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Black–Scholes versus artificial neural networks in pricing FTSE 100 options

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  • Julia Bennell
  • Charles Sutcliffe

Abstract

This paper compares the performance of Black–Scholes with an artificial neural network (ANN) in pricing European‐style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed‐form model. For out‐of‐the‐money options, the ANN is clearly superior to Black–Scholes. For in‐the‐money options, if the sample space is restricted by excluding deep in‐the‐money and long maturity options (3.4% of total volume), then the performance of the ANN is comparable to that of Black–Scholes. The superiority of the ANN is a surprising result, given that European‐style equity options are the home ground of Black–Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed‐form model, or the closed‐form model is less successful than is Black–Scholes for equity options. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Julia Bennell & Charles Sutcliffe, 2004. "Black–Scholes versus artificial neural networks in pricing FTSE 100 options," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(4), pages 243-260, October.
    • Handle: RePEc:wly:isacfm:v:12:y:2004:i:4:p:243-260
    DOI: 10.1002/isaf.254
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    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

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