Research on prediction and recommendation of financial stocks based on K-means clustering algorithm optimization

Abstract

The prediction and recommendation of financial stocks are of great values. This study mainly analyzed the application of K-means clustering algorithm in stock forecasting and recommendation. Firstly, it introduced the k-means algorithm briefly and analyzed its advantages and disadvantages. Then, the k-means algorithm was optimized by introducing artificial fish swarm algorithm (AFSA) to obtain KAFSA. Then 100 stocks of listed companies were taken as the research subject and predicted by KAFSA designed in this study. The prediction results were verified through closing price, price earning ratio, earnings per share and return on net assets. The results showed that there were obvious differences between A and B stocks divided by KAFSA, and the differences of B stocks were significantly larger than those of A stocks. It shows that 100 stocks are well divided into high performance stocks and poor performance stocks through clustering, which provides a good reference for investors to invest in stocks and is worth of further application.

Keywords

K-means clustering algorithm data mining artificial fish swarm algorithm Stock forecast

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