Artificial Neural Network model for Call Options Pricing Using Market Data

Authors

  • Georgios Rigopoulos National and Kapodistrian University of Athens

Keywords:

option pricing, artificial neural network, multilayer perceptron

Abstract

Accurate option pricing is of key importance for markets and traders. This work explores the feasibility of using artificial neural network model in call option pricing, using the traditional Black-Scholes model as a benchmark. We used a multilayer perceptron model trained to learn Black-Scholes function and tested in real option data from thirty-five S&P100 stocks. In our approach testing data is not oriented from the same distribution as training and this is a unique contribution to existing research. Findings demonstrate that artificial neural networks performs well in actual market data. Although further exploration and experimentation is required to reach required robustness and become less ad hoc and data sensitive, it is a promising approach and can play a substantial role in option pricing,.

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References

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Published

2024-11-18

How to Cite

Rigopoulos, G. (2024). Artificial Neural Network model for Call Options Pricing Using Market Data . International Journal of Computer Engineering and Data Science (IJCEDS), 3(4), 21–28. Retrieved from https://ijceds.com/ijceds/article/view/68

Issue

Vol. 3 No. 4 (2023): Volume 3. Issue 4

Section

Articles

License

Copyright (c) 2024 Georgios Rigopoulos

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