Mathematical analyses out of earliest functions of the investigation
All of our analyses work at five form of big date series per of your own 31 companies listed in brand new DJIA in the months of your investigation: this new every single day number of states from good organization’s name in the Monetary Times, the new everyday purchase number of an excellent organization’s stock, new daily natural get back of a great organizations inventory together with every single day get back away from a beneficial business’s stock. Prior to powering correlational analyses, i try to find stationarity and you will normality of every 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).
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