Modeling and Analysis of Effects on Financial Markets from Influence Relationships in Social Media.

Abstract

The data of social media has received much attention to observe and predict real-world events. For example, It is used to predict financial markets, products demand, and voter turnout. While these works regards social media as a sensor of real world, as social media become more popular, it become more natural to think social media significantly effects on real worlds events. The canonical example might be cryptocurrencies, where supply and demand are more susceptible to investor sentiment and therefore interactions within social media cause significant effects on the price of them. On the hypothesis that social media actuate real-world events, we propose a neural network based model to predict the price fluctuations of financial assets, including cryptocurrencies. We model the effect of social media which cannot be directly observed, using an end-to-end neural network, Recurrent Neural Network. By simulating the effect within the social media, we show that the method that models the effect of social media on financial markets can observe and predict the price fluctuations of cryptocurrencies more precisely and stably. By analyzing the model, we suggest that networks within social media can be influential relationships throughout time, even if they are not directly connected, and that the intensity of the influence from social media on financial markets varies depending on the nature of the financial assets.