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There are many related researches on stock price prediction. Support vector machines was applied to build a regression model of historical stock data and to predict the trend of stocks (Malkiel & Fama, 1970).
Particle swarm optimization algorithm is used to optimize the parameters of support vector machine, which can predict the stock value robustly (Malkiel, 2003).
This study improves the support vector machine method, but particle swarm optimization algorithm requires a long time to calculate. LSTM was combined with naive Bayesian method to extract market emotion factors to improve the performance of prediction (Timmermann & Granger, 2004).
Compared with the original LSTM, this combination model is greatly improved with high prediction accuracy and small regression error. Bagging method was used to combine multiple neural net- work method to predict Chinese stock index (including the Shanghai composite index and Shenzhen component index) (Hsu et al., 2016),
Each neural network was trained by back propagation method and Adam optimization algorithm, the results show that the method has different accuracy for prediction of different stock index, but the prediction on close is unsatisfactory. The evolutionary method was applied to predict the change trend of stock price (Ballings et al., 2015).
The deep belief network with inherent plasticity was used to predict the stock price time series (Gerlein et al., 2016). Convolution neural network was applied to predict the trend of stock price (Choudhury et al., 2014).
A forward multi-layer neural network model was created for future stock price prediction by using a hybrid method combining technical analysis variables and basic analysis variables of stock market indicators and BP algorithm (Xia et al., 2013).
The results show that this method has higher accuracy in predicting daily stock price than the technical analysis method. An effective soft computing technology was designed for Dhaka Stock Exchange (DSE) to predict the closing price of DSE (Sands et al., 2015).