Untitled

Our very own analyses work on four variety of go out show for every of 29 companies placed in the latest DJIA during the period of our study: the brand new every single day number of mentions from a good business’s term about Financial Times, the fresh each day exchange quantity of a beneficial business’s inventory, the newest daily absolute return out-of good organization’s stock therefore the every day get back out of a great businesses inventory. Ahead of powering correlational analyses, i identify stationarity and you will normality of each of those 124 go out collection.

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. E. Changing processes inside monetary locations. Proc. Natl. Acad. Sci. You.S.A beneficial. 108, 7674–7678 (2011).

About studies, we for this reason try with the life out of matchmaking between datasets from the figuring Spearman’s rank correlation coefficient, a non-parametric size that produces zero expectation towards normality of one’s hidden study

Podobnik, B., Horvatic, D., Petersen, Good. Meters. Stanley, H. Elizabeth. Cross-correlations ranging from regularity changes and you can speed changes. Proc. Natl. Acad. Sci. U.S.A great. 106, 22079–22084 (2009).

Feng, L., Li, B., Podobnik, B., Preis, T. Stanley, H. E. Hooking up broker-based patterns and you may stochastic type financial segments Vancouver hookup profiles. Proc. Natl. Acad. Sci. U.S.A good. 109, 8388–8393 (2012).

Preis, T., Kenett, D. Y. Stanley, H. E. Helbing, D. Ben-Jacob, Age. Quantifying the brand new behavior of inventory correlations lower than ).

Krawiecki, A great., Holyst, J. A beneficial. Helbing, D. Volatility clustering and you may scaling getting economic day collection due to attractor bubbling. Phys. Rev. Lett. 89, 158701 (2002).

Watanabe, K., Takayasu, H. Takayasu, Yards. An analytical definition of the economic bubbles and you may accidents. Physica Good 383, 120–124 (2007).

Preis, T., Moat, H. S., Bishop, S. Roentgen., Treleaven, P. Stanley, H. Age. Quantifying the latest Electronic Outlines away from Hurricane Sandy towards the Flickr. Sci. Agent. 3, 3141 (2013).

Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. Chater, Letter. Using larger studies to help you expect cumulative conclusion regarding real world. Behav. Notice Sci. (inside the force).