Using boosting for financial analysis and performance prediction: Application to s&p 500 companies, Latin American ADRs and banks

Computational Economics

Volumn 36, Issue 2, 2010, Pages 133-151

  Creamer, Germán ^a   Freund, Yoav ^b  

^a STEVENS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Adaboost; Data mining; Financial analysis; Machine learning

Indexed keywords

EID: 77955703769     PISSN: 09277099     EISSN: 15729974     Source Type: Journal    
DOI: 10.1007/s10614-010-9205-3     Document Type: Article

References (18)

1. Altman E. I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23(4), 589-609 (1968).
2. Breiman L.: Bagging predictors. Machine Learning 24, 123-140 (1996).
3. Breiman L.: Random forests. Machine Learning 45(1), 5-32 (2001a).
4. Breiman L.: Statistical modeling: The two cultures. Statistical Science 16, 199-231 (2001b).
5. Charnes A., Cooper W., Rhodes E.: Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429-444 (1978).
6. Creamer, G., & Freund, Y. (2010). Learning a board balanced scorecard to improve corporate performance. Decision Support Systems. doi: 10. 1016/j. dss. 2010. 04. 004.
7. Dietterich T. G.: Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation 10(7), 1895-1923 (1998).
8. Freund, Y., & Mason, L. (1999). The alternating decision tree learning algorithm. In Machine learning: proceedings of the sixteenth international conference (pp. 124-133). San Francisco: Morgan Kaufmann Publishers Inc.
9. Freund Y., Schapire R. E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119-139 (1997).
10. Friedman J., Hastie T., Tibshirani R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 38(2), 337-374 (2000).
11. Hastie T., Tibishirani R., Friedman J.: The elements of statistical learning. Springer, New York (2003).
12. Jegadeesh N., Kim J., Krische S. D., Lee C. M. C.: Analyzing the analysts: When do recommendations add value?. Journal of Finance 59(3), 1083-1124 (2004).
13. La-Porta R., de Silanes F. L., Shleifer A., Vishny R.: Law and finance. Journal of Political Economy 196, 1113-1155 (1998).
14. Lindenberg E., Ross S. A.: Tobin's Q ratio and industrial organization. Journal of Business 54, 1-32 (1981).
15. Lovell C.: Production frontiers and productive efficiency. In: Fried, H., Lovell, C., Schmidt, S. (eds) The measurement of productive efficiency, pp. 3-67. Oxford University Press, New York (1993).
16. Schapire, R. E. (2002). The boosting approach to machine learning: An overview. In MSRI workshop on nonlinear estimation and classification.
17. Schapire R. E., Freund Y., Bartlett P., Lee W. S.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26(5), 1651-1686 (1998).
18. Witten I. H., Frank E.: Data mining: Practical machine learning tools and techniques. (2nd ed.). Morgan Kaufmann, San Francisco (2005).