SSAAM: Sentiment Signal-based Asset Allocation Method with Causality Information

TL;DR

This paper introduces SSAAM, a novel asset allocation method that leverages sentiment analysis of financial news and causality information to improve stock portfolio rebalancing and outperform traditional approaches.

Contribution

It presents a new approach combining sentiment polarity indexes, change-point detection, and optimization for tactical asset allocation in stocks.

Findings

The proposed method outperforms comparative approaches in asset allocation.

Sentiment polarity indexes effectively inform portfolio rebalancing.

Change-point detection enhances the timing of asset reallocation.

Abstract

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