MSCA Seminar Series - AI in Digital Finance

Link to the meetup event page and participation: https://www.meetup.com/fintech_ai_in_finance/events/306255370/?slug=fintech_ai_in_finance&eventId=305437192&isFirstPublish=true

Speaker

Tiejun Ma (Artificial Intelligence Applications Institute, Informatics, University of Edinburgh)

Contents of the seminar

Existing methods of using financial sentiments relies on equal-weight and static aggregation to manage sentiments from multiple news items. This leads to a critical issue termed "Aggregated Sentiment Homogenization'". This phenomenon occurs when aggregating numerous sentiments, causing representations to converge towards the mean values of sentiment distributions and thereby smoothing out unique and important information.

To address this issue, we introduce a novel method that leverages a dynamic market-news attention mechanism to aggregate news sentiments for market prediction. Our proposed approach learns the relevance of news sentiments to price changes and assigns varying weights to individual news items. By integrating the news aggregation step into the networks for market prediction, it allows for trainable sentiment representations that are optimized directly for prediction.

In addition, the diffusion of financial news into market prices is complex, making it challenging to evaluate the connections between news events and market movements. In this talk we will introduce introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market price and news articles. We conduct extensive experiments on two datasets, encompassing the S\&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ's effectiveness, outperforming advanced market prediction models and latest large-language models. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news.

About Tiejun Ma

Professor Tiejun Ma is Professor in Financial Computing and Risk Modelling within the Artificial Intelligence Applications Institute, Informatics, University of Edinburgh.

Professor Ma’s research focuses on risk analysis and decision-making using quantitative modelling of user’s behaviour with big data analysis techniques (e.g. individual's financial/cyber risk taking and decision behaviour) and fintech.

Dr Ma is also a member of the Risk Management Research and Thought Leadership Committee, of Faculty of Actuaries (IFoA). Recently, Dr Ma’s research projects with leading city industry partners have been twice awarded ‘Certificate of Excellence’ by InnovationUK and he won the Lenovo AI on Fintech Innovation Award (SuperComputing 2017).

Photo by Anna Shvets: https://www.pexels.com/photo/a-person-holding-a-bank-card-5614107/