Stock market scale by artificial insymmetrized patterns

Large and stable indices of the worldwide stock markets such as NYSE and S&P500 together with NASDAQ (the index representing markets of new trends) and WIG (the index of the local stock market of Eastern Europe), are considered. Due to the relation between artificial insymmetrized patterns (AIP) and time series, stationary and temporary properties of stock market indices are identified. By filtering extreme events it is found that fluctuations are self-similar. Snap-shots in time lead to estimates for a temporary state of a market with respect to its history. It appears that close to a crash the AIP representation of a system becomes frozen.