The analyses focus on five particular time collection for every of your 31 enterprises placed in this new DJIA within the period of one’s studies: this new everyday level of says from an excellent organizations identity from the Financial Minutes, new everyday transaction quantity of a businesses inventory, the new every day natural go back out-of a good company’s stock as well as the every single day come back out-of an excellent businesses inventory. Before running correlational analyses, i seek out stationarity and you may normality of each of them 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).
Recommendations
Preis, T., Schneider, J. J. Stanley, H. Elizabeth. Switching techniques into the monetary segments. Proc. Natl. Acad. Sci. U.S.An excellent. 108, 7674–7678 (2011).
From the studies, we therefore test towards lifetime of dating anywhere between datasets because of the figuring Spearman’s score relationship coefficient, a non-parametric scale that renders no expectation about the normality of your own fundamental data
Podobnik, B., Horvatic, D., Petersen, A. Yards. Stanley, H. Age. Cross-correlations anywhere between regularity transform and you will rates change. Proc. Natl. Acad. Sci. U.S.A hookup bars Lloydminster. 106, 22079–22084 (2009).
Feng, L., Li, B., Podobnik, B., Preis, T. Stanley, H. E. Hooking up broker-based models and stochastic models of financial places. Proc. Natl. Acad. Sci. U.S.An excellent. 109, 8388–8393 (2012).
Preis, T., Kenett, D. Y. Stanley, H. Elizabeth. Helbing, D. Ben-Jacob, Age. Quantifying the choices away from inventory correlations around ).
Krawiecki, A beneficial., Holyst, J. An excellent. Helbing, D. Volatility clustering and you may scaling getting monetary day collection due to attractor bubbling. Phys. Rev. Lett. 89, 158701 (2002).
Watanabe, K., Takayasu, H. Takayasu, Meters. An analytical concept of new monetary bubbles and you can injuries. Physica A beneficial 383, 120–124 (2007).
Preis, T., Moat, H. S., Bishop, S. Roentgen., Treleaven, P. Stanley, H. Age. Quantifying the fresh new Electronic Traces out-of Hurricane Exotic with the Flickr. Sci. Rep. 3, 3141 (2013).
Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. Chater, N. Having fun with larger study so you can anticipate collective conclusion in the real world. Behav. Brain Sci. (inside the drive).