Abstract
An accurate prediction of stock market index is important for investors to reduce financial risk. Although quite a number of deep learning methods have been developed for the stock prediction, some fundamental problems, such as weak generalization ability and overfitting in training, need to be solved. In this paper, a new deep learning model named Random Long Short-Term Memory (RLSTM) is proposed to get a better predicting result. RLSTM includes prediction module, prevention module, and three full connection layers. Input of the prediction module is a stock or an index which needs to be predicted. That of the prevention module is a random number series. With the index of Shanghai Securities Composite Index (SSEC) and Standard & Poor's 500 (S&P500), simulations show that the proposed RLSTM can mitigate the overfitting and outperform others in accuracy of prediction.
Highlights
Forecasting future price of a financial asset, such as a stock, is essential for investors as it can reduce the risk of decisionmaking by appropriately determining the future movement of an investment asset. e investors are more likely to buy the stocks whose value is expected to increase in the future
In [8], Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is combined with Artificial Neural Networks (ANNs), which is far better than GARCH alone in predicting market volatility in Latin America
We propose a model named Random Long Short-Term Memory (RLSTM) which is based on Long Short-Term Memory Network (LSTM) and uses a series of random data with uniform distribution against overfitting in forecasting the stock market index. e model contains two modules, that is, prediction module and prevention module. e former acts as the major function in the model, while the latter plays auxiliary role
Summary
Forecasting future price of a financial asset, such as a stock, is essential for investors as it can reduce the risk of decisionmaking by appropriately determining the future movement of an investment asset. e investors are more likely to buy the stocks whose value is expected to increase in the future. In [8], GARCH is combined with Artificial Neural Networks (ANNs), which is far better than GARCH alone in predicting market volatility in Latin America Another important machine learning technique, Support Vector Regression (SVR), is a regression model that can show great ability in predicting future data based on historical data. We propose a model named RLSTM which is based on LSTM and uses a series of random data with uniform distribution against overfitting in forecasting the stock market index.
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