Transfer Learning with Pre-trained ResNet-50: Predicting SPX Option Prices

Abstract

Due to the volatility and complexity of the options market, it has exacerbated the difficulty of predicting option prices and attracted widespread attention. In recent years, many machine learning (ML) techniques have been explored for this purpose. This paper implements the advanced ML models to boost the accurate prediction of the option price, which includes the comparison between traditional ML techniques Random Forest and Support Vector Regression (SVR), as well as the Convolutional Neural Network (CNN) ResNet-50. Additionally, we further compare the performance of ResNet-50 pre-trained on ImageNet through transfer learning. These methods are used to predict the implied volatility, which is then transformed into option pricing using the Black–Scholes Option Pricing Model (BSOPM). Finally, we compare the performance of these models based on error analysis using RMSE and R². The experimental results show that the pre-trained ResNet-50 reduces the error by 6% compared to the un-pretrained ResNet-50, by 51.2% compared to Decision Trees, and by 51.1% compared to SVR. The predicted option prices align well with the actual values, demonstrating the feasibility of applying pre-trained ResNet-50 on option price prediction.