The value of news for economic developments

Larsen, Vegard H.; Thorsrud, Leif Anders

doi:10.1016/j.jeconom.2018.11.013

Cited by 102 publications

(99 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make the textual data applicable for time series analysis, we proceed in three steps illustrated in Figure 1. Technically, these are the same data processing steps as proposed in Larsen and Thorsrud (2018). We provide a summary of the computations below.…”

Section: Datamentioning

confidence: 99%

“…We subjectively chose K = 80 for two reasons. First, this was the choice showing the best statistical results in Larsen and Thorsrud (2018) and Thorsrud (2016b,a). Second, we have experimented with estimating both fewer and more topics.…”

Section: Topic Extractionmentioning

confidence: 99%

“…for the Norwegian economy in Larsen and Thorsrud (2018). In that study, using a very different approach, news topics labeled Funding, Stock market, and IT/startup are among the most influential.…”

Section: News or Noise?mentioning

confidence: 99%

“…For uncovering the narratives relevant for economic fluctuations, the topic modeling approach offers a conceptual advantage over other often applied textual data techniques because it provides interpretable output in a highly automated fashion. 3 Lastly, on the methodological side, we draw on recent advances presented in Larsen and Thorsrud (2018) for constructing time series measures of text, and the model proposed in Thorsrud (2016b,a) for constructing the coincident indexes. Both of these studies explore the relationship between news and economic fluctuations in Norway.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Business Cycle Narratives

Larsen¹,

Thorsrud

2018

SSRN Journal

View full text Add to dashboard Cite

This article quantifies the epidemiology of media narratives relevant to business cycles in the US, Japan, and Europe (euro area). We do so by first constructing daily business cycle indexes computed on the basis of the news topics the media writes about. At a broad level, the most influential news narratives are shown to be associated with general macroeconomic developments, finance, and (geo-)politics. However, a large set of narratives contributes to our index estimates across time, especially in times of expansion. In times of trouble, narratives associated with economic fluctuations become more sparse. Likewise, we show that narratives do go viral, but mostly so when growth is low. While narratives interact in complicated ways, we document that some are clearly associated with economic fundamentals. Other narratives, on the other hand, show no such relationship, and are likely better explained by classical work capturing the market's animal spirits.

show abstract

Section: Datamentioning

confidence: 99%

Section: Topic Extractionmentioning

confidence: 99%

Section: News or Noise?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Business Cycle Narratives

Larsen¹,

Thorsrud

2018

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…news on the business cycle [17]. Scientific discourse modeling is also within the scope of topic models.…”

Section: Fundingmentioning

confidence: 99%

Measuring the diffusion of innovations with paragraph vector topic models

Lenz

Winker

2020

PLoS ONE

View full text Add to dashboard Cite

Measuring the diffusion of innovations from textual data sources besides patent data has not been studied extensively. However, early and accurate indicators of innovation and the recognition of trends in innovation are mandatory to successfully promote economic growth through technological progress via evidence-based policy making. In this study, we propose Paragraph Vector Topic Model (PVTM) and apply it to technology-related news articles to analyze innovation-related topics over time and gain insights regarding their diffusion process. PVTM represents documents in a semantic space, which has been shown to capture latent variables of the underlying documents, e.g., the latent topics. Clusters of documents in the semantic space can then be interpreted and transformed into meaningful topics by means of Gaussian mixture modeling. In using PVTM, we identify innovation-related topics from 170, 000 technology news articles published over a span of 20 years and gather insights about their diffusion state by measuring the topic importance in the corpus over time. Our results suggest that PVTM is a credible alternative to widely used topic models for the discovery of latent topics in (technology-related) news articles. An examination of three exemplary topics shows that innovation diffusion could be assessed using topic importance measures derived from PVTM. Thereby, we find that PVTM diffusion indicators for certain topics are Granger causal to Google Trend indices with matching search terms.

show abstract

Forecasting financial markets with semantic network analysis in the COVID‐19 crisis

Colladon

Grassi

Ravazzolo

et al. 2022

Journal of Forecasting

View full text Add to dashboard Cite

This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic‐related keywords appearing in the text. The index assesses the importance of the economic‐related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID‐19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

show abstract

The value of news for economic developments

Cited by 102 publications

References 26 publications

Business Cycle Narratives

Business Cycle Narratives

Measuring the diffusion of innovations with paragraph vector topic models

Forecasting financial markets with semantic network analysis in the COVID‐19 crisis

Contact Info

Product

Resources

About