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Regular version of the site

Book chapter

Using Emotional Markers’ Frequencies in Stock Market ARMAX-GARCH Model

P. 61-72.
Porshnev A., Lakshina V. V., Редькин И. Е.

We analyze the possibility of improving the prediction of stock market indicators by adding information about public mood ex- pressed in Twitter posts. To estimate public mood, we analysed frequencies of 175 emotional markers - words, emoticons, acronyms and abbreviations - in more than two billion tweets collected via Twitter API over a period from 13.02.2013 to 22.04.2015. We explored the Granger causality relations between stock market returns of S&P500, DJIA, Apple, Google, Facebook, P zer and Exxon Mobil and emotional markers frequencies. We found that 17 emotional markers out of 175 are Granger causes of changes in returns without reverse e ect. These frequencies were tested by Bayes Information Criteria to determine whether they provide additional information to the baseline ARMAX-GARCH model. We found Twitter data can provide additional information and managed to improve prediction as compared to a model based solely on emotional markers.

In book

Edited by: R. Tagiew, D. I. Ignatov, A. Hilbert et al. Vol. 1627. Aachen: CEUR Workshop Proceedings, 2016.