Regulated LSTM Artificial Neural Networks for Option Risks

David Liu and An Wei
Additional contact information
David Liu: Department of Financial and Actuarial Mathematics, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
An Wei: Department of Financial and Actuarial Mathematics, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China

FinTech, 2022, vol. 1, issue 2, 1-11

Abstract: This research aims to study the pricing risks of options by using improved LSTM artificial neural network models and make direct comparisons with the Black–Scholes option pricing model based upon the option prices of 50 ETFs of the Shanghai Securities Exchange from 1 January 2018 to 31 December 2019. We study an LSTM model, a mathematical option pricing model (BS model), and an improved artificial neural network model—the regulated LSTM model. The method we adopted is first to price the options using the mathematical model—i.e., the BS model—and then to construct the LSTM neural network for training and predicting the option prices. We further form the regulated LSTM network with optimally selected key technical indicators using Python programming aiming at improving the network’s predicting ability. Risks of option pricing are measured by MSE, RMSE, MAE and MAPE, respectively, for all the models used. The results of this paper show that both the ordinary LSTM and the traditional BS option pricing model have lower predictive ability than the regulated LSTM model. The prediction ability of the regulated LSTM model with the optimal technical indicators is superior, and the approach adopted is effective.

Keywords: LSTM model; artificial neural network; option pricing (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2674-1032/1/2/14/pdf (application/pdf)
https://www.mdpi.com/2674-1032/1/2/14/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jfinte:v:1:y:2022:i:2:p:14-190:d:830201

Access Statistics for this article

FinTech is currently edited by Ms. Lizzy Zhou

More articles in FinTech from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().