Yang Li, ’20

How can investors identify sentiments embedded in social media, specifically user-generated content, that help forecast stock performance?

By conducting inter-disciplinary research across information systems and finance, we can better understand the value and significance of experts’ opinions and word of mouth advice relating to investments, which allow investors to make better choices in their portfolios.

Our study provides insight into active portfolio management, option pricing and arbitrage trading strategies. For example, mutual fund managers can use stock performance forecasts based on social media to optimize investment portfolio and perform efficient arbitrage strategies. It also adds to the emerging stream of social media related financial studies regarding: (1) the construction of new index to measure sentiments through machine learning and (2) the linkage between some new features embedded in user-generated content and stock performance.