Enhancing portfolio return based on sentiment-of-topic

Abstract

While time-series analysis is commonly used in financial forecasting, a key source of market-sentiments is often omitted. Financial news is known to be making persuasive impact on the markets. Without considering this additional source of signals, only sub-optimal predictions can be made. This paper proposes a notion of sentiment-of-topic (SoT) to address the problem. It is achieved by considering sentiment-linked topics, which are retrieved from time-series with heterogeneous dimensions (i.e., numbers and texts). Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics associated with different sentiment levels are used to quantify market conditions. In particular, sentiment levels are learned from historical market performances and commentaries instead of using subjective interpretations of human expressions. By capturing the domain knowledge of respective industries and markets, an impressive double-digit improvement in portfolio return is obtained as shown in our experiments.