{'fraction_non_alphanumeric': 0.013774104683195593, 'fraction_numerical': 0.0, 'mean_word_length': 5.161016949152542, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Random-Matrix-Theory-Based Analysis of stocks of Markets from Different Countries

Correlations of stocks in time have been widely studied. Both the Random Matrix Theory approach and the graphical visualisation of so-called Minimum Spanning Trees show the clustering of stocks according to industrial sectors. Studying the correlation between stocks traded in markets of different countries we show that the Random Matrix Theory approach is able to separate stocks according to their geographical location, provided they are not strongly correlated. These results are compared with the results from random time series created using the market model, where the main factor is the mean of returns of the stocks of each sector.