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Czasopismo
2017 | 13 | nr 4 | 1--13
Tytuł artykułu

Professionals in the Big Data and Social Media Era : the Empirical Evidence from Poland

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
To meet general objectives of the article, i.e. to check the extent to which the information needs of financial market institutions are satisfied, and to learn about whether there is a transition in this realm triggered by the advent of social media and big data, we surveyed a sample of 415 financial market professionals working in Poland. We also used logit regression models, through which we processed the survey results, to identify which factors are responsible for meeting the needs. We showed that although the information needs of financial market professionals are met to a large degree, still some potential for improvement remains in this regard. We found also that respondent-specific traits are insignificant in explaining the degree of satisfaction with data and information that is used by financial market professionals. Out of firm-specific characteristic and, the value of assets under the institution's management turned out to be the key factors explaining the distribution of responses concerning satisfaction. (original abstract)
Czasopismo
Rocznik
Tom
13
Numer
Strony
1--13
Opis fizyczny
Twórcy
  • University of Economics and Innovation in Lublin, Poland
  • University of Economics and Innovation in Lublin
autor
  • University of Information Technology and Management in Rzeszów
  • CDM Pekao SA Securities
Bibliografia
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  • Chen, H., De, P., Hu, Y., Hwang, B. (2013). Customers As Advisors: The Role of Social Media in Financial Markets. Retrieved from http://papers.ssrn.com/sol3/papers.cfm? abstract_id=1807265.
  • Chen, Y., Chen, H., Gorkhali, A., Lu, Y., Ma, Y., Li, L. (2016). Big Data Analytics and Big Data Science: A Survey. Journal of Management Analytics, 3(1), 1-42.
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  • Einav, L., Levin, J. (2014). The Data Revolution and Economic Analysis, NBER paper. Retrieved from http://web.stanford. edu/~jdlevin/Papers/BigData.pdf (referred on 21/09/2016).
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  • Heston, S.L., Sinha, N.R. (2014). News versus Sentiment: Comparing Textual Processing Approaches for Predicting Stock Returns. SSRN Electronic Journal, DOI: 10.2139/ssrn.2311310. Retrieved from http://finpko.faculty.ku.edu/myssi/ FIN938/Heston %20%26%20Sinha_News%20vs%20Sentiment_WP_2014.pdf.
  • Kahneman, D., Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-91.
  • Kandel, S., Paepcke, A., Hellerstein, J.M., Heer, J. (2012). Enterprise Data Analysis and Visualization: An Interview Study. IEEE Transactions on Visualization and Computer Graphics, 18(2), 2917- 2926.
  • Ko, S., Cho, I., Afzal, S., Yau, C., Chae, J., Malik, A., Beck, K., Jang, Y., Ribarsky, W., Ebert, D.S. (2016). A Survey On Visual Analysis Approaches for Financial Data. Computer Graphics Forum, 35(3), 599-617.
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  • PwC. (2013). Where Have You Been All My Life? How the Financial Services Industry Can Unlock the Value in Big Data. Retrieved from https://www.pwc.com/us/en/financial-services/publications/viewpoints/assets/pwc-unlocking-big-data- value.pdf (referred on 10/09/2016).
  • Schaefer, M., Zhang, L., Wanner, F., Schreck, T., Kahl, R., Keim, D.A. (2011, September). A Novel Explorative Visualization Tool for Financial Time Series Data Analysis. Proceedings from Third International UKVAC Workshop on Visual Analytics, University College London, UK. Retrieved from https://bib.dbvis.de/uploadedFiles/345.pdf (referred on 21/09/2016).
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Typ dokumentu
Bibliografia
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