Momentum, Mean-Reversion, and Social Media: <i>Evidence from StockTwits and Twitter</i>

Agrawal, Shreyash; Azar, Pablo; Lo, Andrew W.; Singh, Taranjit

doi:10.3905/jpm.2018.44.7.085

Cited by 46 publications

(12 citation statements)

References 10 publications

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“…Lastly, research indicates that changes in Twitter sentiment lead to movements in share prices (Bollen et al , 2011). Thus, as share prices shift, market makers who rely on mean reversion are likely to remove liquidity by pulling out of the market, thereby triggering a self-reinforcing feedback loop where liquidity decreases further, leading to more significant price movements (Agrawal et al , 2018). Supporting the latter argument, Agrawal et al (2018) found that extreme sentiment movements lead to a higher demand for shares but a lower supply of liquidity.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, as share prices shift, market makers who rely on mean reversion are likely to remove liquidity by pulling out of the market, thereby triggering a self-reinforcing feedback loop where liquidity decreases further, leading to more significant price movements (Agrawal et al , 2018). Supporting the latter argument, Agrawal et al (2018) found that extreme sentiment movements lead to a higher demand for shares but a lower supply of liquidity. Therefore, shifts in Twitter sentiment may be leading to abnormal changes in the demand for shares, causing shifts in share prices and ultimately negatively affecting liquidity.…”

Section: Resultsmentioning

confidence: 99%

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Measuring the effect of investor sentiment on liquidity

Dunham¹,

García

2020

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PurposeThe purpose of this paper is to examine the effect of firm-level investor sentiment on a firm's share liquidity.Design/methodology/approachThe authors use Bloomberg's firm-level, daily investor sentiment scores derived from firm-level news and Twitter content in a regression model to explain the variability in a firm's share liquidity.FindingsThe results indicate that improvements (deterioration) in investor sentiment derived solely from Twitter content lead to a decrease (increase) in the average firm's share liquidity. Results, although not as strong, are opposite for investor sentiment derived solely from news articles: improvements (deterioration) in news sentiment leads to an increase (decrease) in the average firm's share liquidity.Research limitations/implicationsThe proxy for share liquidity is the bid-ask spread, which may be an imperfect measure of liquidity. The Amihud illiquidity measure was used as an alternative proxy and yield similar results. The results have important implications for investors in assessing the determinants of share liquidity.Practical implicationsThe sample period covers four years (2015–2018), which is determined by the availability of the Bloomberg sentiment data.Social implicationsInvestors increasing use of social media to express views on particular stocks and seek information that might be used in the investment decision-making process. The study links investor sentiment derived from social media (Twitter) to share liquidity.Originality/valueBy examining the relationship between a firm's sentiment and the firm's share liquidity, this paper advances the authors' understanding of the factors that drive a firm's share liquidity. To the authors' knowledge, this is the first study to link investor sentiment derived from firm-level news and Twitter content to a firm's share liquidity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Measuring the effect of investor sentiment on liquidity

Dunham¹,

García

2020

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“…Similarly, Sprenger et al (2014) derived good and bad news from tweets related to the S&P 500 and found that this news has an impact on the market. Agrawal et al (2018) showed that extreme sentiment corresponds to higher demand for and lower supply of liquidity. Furthermore, they showed that negative sentiment has a larger effect on the demand and supply of liquidity than positive sentiment.…”

Section: Introductionmentioning

confidence: 99%

#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic

French

2021

IJFS

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We investigated the differential impacts of a new Twitter-based Market Uncertainty index (TMU) and variables for Bitcoin before and during the COVID-19 pandemic. Results showed that TMU is a leading indicator of Bitcoin returns only during the pandemic, and the effect of the TMU on Bitcoin’s conditional volatility is significantly greater during the pandemic. Furthermore, during the pandemic, the uncertainty content of people’s tweets is impacted by the highly salient Bitcoin market. Taken together, our results suggest that the information contained in virtual communities such as Twitter have a much larger impact on cryptocurrency markets following COVID-19.

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“…available tweets contain valuable information that can be used to forecast stock market returns over and above the impact of asset pricing factors. Agrawal et al (2018) find that a sentiment index constructed based on social media, influences the supply and demand for liquidity in the stock market; they also find evidence supporting the capacity of social media to unveil insights above the respective insights of more traditional news feeds. By focusing on the global equity market, Beckers (2019) compares the predictive content of social media and traditional news to find that the information set delivered by the former is not different from the latter.…”

Section: Introductionmentioning

confidence: 68%

Does It Matter Where You Search? Twitter versus Traditional News Media

Milas¹,

Panagiotidis²,

Dergiades³

2021

J of Money Credit Banking

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We compare news in Twitter with traditional news outlets and emphasize their differential impact on Eurozone's sovereign bond market. We reveal a two-way information flow between Twitter's "Grexit" tweets and "Grexit" mentions in traditional news which suggests not only that both types of news serve as important empirical predictors for the sovereign bond market but also that the "old" (traditional news) and the "new" (Twitter) media are connected; however, the influence of Twitter on traditional news is stronger. Grexit tweets raise the Greek spread more than Grexit mentions in traditional news. Weak contagion effects are reported for Portugal and Ireland.

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Momentum, Mean-Reversion, and Social Media: Evidence from StockTwits and Twitter

Cited by 46 publications

References 10 publications

Measuring the effect of investor sentiment on liquidity

Measuring the effect of investor sentiment on liquidity

#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic

Does It Matter Where You Search? Twitter versus Traditional News Media

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