Facebook's daily sentiment and international stock markets

Siganos, Antonios; Vagenas‐Nanos, Evangelos; Verwijmeren, Patrick

doi:10.1016/j.jebo.2014.06.004

Cited by 170 publications

(96 citation statements)

References 27 publications

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“…We instead use daily mood data from Facebook that are available for twenty international markets. Recent studies by Siganos et al (2013) and Karabulut (2013) have validated the Facebook index, showing that returns and mood are positively related, in line with other conventional sentiment indices. Our study is the first to use Facebook's data to focus on the Monday effect.…”

Section: Introductionsupporting

confidence: 65%

Does Mood Explain the Monday Effect?

Bakar

Siganos

Vagenas‐Nanos

2014

Journal of Forecasting

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A number of studies have explored the sources of the Monday effect, according to which returns are on average negative on Mondays. We contribute to the literature by exploring whether a direct measure of mood explains the Monday effect. In line with psychological literature, a greater proportion of investors is more pessimistic in the early days of the week, and become more optimistic as the week progresses. We use novel daily mood data from Facebook across twenty international markets to explore the impact of mood on the Monday anomaly. We find that the Monday effect disappears after controlling for mood. In line with our hypothesis that mood drives the Monday effect, we find that the Monday effect is more prominent within small capitalisation indices and within collectivist and high uncertainty avoidance countries. Investors could consider mood levels to forecast Mondays with more (less) pronounced negative returns.

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Section: Introductionsupporting

confidence: 65%

Does Mood Explain the Monday Effect?

Bakar

Siganos

Vagenas‐Nanos

2014

Journal of Forecasting

Self Cite

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“…Further, possible extensions of this study could employ other sets of emotional factors and proxies. For instance, recent studies use social media to capture mood and sentiment (Siganos, Vagenas-Nanos & Verwijmeren, 2014). …”

Section: Resultsmentioning

confidence: 99%

Sentiment, mood and outbound tourism demand

Dragouni

Filis

Gavriilidis

et al. 2016

Annals of Tourism Research

122

110

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We investigate spillover effects from sentiment and mood shocks on US outbound tourism demand from 1996 until 2013. We use the Index of Consumer Sentiment and Economic Policy Uncertainty Index as proxies for sentiment and the S&P500 as a proxy for mood. We find a moderate to high interrelationship among sentiment, mood and outbound tourism demand. More importantly, sentiment and mood indicators are net transmitters of spillover shocks to outbound tourism demand. The magnitude of spillover effects sourced by sentiment and mood is time-varying and depends on certain socio-economic and environmental events. Our results have important implications for policymakers and travel agents in their efforts to predict tourism arrivals from key origin countries and to plan their tourism strategy.

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“…The price-volume relations have received considerable attention in finance literature (Karpoff, 1987;Lamoureux andLastrapes, 1990 andWang, 1994 which is in line with existing literature on sentiment extracted from Facebook (Siganos et al, 2014). These findings suggest that the happier the sentiment, the higher the return, and vice-versa.…”

Section: Multivariable Linear Regressionsupporting

confidence: 66%

“…find that the retail investor's opinion extracted from Seeking Alpha can strongly predict future earnings surprise and stock returns. Siganos et al (2014) examine the relations between Facebook's Gross National Happiness Index and trading behavior over 20 international stock markets and they find negative sentiment is related to increased volatility and trading volume. With the method of computational linguistics, Sprenger et al (2014) also extract the sentiment from Twitter with about 250,000 relevant tweets and find this tweet sentiment is closely related to trading volume and stock returns.…”

Section: Machine Learning Text Mining and Linguistic Classificationmentioning

confidence: 99%

“…This category includes sentiment extracted from the Raging Bull (Tumarkin and Whitelaw, 2001), Yahoo! Finance (Antweiler and Frank, 2004;Chen, 2007 andKim andKim, 2014), Wall Street Journal (Tetlock, 2007), Twitter (Bollen et al, 2011), Google Insights (Joseph et al, 2011), Facebook (Siganos et al, 2014) and Google Trends (Da et al, 2015). These studies usually construct a time series proxy for investor sentiment and examine the contemporaneous and lead-lag relations between the constructed sentiment proxy and market variables (short-term returns, abnormal trading volume and excess volatility) as well as give mixed findings on the predictability and correlation coefficient.…”

Section: Introductionmentioning

confidence: 99%

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The role of internet in stock market

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Facebook's daily sentiment and international stock markets

Cited by 170 publications

References 27 publications

Does Mood Explain the Monday Effect?

Does Mood Explain the Monday Effect?

Sentiment, mood and outbound tourism demand

The role of internet in stock market

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