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

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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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Acknowledgements

The authors were supported in part by National Natural Science Foundation of China Grant No. 71271026.

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Authors and Affiliations

  1. School of Science, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Haiyan Mo & Jun Wang

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  1. Haiyan Mo
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  2. Jun Wang
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Correspondence to Haiyan Mo.

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Communicated by V. Loia.

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Mo, H., Wang, J. Return scaling cross-correlation forecasting by stochastic time strength neural network in financial market dynamics . Soft Comput 22, 3097–3109 (2018). https://doi.org/10.1007/s00500-017-2564-0

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