# Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
## 5 Evaluation
### Experimental Setup
#### 5.1.4 Compared Methods  
To evaluate the effectiveness of the proposed deep learning framework, we conduct experiments to compare our results with those of the following methods:  
**ITPP-LSTM**: We use a long short-term memory neural network to construct the proposed important trading point prediction framework (ITPP-LSTM) to evaluate the effectiveness of this method. The LSTM model takes training sequences with the length of the training set and corresponding targets as input.  
**FSPD-LSTM**: Forecasting stock prices directly is a common method in stock performance prediction, and we use an LSTM model that is the same as ITPP-LSTM to carry out this method (FSPD-LSTM). When the ratio of the forecasting price to the current price is above a certain threshold, the fund simulation performs a purchase operation. The optimal threshold is also obtained by backtesting on the validation set.  
**FSPD-SFM**: [PERSON] et al. proposed the state frequency memory (SFM) and applied it to the stock prediction task [15]. Compared to LSTM, SFM decomposes the hidden states of memory cells into multiple frequency components, each of which models a particular frequency of latent trading patterns underlying the fluctuation of the stock price. This method is based on the same dataset as ours and can also be viewed as a specification of the method of forecasting stock prices directly.  
**FSPD-HCRNN**: This method uses our proposed HCRNN model to forecast stock price directly. We use FSPD-HCRNN to compare with ITPP-HCRNN to evaluate the effectiveness of the proposed method of important trading point prediction. This method can also be viewed as a specification of the method of forecasting stock prices directly.  
**FSTD-LSTM**: [PERSON] et al. proposed the usage of an LSTM network for predicting future trends of stock prices, i.e., predicting if the price of a particular stock is going to increase or not in the near future [18]. When the predicted class is \"1\"--in other words, in the case that the network predicts that the stock price will go up--a \"buy\" operation will be triggered; then, the trading strategy is to open a \"buy\" position on the current day and close it on the next day. This method can be viewed as a specification of the method of forecasting stock trends directly.  
**Random Forest**: The Random Forest (RF) is a fundamental and commonly used machine learning classification approach, and we use an RF classifier with the number of trees in the forest set as 200 to construct the proposed framework.  
**RNN**: We use a standard RNN as a comparison with ITPP-LSTM to evaluate the effectiveness of the LSTM setting. This method has the same structure as ITPP-LSTM except that the LSTM is replaced with the RNN.  
**Stacked LSTM**: We use a double-layer LSTM to evaluate the effectiveness of the stacked LSTM setting. The other parameter settings are the same as for ITPP-LSTM.  
**ITPP-HCRNN**: This is the important trading point prediction framework based on our proposed hybrid convolutional RNN.  
**Simplistic:** This method directly takes the previous day's trend in the historical stock price series as the future trend.  
**Random**: This method determines whether to perform a purchase operation based on the hypothesis that the probabilities of prices increasing or decreasing are both 50%.