Hutchinson is from PHZ Partners, Cambridge, Massachusetts. Lo is from the Sloan School of Management, Massachusetts Institute of Technology. Poggio is from the Artificial Intelligence Laboratory and the Center for Biological and Computational Learning, Massachusetts Institute of Technology. This article reports research done within the Massachusetts Institute of Technology Artificial Intelligence Laboratory and the Sloan School of Management's Research Program in Computational Finance. We thank Harrison Hong and Terence Lim for excellent research assistance, and Petr Adamek, Federico Girosi, Chung-Ming Kuan, Barbara Jansen, Blake LeBaron, and seminar participants at the DAIS Conference, the Harvard Business School, and the American Finance Association for helpful comments and discussion. Hutchinson and Poggio gratefully acknowledge the support of an ARPA AASERT grant administered under the Office of Naval Research contract N00014-92-J-1879. Additional support was provided by the Office of Naval Research under contract N00014-93-1-0385, by a grant from the National Science Foundation under contract ASC-9217041 (this award includes funds from ARPA provided under the HPCC program), by the Research Program in Computational Finance, and by Siemens AG. A portion of this research was conducted during Lo's tenure as an Alfred P. Sloan Research Fellow.
A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks
Version of Record online: 30 APR 2012
© 1994 the American Finance Association
The Journal of Finance
Volume 49, Issue 3, pages 851–889, July 1994
How to Cite
HUTCHINSON, J. M., LO, A. W. and POGGIO, T. (1994), A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks. The Journal of Finance, 49: 851–889. doi: 10.1111/j.1540-6261.1994.tb00081.x
- Issue online: 30 APR 2012
- Version of Record online: 30 APR 2012
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