# Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
## 5 Evaluation
### Overall Performance Experiments
#### 5.2.3 Performance Discussion  
As shown in Table 4 and Figure 7, our proposed important trading point prediction framework based on the HCRNN model can provide the best performance among all the baseline methods. Furthermore, as shown in Figure 6 and Table 4, the tendency of the F1 score results is consistent with the simulation results among the different models. We further analyze the results of different methods to explore the reasons why the proposed method outperforms other methods, and the discussion is presented below. **Discussion of effectiveness**: Compared to the performance of simplistic and random methods, all of the other methods show an improvement, suggesting that these methods have a certain predictive ability and are capable of extracting profitable information. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Annualized Return** & **Sharpe Ratio** \\ \hline Random & \(-12.89\%\) & \(-1.63\) \\ Simplistic & \(-12.65\%\) & \(-1.44\) \\ FSPD-SFM [15] & \(-11.92\%\) & \(-0.64\) \\ FSPD-LSTM & 0.51\% & 0.01 \\ FSPD-HCRNN & 5.54\% & 0.17 \\ FSTD-LSTM [18] & 7.46\% & 0.31 \\ Random Forest & 8.17\% & 0.25 \\ RNN & 21.82\% & 0.44 \\ ITPP-LSTM & 28.51\% & 0.52 \\ Stacked LSTM & 47.82\% & 0.58 \\ ITPP-HCRNN & **72.87\%** & **0.78** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results for each method. The best results are in bold. FSPD: forecasting stock prices directly; FSTD: forecasting stock trends directly; SFM: state frequency memory; RNN: recurrent neural network; ITPP: important trading point prediction; LSTM: long short-term memory; HCRNN: hybrid convolutional recurrent neural network. Figure 7: The cumulative profit curve for each method over four years. Although the performances of FSPD-SFM, FSPD-LSTM, FSPD-HCRNN, FSTD-LSTM, and Random Forest are worse than that of the market (18.99%), an important factor is that the transaction cost offsets the profits. The other four methods earn more profit than the market, and they are all based on our proposed important trading point prediction framework. In terms of the comparison of FSPD-LSTM, FSTD-LSTM, and ITPP-LSTM, the improvement of ITPP-LSTM is indicated by both the annualized return and the Sharpe ratio. It can be concluded that the proposed important trading point prediction framework is indeed effective in stock performance prediction tasks; moreover, it has great advantages over the traditional method of forecasting stock prices directly and forecasting stock trends directly. **Discussion of the RNN setting**: Although Random Forest could obtain some profit, the performance of Random Forest is clearly worse than that of the other RNN-based methods, which is probably because the RNN networks can extract temporal information effectively, but Random Forest does not have this ability. This result indicates the significance of using RNNs for sequential modeling in the context of our research. **Discussion of the stacked LSTM setting**: In contrast to the RNN, LSTM has the ability to maintain the long-term memory of the trading patterns from the historical sequence data; thus, IPTT-LSTM outperforms the RNN. In addition, as stacking LSTM layers can enable the characteristics of raw temporal data to be learned from diverse perspectives at each time step, we can see that stacked LSTM shows certain improvements in the experimental results. **Discussion of our proposed HCRNN model**: Unlike the traditional method, which only utilizes RNNs to learn sequential information, the hybrid neural network we propose combines an RNN and CNN to capture both long-term temporal dependencies and local fluctuation features simultaneously during the training process, as they can complement each other. As we can see from Figure 7, our ITPP-HCRNN significantly outperforms all the above-mentioned models for all the test times.