Skip to main content
Log in

How to measure the quality of financial tweets

  • Published:
Quality & Quantity Aims and scope Submit manuscript

Abstract

Twitter text data may be very useful to evaluate from a different perspective financial tangibles, such as share prices, as well as intangible assets, such as company reputation. While twitter data are becoming widely available to researchers, methods aimed at selecting reliable twitter data are, to our knowledge, not yet available. To overcome this problem, and allow to employ twitter data for descriptive and predictive purposes, in this contribution we propose an effective statistical method that formalises and extends a quality index employed in the context of the evaluation of academic research, the h index, renamed T index. Our proposal will be tested on a list of twitterers described by the Financial Times as “the top financial tweeters to follow”, for the year 2013. Using our methodology we rank these twitterers and provide confidence intervals to decide whether they are significantly different. Moreover through a sentiment analysis, we employ the twitters content to estimate graphical models useful in the context of financial systemic risk. To this aim we focus on the Italian bank system and we show how listed banks are connected on the basis of tweets data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Beirlant, J., Einmahl, J.H.J.: Asymptotics for the Hirsch Index. Scand. J. Stat. 37, 355–364 (2010)

    Article  Google Scholar 

  • Ball, P.: Index aims for fair ranking of scientists. Nature 436, 900 (2005)

    Article  Google Scholar 

  • Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  • Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., Weber, I.: Web search queries can predict stock market volumes. PloS one 7(7), e40014 (2012)

    Article  Google Scholar 

  • Burrell, Q.L.: Hirsch’s h-index: a stochastic model. J. Informetr. 1, 16–25 (2007)

    Article  Google Scholar 

  • Cerchiello, P., Giudici, P.: On the distribution of functionals of discrete ordinal variables. Statist. Probab. Lett. 82, 2044–2049 (2012)

  • Choi, H., Varian, H.: Predicting the present with google trends. Econ. Rec. 88(s1), 2–9 (2012)

    Article  Google Scholar 

  • Cruz, M.G.: Modeling, measuring and hedging operational risk. Wiley, Chichester (2002)

    Google Scholar 

  • Conway, R.W., Maxwell, W.L.: A queuing model with state dependent service rates. J. Ind. Eng. 12, 132–136 (1962)

    Google Scholar 

  • Dalla Valle, L., Giudici, P.: A Bayesian approach to estimate the marginal loss distributions in operational risk management. Comput. Stat. Data Anal. 52, 3107–3127 (2008)

    Article  Google Scholar 

  • Evert S.: A simple LNRE model for random character sequences. In: Proceedings of the 7 Journes Internationales d’Analyse Statistique des Donnes Textuelles (JADT 2004), Louvain-la-Neuve pp. 411–422 (2004)

  • Glanzel, W.: On the h-index—a mathematical approach to a new measure of publication activity and citation impact. Scientometrics 67, 315–321 (2006)

    Article  Google Scholar 

  • Hirsch, J.E.: An index to quantify an individual’s scientific research output. In Proceedings of the National Academy of Sciences of the United States of America pp. 16569–1657 (2005)

  • Kennet, R., Raanan, Y.: Operational Risk Management: a pracical approach to intelligent data analysis. Wiley, Chichester (2011)

    Google Scholar 

  • Lauritzen, S.L.: Graphical models. Oxford University Press (1996)

  • Iglesias, J.E., Pecharroman, C.: Scaling the h-index for different scientific ISI fields. Scientometrics 73, 303–320 (2007)

    Article  Google Scholar 

  • Izsak, F.: Maximum likelihood estimation for constrained parameters of multinomial distributions —application to Zipf–Mandelbrot models. Comput. Stat. Data Anal. 51, 1575–1583 (2006)

    Article  Google Scholar 

  • King, D., Ramirez-Cano, D., Greaves, F., Vlaev, I., Beales, S., Darzi, A.: Twitter and the health reforms in the English national health service. Health policy 110, 2–3 (2013)

    Article  Google Scholar 

  • Mandelbrot, B.: On the theory of word frequencies and on related Markovian models of discourse. In: Jakobson, R. (ed.) Structure of Language and its Mathematical Aspects, pp. 190–219. American Mathematical Society, Providence (1962)

    Google Scholar 

  • Pratelli, L., Baccini, A., Barabesi, L., Marcheselli, M.: Statistical Analysis of the Hirsch Index. Scand. J. Stat. 39, 681–694 (2012)

    Article  Google Scholar 

  • Preis, T., Reith, D., Stanley, H.E.: Complex dynamics of our economic life on different scales: insights from search engine query data. Philos. Trans. R. Soc. A 368(1933), 5707–5719 (2010)

    Article  Google Scholar 

  • Sellers, K.F., Borle, S., Shmueli, G.: The COM Poisson model for count data: a survey of methods and applications. Appl. Stoch. Model. Bus. Ind. 28(2), 104–116 (2012)

    Article  Google Scholar 

  • Todeschini, R.: The j-index: a new bibliometric index and multivariate comparisons between other common indices. Scientometrics 87, 621–639 (2011)

    Article  Google Scholar 

  • Whittaker, J.: Graphical models in applied multivariate analysis. Wiley, Chichester (1990)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paola Cerchiello.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cerchiello, P., Giudici, P. How to measure the quality of financial tweets. Qual Quant 50, 1695–1713 (2016). https://doi.org/10.1007/s11135-015-0229-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11135-015-0229-6

Keywords

Navigation