News-based Sentiment Indicators

Abstract

We construct sentiment indices for 20 countries from 1980 to 2019. Relying on computational text analysis, we capture specific language like “fear”, “risk”, “hedging”, “opinion”, and, “crisis”, as well as “positive” and “negative” sentiments, in news articles from the Financial Times. We assess the performance of our sentiment indices as “news-based” early warning indicators (EWIs) for financial crises. We find that sentiment indices spike and/or trend up ahead of financial crises.

A robust textual analysis of the dynamics of Hong Kong property market

“…We begin our analysis by first constructing sentiment indices for the property market in Hong Kong using newspaper information. 2 Using textual information to compile sentiment index is a rapidly growing area (see e.g., Huang et al, 2019;Shapiro et al, 2020;Soo, 2018). For our purpose, we find it satisfactory to use a simple Boolean approach, similar to that of Soo (2018) and Gao and Zhao (2018).…”

Decomposing Climate Risks in Stock Markets

“…News articles encompass diverse narratives and are timely and focused on the risks most pertinent to the market, and in the spirit of canonical asset pricing, news coverage may serve as the best of all publicly available signals. (Huang et al, 2019;Barkema et al, 2021) We propose a novel, machine learning-based method to investigate how market-wide climate mitigation risks are priced in Canadian stocks. Our results provide evidence that stock prices of oil and gas companies incorporate information about climate mitigation policies.…”