Beating the Best: A Neural Network Challenges the Black-Scholes Formula

Document Type

Conference Proceeding

Language

eng

Publication Date

3-1-1993

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

Proceedings., Ninth Conference on Artificial Intelligence for Applications, 1993

Source ISSN

0818638400

Abstract

A neural network model which processes financial input data is presented to estimate the market price of options. The network's ability to estimate option prices is compared to estimates generated by the Black-Scholes model, a traditional financial model. Comparisons reveal that the neural network outperforms the Black-Scholes model in about half of the cases examined. While the two modeling approaches differ fundamentally in their methodology for determining option prices, some common results emerge. While the neural network performs better than Black-Scholes on prices out-of-the-money, estimations near the expiration data are accurate for both.

Comments

Published as a part of Proceedings., Ninth Conference on Artificial Intelligence for Applications, 1993, Orlando FL. DOI.

Linda Salchenberger was affiliated with Loyola University Chicago at the time of publication.

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