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Soft Computing

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23.03.2017 | Methodologies and Application

Return scaling cross-correlation forecasting by stochastic time strength neural network in financial market dynamics

verfasst von: Haiyan Mo, Jun Wang

Erschienen in: Soft Computing | Ausgabe 9/2018

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Abstract

A return scaling cross-correlation function of exponential parameter is introduced in the present work, and a stochastic time strength neural network model is developed to predict the return scaling cross-correlations between two real stock market indexes, Shanghai Composite Index and Shenzhen Component Index. In the proposed model, the stochastic time strength function gives a weight for each historical data and makes the model have the effect of random movement. The empirical research is performed in testing the model forecasting effect of long-term cross-correlation relationships by training short-term cross-correlations, and a corresponding comparison analysis is made to the backpropagation neural network model. The empirical results show that the proposed neural network is advantageous in increasing the forecasting precision.

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Titel: Return scaling cross-correlation forecasting by stochastic time strength neural network in financial market dynamics
verfasst von: Haiyan Mo
Jun Wang
Publikationsdatum: 23.03.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 9/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2564-0

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