Predicting Funding Flows in Public and Private Markets (Master’s Capstone Project)

Description

In the dynamic world of finance, anticipating market trends and making informed decisions is paramount. Predicting funding flows plays a crucial role in this endeavor, offering insights into market sentiment and investment opportunities.

Our project seeks to develop a robust framework for forecasting funding flows in public and private markets, leveraging data science, time series analysis, and NLP techniques. Despite the complexity posed by market data and external factors, we are confident that advanced data analysis and machine learning can propel us forward in this challenging domain.

We are currently working on combining the finance (Refinitive Workspace) and publications (SemOpenAlex) data with patents (US Patents), investment funds and firms data (Refinitve Workspace). Using concept-ontology (SemOpenAlex)author disambiguation (S2AND) and Affiliation Linking (S2AFF) we are creating edges connecting diverse data sources into a super graph. This graph will then be leveraged using Graph Nueral Networks for predicting funding flows in private and public sector.