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.
Stock index prediction aims to predict the future price of stock indexes, which plays a key role in seeking the maximum profit from stock investment. However, It has been proven to be a very difficult task because of its innate complexity, dynamics, and uncertainty. With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as long short-term memory networks (LSTMs) to capture the complex patterns hidden in market trends. In this paper, we propose a Long-term Recurrent Convolutional Network (LRCN), which combines convolutional layers and long-range temporal recursion and is end-to-end trainable. In the LRCN model, the two-dimensional convolutional neural network (2D-CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) learns the long-term temporal dependencies to improve stock index prediction. To evaluate the effectiveness of LRCN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed LRCN can significantly outperform several existing highly competitive methods.
With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as recurrent neural networks (RNNs) and attention mechanisms to capture the complex patterns hidden in stock market trends. Most existing approaches to this task employ an attention mechanism that primarily relies on the information extracted from input features but fails to consider the other important factors (e.g. trading volume and position), which can potentially enhance these attention-based approaches. Motivated by the observation, we extend the attention mechanism with features needed for stock performance prediction in this paper. Specifically, we propose a volume-aware positional attention-based recurrent neural network (VPA-RNN) for this task. First, we propose a generic method of adding position awareness to the attention mechanism. Next, the trading volume is incorporated into the original attention distribution to form a revised distribution. To evaluate the effectiveness of VPA-RNN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed VPA-RNN can significantly outperform several existing highly competitive methods.
With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as recurrent neural networks (RNNs) and attention mechanisms to capture the complex patterns hidden in stock market trends. Most existing approaches to this task employ an attention mechanism that primarily relies on the information extracted from input features but fails to consider the other important factors (e.g., trading volume and position), which can potentially enhance these attention-based approaches. Motivated by the observation, we extend the attention mechanism with features needed for stock performance prediction in this article. Specifically, we propose a volume-aware positional attentionbased recurrent neural network (VPA-RNN) for this task. First, we propose a generic method of adding position awareness to the attention mechanism. Next, the trading volume is incorporated into the original attention distribution to form a revised distribution. To evaluate the effectiveness of VPA-RNN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed VPA-RNN can significantly outperform several existing highly competitive methods.
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