{'fraction_non_alphanumeric': 0.019230769230769232, 'fraction_numerical': 0.0, 'mean_word_length': 5.702970297029703, '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}}
Emergent Properties of Price Processes in Artificial Markets

Motivation: Financial markets are known to exhibit certain stylized properties that hold across a broad range of markets and institutions. Example of these properties are volatility clustering, the leptokurtic (“fat-tailed”) distribution of returns, power law scaling of the tail of the return distribution, and significant long range autocorrelation of absolute returns paired with no such autocorrelation in raw returns. Theoretical models in general have a hard time explaining these facts. Market simulations with artificial traders might be able to provide qualitative explanations of some of these phenomena.