Our very own analyses work at four style of date show for each and every of your own 29 enterprises placed in the fresh DJIA in the period of one’s investigation: the newest each and every day number of states regarding a good businesses title from the Economic Minutes, the newest day-after-day transaction quantity of a company’s stock, the latest day-after-day pure return out-of an effective organization’s stock while the everyday go back away from good organizations stock. Before powering correlational analyses, i try to find stationarity and you can normality of any of those 124 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 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).
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