Research on Option Pricing Prediction Based on Deep Learning

As a financial derivative with high leverage, options have prominent hedging and arbitrage capabilities. Traditional pricing methods for European options, such as the Black–Scholes pricing model, the Merton model, and the Heston model, rely on strict assumptions and simulate option price trends using stochastic processes. However, due to the discrepancies between these assumptions and actual market conditions, these traditional models often fail to reflect real-world option pricing accurately. Therefore, this paper adopts a data-driven approach using deep learning algorithms to simulate the option pricing process. Based on the classical Black–Scholes pricing theory, we explore the feasibility of using BP neural networks and LSTM neural networks for option pricing prediction. Using historical data of the SSE 50ETF options, we build two predictive models and use MSE, MAE, and R-squared as evaluation metrics to assess prediction accuracy. Experimental results show that the LSTM model significantly outperforms others in predicting the price of SSE 50ETF options.