When Sentiment Is News: The Polarity Pattern Approach

Abstract

We present a new approach and set of dictionaries for the analysis of sentiments in financial and business news headlines at the firm level. The proposed Polarity Pattern Approach (PPA) captures the deep structure of context and syntax and reduces measurement errors evident in previous approaches for classifying the tone of words and sentences in news headlines. We demonstrate the superior predictive validity of PPA using an out-of-sample dataset. As expected, negative news headlines are negatively related to stock returns and positively related to trading volumes, while positive news headlines are positively related to both, especially for finance news. Prior studies report a non-significant or, counter intuitively, negative relationship between positive news and stock returns, which we attribute to measurement error in previous approaches. Additionally, using market activities, we jointly estimate cross-sectional regression to compare the explanatory power of the PPA approach with commonly used methods. We show how PPA’s tone assignment significantly outperforms other dictionaries and approaches, including the commercial package (RavenPack) that uses both machine learning and deep learning. The PPA, including dictionaries, provides finance researchers with a new, more valid method of sentiment analysis for media news.

Link to PPA tone assignment dataset: https://www.whensentimentisnews.com

Keywords: Financial news headlines, sentiment analysis, textual analysis, bag of words, machine learning, deep learning, topic analyses, semantic and syntactic structure, stock market returns and volume.

JEL Classification: G1, G12, G4, G41