Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty
Paul Trust, Ahmed Zahran, Rosane Minghim

TL;DR
This paper explores using weak supervision and machine learning to classify news articles for measuring economic policy uncertainty, providing a scalable alternative to manual methods and enabling better macroeconomic forecasting.
Contribution
It introduces a weak supervision approach for classifying news related to economic policy uncertainty, improving scalability and accuracy over previous keyword-based methods.
Findings
The weak supervision method effectively classifies news articles for EPU.
The generated EPU index predicts macroeconomic performance.
The approach reduces reliance on manual annotation.
Abstract
The need for timely data analysis for economic decisions has prompted most economists and policy makers to search for non-traditional supplementary sources of data. In that context, text data is being explored to enrich traditional data sources because it is easy to collect and highly abundant. Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU). Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals. Quantifying EPU is of great importance to policy makers, economists, and investors since it influences their expectations about the future economic fundamentals with an impact on their policy, investment and saving decisions. Most of the previous work using news articles for measuring EPU are either…
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Taxonomy
TopicsMarket Dynamics and Volatility · Monetary Policy and Economic Impact · Stock Market Forecasting Methods
