Our analyses work at four type of time show each of 29 organizations listed in this new DJIA when you look at the several months of our data: the fresh new each day amount of states of a organization’s title regarding the Monetary Times, brand new every day exchange quantity of an effective company’s inventory, new daily pure get back off an excellent businesses stock in addition to each and every day get back away from good businesses inventory. Before running correlational analyses, we identify stationarity and you can normality of any ones 124 time show.
To check for stationarity, we first run an Augmented Dickey-Fuller test on each of these company name mention, daily transaction volume, daily absolute return and daily return time series. With the exception of the time series of mentions of Coca-Cola in the Financial Times, we reject the null hypothesis of a unit root for all time series, providing support for the assumption of stationarity of these time series (company names mentions: Coca-Cola Dickey-Fuller = ?3.137, p = 0.099; all other Dickey-Fuller < ?3.478, all other ps < 0.05; daily transaction volume: all Dickey-Fuller < ?3.763, all ps < 0.05; daily absolute return: all Dickey-Fuller < ?5.046, all ps < 0.01; daily return: all Dickey-Fuller < ?9.371, all ps < 0.01). We verify the results of the Augmented Dickey-Fuller test with an alternative test for the presence of a unit root, the Phillips-Perron test. Here, we reject the null hypothesis of a unit root for all company name, transaction volume, absolute return and return time series, with no exceptions, again providing support for the assumption of stationarity of these time series (company names mentions: all Dickey-Fuller Z(?) < ?, all ps < 0.01; daily transaction volume: all Dickey-Fuller Z(?) < ?, all ps < 0.01; daily absolute return: all Dickey-Fuller Z(?) < ?, all ps < 0.01; daily return: all Dickey-Fuller Z(?) < ?, all ps < 0.01).
To check for normality, we run a Shapiro-Wilk test on each of our company name mention, daily transaction volume, daily absolute return and daily return time series. We find that none of our 124 time series have a Gaussian distribution (company names mentions: all W < 0.945, all ps < 0.01; daily transaction volume: all W < 0.909, all ps < 0.01; daily absolute return: all W < 0.811, all ps < 0.01; daily return: all W < 0.962, all ps < 0.01).
Sources
Preis, T., Schneider, J. J. Stanley, H. Age. Changing processes from inside the economic places. Proc. Natl. Acad. Sci. You.S.Good. 108, 7674–7678 (2011).
In the data, we ergo shot on lives regarding relationship ranging from datasets by calculating Spearman’s rating correlation coefficient, a non-parametric measure that renders no presumption regarding normality of underlying research
Podobnik, B., Horvatic, D., Petersen, Good. Meters. Stanley, H. E. Cross-correlations ranging from regularity transform and you can rate transform. Proc. Natl. Acad. Sci. U.S.Good. 106, 22079–22084 (2009).
Feng, L., Li, B., Podobnik, B., Preis, T. Stanley, H. Elizabeth. Hooking up broker-established patterns and stochastic models of financial locations. Proc. Natl. Acad. Sci. You.S.A beneficial. 109, 8388–8393 (2012).
Preis, T., Kenett, D. Y. Stanley, H. Age. Helbing, D. Ben-Jacob, Elizabeth. https://datingranking.net/local-hookup/milwaukee/ Quantifying the latest conclusion of inventory correlations below ).
Krawiecki, A good., Holyst, J. A beneficial. Helbing, D. Volatility clustering and you may scaling to have monetary date collection due to attractor bubbling. Phys. Rev. Lett. 89, 158701 (2002).
Watanabe, K., Takayasu, H. Takayasu, Meters. A mathematical definition of the brand new economic bubbles and you can crashes. Physica A 383, 120–124 (2007).
Preis, T., Moat, H. S., Bishop, S. Roentgen., Treleaven, P. Stanley, H. E. Quantifying this new Electronic Contours away from Hurricane Exotic with the Flickr. Sci. Associate. step 3, 3141 (2013).
Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. Chater, Letter. Having fun with huge investigation in order to anticipate cumulative conclusion regarding real life. Behav. Attention Sci. (during the press).