Synergy between stock prices and investor sentiment in social media

Liu, Qing; Lee, Woon-Seek; Huang, Minghao; Wu, Qingjun

doi:10.1016/j.bir.2022.09.006

Cited by 16 publications

(11 citation statements)

References 40 publications

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“…For the six companies I analyzed, none of them had a strong correlation between sentiment score and percent change in stock price, showing that tweets about sustainability practice doesn't necessarily sway stock prices. This goes against the findings by Liu et. al (2023) where they conclude that there is an overall weak but significant positive synergy(correlation) between stock prices and investor sentiment meaning that although both variables tend to go up in response to each other, the relationship is not very strong.…”

Section: Discussioncontrasting

confidence: 97%

Analyzing the Sentiment of Tweets Regarding Sustainability Practices to Predict Stock Performance

Chakkera,

Anderson,

White

et al. 2023

J Stud Res

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In recent years, sustainability has entered the mainstream consciousness of citizens, policy makers and companies across the globe. A key factor of sustainability in the investing world is ESG (environmental, social, governance) scores that demonstrate how sustainable the actions and decisions of a company are for employees, shareholders, and its ecosystem. In the United States, there has been a political divide in ESG values as investors seek to balance their drive to maximize shareholder value and promote positive change in society for all stakeholders. One platform that debate has occurred on is Twitter where users express their opinions on various companies. I analyzed the sentiment of users’ tweets on companies that have been regarded as sustainability leaders and companies that are laggards in this area. My analysis was completed through the Python coding language, a programming language designed for statistical computing and graphics. This data was then correlated with the stock performances of these respective companies during specific time frames of high Twitter activity. Ultimately, I sought to determine whether social media has a significant influence on the stock market and whether that will play a large part in driving sustainable change which will improve the environmental and social consciousness in the United States. Future research can identify what specific ideas or principles regarding sustainability are most widely discussed on Twitter and how this in turn relates to the decisions that companies make on key public policy issues.

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Section: Discussioncontrasting

confidence: 97%

Analyzing the Sentiment of Tweets Regarding Sustainability Practices to Predict Stock Performance

Chakkera,

Anderson,

White

et al. 2023

J Stud Res

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“…4 and 5 reveal that Twitter sentiment fails to adequately capture the Consumer Confidence Index (CCI) in Indonesia. This observation aligns with several studies demonstrating that while social media is pivotal in representing public opinion, it does not consistently exhibit a directly proportional impact on consumer confidence (Abbas et al, 2022;Li et al, 2022;Liu et al, 2023;Primananda et al, 2022). It is noteworthy, however, that sentiment originating from news accounts demonstrates a higher correlation compared to sentiment from other accounts.…”

Section: Resultssupporting

confidence: 90%

Tweeting the economy: Analyzing social media sentiments and macroeconomic indicators

Fitriani,

Amalyah Agus

2023

JIBE

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This research aims to examine the correlation between social media sentiment and the Consumer Confidence Index (CCI) as well as Gross Domestic Product (GDP) in Indonesia. Data were collected through web scraping from Twitter (now also known as X) spanning from 2019 to 2022 on a monthly basis. Using Pearson and Kendall’s Tau correlation tests, the study found that the correlation between Twitter sentiment and the CCI is not significant. However, there is a significant correlation between Twitter sentiment from news accounts and GDP The findings indicate that the views and perceptions expressed in social media sentiment, particularly from news accounts on Twitter, could serve as an initial indicator of Indonesia's GDP.

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“…Here are some future directions for SPF: + In addition to employing time series data for forecasting, it is important to explore and use alternative data sources for stock price prediction. Specifically, exploiting sentiments extracted from various platforms, such as social media and news outlets [91][92][93][94] can significantly enhance our ability to comprehend and accurately predict market movements.…”

Section: Future Directionsmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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Synergy between stock prices and investor sentiment in social media

Cited by 16 publications

References 40 publications

Analyzing the Sentiment of Tweets Regarding Sustainability Practices to Predict Stock Performance

Analyzing the Sentiment of Tweets Regarding Sustainability Practices to Predict Stock Performance

Tweeting the economy: Analyzing social media sentiments and macroeconomic indicators

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

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