Extreme Topic Model for Market eAlert Service

Abstract

Although past research in finance showed that future investment returns are hardly predictable, predictions in volatility are found possible. Financial news is known to be making persuasive impacts on investor behavior, and hence injecting disturbances to markets. While there exists many prediction services, most of them do not factor in financial news directly. Previously proposed supervised topic model provides an avenue to associate volatility with news, yet it displays poor resolutions at extreme regions. To address this problem, we propose a novel extreme topic model to provide a better service for market alerts. By mapping extreme events into Poisson point processes, volatile regions are magnified to reveal their hidden volatility-topic relationships. Derivative approach is used to quantify the volatility of financial instruments. It captures immediate, collective responses from investors on market events, e.g., Brexit. By acquiring domain knowledge on how financial news influence investor behavior, accurate volatility predictions are made under extreme conditions as shown by the improved prediction accuracy in our experiments.