Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market
With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing w...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6641298 |
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| author | Fang Wang Sai Tang Menggang Li |
| author_facet | Fang Wang Sai Tang Menggang Li |
| author_sort | Fang Wang |
| collection | DOAJ |
| description | With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network. |
| format | Article |
| id | doaj-art-3c98583cd11944c2ad9122c80c648ff7 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3c98583cd11944c2ad9122c80c648ff72025-02-03T00:58:58ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66412986641298Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock MarketFang Wang0Sai Tang1Menggang Li2School of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Humanities, Social Sciences & Law, Harbin Institute of Technology, Harbin, ChinaBeijing Laboratory of National Economic Security Early-Warning Engineering, Beijing Jiaotong University, Beijing 100044, ChinaWith a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.http://dx.doi.org/10.1155/2021/6641298 |
| spellingShingle | Fang Wang Sai Tang Menggang Li Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market Complexity |
| title | Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market |
| title_full | Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market |
| title_fullStr | Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market |
| title_full_unstemmed | Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market |
| title_short | Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market |
| title_sort | advantages of combining factorization machine with elman neural network for volatility forecasting of stock market |
| url | http://dx.doi.org/10.1155/2021/6641298 |
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