Predicting how a crashing or bubbling asset affects a financial network through sentiment diffusion

Network theory within finance has helped model the underlying relationships between different companies and assets. The purpose of this is to understand how a network of assets will react if a particular asset performs above or below expectations. It is my goal to use financial news and social media data to generate a network and predict how sentiment will diffuse through that network of assets when one asset enters a "bubbling" or "crashing" state as defined in the current finance literature. The reason why this research is relevant to the financial community is that it could reveal information about which assets are likely to be positively or negatively affected by an asset that is going through an extreme market event.

In the first stage, I will develop a more accurate way of determining the semantic polarity of a financial article by fine-tuning a language model. I will do this by exposing the model to a large financial news corpus so that it understands financial terms, and I will utilise recent advancements in Natural Language Processing to deliver state-of-the-art performance. Creating this language model and making it open source will make it easier for others to achieve state-of the-art performance.

Following that, to produce a financial network I will use a co-occurrence network where connections are stronger if assets appear in the same news articles and compare that a network based on how different assets are spoken about. To do this I will utilise the Masked Language Model (MLM) training objective to fine-tune a language model which will work out the next most likely assets to take the place of a masked asset name over a given time period. By doing this I will know which assets have been written about in a similar way. This has the potential to expose underlying relationships between companies. Finally, in order to analyse how sentiment will diffuse through a network I will compare the diffusion to an epidemiological model in which nodes of the network are split up into susceptible, infected and recovered categories. In order to predict how sentiment will diffuse through a network I will make the most of recent advancements in network embedding and link prediction. The implication of this final stage is that organisations will be able to predict how a bubbling asset will affect different assets within its network.