Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry Sectors Article Swipe
Woojung Kim
,
Jiyoung Jeon
,
Minwoo Jang
,
Sanghoe Kim
,
Hee Soo Lee
,
SangHyuk Yoo
,
Jae Joon Ahn
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14167314
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14167314
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14167314
- https://www.mdpi.com/2076-3417/14/16/7314/pdf?version=1724127236
- OA Status
- gold
- Cited By
- 1
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401975556
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4401975556Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app14167314Digital Object Identifier
- Title
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Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry SectorsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-20Full publication date if available
- Authors
-
Woojung Kim, Jiyoung Jeon, Minwoo Jang, Sanghoe Kim, Hee Soo Lee, SangHyuk Yoo, Jae Joon AhnList of authors in order
- Landing page
-
https://doi.org/10.3390/app14167314Publisher landing page
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https://www.mdpi.com/2076-3417/14/16/7314/pdf?version=1724127236Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/14/16/7314/pdf?version=1724127236Direct OA link when available
- Concepts
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Business, Stock market, Feature selection, Industrial organization, Computer science, Artificial intelligence, Geography, Context (archaeology), ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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