Machine Learning Driven Options Pricing Model for Changing Market Conditions

This paper presents the design and implementation of an Adaptive Options Pricing Model utilizing deep learning techniques to enhance the accuracy of options pricing in volatile financial markets. Traditional models such as the Black-Scholes and Binomial frameworks have served as foundational tools in options pricing; however, they are constrained by assumptions of constant volatility and market efficiency, which often fail to reflect real-world dynamics. To address these limitations, the proposed model incorporates historical pricing data alongside real-time market sentiment derived from news articles and social media. By leveraging advanced deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, the model dynamically adapts to changing market conditions, capturing complex, nonlinear relationships in the data. The performance of the model is evaluated against traditional pricing methods, demonstrating significant improvements in pricing accuracy and responsiveness to market fluctuations. This research contributes valuable insights for traders and risk managers seeking to optimize their strategies in the derivatives market.