## 5 Conclusions and Future Work  
Although stock market trends change rapidly and can cause significant price fluctuations due to various reasons, the STBC proposed in this study can promptly adjust future stock price trends and reduce prediction errors, achieving more accurate stock price predictions. Furthermore, by using fuzzy membership functions optimized through GAs, this study has derived the best parameters for technical indicators, established a fuzzy system, and combined it with technical indicator application rules to infer the best trading  
Figure 11: Impact of COVID-19 on ETF50 stock prices.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Period** & **Best Buy and Hold Returns** & **Buy and Hold Returns** & **The Proposed Returns** \\ \hline
1 July 2019–31 December 2019 & 18.46 & 17.30 & 21.60 \\
1 August 2019–31 January 2020 & 10.41 & 9.29 & 19.76 \\
2 September 2019–27 February 2020 & 9.61 & 8.24 & 9.18 \\
1 October 2019–31 March 2020 & \(-\)8.96 & \(-\)11.40 & 18.02 \\
1 November 2019–30 April 2020 & \(-\)4.73 & \(-\)5.10 & 6.05 \\
2 December 2019–29 May 2020 & \(-\)7.95 & \(-\)8.25 & 0.46 \\
2 January 2020–30 June 2020 & \(-\)7.31 & \(-\)7.93 & 4.29 \\
3 February 2020–31 July 2020 & 19.06 & 16.56 & 19.44 \\
2 March 2020–31 August 2020 & 20.03 & 16.54 & 25.27 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of returns across the COVID-19 event.  
points. This approach has been tested through simulated trading, resulting in a positive return rate.  
We will further discuss improvements to the correction method and the fuzzy system, outlining two main areas for future enhancement:  
Firstly, we can increase the number of technical indicator membership functions and application rules to more accurately determine the best buying and selling points, achieving better results. By introducing more technical indicators, the model's adaptability to different market conditions can be improved.  
Secondly, although STBC can reduce daily prediction errors, frequent corrections might be caused by a few days of significant anomalies, leading to substantial fluctuations in future corrected stock prices. To address this issue, we should limit the correction values to within a reasonable range by using data and errors from previous days to infer the appropriate correction value for future predictions, thus reducing the frequency of corrections. This approach can enhance the model's stability and accuracy, thereby increasing the return rate.  
Finally, while the genetic fuzzy system achieves returns during significant market upswings, its conservative trading strategy limits high profitability. Therefore, we can consider incorporating additional indicators, such as Bollinger Bands, to further optimize entry points and enhance the flexibility and profitability of the trading strategy. These improvements will make the model more stable and effective under various market conditions. The proposed method can also be implicated for other stock markets or financial instruments. It can also contribute to the existing body of knowledge in financial forecasting and machine learning.  
Conceptualization, R.-I.C.; methodology, R.-I.C. and C.-H.W.; software, L.-C.W. and Y.-F.L.; validation, C.-H.W., L.-C.W. and Y.-F.L.; Formal analysis, R.-I.C.; investigation, C.-H.W., L.-C.W. and Y.-F.L.; resources, C.-H.W. and L.-C.W.; data curation, R.-I.C., L.-C.W. and Y.-F.L.; writing--original draft, C.-H.W. and Y.-F.L.; writing--review & editing, R.-I.C. and C.-H.W.; visualization, C.-H.W. and Y.-F.L.; supervision, R.-I.C.; project administration, R.-I.C., [PERSON]-H.W. and L.-C.W.; funding acquisition, C.-H.W. All authors have read and agreed to the published version of the manuscript.  
This research and APC were funded by NSTC Taiwan, grant number NSTC 112-2221-E-130-001.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.