Neural network earnings per share forecasting models: A comparison of backward propagation and the genetic algorithm

Decision Support Systems

Volumn 47, Issue 1, 2009, Pages 32-41

  Cao, Qing ^a   Parry, Mark E ^b  

^a TEXAS TECH UNIVERSITY   (United States)

^b UNIVERSITY OF MISSOURI KANSAS CITY   (United States)

Author keywords

Artificial neural network; Comparative analysis; Forecasting methods

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; DECISION SUPPORT SYSTEMS; DECISION THEORY; EARNINGS; FORECASTING; GENETIC ALGORITHMS; MAXIMUM LIKELIHOOD ESTIMATION;

ARTIFICIAL NEURAL NETWORK; BACKWARD PROPAGATION; COMPARATIVE ANALYSIS; DECISION SUPPORTS; EARNINGS PER SHARES; ESTIMATION PROCEDURES; FORECASTING ACCURACIES; FORECASTING METHODS; FORECASTING MODELS; FUNDAMENTAL ANALYSIS; MULTIVARIATE FORECASTING; NETWORK FUNCTIONS; NETWORK WEIGHTS; NEURAL NETWORK MODELS; SIMULTANEOUS OPTIMIZATIONS; STOCK PRICES; UNIVARIATE; UNIVARIATE MODELS;

NEURAL NETWORKS;

EID: 63049127958     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2008.12.011     Document Type: Article

References (38)

1. Abarbanell J.S., and Bushee B.J. Fundamental analysis, future EPS, and stock prices. Journal of Accounting Research 35 1 (1997) 1-24
2. Abarbanell J.S., and Bushee B.J. Abnormal returns to a fundamental analysis strategy. The Accounting Review 73 1 (1998) 19-45
3. Bathke A.W., and Lorek K.S. The relationship between time-series models and the security market's expectation of quarterly earnings. The Accounting Review 59 2 (1984) 163-176
4. Beneish M.D., Lee C.M.C., and Tarpley R.L. Contextual fundamental analysis through the prediction of extreme returns. Review of Accounting Studies 6 2,3 (2001) 165-189
5. Brown L.D., and Rozeff M.S. Univariate time-series models of quarterly accounting earnings per share: a proposed model. Journal of Accounting Research 17 (1979) 179-189 (Spring)
6. Callen J.L., Kwan C.C.Y., Yip P.C.Y., and Yuan Y. Neural network forecasting of quarterly accounting earnings. International Journal of Forecasting 12 4 (1996) 475-482
7. Conroy R., and Harris R. Consensus forecasts of corporate earnings: analysts' forecasts and time series methods. Management Science 33 6 (1987) 725-738
8. Cui G., Wong M.L., and Lui H.K. Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science 52 4 (2006) 597-612
9. Dorsey R.E., and Mayer W.J. Genetic algorithms for estimation problems with multiple optima, nondifferentiability, and other irregular features. Journal of Business & Economic Statistics 13 3 (1995) 53-66
10. Foster G. Quarterly accounting data: time series properties and predictive ability results. The Accounting Review 52 1 (1977) 1-21
11. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning (1989), Addison-Wesley, Reading, MA
12. Griffin P. The time series behavior of quarterly earnings: preliminary evidence. Journal of Accounting Research 15 Spring (1977) 71-83
13. Guerard J.B. Linear Constraints, Robust-Weighting and Efficient Composite Modeling. Journal of Forecasting 6 3 (1987) 193-199
14. Haykin S. Neural Networks: a Comprehensive Foundation. 2nd Edition (1998), Prentice-Hall, Englewood Cliffs, NJ
15. Huang S.J., Chiu N.H., and Chen L.W. Integration of the grey relational analysis with genetic algorithm for software effort estimation. European Journal of Operational Research 188 3 (2008) 898-909
16. Kaparthi S., and Suresh N.C. Performance of selecting part-machine grouping technique for data sets of wide ranging sizes and imperfection. Decision Sciences 25 4 (1994) 515-532
17. Kim Y., Street N.K., Russell G.J., and Menczer F. Customer targeting: a neural network approach guided by genetic algorithms. Management Science 51 2 (2005) 264-276
18. Kimbrough S.O., Koehler G.J., Lu M., and Wood D.H. On a feasible-infeasible two-population (FI-2Pop) genetic algorithm for constrained optimization: distance tracing and no free lunch. European Journal of Operational Research 190 2 (2007) 310-327
19. Kuflik T., Boger Z., and Shoval P. Filtering search results using an optimal set of terms identified by an artificial neural network. Information Processing & Management 42 2 (2006) 469-483
20. Kuldeep K., and Sukanto B. Artificial neural network vs. linear discriminant analysis in credit ratings forecast; a comparative study of prediction performances. Review of Accounting & Finance 5 3 (2006) 216-227
21. Landajo M., and de Andrés J. Robust neural modeling for the cross-sectional analysis of accounting information. European Journal of Operational Research 177 2 (2007) 1232-1252
22. Lenard M.J., Alam P., and Madey G.R. The application of neural networks and a qualitative response model to the auditor's going concern uncertainty decision. Decision Sciences 26 2 (1995) 209-224
23. Lev B., and Thiagarajan S.R. Fundamental information analysis. Journal of Accounting Research, 31 2 (1993) 190-215
24. Lobo G.J., and Nair R.D. Combining judgmental and statistical forecasts: an application to earnings forecasts. Decision Sciences 21 2 (1990) 446-460
25. Lorek K.S., and Willinger G.L. A multivariate time-series prediction model for cash-flow data. The Accounting Review 71 1 (1996) 81-102
26. Qi M. Nonlinear predictability of stock returns using financial and economic variables. Journal of Business & Economic Statistics 17 4 (1999) 419-429
27. Sexton R.S., Dorsey R.E., and 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
28. Sexton R.S., Dorsey R.E., and Johnson J.D. Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. European Journal of Operational Research 114 3 (1999) 589-601
29. Sexton R.S., Sriram R.S., and Etheridge H. Improving decision effectiveness of artificial neural networks: a modified genetic algorithm approach. Decision Sciences 34 3 (2003) 421-442
30. Sexton R.S., Dorsey R.E., and Sikander N.A. Simultaneous optimization of neural network function and architecture algorithm. Decision Support Systems 36 3 (2004) 283-296
31. Thieme R.J., Song M., and Calanton R.J. Artificial neural network decision support systems for new product development project selection. Journal of Marketing Research 37 2 (2000) 499-506
32. Wang K.J., Chen J.C., and Lin Y.S. A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory. Production Planning & Control 16 7 (2005) 665-680
33. Watts R.L., and Leftwich R.W. The time series of annual accounting EPS (in research reports). Journal of Accounting Research 15 2 (1977) 253-271
34. Wolpert D.H., and Macready W.G. No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010 (1995), Santa Fe Institute
35. Wolpert D.H., and Macready W.G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 67 (1997) 67-82
36. Wu R.C., Chen R.S., and Chian S.S. Design of a product quality control system based on the use of data mining techniques. IIE Transactions 38 1 (2006) 39-51
37. Zhang W., Cao Q., and Schniederjans M. Neural network earnings per share forecasting models: a comparative analysis of alternative methods. Decision Sciences 35 2 (2004) 205-237
38. Zhu D., Premkumar G., Zhang X., and Chu C.H. Data mining for network intrusion detection: a comparison of alternative methods. Decision Sciences 32 4 (2001) 635-653