Stock Prediction and Analysis Based on Support Vector Machine

Menglei Hu; Zili Tang; Xiaoxian Xie; Min Jiang

doi:10.54097/fbem.v5i2.1739

Authors

Menglei Hu
Zili Tang
Xiaoxian Xie
Min Jiang

DOI:

https://doi.org/10.54097/fbem.v5i2.1739

Keywords:

Support vector machine, Stock forecast, Parameter optimization.

Abstract

The changes of the stock market are closely related to the market development and economic research trends of the whole country. Correctly predicting the stock price trend is not only beneficial for investors to make correct investment management decisions, but also plays an important role in promoting the effective allocation of resources and improving market effectiveness. significance. To this end, the daily closing prices of Shanghai Hengrui Medicine and Baosteel Co., Ltd. are used as the basic data, and the support vector machine of python software is used to empirically predict and analyze the development of my country's stock market. The accuracy rate is as high as about 90%; then the parameter optimization is carried out on the basis of the model again, and it is also concluded that the accuracy of stock prediction is as high as about 90%, indicating that the stock prediction accuracy rate based on support vector machine is ideal and has meaningful.

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