Proposal for Turning Point Detection Method using Financial Text and Transformer

Abstract

In this study, we demonstrate whether analysts' sentiment toward individual stocks is useful for stock market analysis. This can be achieved by creating a polarity index in analyst reports using natural language processing. In this study, we calculated anomaly scores for the created polarity index using anomaly detection algorithms. The results show that the proposed method is effective in detecting the turning point of the polarity index.