Textual Sentiment and Sector specific reaction

Elisabeth Bommes, Cathy Yi-Hsuan Chen and Wolfgang Härdle

No 2018-043, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: News move markets and contains incremental information about stock reactions. Future trading volumes, volatility and returns are a ected by sentiments of texts and opinions expressed in articles. Earlier work of sentiment distillation of stock news suggests that risk prole reactions might differ across sectors. Conventional asset pricing theory recognizes the role of a sector and its risk uniqueness that differs from market or rm specic risk. Our research assesses whether incorporating the sentiment distilled from sector specic news carries information about risk proles. Textual analytics applied to about 600K articles leads us with lexical projection and machine learning to classication of sentiment polarities. The texts are scraped from offcial NASDAQ web pages and with Natural Language Processing (NLP) techniques, such as tokenization, lemmatization, a sector specic sentiment is extracted using a lexical approach and a nancial phrase bank. Predicted sentence-level polarities are aggregated into a bullishness measure on a daily basis and fed into a panel regression analysis with sector indicators. Supervised learning with hinge or logistic loss and regularization yields good prediction results of polarity. Compared with standard lexical projections, the supervised learning approach yields superior predictions of sentiment, leading to highly sector specic sentiment reactions. The Consumer Staples, Health Care and Materials sectors show strong risk prole reactions to negative polarity.

Keywords: Investor Sentiment; Attention Analysis; Sector-specic Reactions; Volatility; Text Mining; Polarity (search for similar items in EconPapers)
JEL-codes: C81 G14 G17 (search for similar items in EconPapers)
Date: 2018
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