Data-driven Simulation and Forecasting of Option Pricing: A Particle Swarm Optimization Approach

Articles in Press

Document Type : Research Paper

Authors

1 Department of Accounting, Ke.C., Islamic Azad University, Kerman, Iran.

2 Department of Economic, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran.

10.22103/jak.2025.25456.4192

Abstract

Objective: This study aims to design and propose a novel framework for option pricing using the Particle Swarm Optimization (PSO) algorithm. The framework focuses on separately predicting bid and ask prices to overcome the limitations of traditional analytical models such as the Black–Scholes model and to utilize machine learning capabilities for more accurate modeling of price behavior.
 
Method: The research is data-driven and based on a machine learning approach. A Multilayer Perceptron (MLP) neural network was designed and trained to predict option prices, while the network weights and linear regression coefficients were optimized using the PSO algorithm to enhance prediction accuracy and minimize error. The dataset includes 840 observations from 42 stocks with traded options during the 2014–2023 period. Model performance was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²).
 
Results: Results indicate that PSO-based optimization significantly reduces prediction errors and improves the model’s stability compared to traditional methods.
 
Conclusion: The proposed PSO–MLP framework serves as an effective tool for option market participants, enabling more accurate, data-driven forecasts and better-informed investment decisions while reducing transaction risk.

Keywords

Main Subjects

References

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Journal of Accounting Knowledge

Articles in Press, Accepted Manuscript
Available Online from 22 December 2025

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  • Receive Date: 24 June 2025
  • Revise Date: 16 November 2025
  • Accept Date: 01 December 2025

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