# Empirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning
## 7 Conclusions and Future Work  
In this paper, we empirically analyzed the performance of automated stock trading based on deep reinforcement learning through experiments to validate the contents of [10]. We conducted empirical analysis in three ways to determine whether it is possible to generalize automated stock trading, including several DRL-based trading agents. First, we added a new trading agent, which is named 'Remake Ensemble', and it is based on the ensemble strategy. Since Remake Ensemble showed no significant performance difference compared to other agents, we concluded that adding several different DRL algorithms to the ensemble-based trading agent would make it difficult to increase the robustness of the model. Furthermore, we expanded the trading model to the KOSPI and JPX markets for analysis and wondered if there was a way to increase the model's performance in each market. As one of the methods, the trading strategy was changed to a more conservative and general form by adjusting several model hyperparameters. A new trading strategy was applied based on the KOSPI, which had the lowest performance. Finally, by changing the trading strategy of the original model, we observed that the model's robustness could be improved in the KOSPI and JPX markets. The average performance of all agents was better than before changing the trading strategy, and the whole performance was better for individual iterations. However, the performance was low when the same model was applied to the Dow Jones 30. This empirical analysis is only one possible direction toward a robust automated stock trading system. It cannot be guaranteed that it is optimal for a more stable and robust model. If the experimental iteration exceeds five times, it is unknown whether the PPO and the ensemble strategy still guarantee a higher cumulative return than the KOSPI30 for all iterations. Further, there is no promise that the model will yield stable returns if the trading period or timing are changed. Although it is clear that the ensemble strategy presented in [10] is a novel and innovative approach, we thought the automated stock trading system still had many potential improvements that have not yet been empirically demonstrated apart from the experiments we conducted. The key is generalization and higher profit. One of the methods related to generalization is the possibility of performance improvement in the Dow Jones 30 and JPX \begin{table}
\begin{tabular}{c c c c c c} \hline
**Iteration** & **JPX30** & **A2C** & **DDPG** & **PPO** & **Ensemble** & **Remake** \\ \hline
1 & & **100.422\%** & **96.477\%** & 50.446\% & **77.599\%** & **109.832\%** \\
2 & & **84.178\%** & **79.738\%** & 58.264\% & **92.764\%** & 57.523\% \\
3 & 71.51\% & **110.631\%** & **76.879\%** & 54.933\% & **80.807\%** & **82.248\%** \\
4 & & **104.585\%** & **90.693\%** & **81.633\%** & **79.084\%** & **90.764\%** \\
5 & & 61.004\% & **101.648\%** & 45.742\% & 62.45\% & 65.715\% \\ \hline  & **mean** & **92.164\%** & **89.087\%** & 58.204\% & **78.397\%** & **81.216\%** \\ \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation comparison for five agents after changing the trading strategy in the JPX market from 4 January 2016 to 21 July 2020 (1134 days). markets through customizing the trading strategy, as in the KOSPI market. Suppose it is possible ensuring a higher return than the benchmark index of the three markets. In that case, it can be challenging to make a general model guarantee stable returns in more diverse markets such as the S&P 500 and KOSDAQ. These things can be called market generalization. The other task is to create a model that assures higher profits than the benchmark index during all trading intervals in various markets. It can be called time generalization. The original model verifies the agent's performance based on the current SR, but other metrics such as CR can be used instead of the SR.