Option pricing using deep learning approach based on LSTM-GRU neural networks: Case of London stock exchange

Habib Zouaoui; Meryem-Nadjat Naas; Habib Zouaoui; Meryem-Nadjat Naas

doi:10.3934/DSFE.2023016

Data Science in Finance and Economics

2023, Volume 3, Issue 3: 267-284. doi: 10.3934/DSFE.2023016

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Option pricing using deep learning approach based on LSTM-GRU neural networks: Case of London stock exchange

Habib Zouaoui ^,,
Meryem-Nadjat Naas

Faculty of Economic Sciences, University of Relizane, w. Relizane, B.P: 48000, Algeria

Received: 01 June 2023 Revised: 25 July 2023 Accepted: 27 July 2023 Published: 10 August 2023
JEL Codes: C45, E47, G17

This study is a review of literature on machine learning to examine the potential of deep learning (DL) techniques in improving the accuracy of option pricing models versus the Black-Scholes model and capturingcomplex features in financial data.

Neural networks and other machine learning models have been proposed for option pricing and have improved accuracy compared withtraditional models. However, such use of machine learning also presents practical challenges such as data availability and quality, computational resources, model selection and validation, interpretability and overfitting. This study discusses several of these challenges and highlights the need for careful evaluation and validation of machine learning models in London option pricing during the Coronavirus disease 2019 pandemic. Moreover, to investigate the quality of the models used, we compare the performances of these algorithms in option pricing through the application of significance statistical tests.
- black scholes,
- deep learning,
- GRU, LSTM,
- option pricing,
- RNN
Citation: Habib Zouaoui, Meryem-Nadjat Naas. Option pricing using deep learning approach based on LSTM-GRU neural networks: Case of London stock exchange[J]. Data Science in Finance and Economics, 2023, 3(3): 267-284. doi: 10.3934/DSFE.2023016

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Abstract

This study is a review of literature on machine learning to examine the potential of deep learning (DL) techniques in improving the accuracy of option pricing models versus the Black-Scholes model and capturingcomplex features in financial data.

Neural networks and other machine learning models have been proposed for option pricing and have improved accuracy compared withtraditional models. However, such use of machine learning also presents practical challenges such as data availability and quality, computational resources, model selection and validation, interpretability and overfitting. This study discusses several of these challenges and highlights the need for careful evaluation and validation of machine learning models in London option pricing during the Coronavirus disease 2019 pandemic. Moreover, to investigate the quality of the models used, we compare the performances of these algorithms in option pricing through the application of significance statistical tests.

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