Abstract
The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.
Highlights
According to the statistics of China Securities Depository and Clearing Corporation, as of March 2020, there are 163.3 million securities investors in China
Combining the features of the Ensemble empirical mode decomposition (EEMD) method based on the improved empirical mode decomposition method and the long-short term memory (LSTM) machine learning algorithm, this paper proposes a hybrid LSTM-EEMD method for stock index price prediction
By comparing the three evaluation indicators of RMSE, MAE, and R2 of the LSTM, LSTM-Empirical mode decomposition (EMD), and LSTM-EEMD prediction methods in Table 1, we found the prediction results of the LSTM-EEMD prediction method in the four sequence data are better than the LSTM-EMD prediction method. e experimental results show that the LSTMEEMD prediction method is better than the LSTM-EMD prediction method
Summary
According to the statistics of China Securities Depository and Clearing Corporation, as of March 2020, there are 163.3 million securities investors in China. With the speedy development of big data application technology, especially the application of machine learning and deep learning in the financial field, it has a profound impact on investors. In technical analysis, people widely use mathematical statistical techniques to analyze historical stock price trends and predict recent stock prices. Many researchers have applied a variety of machine learning algorithms to analyze and predict stock prices, such as neural networks, multicore learning [12], stepwise regression analysis [13], and deep learning [14, 15]. In the fundamental analysis [16,17,18], people mainly use natural language processing to analyze the company’s financial news and financial statements to predict the future stock price trend
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