Tweets, Google trends, and sovereign spreads in the GIIPS

Dergiades, Theologos; Milas, Costas; Panagiotidis, Theodore

doi:10.1093/oep/gpu046

Cited by 68 publications

(37 citation statements)

References 32 publications

(4 reference statements)

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“…(Stephens-Davidowitz, 2014). As discussed in Choi and Varian (2012) and Dergiades et al (2015), it is possible to use Google Trends to compute a time series index of the volumes of queries entered by users into Google in each given U.S. state. This search intensity index is based on query shares normalized between from 0 to 100.…”

Section: Evidence From Google Trendsmentioning

confidence: 99%

Pink work: Same-sex marriage, employment and discrimination

Sansone

2019

Journal of Public Economics

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This paper analyzes how the legalization of same-sex marriage in the U.S. affected gay and lesbian couples in the labor market. Results from a difference-in-difference model show that both partners in same-sex couples were more likely to be employed, to have a full-time contract, and to work longer hours in states that legalized same-sex marriage. In line with a theoretical search model of discrimination, suggestive empirical evidence supports the hypothesis that marriage equality led to an improvement in employment outcomes among gays and lesbians and lower occupational segregation thanks to a decrease in discrimination towards sexual minorities.

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Section: Evidence From Google Trendsmentioning

confidence: 99%

Pink work: Same-sex marriage, employment and discrimination

Sansone

2019

Journal of Public Economics

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“…Dergiades, Milas, and Panagiotidis () analysed the influence of Twitter, Facebook, and Google Trends on the fluctuation of European financial market variables. By using a multivariate system, they proved a significant effect in the short term mainly to the bond yield differential of the Greek–German state.…”

Section: Related Workmentioning

confidence: 99%

“…Preis, Moat, and Stanley (2013) suggested that the huge data collected from social media visualizing the interaction between Internet users can explain the behaviour of market movements. Dergiades, Milas, and Panagiotidis (2015) analysed the influence of Twitter, Facebook, and Google Trends on the fluctuation of European financial market variables. By using a multivariate system, they proved a significant effect in the short term mainly to the bond yield differential of the Greek-German state.…”

Section: Previous Studies On Online Investor Sentimentmentioning

confidence: 99%

Big data tools for Islamic financial analysis

Mnif

Jarboui

Hassan

et al. 2020

Intell Sys Acc Fin Mgmt

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Summary Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.

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“…Dergiades et al . () employ both Google and Twitter data to investigate their effect on the eurozone bond markets. Da et al .…”

Section: Related Literaturementioning

confidence: 99%

Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil

Bampinas

Panagiotidis

Rouska

2019

Scottish J Political Eco

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This study explores the relationship between Google search activity and the conditional volatility of oil and gold spot market returns. By aggregating the volume of queries related to the two commodity markets in the spirit of Da et al. (2015), we construct a weekly Searching Volume Index (SVI) for each market as proxy of households and investors information demand. We employ a rolling EGARCH framework to reveal how the significance of information demand has evolved through time. We find that higher information demand increases conditional volatility in gold and oil spot market returns. Information flows from Google SVI's reduce the proportion of the significant volatility asymmetry produced by negative shocks in both commodity markets. The latter is more profound in the gold market. *University of Macedonia 1 See for example http://www.gold.org/research/gold-demand-trends/gold-demand-trends-fullyear-2016 and https://www.bloomberg.com/view/articles/2016-09-16/the-problem-with-the-oil-etf-itdoes-a-great-job.

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Tweets, Google trends, and sovereign spreads in the GIIPS

Cited by 68 publications

References 32 publications

Pink work: Same-sex marriage, employment and discrimination

Pink work: Same-sex marriage, employment and discrimination

Big data tools for Islamic financial analysis

Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil

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