Supplementary Data and Code: Change Point Analysis to decode Economic Crisis Information
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Raw data, results and Python code of the corresponding publication "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation" (accepted in: Entropy; Section: Complexity; Special Issue: Complexity in Finance). The change point analysis can be performed using the documented Python package <em>antiCPy</em>. Some further helpful Python scripts are provided here under a <em>GNU General Public License v3.0.</em> In Data_Generation you can find a list of S&P500 companies which are considered in the analysis, a jupyter notebook to create the correlation time series. Data_Preprocessing contains the S&P500 mean market correlation Financial_Time_Series_Centered_Interval__42days.csv, the Python code to thin it, the thinned data saved as .npy file, the time scale is saved as integer numbers in thinned_time_thinning40.npy, as datetime in TimeScale_FinancialData.npy. In Change_Point_Analysis you find the following files: cp_probs...npy contain the joint probabilities of the corresponding change point configurations (The joint probabilities are saved corresponding to the order in which Python's <em>itertools.combinations() </em>creates the configurations. This holds also for the cp_probs_5_cps.npy for which the whole combinations array is not saved for memory reasons), cp_pdfs...npy contain the marginal probability density functions of the ordinal change point positions averaged over all configurations, cp_configs...npy contain the configurations, segment_fit...npy contain the segment fit data, segment_fit_variance...npy contain corresponding variances, In the case of five change points only the plotted 1st, 13th and 26th most probable configuration in config_ranking_CP1_5.npy for memory reasons. the data and results of figure 3 for each crisis event can be found in the corresponding directory's folders: blue corresponds to the pre-crisis data segments, green corresponds to the data segments up to the green vertical dotted line, red corresponds to the longest data segments incorporating near and in-crisis data.
