Abstract
Discretization of time series and encoding it as a string in a finite alphabet allows application of information theory methods developed for discrete signals. Computing information values of n-grams extracted from such string leads to introduction of events as occurrences of n-grams that possess specific properties, e.g. abnormally high (or low) information value. We define information value of an n-gram via maximum entropy lifts over frequency dictionaries. We also look for correlation between market events and n-gram events. The paper shows that the proposed method of time series analysis when applied to events study may provide new insightful perspective.
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Notes
- 1.
The source of the data used throughout this work is the publicly available financial data from Yahoo!Finance unless indicated otherwise.
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Borovikov, I., Sadovsky, M. (2016). N-gram Events for Analysis of Financial Time Series. In: Battiston, S., De Pellegrini, F., Caldarelli, G., Merelli, E. (eds) Proceedings of ECCS 2014. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-29228-1_14
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DOI: https://doi.org/10.1007/978-3-319-29228-1_14
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