## 5 Experimental Results
### Results for Each Sector in DFSS  
The optimal feature selection algorithms and combinations for each sector were determined by analyzing their average results. Additionally, the importance and proportion of feature classes in each sector were assessed. Table 3 presents a summary of the optimal outcomes for each sector, reflecting the average results from 60 experimental configurations per sector. Consistent with the overall results, the wrapper method exhibits the best performance in four out of ten sectors. Intriguingly, despite its generally lower performance, the filter method proves effective in three out of ten sectors. It is noteworthy that no sector demonstrated optimal results with the ensemble method, possibly due to the influence of outliers within the algorithm's impact. In the majority of sectors, price and market sentiment indicators are commonly included, with market sentiment often emerging as the most significant. This indicates that, typically, price is a secondary feature, while market sentiment plays a primary role (considering price's high inclusion rate but relatively lower significance). Most sectors exhibit an optimal set of features. However, in the sectors of communication services and utilities, a random selection approach yielded the best results. This could be ascribed to both sectors being highly regulated and less affected by economic fluctuations, as they provide essential services. Such characteristics impact the profitability and operations of companies within these sectors. Figure 3 presents graphs summarizing the average results for each sector, illustrating the performance and stability outcomes of various algorithms. These results indicate distinct patterns of algorithmic superiority in different sectors. In the energy and healthcare sectors, aligning with the general sector results, the wrapper method exhibits high performance. In contrast, in the materials and consumer staples sectors, the filter method shows higher performance, despite its relative ineffectiveness in other sectors. Table 4 details the optimal feature sets for the materials, consumer discretionary, and financial sectors. Results for all sectors are compiled in Appendix A. The interpretations of these results are as follows: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sector** & \begin{tabular}{c} **Algorithm** \\ **(Feature Count)** \\ \end{tabular} & **Method** & \begin{tabular}{c} **1 st Ranked Feature** \\ **Class by Percentage (\%)** \\ \end{tabular} & \begin{tabular}{c} **1 st Ranked Feature** \\ **Class in Feature** \\ **Importance** \\ \end{tabular} \\ \hline Energy & RFE (20) & Wrapper & Price (50) & Economic \\ \hline Material & Variance Threshold (34) & Filter & Price (100) & Financial \\ \hline Industrial & ANOVA F-value (20) & Filter & Market Sentiment (80) & Market Sentiment \\ \hline \begin{tabular}{c} Consumer \\ Discretionary \\ \end{tabular} & Mutual Information (13) & Filter & \begin{tabular}{c} Financial \\ (28.6) \\ \end{tabular} & Market Sentiment \\ \hline Consumer Staples & SHAP (13) & Wrapper & Market Sentiment (60) & Price \\ \hline \multirow{2}{*}{Health Care} & \
\begin{tabular}{c} Embedded Random \\ Forest (7) \\ \end{tabular} & Embedded & Price (25) & Economic \\ \hline Financial & SHAP (47) & Wrapper & Market Sentiment (100) & Technical \\ \hline IT & RFE (7) & Wrapper & Market Sentiment (40) & Price \\ \hline
\begin{tabular}{c} Communication \\ Services \\ \end{tabular} & NOFS (13) & Random Selection & Price (37.5) & Market Sentiment \\ \hline Utilities & NOFS (13) & Random Selection & Price (37.5) & Market Sentiment \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the first ranked feature class analysis by sector. Figure 3: Results for each of ten sectors in DFSS (red box is a feature selection algorithm with a high-friedman rank compared to a low standard deviation). Material: All price features are included, with technical and financial indicators following.