doi: 10.1685/journal.caim.385

Windows of predictability in Financial Markets: a Shannon Entropy approach

Leon Zingales


In this paper a Shannon entropy criterium to evaluate the largest evaluation of forecasting in time series
with broadband Fourier spectra is introduced. The method is based on the comparison between the empiric distribution
of values and the Gaussian one, the latter corresponding to a complete random state in which efficient market hypothesis
is valid and no forecasting is possible. The model is useful in financial data predictions as it allows to quantify
the maximum percentage of successes in providing the correct sign of the following price rate, giving rise to
tangible gains in terms of forecast performance. The model is tested in German Dax allowing to validate the chaotic
framework to make predictions, as its forecasting percentage is near to the upper limit of successes provided by Shannon
entropy criterium in each time window analyzed.

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Communications in Applied and Industrial Mathematics
ISSN: 2038-0909