Abstract
Stock market prediction has always been an important research topic in the financial field. In the past, inventors used traditional analysis methods such as K-line diagrams to predict stock trends, but with the progress of science and technology and the development of market economy, the price trend of a stock is disturbed by various factors. The traditional analysis method is far from being able to resolve the stock price fluctuations in the hidden important information. So, the prediction accuracy is greatly reduced. In this paper, we design a new model for optimizing stock forecasting. We incorporate a range of technical indicators, including investor sentiment indicators and financial data, and perform dimension reduction on the many influencing factors of the retrieved stock price using depth learning LASSO and PCA approaches. In addition, a comparison of the performances of LSTM and GRU for stock market forecasting under various parameters was performed. Our experiments show that (1) both LSTM and GRU models can predict stock prices efficiently, not one better than the other, and (2) for the two different dimension reduction methods, both the two neural models using LASSO reflect better prediction ability than the models using PCA.
Highlights
convolutional neural network (CNN) is a type of neural network that has been increasingly popular in recent years
Li et al [9] introduced the stock indicator with investor sentiment based on the long-term and short-term memory neural networks (LSTM) model to predict the CS1300 index value, and the research results showed that the model was better than the support vector machine method in prediction accuracy
Hu [11] reduced the dimension of stock technical analysis indicators by Principal component analysis (PCA) and LASSO methods before using the LSTM model to predict. e results demonstrated that compared with the LASSO-LSTM model, the PCA-LSTM model can significantly reduce data redundancy and enhance prediction accuracy
Summary
Stock market prediction has always been an important research topic in the financial field. Li et al [9] introduced the stock indicator with investor sentiment based on the LSTM model to predict the CS1300 index value, and the research results showed that the model was better than the support vector machine method in prediction accuracy. This model does not reduce the dimension of stock indicator. Jiawei and Murata [10] attempted to identify the influencing factors of stock market trend prediction through the LSTM model, which used a preprocessing algorithm to reduce the dimension of stock features and a sentiment analyzer to present financial news for stock trend prediction. By comparing the accuracy and stability of the LASSO-LSTM, LASSO-GRU, PCA-LSTM, and PCA-GRU models, the optimal forecasting model may be recommended
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