###### Abstract  
For several years, a growing interest among numerous researchers and investors in predicting stock price movements has spurred extensive exploration into employing advanced deep learning models. These models aim to develop systems capable of comprehending the stock market's complex nature. Despite the immense challenge posed by the diverse factors influencing stock price forecasting, there remains a notable lack of research focused on identifying the essential feature set for accurate predictions. In this study, we propose a Dynamic Feature Selection System (DFSS) to predict stock prices across the 10 major industries, as classified by the FnGuide Industry Classification Standard (FICS) in South Korea. We apply 16 feature selection algorithms from filter, wrapper, embedded, and ensemble categories. Subsequently, we adjust the settings of industry-specific index data to evaluate the model's performance and robustness over time. Our comprehensive results identify the optimal feature sets that significantly impact stock prices within each sector at specific points in time. By analyzing the inclusion ratios and significance of the optimal feature set by category, we gain insights into the proportion of feature classes and their importance. This analysis ensures the interpretability and reliability of our model. The proposed methodology complements existing methods that do not consider changes in the types of variables significantly affecting stock prices over time by dynamically adjusting the input variables used for learning. The primary goal of this study is to enhance active investment strategies by facilitating the creation of diversified portfolios for individual stocks across various sectors, offering robust models and feature sets that consistently demonstrate high performance across industries over time. feature selection; Korean industry sectors; dynamic; stock prediction; optimal feature set +
Footnote †: journal: Medicalfocused on enhancing the predictability of financial markets and providing more accurate decision-making tools for investors. There is a notable scarcity of research focused on identifying effective combinations of factors influencing stock prices. The dynamics of the stock market are affected by various elements, including the market value of stocks, company performance, government policies, a country's gross domestic product (GDP), inflation rates, and unexpected events such as natural disasters [8]. Additionally, factors like short-term price trends, economic conditions, corporate solvency, and profitability, and the impact of behavioral psychology are acknowledged as significant [5]. Current research is exploring these complex factors through the development of various technical indicators and advancements in computer science. However, the pursuit of effective feature sets for stock price prediction continues to be a formidable challenge [9; 10; 11; 12]. In the realm of machine learning and deep learning algorithms, feature selection is a critical step for avoiding overfitting and mitigating the curse of dimensionality. This process is essential for identifying key feature sets that significantly influence the prediction of the target feature. Active exploration of this process is ongoing in various AI fields [13; 14]. Its significance is especially pronounced in the stock market due to its dynamic nature and the unique characteristics of each sector. Employing dynamic methods, rather than a static approach, becomes imperative for feature selection, aligning with the continuously evolving conditions and sector-specific characteristics of the stock market. Identifying the appropriate combination of features for each situation is crucial. Thus, prioritizing effective feature management in stock price prediction emerges as a significant and necessary challenge, one that should be addressed before focusing on enhancements in algorithm complexity.