Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

Complexity 2021:1-12 (2021)
  Copy   BIBTEX


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 technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price index, the Dow Jones industrial average index, the Shanghai Stock Exchange Composite index, and the Shenzhen Securities Component Index 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.



    Upload a copy of this work     Papers currently archived: 76,442

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles


Added to PP

1 (#1,501,648)

6 months
1 (#454,876)

Historical graph of downloads

Sorry, there are not enough data points to plot this chart.
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations

References found in this work

Finding Structure in Time.Jeffrey L. Elman - 1990 - Cognitive Science 14 (2):179-211.

Add more references