Forecasting GDP in Europe with Textual Data

Luca Barbaglia, Sergio Consoli, Sebastiano Manzan
1/14/2024
cs.CEcs.AIcs.CL

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

We evaluate the informational content of news-based sentiment indicators for forecasting Gross Domestic Product (GDP) and other macroeconomic variables of the five major European economies. Our data set includes over 27 million articles for 26 major newspapers in 5 different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast macroeconomic variables and their predictive content is robust to controlling for other indicators available to forecasters in real-time.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty

80% match2022

Wage Sentiment Indices Derived from Survey Comments via Large Language Models

79% match2025

Effects of Daily News Sentiment on Stock Price Forecasting

78% match2023

Enhancing Inflation Nowcasting with LLM: Sentiment Analysis on News

78% match1 repo42024

Not All News Is Equal: Topic- and Event-Conditional Sentiment from Finetuned LLMs for Aluminum Price Forecasting

78% match2026

On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?

77% match2023

Cite this paper

@article{barbaglia2024forecasting,
  title  = {Forecasting GDP in Europe with Textual Data},
  author = {Luca Barbaglia and Sergio Consoli and Sebastiano Manzan},
  year   = {2024},
  eprint = {2401.07179},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.07179v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.07179.svg)](https://codeofpaper.com/paper/2401.07179)

Discussion