Stock Price Pattern Prediction Based on Complex Network and Machine Learning

Hongduo Cao,Hanyu Zhang,Tiantian Lin,Ying Li

doi:10.1155/2019/4132485

Abstract

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock’s price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. Firstly, we propose a new pattern network construction method for multivariate stock time series. The price volatility combination patterns of the Standard & Poor’s 500 Index (S&P 500), the NASDAQ Composite Index (NASDAQ), and the Dow Jones Industrial Average (DJIA) are transformed into directed weighted networks. It is found that network topology characteristics, such as average degree centrality, average strength, average shortest path length, and closeness centrality, can identify periods of sharp fluctuations in the stock market. Next, the topology characteristic variables for each combination symbolic pattern are used as the input variables for K-nearest neighbors (KNN) and support vector machine (SVM) algorithms to predict the next-day volatility patterns of a single stock. The results show that the optimal models corresponding to the two algorithms can be found through cross-validation and search methods, respectively. The prediction accuracy rates for the three indexes in relation to the testing data set are greater than 70%. In general, the prediction ability of SVM algorithms is better than that of KNN algorithms.

Highlights

Stock price volatility patterns classification and prediction is a very important problem in stock market research
We train the optimal prediction models based on K-nearest neighbors (KNN) and support vector machine (SVM) algorithms by the obtained network topology characteristic variables, and predict next-day patterns of three single stock indexes using the testing data set
From the analyses of the average strength, average shortest path length, average degree centrality, and closeness centrality of the price pattern network every 30 days, it is found that when the overall stock market is in a period of dramatic fluctuations, the average strength, average degree centrality, and closeness centrality reach their maximum values, while the average shortest path length reaches its minimum value

Summary

Introduction

Stock price volatility patterns classification and prediction is a very important problem in stock market research. The prediction of stock price trends is a classified prediction of stock price fluctuation patterns [1]. Many studies have focused on predicting stock price patterns rather than predicting the absolute prices of stocks [2,3,4]. Most studies have focused on the volatility patterns of a single stock based on its own historical attributes [5, 6] and have paid less attention to the comovement of related stocks and information pertaining to the overall market. A few studies have used historical information regarding related stocks as the input variables for prediction and shown that the price fluctuations in a single stock are not isolated and are often influenced by the trends of multiple related stocks [7, 8]

Methods

Results

Conclusion