Social media big data and capital markets—An overview

The behavioral finance literature has found that investor sentiment has predictive ability for equity returns. This differs from standard finance theory, which provides no role for investor sentiment. We examine the relationship between investor sentiment and stock returns by employing textual analysis on social media posts. We find that our investor sentiment measure has a positive and significant effect on abnormal stock returns. These findings are consistent across a number of different models and specifications, providing further evidence against non-behavioral theories. JEL-Classification: G12, G13, G14

show abstract

“…See, for example, Wurgler (2006, 2007) 2 SeeNardo et al (2016),Bukovina (2016) andZhou (2018) for a review of the recent literature.…”

mentioning

confidence: 99%

Stock returns and investor sentiment: textual analysis and social media

2019

View full text Add to dashboard Cite

show abstract

“…Compared to traditional data-processing techniques, BDA are capable of reducing both computational times (thanks to cloud computing, machine learning and AI) and researchers’ biases regarding which parameters have to be considered to improve models (Fosso Wamba et al 2015 ). Indeed, several finance and marketing researches have demonstrated that BDA can be effectively used to develop accurate predictive models that can represent excellent support for financial decision making (Bukovina 2016 ) and default prediction modelling (Alaka et al 2018 ).…”

Section: Sme Default Prediction: a Research Agendamentioning

confidence: 99%

Rethinking SME default prediction: a systematic literature review and future perspectives

et al. 2021

View full text Add to dashboard Cite

Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

show abstract

“…Many other researchers perused several social big data papers such as ( Bukovina, 2016 ) by reviewing technical analysis of social media to examine the behavior of capital markets, ( Martin and Schuurman, 2019 ) by surveying social media data for qualitative geographic analysis, ( Arnaboldi et al, 2017 ) by surveying the relationship between social big data analysis and the accounting function, ( Bello-Orgaz et al, 2016 ) by reviewing the big data analytic algorithms in social media and their applications, ( Peng et al, 2016 ) by conducting a survey to explore the architecture of influence analysis in social big data, and ( Guellil and Boukhalfa, 2015 , Gole and Tidke, 2015 , Paul et al, 2017 ) by surveying big data mining in social media.…”

Section: Related Work and Motivationmentioning

confidence: 99%