Stock Price Pattern Prediction Based on Complex Network and Machine Learning
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/4132485 |
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| _version_ | 1850175993846693888 |
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| author | Hongduo Cao Tiantian Lin Ying Li Hanyu Zhang |
| author_facet | Hongduo Cao Tiantian Lin Ying Li Hanyu Zhang |
| author_sort | Hongduo Cao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c0909afa2fc744ebaaaa84b5ceccbd18 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c0909afa2fc744ebaaaa84b5ceccbd182025-08-20T02:19:22ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/41324854132485Stock Price Pattern Prediction Based on Complex Network and Machine LearningHongduo Cao0Tiantian Lin1Ying Li2Hanyu Zhang3Business School, Sun Yat-sen University, Guangzhou 510275, ChinaBusiness School, Sun Yat-sen University, Guangzhou 510275, ChinaBusiness School, Sun Yat-sen University, Guangzhou 510275, ChinaBusiness School, Sun Yat-sen University, Guangzhou 510275, ChinaComplex 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.http://dx.doi.org/10.1155/2019/4132485 |
| spellingShingle | Hongduo Cao Tiantian Lin Ying Li Hanyu Zhang Stock Price Pattern Prediction Based on Complex Network and Machine Learning Complexity |
| title | Stock Price Pattern Prediction Based on Complex Network and Machine Learning |
| title_full | Stock Price Pattern Prediction Based on Complex Network and Machine Learning |
| title_fullStr | Stock Price Pattern Prediction Based on Complex Network and Machine Learning |
| title_full_unstemmed | Stock Price Pattern Prediction Based on Complex Network and Machine Learning |
| title_short | Stock Price Pattern Prediction Based on Complex Network and Machine Learning |
| title_sort | stock price pattern prediction based on complex network and machine learning |
| url | http://dx.doi.org/10.1155/2019/4132485 |
| work_keys_str_mv | AT hongduocao stockpricepatternpredictionbasedoncomplexnetworkandmachinelearning AT tiantianlin stockpricepatternpredictionbasedoncomplexnetworkandmachinelearning AT yingli stockpricepatternpredictionbasedoncomplexnetworkandmachinelearning AT hanyuzhang stockpricepatternpredictionbasedoncomplexnetworkandmachinelearning |