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Financial time series prediction by a random data-time effective RBF neural network

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Abstract

An improved neural network of time series predicting is presented in this paper. We introduce a random data-time effective radial basis function neural network in determination of the output weights, the center vectors and the widths in the hidden layer of the network. In the training modeling, we consider that the historical data on the financial market is key to the investors’ decision-making for their investing positions, and the impact of historical data depends closely on the time. We develop a random data-time effective function to describe this impact strength, and a weight is given to each of the historical data, where a drift function and a random Brownian volatility function are applied to express the behavior of the time strength. Further, this neural network is applied to the prediction of financial price series of crude oil, SSE, N225 and DAX. The empirical experiments show that the proposed neural network results in better performance in financial time series forecasting and is advantageous in increasing the forecasting precision.

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Acknowledgments

The authors were supported in part by National Natural Science Foundation of China Grant No. 71271026 and Grant No. 10971010.

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Authors and Affiliations

  1. Institute of Financial Mathematics and Financial Engineering, School of Science, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Hongli Niu & Jun Wang

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  1. Hongli Niu
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  2. Jun Wang
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Correspondence to Jun Wang.

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Communicated by G. Acampora.

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Niu, H., Wang, J. Financial time series prediction by a random data-time effective RBF neural network. Soft Comput 18, 497–508 (2014). https://doi.org/10.1007/s00500-013-1070-2

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