Nowcasting Euro Area GDP with News Sentiment: A Tale of Two Crises

Sáiz, Lorena; Ashwin, Julian; Kalamara, Eleni

doi:10.2139/ssrn.3971974

Cited by 6 publications

(4 citation statements)

References 41 publications

(44 reference statements)

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“…We further demonstrate the practical utility of FinEntity in investment and regulatory applications, extending the work of Ashwin et al (2021); Wong et al (2022). Collaborating with a regulatory agency, we apply the fine-tuned PLMs to a unique cryptocurrency news dataset.…”

Section: Introductionmentioning

confidence: 82%

FinEntity: Entity-level Sentiment Classification for Financial Texts

Tang,

Yang,

Huang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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In the financial domain, conducting entity-level sentiment analysis is crucial for accurately assessing the sentiment directed toward a specific financial entity. To our knowledge, no publicly available dataset currently exists for this purpose. In this work, we introduce an entity-level sentiment classification dataset, called FinEntity, that annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. We document the dataset construction process in the paper. Additionally, we benchmark several pre-trained models (BERT, FinBERT, etc.) and ChatGPT on entity-level sentiment classification. In a case study, we demonstrate the practical utility of using FinEntity in monitoring cryptocurrency markets. The data and code of FinEntity is available at https://github.com/yixuantt/ FinEntity. * The views and analysis expressed in this paper are those of the authors and do not necessarily represent the views of HKMA. All errors are the authors' own.1 https://www.wsj.com/articles/microsoft-and-googlewill-both-have-to-bear-ais-costs-11674006102

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Section: Introductionmentioning

confidence: 82%

FinEntity: Entity-level Sentiment Classification for Financial Texts

Tang,

Yang,

Huang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…This work joins an emerging literature that uses textual data from news to forecast economic variables with a focus, in particular, on the US (Ardia et al, 2019;Barbaglia et al, 2023;Ellingsen et al, 2022;Shapiro et al, 2022), the UK (Rambaccussing and Kwiatkowski, 2020;Kalamara et al, 2022), France (Bortoli et al, 2018), Germany (Feuerriegel and Gordon, 2019), Italy (Aprigliano et al, 2023), Norway (Larsen and Thorsrud, 2019), and Spain (Aguilar et al, 2021). There are very few studies in economics and finance that perform a textual analysis of news across several countries and languages (e.g., Baker et al, 2016;Ashwin et al, 2021). Although this adds considerable complications, in particular in the computational aspect, we believe that it offers the opportunity to validate the robustness of our findings across countries and to reveal country-specific characteristics of the relationship between news and macroeconomic variables.…”

Section: Introductionmentioning

confidence: 96%

Forecasting GDP in Europe with Textual Data

2021

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“…This work joins an emerging literature that uses textual data from news to forecast economic variables with a focus, in particular, on the United States (Ardia et al, 2019; Barbaglia et al, 2023; Ellingsen et al, 2022; Shapiro et al, 2022), the United Kingdom (Kalamara et al, 2022; Rambaccussing & Kwiatkowski, 2020), France (Bortoli et al, 2018), Germany (Feuerriegel & Gordon, 2019), Italy (Aprigliano et al, 2023), Norway (Larsen & Thorsrud, 2019), and Spain (Aguilar et al, 2021). There are very few studies in economics and finance that perform a textual analysis of news across several countries and languages (e.g., Ashwin et al, 2021; Baker et al, 2016). Although this adds considerable complications, in particular in the computational aspect, we believe that it offers the opportunity to validate the robustness of our findings across countries and to reveal country‐specific characteristics of the relationship between news and macroeconomic variables.…”

Section: Introductionmentioning

confidence: 99%

Forecasting GDP in Europe with textual data

Barbaglia,

Consoli,

Manzan

2024

J of Applied Econometrics

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SummaryWe 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 dataset includes over 27 million articles for 26 major newspapers in five 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.

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Nowcasting Euro Area GDP with News Sentiment: A Tale of Two Crises

Cited by 6 publications

References 41 publications

FinEntity: Entity-level Sentiment Classification for Financial Texts

FinEntity: Entity-level Sentiment Classification for Financial Texts

Forecasting GDP in Europe with Textual Data

Forecasting GDP in Europe with textual data

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