Our very own analyses run five brand of day collection for every of the 29 organizations listed in the fresh new DJIA from inside the months of your analysis: the newest daily quantity of says from a good organizations term on the Monetary Minutes, the brand new each and every day exchange number of an excellent businesses stock, the newest daily natural go back from good company’s stock as well as the everyday return away from an excellent organizations inventory. Before running correlational analyses, i seek stationarity and you may normality of any of those 124 big date 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 https://datingranking.net/local-hookup/pomona 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).
Records
Preis, T., Schneider, J. J. Stanley, H. E. Switching process in economic segments. Proc. Natl. Acad. Sci. You.S.A good. 108, 7674–7678 (2011).
Regarding studies, we hence take to towards the life away from matchmaking ranging from datasets from the figuring Spearman’s rank relationship coefficient, a non-parametric scale that produces no presumption about the normality of root study
Podobnik, B., Horvatic, D., Petersen, An excellent. Yards. Stanley, H. Elizabeth. Cross-correlations ranging from frequency changes and you may price changes. Proc. Natl. Acad. Sci. U.S.A great. 106, 22079–22084 (2009).
Feng, L., Li, B., Podobnik, B., Preis, T. Stanley, H. Age. Connecting representative-founded habits and you can stochastic models of financial markets. Proc. Natl. Acad. Sci. U.S.An excellent. 109, 8388–8393 (2012).
Preis, T., Kenett, D. Y. Stanley, H. Elizabeth. Helbing, D. Ben-Jacob, E. Quantifying this new choices out-of inventory correlations below ).
Krawiecki, A beneficial., Holyst, J. Good. Helbing, D. Volatility clustering and you will scaling getting economic date collection due to attractor bubbling. Phys. Rev. Lett. 89, 158701 (2002).
Watanabe, K., Takayasu, H. Takayasu, Meters. A statistical definition of the newest financial bubbles and accidents. Physica Good 383, 120–124 (2007).
Preis, T., Moat, H. S., Bishop, S. Roentgen., Treleaven, P. Stanley, H. E. Quantifying the Electronic Outlines away from Hurricane Sandy with the Flickr. Sci. Associate. step 3, 3141 (2013).
Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. Chater, N. Playing with large investigation in order to assume collective decisions throughout the real life. Behav. Attention Sci. (from inside the press).