Analysis and Time-series Forecasting of Corporate Stock Price

Abstract

Fluctuations in the stock market represent the changes in the national economy objectively, stock price prediction predicts the future trend of stocks using the past data, which has been widely focused on. Some machine learning algorithms, such as linear fitting and sequence mining, are used to predict the stock market. However, linear fitting faces the problem of overfitting and black relationships with historical data, while sequence mining is short in efficiency and lacks dynamic adaptations. State-of-the-art methods using attention mechanism in neural networks have shown exceptional performance targeting sequential prediction and classification. In this paper, we propose a combined LSTM-CNN attention model to explore the role of attention mechanism in Long Short-Term Memory (LSTM) network-based stock price movement prediction. Experimental results show that our LSTM-CNN-attention model can provide an accurate prediction and reliable trial on the stock price prediction, and the attention mechanism significantly improves the model performance in the stock market.