Research on Clustering Application Based on Stock Association Network

Abstract

Abstract This article uses stock market data and text information to construct related complex networks of stock information, and compares the cluster analysis effect of three community discovery algorithms base on two networks to discuss how to classify stocks in order to give Investors provide better reference. First, transform the heterogeneous data into structured data by obtaining and preprocessing. Then, build an association network based on the similarity of stock price fluctuations and the correlation of stock text information. Finally, using three different cluster analysis algorithms to analyze the stock association network and the text similarity network to compare the effects of different algorithms on stock classification.