Barney, Douglas K ^a   Finley Graves, O ^b   Johnson, John D ^b  

^a INDIANA UNIVERSITY SOUTHEAST   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0005244792     PISSN: 02784254     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0278-4254(98)10018-2     Document Type: Article

References (57)

1. Aldrich, J.H., Nelson, F.D., 1984. Linear Probability, Logit, and Probit Models. Sage Publications, Beverly Hills, CA.
2. Beaver, W.H., 1966. Financial ratios as predictors of failure. Journal of Accounting Research 4 (suppl.) 71-111.
3. Calantone R., Di Benedetto C., Errunza V. The use of discrete variable selections for credit evaluations. Omega International Journal of Management Science. 16(5):1988;469-480.
4. DeGroot, M.H., 1986. Probability and Statistics, 2nd ed., Addison-Wesley, Reading, MA.
5. Dorsey R.E., Mayer W.J. Genetic algorithms for estimation problems with multiple optima, non-differentiability, and other irregular features. Journal of Business and Economic Statistics. 13(1):1995;53-66.
6. Dorsey, R.E., Mayer, W.J., 1994. Optimization using genetic algorithms. In: Johnson, J.D., Whinston, A.B. (Eds.), Advances in Artificial intelligence in economics. Finance and Management, vol. 1, JAI Press, Greenwich, CT, pp. 69-91.
7. Dorsey, R.E., Johnson, J.D., Mayer, W.J., 1992. The genetic adaptive neural network training (GANNT) algorithm for genetic feedforward artificial neural systems. Unpublished working paper, University of Mississippi.
8. Dorsey, R.E., Johnson, J.D., Mayer, W.J., 1994. A genetic algorithm for the training of feedforward neural networks. In: J.D. Johnson and A.B. Whinston, (Eds.), Advances in Artificial Intelligence in Economics, Finance, and Management, vol. 1, JAI Press, Greenwich, CT, pp. 93-111.
9. Farm Financial Standards Task Force (FFSTF), 1991. Recommendations of the Farm Financial Standards Task Force. Farm Financial Standards Task Force, Washington, DC.
10. Farmers Home Administration (FmHA), 1989a. Farm and Home Plan. Form FmHA 431-2. Rev. United States Department of Agriculture, Farmers Home Administration, Washington, DC.
11. Farmers Home Administration (FmHA), 1991. Farmers Home Administration: Report for Fiscal Year 1990. United States Department of Agriculture, Farmers Home Administration Washington, DC.
12. Farmers Home Administration (FmHA), 1997. Classification of Farm Loan Programs Borrower Accounts. FmHA Instruction 2006-W. Rev. United States Department of Agriculture, Farmers Home Administration, Washington, DC.
13. Farmers Home Administration (FmHA), 1989b. Classification of Farmer Program Borrower Account. Form FmHA 2006-20. Rev. United States Department of Agriculture, Farmers Home Administration, Washington, DC.
14. Farmers Home Administration (FmHA), 1983. Proposed Rules. Federal Register 48 (217), 51312-51317.
15. Farmers Home Administration (FmHA), 1990. This is FmHA. Program Aid Number 973. United States Department of Agriculture, Farmers Home Administration, Washington, DC.
16. Financial Accounting Standards Board (FASB), 1984. Statement of Financial Accounting Concepts No. 5. Recognition and Measurement in Financial Statements of Business Enterprises. Financial Accounting Standards Board, Stamford, CT.
17. Frydman H., Altman E., Kao D. Introducing recursive partitioning for financial classification: the case of financial distress. The Journal of Finance. 40(1):1985;269-291.
18. Funahashi K.I. On the approximate realization of continuous mappings by neural networks. Neural Networks. 2(3):1989;183-192.
19. General Accounting Office (GAO), 1989. Farmers Home Administration: Sounder Loans Would Require Revised Loan-Making Criteria. General Accounting Office, Washington, DC.
20. General Accounting Office (GAO), 1990. Farmers Home Administration: Changes Needed in Loan Servicing Under the Agricultural Credit Act. General Accounting Office, Washington, DC.
21. General Accounting Office (GAO), 1992. Report to Congressional Committee, Farmers Home Administration: Billions of Dollars in Farm Loans are at Risk. General Accounting Office, Washington, DC.
22. General Accounting Office (GAO), 1988. Report to the Secretary of Agriculture; Financial Audit: Farmers Home Administration's Losses Have Increased Significantly. General Accounting Office, Washington DC.
23. Gilbert L.R., Menon K., Schwartz K.B. Predicting bankruptcy for firms in financial distress. Journal of Business Finance & Accounting. 17(1):1990;161-171.
24. Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.
25. Hawley D.D., Johnson J.D., Raina D. Artificial neural systems: a new tool for financial decision-making. Financial Analysts Journal. 46(6):1990;63-72.
26. Hecht-Neilsen, R., 1990. Neurocomputing. Addison-Wesley, Reading, MA.
27. Hornik K., Stinchcombe M., White H. Multilayer feed-forward networks are universal approximators. Neural Networks. 2(5):1989;359-366.
28. Huang C.-S., Dorsey R.E., Boose M.A. Life insurer financial distress prediction: a neural network model. Journal of Insurance Regulation. 3(2):1995;131-167.
29. Johnson R.B., Hagan A.R. Agricultural loan evaluation with discriminant analysis. Southern Journal of Agricultural Economics. 5(2):1973;57-62.
30. Judge, G.G, Griffiths, W.E., Hill, R.C., Lütkepohl, H., Lee, T.-C., 1985. The Theory and Practice of Econometrics, 2d ed. Wiley, New York.
31. Klinefelter D. FmHA: an addendum. Agri Finance. 34(5):1992;5.
32. LeCun, Y., 1986. Learning processes in an asymmetric threshold network. In: Bienenstock, E., Fogelman Souli, F., Weisbuch, G. (Eds.), Disordered Systems and Biological Organization. Springer, Berlin, pp. 233-240.
33. Lee T.-H., White H., Granger C.W.J. Testing for neglected nonlinarity in time series model: a comparison of neural network methods and alternative tests. Journal of Econometrics. 56(3):1993;269-290.
34. Merry, C.E., 1998. Administrator, Farm Financial Standards Council. Telephone conversation with Douglas Barney, 1 December 1998.
35. National Commission on Agricultural Finance (NCAF), 1989. Report of the National Commission on Agricultural Finance. National Commission on Agricultural Finance, Washington, DC.
36. Ohlson J. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 18(1):1980;109-131.
37. Parker, D., 1985. Learning Logic. Technical Report TR-87. Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA.
38. Platt H.D., Platt M.B. A note on the use of industry-relative ratios in bankruptcy prediction. Journal of Banking and Finance. 15(6):1991;1183-1194.
39. Price Waterhouse (PW), 1991. A Credit Management Study for the Farmers Home Administration's Farmer Program Loans. Price Waterhouse, Washington, DC.
40. Reimund, D.A., Gale, F., 1992. Structural Change in the US. Farm Sector, 1974-87: 13th Annual Family Farm Report to Congress. Agriculture Information Bulletin No. 647. US Department of Agriculture, Economic Research Service, Washington, DC.
41. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986a. Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (Eds.), Parallel Distributed Processing: Exploration in the Microstructure of Cognition. MIT Press, Cambridge, MA, pp. 318-362.
42. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by backpropagating errors. Nature. 323(6088):1986;533-536.
43. Salassi, M.E., Gatton, D.E., 1987. Regional Farm Financial and Operating Characteristics by Sales Class and Farm Type, January 1985. US Department of Agriculture, Economic Research Service, Agriculture and Rural Economy Division, Washington, DC.
44. Salchenberger L.M., Cinar E.M., Lash N.A. Neural networks: a new tool for predicting thrift failures. Decision Sciences. 23(4):1992;899-916.
45. Sexton R.S., Dorsey R.E., Johnson J.D. Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decision Support Systems. 22(2):1998;171-185.
46. Stam, J.M., Koening, S.R., Bentley, S.E., Gale, H.F., 1991. Farm Financial Stress, Farm Exits, and Public Sector Assistance to the Farm Sector in the 1980's. Agricultural Economic Report No. 645. US Department of Agriculture, Economic Research Service, Washington, DC.
47. Tam K.Y., Kiang M.Y. Managerial applications of neural networks: the case of bank failure predictions. Management Science. 38(7):1992;926-947.
48. US Congress, 1984. House Committee on Government Operations. Ignore the Law and Approve the Form: FmHA's Coordinated Financial Statement. Fifty-First Report by the Committee on Government Operations. 98th Cong. 2d sess. H. Report 98-1111. US Government Printing Office, Washington, DC.
49. US Congress, 1985. Public Law 99-198. 99 United States Statutes at Large 1325. US Government Printing Office, Washington, DC.
50. US Congress, 1998a. Consolidated Farm and Rural Development Act of 1961. 7 United States Code 1921. US Government Printing Office, Washington, DC.
51. US Congress, 1998b. Food Security Act of 1985. 7 United States Code 1989. US Government Printing Office, Washington, DC.
52. US Congress, 1998c. United States Housing Act of 1937. 42 United States Code 1471. US Government Printing Office, Washington, DC.
53. Wasserman, P.D., 1989. Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York.
54. Werbos, P., 1974. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Ph.D. dissertation. Harvard University.
55. White H. Neural-network learning and statistics. AI Expert. 4(12):1989;48-52.
56. White H. Some asymptotic results for learning in single hidden layer feedforward network models. Journal of the American Statistical Association. 84(408):1989;1003-1031.
57. Wilson R.L., Sharda R. Bankruptcy prediction using neural networks. Decision Support Systems. 11(5):1994;545-557.