# Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
## 1 Introduction  
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. Various models have been used to predict stock performance by many economic analysts and stock traders, including quantile autoregression model (QAR) [1], hidden Markov model [2], deep neural network (DNN) [3], recurrent neural network (RNN) [5, 6] and Long short-term memory (LSTM) [7, 8, 9]. However, due to the dynamic and complex nature of the stock market, as well as its many intertwined factors, it is not easy to establish an effective forecasting model. Experienced investors predict stock trends by analyzing the changes in stock prices and volumes over time. Some theories have been developed to predict the trend of stock prices, such as the Elliott Wave Theory [26], presented by [PERSON]. [PERSON] 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. In addition, 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. Besides masspsychology, the stock price system is also influenced by many kinds of information such as government policy, corporate performance, and breaking news. Due to the remarkable result of deep learning in various domains such as computer vision [29], natural language processing [30], and network security [31]. In recent years, many methods based on deep learning have been proposed to forecast stock prices and have drawn some essential conclusions [32]. Nevertheless, when applying these methods to the development of trading systems, a common phenomenon occurs: the prediction price turns out to be a very much delayed version of the price from a time step before, as shown in Figure 1. It seems that the point-by-point prediction closely matches the real price curve, but in fact, this is deceptive, and the prediction tends to simply repeat the trajectory of historical prices with a delay. The delay occurs because the predicted price is close to the last time step of the input stock price sequence. After training for many iterations with an MSE loss function, the LSTM model tends to output a value close to the last time step of the input sequence as the predicted stock price of the next time step in order to minimize the MSE loss. From the figure, we can see that the prediction results in the gray dotted boxes moving in the opposite direction to reality, which is the lagging phenomenon that causes a poor prediction performance. This leads us to rethink the feasibility of pursuing a model that is capable of precisely predicting the stock price or trend at any time. On the one hand, stock prices are influenced not only by the inherent law of fluctuations but also many unpredictable external factors, such as breaking news and national policy; i.e., stock price data are usually noisy. Even if deep neural networks have demonstrated their capability in time-series data mining, it is unrealistic to predict stock prices accurately. In contrast, feeding historical data into the neural network indiscriminately may decrease the prediction performance because too much noise will also be fed into the network. On the other hand, the primary goal of developing a trading system is to ensure profitability, and there is no need to precisely predict the price all the time. Catching potential high-margin trading opportunities is also significant and valuable.