News headlines transforming into economic graphs

Can News Headlines Predict the Economy? How Textual Data Is Changing Tail Risk Forecasting

Mira Elwood in Business & Economy August 2025 • 4 min read.

"Uncover the hidden signals in news articles and how they're reshaping macroeconomic forecasts in real-time, offering a new edge in predicting economic tail risks."

In times of economic uncertainty, like the Global Financial Crisis and the COVID-19 pandemic, accurately predicting tail risks becomes essential. Macroeconomic forecasts need to be reliable so that policymakers and central banks can get a better grasp on when the economy is heading for a period of high economic risk. Quantile predictions, which offer a detailed view of potential outcomes, are increasingly becoming important.

Recent work in macroeconomic forecasting is using textual data, analyzing news articles and reports to find important economic signals. Textual data can provide more timely information. Researchers use this data to understand the narratives shaping economic events, since narratives can influence economic outcomes. Quantifying these narratives is a valuable task.

A recent study analyzes whether textual data adds value to macroeconomic quantile predictions. The study uses a data-driven method to analyze news articles along with economic indicators, providing monthly tail risk forecasts for employment, industrial production, inflation, and consumer sentiment. It uses a range of quantiles to assess the benefits of text-based predictors compared to traditional methods.

Decoding the News: How Textual Data Enhances Economic Forecasting

News headlines transforming into economic graphs

The research uses news-based data along with FRED-MD economic indicators to make quantile predictions for several factors, such as employment and consumer sentiment. The results show that news data contains valuable information not found in standard economic indicators. By using this information, forecasters can improve tail risk predictions.

The study uses the Correlated Topic Model (CTM) to analyze about 800,000 newspaper articles from The New York Times and The Washington Post, extracting numerical text-based predictors. The CTM identifies word clusters, or topics, and their proportions, indicating media coverage. The analysis also uses tone-adjusted topic proportions to determine whether the tone of a topic is positive or negative.

Macroeconomic Predictors: Uses FRED-MD database.
Unadjusted Text-Based Predictors: Incorporates raw topic proportions from news articles.
Tone-Adjusted Text-Based Predictors: Combines topic proportions with sentiment analysis to gauge positive or negative tones.

To prevent overfitting, the study uses Bayesian quantile regressions (QRs) with shrinkage priors. These methods are effective for forecasting in high-dimensional settings. The study also uses non-linear models, including Gaussian Process Regressions and QR Forests, to capture complex predictive relationships. These methods help to evaluate the empirical differences between linear and non-linear models.

The Future of Forecasting: Integrating News and Economic Data

The study's findings suggest that combining textual data with economic indicators improves tail risk forecasts, particularly in extreme economic situations. Adding tone-adjusted text-based predictors enhances forecast accuracy compared to using unadjusted predictors alone. Non-linear models capture predictive relationships better than linear models. By using textual data and advanced analytical methods, forecasters can gain valuable insights for predicting economic tail risks.

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This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2302.13999,

Title: Forecasting Macroeconomic Tail Risk In Real Time: Do Textual Data Add Value?

Subject: econ.em

Authors: Philipp Adämmer, Jan Prüser, Rainer Schüssler

Published: 27-02-2023

Everything You Need To Know

How can news headlines be utilized to forecast economic downturns?

News headlines can be analyzed to forecast economic downturns by extracting valuable signals from textual data. Researchers leverage news articles and reports to identify important economic signals. The process involves using a data-driven method to analyze news articles along with established economic indicators. This approach provides monthly tail risk forecasts for key areas like employment, industrial production, inflation, and consumer sentiment. By incorporating this textual data, forecasters aim to gain timely insights into potential economic risks, enhancing the accuracy of predictions compared to relying solely on traditional economic indicators like those from the FRED-MD database.

What specific types of textual data are used to enhance macroeconomic forecasts?

The study uses several types of textual data to enhance macroeconomic forecasts. The primary source is news articles from The New York Times and The Washington Post, comprising about 800,000 articles. This data is processed using the Correlated Topic Model (CTM) to extract numerical text-based predictors. These predictors include raw topic proportions, representing the frequency of certain word clusters or topics within the news articles. Additionally, tone-adjusted text-based predictors are used. These combine topic proportions with sentiment analysis to gauge whether the tone of a topic is positive or negative, adding another layer of insight to the analysis.

What is the role of the Correlated Topic Model (CTM) in analyzing news articles for economic forecasting?

The Correlated Topic Model (CTM) plays a crucial role in analyzing news articles for economic forecasting. The CTM identifies word clusters, known as topics, within the vast amount of textual data from news articles. It determines the proportions of these topics, indicating how frequently different themes are covered in the media. This process allows researchers to quantify the narratives shaping economic events. By understanding these narratives, forecasters can identify hidden signals relevant to economic trends. CTM is essential for extracting numerical data from text, which can then be used in conjunction with economic indicators to improve the accuracy of economic forecasts.

How do the different types of text-based predictors (unadjusted vs. tone-adjusted) impact the accuracy of economic forecasts?

The study highlights that tone-adjusted text-based predictors enhance forecast accuracy compared to using unadjusted predictors alone. Unadjusted text-based predictors incorporate raw topic proportions from news articles, offering a basic view of media coverage. However, by combining topic proportions with sentiment analysis, tone-adjusted predictors offer a more nuanced understanding. This adjustment allows forecasters to assess the positive or negative tone associated with a topic, providing additional context that can be crucial in predicting economic tail risks. The added information from sentiment analysis refines the forecasts, making them more sensitive to market sentiment and economic events.

What analytical methods are employed to process textual data and economic indicators for forecasting tail risks, and why are they chosen?

The study utilizes several advanced analytical methods to process textual data and economic indicators for forecasting tail risks. Bayesian quantile regressions (QRs) with shrinkage priors are used to prevent overfitting, which is a risk when dealing with high-dimensional data. Non-linear models, including Gaussian Process Regressions and QR Forests, are employed to capture complex predictive relationships that linear models might miss. These methods are chosen for their ability to handle the complexity of the data and provide a detailed view of potential economic outcomes, which is particularly valuable for predicting tail risks. This detailed view is provided by a range of quantiles, allowing for a comprehensive assessment of the potential economic scenarios.