Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

The economic policy uncertainty index for Flanders, Wallonia and Belgium

This research note describes the construction of news-based Economic Policy Uncertainty (EPU) indices for Flanders, Wallonia and Belgium. The indices are computed from January 2001 until May 2020. Important domestic and more global events coincide with spikes in the indices. The COVID-19 pandemic represents the highest point, reflecting very strong consecutive Belgian newspaper attention to economic policy uncertainty. The monthly values of the EPU indices for Flanders, Wallonia and Belgium are published on www.policyuncertainty.com

The R package sentometrics to compute, aggregate and predict with textual sentiment

We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index

Semi-supervised text mining for monitoring the news about the ESG performance of companies

We present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening

Semi-supervised text mining for monitoring the news about the ESG performance of companies

We present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening

Overall survival in the OlympiA phase III trial of adjuvant olaparib in patients with germline pathogenic variants in BRCA1/2 and high-risk, early breast cancer

International audienc