We construct a real-time market sentiment index with three main components: sentiment extracted from social media, from market data, and option implied information. We investigate the interrelationships between these three components, generate an economically meaningful sentiment index for the whole market, and investigate its predictive power.
The market is driven not only by fundamental economic factors, but also by factors including social sentiment, political news and events. To better predict market movement, we need to study these multi-faceted influences. The most common strategy in current market prediction practice is to concatenate the features of various information sources into one super feature vector, which treats each information source separately and ignores their interactions. This practical challenge has led us to develop a supervised tensor regression learning approach in our investigation. Our tensor-based AI market predictor captures the relationships and interdependencies among various information sources. Furthermore, our AI market predictor adopts a sequence of tensors to reflect the time sequence of gathered information, as financial markets often experience sudden regime shifts near phase transitions due to financial crisis. In the face of a market turbulence, the correlation structure between stock prices changes. Our tensor-based AI market predictor enables us to empirically extract complex relationships from price time series and to predict abrupt changes by inferring the forthcoming dynamics of stock prices.