Mathematical analyses regarding basic characteristics of your studies

Mathematical analyses regarding basic characteristics of your studies

Our analyses work at four sorts of go out show for every of one’s 31 people placed in the brand new DJIA in several months your study: the brand new daily quantity of mentions of an excellent company’s term regarding Financial Moments, new every day exchange level of an effective businesses inventory, the fresh new everyday absolute get back away from an excellent organization’s inventory together with each and every day get back from a good businesses inventory. In advance of running correlational analyses, we try to find stationarity and you can normality of each and every ones 124 date 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 procedure inside the financial areas. Proc. Natl. Acad. Sci. You.S.A beneficial. 108, 7674–7678 (2011).

On the data, i ergo test on existence from matchmaking between datasets by the figuring Spearman’s score relationship coefficient, a low-parametric level that makes no expectation concerning the normality of your fundamental studies

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

Feng, L., Li, B., Podobnik, B., Preis, T. Stanley, H. E. Hooking up broker-based designs and stochastic models of financial places. 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 fresh new conclusion off stock correlations less https://datingranking.net/local-hookup/norfolk/ than ).

Krawiecki, A great., Holyst, J. Good. Helbing, D. Volatility clustering and scaling having financial date show because of attractor bubbling. Phys. Rev. Lett. 89, 158701 (2002).

Watanabe, K., Takayasu, H. Takayasu, Yards. A mathematical concept of the latest economic bubbles and you will crashes. Physica A great 383, 120–124 (2007).

Preis, T., Moat, H. S., Bishop, S. Roentgen., Treleaven, P. Stanley, H. Elizabeth. Quantifying the new Digital Contours out of Hurricane Exotic to the Flickr. Sci. Associate. step three, 3141 (2013).

Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. Chater, N. Playing with huge study to predict cumulative behavior regarding real world. Behav. Notice Sci. (inside the push).