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A Hybrid Model Combined Deep Learning Approaches in Stock Price Prediction

Stock price forecasting is always an important task, but because many factors determine the stock price, the variability of its market makes the stock prediction complex and challenging. The stock price prediction is indispensable because the correct stock price prediction can help investors make better decisions and reduce investment risks. The historical price of the stock is only one of the many factors that affect the trend of stock price. Therefore, researchers combine various factors and use machine learning to put forward new models to promote stock price prediction constantly. Nowadays, machine learning has become more widely used in stock price prediction. Compared with traditional technologies, machine learning can predict time series data with high precision and minimize errors and risks. This study aims to review and summarize some existing literature and determine the prediction direction and challenges of the future stock market. We classified the works of literature with similar research methods and backgrounds, and we also introduced the main models. Three categories formed: Support Vector Machine (SVM) studies, Empirical Mode Decomposition (EMD), and Long-Short Term Memory (LSTM) studies, and studies using morphological similarity clustering and hierarchical temporal memory. The last two sections provide the overall conclusions and future research challenges