Deep Residual Convolutional Long Short-term Memory Network for Option Price Prediction Problem

In the realm of financial markets, the precise prediction of option prices remains a cornerstone for effective portfolio management, risk mitigation, and ensuring overall market equilibrium. Traditional models, notably the Black-Scholes, often encounter challenges in comprehensively integrating the multifaceted interplay of contemporary market variables. Addressing this lacuna, this study elucidates the capabilities of a novel Deep Residual Convolution Long Short -term Memory (DR-CLSTM) network, meticulously designed to amalgamate the superior feature extraction prowess of Convolutional Neural Networks (CNNs) with the unparalleled temporal sequence discernment of Long Short-term Memory (LSTM) networks, further augmented by deep residual connections. Rigorous evaluations conducted on an expansive dataset, representative of diverse market conditions, showcased the DR-CLSTM's consistent supremacy in prediction accuracy and computational efficacy over both its traditional and deep learning contemporaries. Crucially, the integration of residual pathways accelerated training convergence rates and provided a formidable defense against the often detrimental vanishing gradient phenomenon. Consequently, this research positions the DR-CLSTM network as a pioneering and formidable contender in the arena of option price forecasting, offering substantive implications for quantitative finance scholars and practitioners alike, and hinting at its potential versatility for broader financial instrument applications and varied market scenarios.