Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information

This study demonstrates whether financial text is useful for the tactical asset allocation method using stocks. This can be achieved using natural language processing to create polarity indexes in financial news. We perform clustering of the created polarity indexes using the change point detection algorithm. In addition, we construct a stock portfolio and rebalanced it at each change point using an optimization algorithm. Consequently, the proposed asset allocation method outperforms the comparative approach. This result suggests that the polarity index is useful for constructing the equity asset allocation method.

Automated summary

This study demonstrates the effectiveness of financial text in the tactical asset allocation method using stocks. By creating polarity indexes in financial news through natural language processing, clustering them using change point detection algorithm, constructing a stock portfolio, and optimizing it at each change point, the proposed method outperforms the comparative approach, suggesting the usefulness of polarity index in equity asset allocation.