The Interaction Between Microblog Sentiment and Stock Returns: An Empirical Examination

Deng, Shuyuan; Huang, Zhijian; Sinha, Atish P.; Zhao, Huimin

doi:10.25300/misq/2018/14268

Cited by 114 publications

(84 citation statements)

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“…Although the analysis is exploratory, we find a reasonable similarity in patterns between upbeat sentiments on tweets and a bullish market trend. This is consistent with the previous research on the effect of user sentiment on Sock-Twits on the stock returns (Deng, Huang, Sinha, & Zhao, 2018). However, it is too soon to conclude whether positive sentiments or negative sentiments predict cryptocurrency market prices.…”

Section: Discussionsupporting

confidence: 90%

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

Park

Lee

2019

Drustvena istrazivanja

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Cryptocurrencies have embraced Twitter as a major channel of communication. Employing social network analysis and sentiment analysis, this study investigates the Twitter-mediated communication behaviors among cryptocurrencies. This study determines whether a significant association exists between cryptocurrencies' Twitter networks and their credit scores. Data were drawn from the Twitter pages of several top cryptocurrencies. The results indicate that reply-mention networks had the densest structure, that the following-follower network structure was correlated with the reply-mention structure, and that the reply-mention and co-tweet networks were positively correlated. The results also indicate that cryptocurrencies' active networking strategies affected their credit scores and more importantly, that cryptocurrencies frequently linked with fellow currencies tended to have high credit scores.

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Section: Discussionsupporting

confidence: 90%

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

Park

Lee

2019

Drustvena istrazivanja

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“…First, by identifying social media-driven innovation as an important new IT-enabled capability and unpacking the actions, interactions, and decisions of individuals that underlie it, we contribute to the literature on IT capabilities (Kim et al, 2011;Rai & Tang, 2010;Saldanha et al, 2017), with a specific focus on social media as one such capability. Prior research has shown that consumer conversations on social media can have a rapid and substantial impact on organizational outcomes such as sales and stock valuations (Deng et al, 2018;Luo & Zhang, 2013;Oh et al, 2017). Research has also shown that the capability to engage with consumers via social media can positively influence organizational outcomes (Candi et al, 2018;Lehrer, Wieneke, Vom Brocke, Jung, & Seidel, 2018).…”

Section: Contributions and Implicationsmentioning

confidence: 99%

“…These conversations have substantial implications for organizations. For example, they can influence organizations' product sales (Dewan & Ramaprasad, 2014;Oh, Roumani, Nwankpa, & Hu, 2017) and even stock market valuations (Deng, Huang, Sinha, & Zhao, 2018;Luo & Zhang, 2013). An increasing number of organizations thus try to tap into consumer conversations on social media for innovation purposes (Roberts et al, 2016;Tan & Zhan, 2017).…”

Section: Introductionmentioning

confidence: 99%

Unpacking the social media–driven innovation capability: How consumer conversations turn into organizational innovations

Patroni¹,

Briel

Recker

2022

Information & Management

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Given the scale and scope of consumer conversations on social media, it is both possible and challenging for organizations to identify valuable ideas from those conversations that could lead to successful innovations. We examine how a large retailer developed a new capability to leverage public social media conversations for innovation through a process of filtering, assessing, converting, and deploying. By inductively developing a process model of how the retailer managed to convert consumer conversations on social media into organizational innovation, our study sheds light on the microfoundations of this important capability.

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“…Additionally, Deng et al (2018) and Li, Dalen and Rees (2018) analyze sentiment within big data. The former authors show the influence of microblog sentiment on stock returns, while the latter verify that stock microblog features serve as proxies for market sentiment.…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Big data analytics: achievements, challenges, and research trends

Henriques

Meirelles

Cunha

2020

Ind. Jour. Manag. & Prod.

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Big data applications combined with analytical tools foster prediction techniques that impact societal, economic, and political changes. After almost a decade of studies, this paper proposes to identify major debates on big data analytics, presenting its evolution over the past years and identifying its research tendencies. We limited our research to the top eight journals in information systems. Our findings suggest that big data analytics is apparently reaching a plateau, which might be confirmed by publications in the following years. The paper contributes to the current debate on big data by identifying ongoing studies in the research community. In addition, it provides a critical analysis of the field development, from its perceived benefits to its unimagined consequences. Finally, we conclude that other perspectives on big data analytics might include a new wave of studies and that new paths beyond productivity gains can be explored.

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The Interaction Between Microblog Sentiment and Stock Returns: An Empirical Examination

Cited by 114 publications

References 0 publications

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

Unpacking the social media–driven innovation capability: How consumer conversations turn into organizational innovations

Big data analytics: achievements, challenges, and research trends

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