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Influence of Transfer Entropy in the Short-Term Prediction of Financial Time Series Using an ∊-Machine
José Crispín Zavala-Díaz1, Joaquín Pérez-Ortega2, Nelva Nely Almanza-Ortega3
1Faculty of Accounting, Administration and Informatics, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico.
Abstract:
Predicting the values of a financial time series is mainly a function of its price history, which depends on several factors, internal and external. With this history, it is possible to build an ∊-machine for predicting the financial time series. This work proposes considering the influence of a financial series through the transfer of entropy when the values of the other financial series are known. A method is proposed that considers the transfer of entropy for breaking the ties that occur when calculating the prediction with the ∊-machine. This analysis is carried out using data from six financial series: two American, the S&P 500 and the Nasdaq; two Asian, the Hang Seng and the Nikkei 225; and two European, the CAC 40 and the DAX. This work shows that it is possible to influence the prediction of the closing value of a series if the value of the influencing series is known. This work showed that the series that transfer the most information through entropy transfer are the American S&P 500 and Nasdaq, followed by the European DAX and CAC 40, and finally the Asian Nikkei 225 and Hang Seng.
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