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Using Implicit Preference Relations to Improve Content Based Recommending Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Twitter View of the Brazilian Stock Exchange Market

Authors : Hugo S. Santos, Alberto H. F. Laender, Adriano C. M. Pereira

Published in: E-Commerce and Web Technologies

Publisher: Springer International Publishing

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Abstract

In this paper, we present a view of the Brazilian stock exchange market based on a large characterization and analysis of Twitter data. In our analysis, we show that events and news about the stock market are capable of generating peaks of publications by Twitter users and that the frequency of posts follows the starting of the exchange trading day and maintains for about three hours after the stock market closing hour. Moreover, based on a survey conducted with a specific niche of Twitter users, we have been able to estimate that 0.5 % of those users have some knowledge of the Brazilian stock market and are mostly individual investors interested in publishing and consuming news about this market, having 45 % of them used Twitter as a source for investment decisions. Finally, we have observed that the total number of orders and the financial volume are positively correlated for 66 % of the stocks mentioned on Twitter, whereas the oscillation and maximum oscillation dimensions present no correlation.

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previous chapter The Interactive Effect of Review Rating and Text Sentiment on Review Helpfulness
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Footnotes
3
Infomoney: Paraná’s governor vetoes through Twitter COPEL’s price realignments; stocks turn down - http://​goo.​gl/​T4YTEt
 
4
IBOVESPA stocks are responsible for 80 % of the BOVESPA financial movement.
 
7
 
8
Monetary and Exchange Commission - http://​www.​cvm.​gov.​br
 
Literature
1.
Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques - Part II: Soft computing methods. Expert Syst. Appl. 36(3), 5932–5941 (2009)CrossRef
2.
Martinez, L.C., da Hora, D.N., de M. Palotti, J.R., Meira Jr, W., Pappa, G.L.: From an artificial neural network to a stock market day-trading system: a case study on the BM&F BOVESPA. In: Proceedings of the International Joint Conference on Neural Networks, pp. 2006–2013 (2009)
3.
Silva, E., Castilho, D., Pereira, A., Brando, H.: A neural network based approach to support the market making strategies in high-frequency trading. In: Proceedings of the International Joint Conference on Neural Networks, pp. 845–852 (2014)
4.
Fama, E.F., et al.: The adjustment of stock prices to new information. Int. Econ. Rev. 10(1), 1–21 (1969)CrossRef
5.
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600 (2010)
6.
Maia, M., Almeida, J., Almeida, V.: Identifying user behavior in online social networks. In: Proceedings of the 1st Workshop on Social Network Systems, pp. 1–6 (2008)
7.
Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)
8.
Zhang, W., Skiena, S.: Trading strategies to exploit blog and news sentiment. In: Proceedings of the Fourth International Conference on Weblogs and Social Media, pp. 375–378 (2010)
9.
Ruiz, E.J., Hristidis, V., Castillo, C., Gionis, A., Jaimes, A.: Correlating financial time series with micro-blogging activity. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 513–522 (2012)
10.
Gilbert, E., Karahalios, K.: Widespread worry and the stock market. In: Proceedings of the International Conference on Weblogs and Social Media, pp. 59–65 (2010)
11.
Cha, M., Benevenuto, F., Haddadi, H., Gummadi, P.K.: The world of connections and information flow in Twitter. IEEE Trans. Syste., Man, Cybern., Part A 42(4), 991–998 (2012)CrossRef
12.
Sprenger, T.O., Tumasjan, A., Sandner, P.G., Welpe, I.M.: Tweets and trades: the information content of stock microblogs. Eur. Financ. Manag. 20, 926–957 (2013)CrossRef
13.
Sharma, N.K., Ghosh, S., Benevenuto, F., Ganguly, N., Gummadi, K.: Inferring who-is-who in the Twitter social network. ACM SIGCOMM Comput. Commun. Rev. 42(4), 533–538 (2012)CrossRef
14.
Cheong, M., Lee, V.: Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base. In: Proceedings of the 2nd ACM Workshop on Social Web Search and Mining, pp. 1–8 (2009)
15.
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRef
16.
Sprenger, T.O.: Tweettrader.net: leveraging crowd wisdom in a stock microblogging forum. In: Proceedings of the International Conference on Weblogs and Social, The AAAI Press (2011)
17.
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter. In: Proceedings of the 7th Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, July 2010
18.
Fama, E.F.: The behavior of stock-market prices. J. Bus. 38(1), 34–105 (1965)CrossRef
19.
Zar, J.H.: Significance testing of the spearman rank correlation coefficient. J. Am. Statis. Assoc. 67(339), 578–580 (1972)CrossRefMATH
20.
Cohen, J., Cohen, P., West, S.G., Aiken, L.S.: Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge, London (2013)
Metadata
Title
A Twitter View of the Brazilian Stock Exchange Market
Authors
Hugo S. Santos
Alberto H. F. Laender
Adriano C. M. Pereira
Copyright Year
2015
Book
E-Commerce and Web Technologies

Print ISBN: 978-3-319-27728-8

Electronic ISBN: 978-3-319-27729-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-27729-5_9

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