We apply Zipf power law to financial time series of WIG 20 index daily changes (open-close). Thanks to translation of time series signal into the sequence of 2k+1 "spin-like" states, where k=0, 1/2, 1, 3/2, ... one is able to discretise any time series increaments with almost arbitrary accuracy. This procedure leads in the simplest non-trivial case (k = 1/2) to binary data projection but more sophisticated projections are also possible. The formalism allows then to use Zipf power law to describe the intrinsic structure of time series. The fast algorithm for this implementation was constructed from Matlab^TM software. The method, called Zipf strategy, is then applied in the simplest case k = 1/2 to WIG 20 open and close daily data to make short-time predictions.The results of forecast effectiveness with respect to different time window sizes and partition divisions (word lengths in Zipf language) are presented. The various investment strategies improving ROI (return of investment) for WIG 20 futures are revealed. We show that the Zipf strategy is the appropriate and effective tool to make short term predictions and therefore, to evaluate short term investments on the basis of historical stock index data. Our findings support also the existence of long memory in financial data.

1. PRESENTATION: On the Zipf strategy for short-time investments in WIG 20 futures, PDF document, version 1.4, 0.6MB

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