Hypothesizing Multimodal Influence: Assessing the Impact of Textual and Non-Textual Data on Financial Instrument Pricing Using NLP and Generative AI

Abstract

This paper presents an advanced conceptual framework for the analysis of textual data in the context of financial securities, hypothesizing that a comprehensive evaluation of events within the broader economic environment, particularly through their descriptions, significantly influences the pricing of financial instruments.

This research extends beyond the traditional scope of Natural Language Processing by proposing the inclusion of non-textual data forms such as images, videos, and audio in the analysis. Further, it acknowledges the recent developments in Generative Artificial Intelligence, suggesting its application to expand the breadth of textual analysis through the generation of varied textual datasets. The hypothesis posits that the systematic analysis of these diverse multimodal textual inputs, surpassing the conventional verbal text, could enhance the decision-making process in financial asset management. This study aims to elucidate the potential effects of this methodological advancement on financial market fluctuations and outlines the most pertinent NLP methodologies for the empirical investigation of the hypothesis in future scholarly work.

Keywords: Financial Markets, Natural Language Processing (NLP), Generative Artificial Intelligence, Multimodal Data Analysis, Economic Context Analysis, Textual Data in Finance, Non-Textual Data Integration, Sentiment Analysis, Market Dynamics, Automated Decision-Making

JEL Classification: G00, G10, G20