Abstract
This paper demonstrates how the boosting approach can support the financial analysis functions in two ways: (1) As a predictive tool to forecast corporate performance, and rank accounting and corporate variables according to their impact on performance, and (2) As an interpretative tool to generate alternating decision trees that capture the non-linear relationship among accounting and corporate governance variables that determine performance. We compare our results using Adaboost with logistic regression, bagging, and random forests. We conduct 10-fold cross-validation experiments on one sample each of S&P 500 companies, American Depository Receipts (ADRs) of Latin American companies and Latin American banks. Adaboost results indicate that large companies perform better than small companies, especially when these companies have a limited long-term assets to sales ratio. Performance improves for large LAADR companies when the country of residence is characterized by a weak rule of law. In the case of S&P 500 companies, performance increases when the compensation for top officers is mostly variable.
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Acknowledgments
Authors thank the editor Hans M. Amman, David Waltz, Tony Jebara, Sal Stolfo, Vasant Dhar, Salvatore Cantale, Paul Spindt, Tom Noe, TomReese, Kenneth Jameson, Greg Buchholz, John M. Trapani, and participants of the 2004 IASTED Financial Engineering and Applications conference, 2003 Latin American Studies Association meeting, 2001 Tulane University Latin American Research Consortium meeting, and 2000 Eastern Finance Association meeting for their helpful comments on partial versions of this paper, and to Patrick Jardine for proof-reading the article. GC also thanks the institutional support of Stevens Institute of Technology, Tulane University, the Board of Regents of the State of Louisiana, and the Center for Computational Learning Systems at Columbia University, and research support of Carolina Gomez, Monica Garcia, Leonardo Serrano, Marcelo Gonzalez, Juan Carlos Otalora, and Julian Benavides. The opinions presented are the exclusive responsibility of the authors.
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This paper is based on an earlier work: Predicting Performance and Quantifying Corporate Governance Risk for Latin American ADRs and Banks. In Proceedings of the Financial Engineering and Applications conference, MIT-Cambridge, 2004.
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Creamer, G., Freund, Y. Using Boosting for Financial Analysis and Performance Prediction: Application to S&P 500 Companies, Latin American ADRs and Banks. Comput Econ 36, 133–151 (2010). https://doi.org/10.1007/s10614-010-9205-3
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DOI: https://doi.org/10.1007/s10614-010-9205-3