Emotions in macroeconomic news and their impact on the European bond market

Consoli, Sergio; Pezzoli, Luca Tiozzo; Tosetti, Elisa

doi:10.1016/j.jimonfin.2021.102472

Cited by 18 publications

(4 citation statements)

References 42 publications

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“…14 The linkage between the SMoEI and the Spanish market does not seem surprising. Studying the effect of news and emotions on Italian and Spanish government bond yields, Consoli et al ( 2021 ) recently confirmed that news plays an important role in determining the related changes, stressing an interesting aspect: Italy is mainly concerned about national economic context, while Spain mainly looks at international events. According to the authors, there seems to emerge an emotive spillover effect: emotions generated by the Italian political turmoil propagate to the Spanish news affecting the neighbourhood market.…”

Section: Resultsmentioning

confidence: 99%

“…According to Gotthelf and Uhl ( 2018 ), the sentiment of newspaper articles explains and forecasts changes in the term structure of US government bonds, since investor decision-making is becoming—due to growing uncertainty—increasingly dependent on news and sentiment. Exploiting an open-source database known as Global Database of Events, Language and Tone, Consoli et al ( 2021 ) show that negative emotions mined from news significantly enhance the predictive power of government yield models.…”

Section: Social Mood and Financial Markets: Setting The Issuementioning

confidence: 99%

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The Impact of the Social Mood on the Italian Sovereign Debt Market: A Twitter Perspective

Carnazza

2023

Ital Econ J

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By analysing the relationship between a new experimental daily index based on Twitter data (the Istat’s Social Mood on Economy Index—SMoEI) and the structure of Italian (and Spanish) sovereign interest rates, our work sheds new light on the great significance of the interconnections between economic sentiment and the Italian sovereign bond market. A placebo test performed on Spain introduces a possible extension of this linkage to the European market, highlighting the deep integration of financial markets within the European Monetary Union. Within a VAR and VECM framework with daily frequency data (2016–2022), we show that public shaping mechanisms play a role in the cost of debt financing. Our analysis emphasises the importance of economic sentiment when it comes to financial markets, putting the role of macroeconomic fundamentals in a different light. This result should be interpreted with caution, as updates to fundamentals can be affected by a time lag. In any case, recognising the importance of this index has at least two implications: on the one side, the SMoEI could represent a more responsive indicator for predicting investor sentiment; on the other side, media channels—as well as the European and national institutions—should gain relevance for their potential impact on collective sentiment, encouraging the importance of economic education and non-alarmist communication.

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Section: Resultsmentioning

confidence: 99%

Section: Social Mood and Financial Markets: Setting The Issuementioning

confidence: 99%

The Impact of the Social Mood on the Italian Sovereign Debt Market: A Twitter Perspective

Carnazza

2023

Ital Econ J

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“…The variables about the aviation sector, namely the number of passengers and average revenues of Iacus et al (2020), and the air quality indicators become available in 2010 and 2013, respectively. In 2015, AirB&B review figures started alongside the hundreds of sentiment and media attention related measures extracted from the GDELT database (Consoli et al, 2021). Finally, in 2020 COVID-19 indicators entered the data set together with mobility indicators of Santamaria et al (2020).…”

Section: The Information Setmentioning

confidence: 99%

Testing Big Data in a Big Crisis: Nowcasting under COVID-19

et al. 2022

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“…Numerous studies on news articles have been conducted in the past, though without using any algorithms ((Tetlock, 2007), (Baker et al, 2015)) or designing algorithms dependent on sentiment lexicons. For example, (Consoli et al, 2021) use the Loughran McDonald dictionary (Loughran and Mcdonald, 2011) to extract negative emotion from financial newspapers as explainable variable to predict sovereign debt spread. Thanks to recent progress in natural language understanding, it becomes possible to monitor economic narratives and opinions expressed in written texts.…”

Section: Introductionmentioning

confidence: 99%

A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives

Hu¹,

Paroubek²

2021

Proceedings of the Third Workshop on Economics and Natural Language Processing

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In this paper about aspect-based sentiment analysis (ABSA), we present the first version of a fine-grained annotated corpus for targetbased opinion analysis (TBOA) to analyze economic activities or financial markets. We have annotated, at an intra-sentential level, a corpus of sentences extracted from documents representative of financial analysts' most-read materials by considering how financial actors communicate about the evolution of event trends and analyze related publications (news, official communications, etc.). Since we focus on identifying the expressions of opinions related to the economy and financial markets, we annotated the sentences that contain at least one subjective expression about a domain-specific term. Candidate sentences for annotations were randomly chosen from texts of specialized press and professional information channels over a period ranging from 1986 to 2021.Our annotation scheme relies on various linguistic markers like domain-specific vocabulary, syntactic structures, and rhetorical relations to explicitly describe the author's subjective stance. We investigated and evaluated the recourse to automatic pre-annotation with existing natural language processing technologies to alleviate the annotation workload. Our aim is to propose a corpus usable on the one hand as training material for the automatic detection of the opinions expressed on an extensive range of domain-specific aspects and on the other hand as a gold standard for evaluation TBOA.In this paper, we present our pre-annotation models and evaluations of their performance, introduce our annotation scheme and report on the main characteristics of our corpus.

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Emotions in macroeconomic news and their impact on the European bond market

Cited by 18 publications

References 42 publications

The Impact of the Social Mood on the Italian Sovereign Debt Market: A Twitter Perspective

The Impact of the Social Mood on the Italian Sovereign Debt Market: A Twitter Perspective

Testing Big Data in a Big Crisis: Nowcasting under COVID-19

A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives

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