Social media bots and stock markets

Fan, Rui; Talavera, Oleksandr; Tran, Vu

doi:10.1111/eufm.12245

Cited by 49 publications

(27 citation statements)

References 58 publications

(75 reference statements)

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“…Leitch and Sherif (2017) constructed a Twitter sentiment score to forecast stock returns for the firms. Many recent researches have also established that the user generated content on the social media has a significant influence on the movements in the stock markets (Fan et al, 2020a(Fan et al, , 2020bvan Dieijen et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Social Media and the Stock Markets: An Emerging Market Perspective

Agarwal

Kumar

Goel

2021

Journal of Business Economics and Management

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There are numerous studies that examine the impact of social media on the stock market performance but there is a paucity of such evidences from the emerging economies. Today many multinational banks and other financial conglomerates from the developed countries are expanding their operations to the emerging markets, known for their rapid growth. The businesses in developed countries prefer using social media to reach out to their stakeholders. This might be a challenge as emerging markets are very different from the developed markets in terms of infrastructure and stock market development. This study performs the sentiment analysis of the tweets about the Indian companies that are a part of Nifty50 or any sectorial index, for a period of 15 months. The results from the Granger-causalty tests indicate that the Twitter sentiments have a significant relationship with the indices related to the banking and financial sectors of the Indian stock markets. Results from the Impulse Response Function reveal that, on the index returns, the impact of the negative sentiments stays for a longer period of time than the positive sentiments. This study would help businesses use social media effectively for information sharing and dissemination in the new environment.

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Section: Literature Reviewmentioning

confidence: 99%

Social Media and the Stock Markets: An Emerging Market Perspective

Agarwal

Kumar

Goel

2021

Journal of Business Economics and Management

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“…Discussion. From the distribution polygons, it can be seen that there is a relationship between: L2 = f (L1); L5 = f (L3) and L4 = f (G) per general secondary education per secondary school student, which shows direct regression with an increase per unit L1, L2 increases by 0.59 units [9][10][11][12]. From line number 2 in table.…”

Section: Resultsmentioning

confidence: 88%

Relationships that determine the quantitative block of financing in the scientific-informational and educational-production environment

Sivak¹,

Volkova

Pankratyeva

et al. 2021

E3S Web Conf.

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The efficiency of sowing and harvesting processes in agriculture is one of the most important components of the success of an agricultural firm. Natural and climatic conditions typical for the majority of Russian agricultural territories impose restrictions on the timing and the fundamental possibility of sowing and harvesting. Therefore, the low efficiency of sowing and harvesting processes leads to a corresponding decrease in the volume of crop production. A number of works [1-4] consider various approaches to increasing the efficiency of sowing and harvesting, however, a systematic approach to the management of agricultural company processes requires the identification of the most significant reasons for the decrease in the efficiency and addresses as a priority their elimination (or reduction in the strength of their influence). The paper analyzes the reasons for the inefficiencies of sowing and harvesting processes and provides recommendations for reducing the impact of these reasons.

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“…07), we examine L7, L9, and L10 for the correlability with each other. The results are presented in Table 02 (Dvouletý et al, 2019;Fan et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Indicators Of The Quality Block Of Innovation-Based Economy

Volkova¹

2022

European Proceedings of Social and Behavioural Sciences

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The given paper considers the quality block with its interrelations as a consequence of the quantity block which determines the financial component for all types of education, beginning from the secondary-level education to higher and vocational secondary education. On the one hand, there has been the share of the employed population with a particular set of education quality in total employment of the population. On the other hand, there has been the proportion of individuals with this quality of the total population in %. There are the polygons of indicators distribution. Using correlation and regression analysis and statistical data processing, we identified the number of consistent patterns that allowed a deeper analysis of the selected indicators. Based on the found dependences of education expenditures and employment in accordance with the specialty in the proportion of individuals with higher and vocational education, an interesting conclusion has been made. In our opinion, in due course, no one will work in his/her degree field. In the modern innovation-based economy, the explanation for this is that professionals in these specialties will not be in demand in seven years, due to the fact that the specialty field is becoming obsolete because of the production modernization, and therefore the state does not allocate funds for training specialists in this field.

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Social media bots and stock markets

Cited by 49 publications

References 58 publications

Social Media and the Stock Markets: An Emerging Market Perspective

Social Media and the Stock Markets: An Emerging Market Perspective

Relationships that determine the quantitative block of financing in the scientific-informational and educational-production environment

Indicators Of The Quality Block Of Innovation-Based Economy

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