## 6 Conclusions  
In this paper, we propose the Dynamic Feature Selection System (DFSS) for predicting stock prices in ten major Korean FICS industry sectors. The DFSS employs 16 different feature selection algorithms, focusing on both performance and stability. Our experimental results show that the DFSS generally outperforms random feature selection in both areas. The optimal performance algorithms and their corresponding feature sets vary across sectors, and the percentage of features included changes over time within the same sector. These findings are corroborated in Section 5.3, demonstrating the model's capability to capture temporal variations and determine sector-specific optimal features. This aspect was previously overlooked in existing research. Integrating DFSS into stock algorithm trading systems could enhance their sophistication and aid investors by providing reliable, dynamically updated information.  
Future research could enhance the system's performance by employing Attention or GAN-based feature selection methods, which are increasingly prevalent in AI. Additionally, the model could be applied to various financial markets, such as futures, options, bonds, and real estate, as well as tested in diverse countries like the United States and China to evaluate its robustness.  
Investigation, W.K., J.J., M.J. and S.K.; Software, W.K.; Formal analysis, W.K. and J.J.; Writing-original draft, W.K., J.J., M.J. and S.K.; Writing-review & editing, W.K., H.L., S.Y. and J.A.; Supervision, H.L., S.Y. and J.A.; Resources, J.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1A2C1094211).  
**Table A1.**_Cont._  
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Industrial**} \\ \hline
**Feature Class** & **Percentage** & **Feature Class** &
\begin{tabular}{c} **Average Importance** \\ **Ranking of Feature** \\ \end{tabular} \\ \hline Market Sentiment & 0.800 & Market Sentiment & 4.250 \\ Price & 0.625 & Financial & 10.000 \\ Technical & 0.455 & Technical & 11.800 \\ Financial & 0.143 & Price & 13.000 \\ Economic & 0 & Economic & - \\ Fundamental & 0 & Fundamental & - \\ \hline \multicolumn{4}{c}{**Consumer Discretionary**} \\ \hline
**Feature Class** & **Percentage** & **Feature Class** &
\begin{tabular}{c} **Average Importance** \\ **Ranking of Feature** \\ \end{tabular} \\ \hline Financial & 0.286 & Market Sentiment & 2.000 \\ Price & 0.250 & Technical & 5.750 \\ Market Sentiment & 0.200 & Economic & 6.667 \\ Technical & 0.182 & Fundamental & 8.000 \\ Economic & 0.176 & Price & 8.500 \\ Fundamental & 0.125 & Financial & 10.500 \\ \hline \multicolumn{4}{c}{**Consumer Staples**} \\ \hline
**Feature Class** & **Percentage** & **Feature Class** &
\begin{tabular}{c} **Average Importance** \\ **Ranking of Feature** \\ \end{tabular} \\ \hline Market Sentiment & 0.600 & Price & 5.333 \\ Price & 0.375 & Technical & 6.333 \\ Technical & 0.273 & Market Sentiment & 9.000 \\ Financial & 0.143 & Financial & 10.000 \\ Economic & 0 & Economic & - \\ Fundamental & 0 & Fundamental & - \\ \hline \multicolumn{4}{c}{**Health Care**} \\ \hline
**Feature Class** & **Percentage** & **Feature Class** &
\begin{tabular}{c} **Average Importance** \\ **Ranking of Feature** \\ \end{tabular} \\ \hline Price & 0.250 & Economic & 1.000 \\ Market Sentiment & 0.200 & Market Sentiment & 3.000 \\ Technical & 0.136 & Technical & 3.667 \\ Economic & 0.059 & Price & 6.500 \\ Financial & 0 & Financial & - \\ \hline \multicolumn{4}{c}{**Financial**} \\ \hline
**Feature Class** & **Percentage** & **Feature Class** &
\begin{tabular}{c} **Average Importance** \\ **Ranking of Feature** \\ \end{tabular} \\ \hline Market Sentiment & 1 & Technical & 19.050 \\ Price & 1 & Economic & 22.667 \\ Technical & 0.909 & Price & 26.000 \\ Economic & 0.529 & Financial & 29.333 \\ Financial & 0.429 & Market Sentiment & 31.800 \\ Fundamental & 0.250 & Fundamental & 44.000 \\ \hline \hline \end{tabular}