The relationship between twitter and stock prices. Evidence from the US technology industry

Teti, Emanuele; Dallocchio, Maurizio; Aniasi, Alberto

doi:10.1016/j.techfore.2019.119747

Cited by 46 publications

(30 citation statements)

References 16 publications

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“…These results provide a more accurate view of the influence of investor sentiment and a greater understanding of stock performance in reaction to this sentiment, as [86] states that different trading strategies based on this index outperform the benchmark ETF index and [80] find a relationship between stock prices and their sentiment index when the company's coverage in social networks is extensive. In summary, as a consequence of the evidence found, Bloomberg investor sentiment index has a slight influence (causality) on idiosyncratic shocks, possibly due to the construction of the index itself, which includes the news published in an aggregated way instead of considering them individually, as in [38].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, some empirical works, which analyzed high frequency data (daily), consider the usual statistical properties of the series like heteroscedasticity [52], an issue we also consider. In a first stage we extract the idiosyncratic shocks of the daily performance, the daily variations of the log-volume and the log-average of Bloomberg's investor sentiment index, unlike [80].…”

Section: Econometric Model For Analyzing Causalitymentioning

confidence: 99%

“…In summary, based on the literature reviewed, we can group the empirical studies together by two fundamental characteristics: on the one hand, those papers that do not consider an asset valuation model to measure the relationship between investor sentiment and market returns of assets [12,17,28,37,79,80], versus those who do [7,9,11,62,64,66,67,81,82]; and on the other, studies that develop their own sentiment indexes [11,35,41,71,82] versus those using indexes developed by specialized investors or economic agents [9,36,64,[82][83][84].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Sánchez

Vega

2021

Mathematics

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A part of the financial literature has attempted to explain idiosyncratic asset shocks through investor behavior in response to company news and events. As a result, there has been an increase in the development of different investor sentiment measurements. This paper analyses whether the Bloomberg investor sentiment index has a causal relationship with the abnormal returns and volume shocks of major European Union (EU) financial companies through a sample of 85 financial institutions over 4 years (2014–2018) on a daily basis. The i.i.d. shocks are obtained from a factorial asset pricing model and ARMA-GARCH-type process; then we checked whether there is both individual and joint causality between the standardized residuals. The results show that the explanatory capacity of the shocks of the firm Bloomberg sentiment index is low, although there is empirical evidence that the effects correspond more to the situation of the financial subsector (banks, real estate, financial services and insurance) than to the company itself, with which we conclude that the sentiment index analyzed reflects a sectorial effect more than individual one.

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Section: Discussionmentioning

confidence: 99%

Section: Econometric Model For Analyzing Causalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Sánchez

Vega

2021

Mathematics

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“…Matta et al's [49] study proved that the investment professionals in Bitcoin use social media activity and information extracted by a web search and found it helpful. Investors search social media when making decisions since it is proved that sentiment analysis captures information not embedded in prices [50]. News and information extracted from online social media (blogs, Twitter feeds, etc.)…”

Section: Data and Criteriamentioning

confidence: 99%

Cryptocurrency Portfolio Selection—A Multicriteria Approach

2021

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This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.

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“…For example, in 2019, Elodie Michaux conducted research that identified a correlation between stock returns and tweets, though she was unable to find any sort of correlation regarding tweet count and stock trading volume proving that the stock prices can be affected by tweets. In addition, ScienceDirect, a worldrenowned science magazine, shares multiple accounts of research that produced similar results, indicating that Twitter coverage and positive mood in tweets are inevitably going to boost the stock prices [6,7]. Through the lens of such research in various search engines and repositories, it becomes apparent that an undeniable connection between the stock market and Twitter exists.…”

Section: Introductionmentioning

confidence: 99%

Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market

Kim¹,

Lee²,

Lee³

et al. 2021

IJDSA

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Over the past few years, Twitter has rapidly grown into a prominent social media platform, and various research papers have attempted to prove the relationship between the stocks and the tweets made on Twitter. The purpose of this research paper is to investigate the specific connection between Elon Musk's twitter and the stock value of Tesla. The primary form of analysis used was Exploratory Data Analysis to be able to more easily distinguish patterns within our dataset, which was preprocessed to exclude any stopwords. Utilizing various graphs and Machine Learning algorithms such as Logistic Regression and Support Vector Machine, we wrote this research paper that respectively analyzes the change in the close price of Tesla's stock and Elon Musk's Twitter engagement, including tweets, likes, and retweets dating from the start of 2015 up until July of 2020. Furthermore, the article illustrates the contents of Elon Musk's tweets and allows a deeper understanding of other correlations that may exist through the use of Machine Learning to perform Sentiment Analysis. This was achieved by categorizing Elon's tweets into three different tones (positive, negative, and neutral) and seeing how the underlying mood would correspondingly affect Tesla's stock value. The combination of such techniques and factors allowed for a conclusive result in which a distinct correlation was apparent: an increase in the number of tweets/engagement would lead to an increase in the closing price of Tesla, as well as vice versa.

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The relationship between twitter and stock prices. Evidence from the US technology industry

Cited by 46 publications

References 16 publications

Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Cryptocurrency Portfolio Selection—A Multicriteria Approach

Elon Musk’s Twitter and Its Correlation with Tesla’s Stock Market

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