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Research on intelligence analysis technology of financial industry data based on genetic algorithm

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Abstract

Based on the improved genetic algorithm and the BP neural network, the financial data analysis model is built on the basis of the data research problem in the financial industry, especially the stock market economy. Genetic algorithm (GA) is a bionic algorithm that simulates the evolutionary mechanism of “natural selection and survival of the fittest.” Through a suitable fitness function, it enables the high-quality individuals to inherit the next generation with a larger probability. Using this method to select variables, we can optimize the variables that affect the stock price and effectively solve the selection problem of the input layer variables of the neural network. On the basis of genetic algorithm, a new GA-BP model based on genetic algorithm and neural network is proposed, and it is applied to the prediction and analysis of securities price or trend in the securities intelligence analysis system. By selecting the Shanghai and Shenzhen 300 index data, the model based on genetic algorithm is used to carry out the experiment, and the experimental results show that the method can effectively reduce the number of variables while ensuring the accuracy of the prediction.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: U1536114) and the National Natural Science Foundation of China (Grant: 61672393).

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Authors and Affiliations

  1. Faculty of Mathematics and Statistics, Hubei University, Wuhan, 430062, China

    Xiaojuan Wang, Lanshan Gan & Songlin Liu

  2. Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan, 430062, China

    Xiaojuan Wang & Songlin Liu

Authors
  1. Xiaojuan Wang
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  2. Lanshan Gan
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  3. Songlin Liu
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Correspondence to Songlin Liu.

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Wang, X., Gan, L. & Liu, S. Research on intelligence analysis technology of financial industry data based on genetic algorithm. J Supercomput 76, 3391–3401 (2020). https://doi.org/10.1007/s11227-018-2584-2

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  • DOI: https://doi.org/10.1007/s11227-018-2584-2

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