Nowadays, the stock market generates a large amount of data every day, and the growth rate of these data is amazing. This situation leads to many problems faced by stock investors and companies, such as lack of ability to analyze massive data, fast data update, redundancy of data, and reduction of effective utilization of data. In order to make a better prediction, this paper constructs a bionic computing intelligent algorithm based on support vector machine, establishes an improved GASVM parameter optimization model based on the support vector machine model, and finally applies it to the financial market to predict the short-term stock price in the selected stock pool. The empirical results show that the prediction results are higher than the random prediction accuracy under different parameter optimization algorithms, which shows that it is effective to optimize the model by optimizing the parameters of support vector machine. Compared with neural network model or other traditional artificial intelligence algorithms, the model established in this paper is more economical and applicable, and can give investors guidance to a certain extent.
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