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 [
1]. Particle swarm optimization algorithm is used to optimize the parameters of support vector machine, which can predict the stock value robustly [
2]. 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 [
3]. This method can be used to predict financial markets in completely different time scales with other variables. The emotional analysis model integrated with the LSTM time series learning model to obtain a robust time series model for predicting the opening price of stocks, and the results showed that this model could improve the accuracy of prediction [
11]. Jia [
12] discussed the effectiveness of LSTM for predicting stock price, and the study showed that LSTM is an effective method to predict stock profits. Real-time wavelet denoising was combined with LSTM network to predict the east Asian stock index, which corrected some logic defects in previous studies [
13]. 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 network method to predict Chinese stock index (including the Shanghai composite index and Shenzhen component index) [
4], 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 [
5]. The deep belief network with inherent plasticity was used to predict the stock price time series [
6]. Convolutional neural network was applied to predict the trend of stock price [
7]. 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 [
8]. 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 [
9]. The comparison experiment with artificial neural network and adaptive neural fuzzy reasoning system shows that this method is more effective. Artificial bee colony algorithm was combined with wavelet transforms and recurrent neural network for stock price forecasting. Many international stock indices were simulated for evaluation, including the Dow Jones industrial average (DJIA), London FTSE 100 index (FTSE), Tokyo Nikkei-225 index (Nikkei) and the Taiwan stock exchange Capitalization Weighted Stock Index (TAIEX). The simulation results show that the system has good prediction performance and can be applied to real-time trading system of stock prediction.
A multi-output speaker model based on RNN-LSTM was used in the field of speech recognition [
14]. The experimental results show that the model is better than a single speaker model, and fine-tuning under the infrastructure when adding new output branches. Obtaining a new output model not only reduces memory usage but also better than training a new speaker model. A multi-input multi-output convolutional neural network model (MIMO-Net) was designed for cell segmentation of fluorescence microscope images [
15]. The experimental results show that this method is superior to the most state-of-the-art deep learning based segmentation method.
Inspired by the above research, considering that some parameters and indicators of a stock are associated with one another, it is necessary to design a multi-value associated neural network model that can handle multiple associated prices of the same stock and output these parameters and indicators at the same time. For this purpose, it is proposed an associated neural network model based on LSTM deep recurrent network which is established by historical data and for predicting the opening price, lowest price and highest price of the stock on the next day.