Multimodal Financial Sentiment Analysis for Predicting Financial Markets
Provider: Grantová agentura ČR
Programme: Standardní projekty
Implementation period: 01.01.25 - 31.12.27
Workplace:
Fakulta ekonomicko-správní - Centrum pro vědu a výzkum
Investigator: Hájek PetrTeam member: Novotný Josef | Munk Michal | Krátký Martin | Asante Andrew | Kebede Zeru Kifle
Description:
Financial sentiment analysis is increasingly recognised as a critical decision support tool for financial market participants. Previous approaches to financial sentiment analysis have focused primarily on textual sentiment analysis due to the abundance of text-based financial data, such as news articles and social media posts. By incorporating multiple data types, multimodal financial sentiment analysis can provide a more comprehensive understanding of individual stakeholder sentiment by capturing non-verbal cues such as facial expressions and tone of voice. Here, we propose to investigate how multimodal financial sentiment analysis could improve the predictability of financial markets, including equity, foreign exchange, cryptocurrency and commodity markets. To this end, we combine multimodal financial sentiment with quantitative financial indicators in interpretable deep learning prediction models.
Financial sentiment analysis is increasingly recognised as a critical decision support tool for financial market participants. Previous approaches to financial sentiment analysis have focused primarily on textual sentiment analysis due to the abundance of text-based financial data, such as news articles and social media posts. By incorporating multiple data types, multimodal financial sentiment analysis can provide a more comprehensive understanding of individual stakeholder sentiment by capturing non-verbal cues such as facial expressions and tone of voice. Here, we propose to investigate how multimodal financial sentiment analysis could improve the predictability of financial markets, including equity, foreign exchange, cryptocurrency and commodity markets. To this end, we combine multimodal financial sentiment with quantitative financial indicators in interpretable deep learning prediction models.
