An Approach Towards Developing Relationally Intelligent Multimodal Framework for Stock Movement Prediction (Student Abstract)

Abstract

The dependency of stock prices on a multitude of factors makes the task of prediction exceedingly challenging. Given the volatile nature of stock data, it is imperative to integrate multiple sources of information to accurately encompass the various factors that influence market trends. To capture these complex dynamics, several multimodal methodologies have been proposed, integrating market data, technical indicators, and textual information. However, it is claimed that these coarse-grained information sources do not offer a holistic view of the market. Furthermore, these sources are stock-specific and do not elucidate the interconnections between various stocks. To address this deficiency, we propose a multimodal approach that incorporates this relational aspect alongside fine-grained information sources. The applicability of our framework is underscored by empirical results, which demonstrate the superiority of our approach.