Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 043

Textual Sentiment and Sector specific reaction

Elisabeth Bommes
Cathy Yi-Hsuan Chen
Wolfgang Karl Härdle

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.

Investor Sentiment, Attention Analysis, Sector-specic Reactions, Volatility, Text Mining, Polarity

JEL Classification:
C81, G14, G17