Enhancing Option Pricing Precision in FinancialMarkets with a Hybrid Ga-Bp Neural NetworkApproach

Accurate option pricing is critical for the effective functioning of financial markets, providing traders andinvestors with the means to hedge risks and capitalize on market movements. Traditional models such as the BlackScholes, Binomial Tree, Trinomial Tree, Monte Carlo Simulation, and the Garman-Kohlhagen model have long beenthe standard for option pricing. However, these models often face limitations in capturing market complexities andextreme events. We propose here a hybrid approach that combines Genetic Algorithm (GA) optimization withBackpropagation (BP) neural networks to enhance the precision of option pricing. It uses HS300 index stock data from2013 to 2022, including stock prices, volumes, and price changes. The hybrid GA-BP model is tested for its ability tomake more accurate price predictions. The model helps investors make better decisions by improving pricing strategiesand managing risks effectively. The Hybrid GA-BP neural network model leverages the global search capabilities ofGA to optimize the initial weights and biases of the BP neural network, thereby avoiding local minima and improvingconvergence rates. This integrated model is trained and tested on historical market data, with its performancebenchmarked against traditional models. Empirical results demonstrate that the Hybrid GA-BP neural network modelsignificantly outperforms traditional models in terms of pricing accuracy. The model shows superior precision whencomparing actual market prices with predicted prices, reducing errors and increasing reliability. This enhancement inpricing precision can lead to more informed trading decisions and better risk management strategies. The findings ofthis research contribute to the growing body of knowledge in financial engineering by showcasing the potential ofhybrid machine learning approaches in financial modeling. The Hybrid GA-BP neural network model presents apromising tool for practitioners and researchers aiming to improve option pricing methodologies in increasinglycomplex financial markets.