Abstract
Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system. However, precise stock price prediction is challenging because of the complexity of the internal structure of the stock price system and the diversity of external factors. Although research on forecasting stock prices has been conducted continuously, there are few examples of the successful use of stock price forecasting models to develop effective trading systems. Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important trading points (IPs) that are more likely to be followed by a significant stock price rise to capture potential high-margin opportunities. In the HCRNN model, the convolutional neural network (CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) approach learns the long-term temporal dependencies to improve stock performance prediction. Comprehensive experiments on real stock market data prove the effectiveness of our proposed framework. Our proposed method ITPP-HCRNN achieves an annualized return that is 278.46% more than that of the market.
Highlights
Comparison results indicate that focusing on the important trading points which are more likely to be a high-margin opportunity rather than predicting the stock price or trend at every time point can result in more profits
The above studies have generally neglected transaction costs and taxes in the profit evaluation of the proposed methods when used in real-world applications, whereas, transaction costs and taxes are crucial to the return of a quantitative investment strategy. We address this challenge by imitating the analysis process of human investors, and with this inspiration, we propose a deep learning framework to predict the important trading points (IPs) that are more likely to be followed by a significant stock price rise
We propose an hybrid convolutional recurrent neural network (HCRNN) that combines the advantages of both convolutional neural network (CNN) and long short-term memory (LSTM)
Summary
The stock market has been regarded as an investment channel with great profit potential and has been studied by many people for many decades [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]. Stock performance prediction aims to predict the future price or trend of stocks in order to achieve the maximum profit from stock investment. Elliott proposed that trends in financial prices resulted from investors’ psychology; he found that the fluctuations of mass psychology always appeared in the same repeated fractal pattern— that is, the “volatility” of financial markets. The very famous Gann Theory [27], golden ratio theory [28], and other theories have been presented. These theories, which are widely recognized by stock investors, reveal the inherent law of fluctuations. The stock price system is influenced by many kinds of information such as government policy, corporate performance, and breaking news
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