In the last decade, the arrival of new forms of social media has drastically increased the amount of personal data generated online. The massive amount of data available has shown a lot of opportunities for industries and research. In particular, increasing numbers of quantitative investors start to rely on alternative data to adapt their position in the market. However, it is still unclear whether aggregated online data could generate excess returns in active investing and allow refining positions on the stock market. In the present talk, we propose to tackle the question by focusing on three underlying themes. First, we will introduce one of the first viable approaches to the estimation of individual-level ideological positions derived from social media content. Second, we will show how a consensus model can be used to predict opinion evolution in online collective behaviour and how the "wisdom of the crowd" relates to group influence. Finally, we will explore whether aggregated opinion signals have potential to predict financial fundamentals and build an edge on the market.
For catering purposes, please register by 11 October.