Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry Sectors
Abstract
:1. Introduction
2. Literature Review
2.1. Stock Price Prediction
2.2. Feature Selection
2.3. Feature Selection for Stock Price Prediction
3. Methodology
3.1. Filter Method
3.2. Wrapper Method
3.3. Embedded Method
3.4. Ensemble Method
4. Empirical Study
4.1. Data Preprocessing
4.2. Performance Evaluation
4.3. Rank Comparison
5. Experimental Results
5.1. Average Results across All Sectors in DFSS
5.2. Results for Each Sector in DFSS
5.3. The Dynamic Nature of DFSS
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Energy | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
Price | 0.500 | Economic | 2.000 |
Technical | 0.500 | Market Sentiment | 8.500 |
Market Sentiment | 0.400 | Technical | 10.727 |
Financial | 0.286 | Price | 10.750 |
Economic | 0.059 | Financial | 15.000 |
Fundamental | 0 | Fundamental | - |
Material | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
Price | 1 | Financial | 10.500 |
Technical | 0.591 | Price | 11.000 |
Financial | 0.571 | Technical | 17.769 |
Fundamental | 0.500 | Economic | 23.800 |
Economic | 0.294 | Fundamental | 28.750 |
Market Sentiment | 0 | Market Sentiment | - |
Industrial | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
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 | - |
Consumer Discretionary | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
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 |
Consumer Staples | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
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 | - |
Health Care | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
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 | - |
Fundamental | 0 | Fundamental | - |
Financial | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
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 |
IT | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
Market Sentiment | 0.400 | Price | 1.500 |
Price | 0.250 | Technical | 4.000 |
Technical | 0.136 | Market Sentiment | 6.500 |
Economic | 0 | Economic | - |
Financial | 0 | Financial | - |
Fundamental | 0 | Fundamental | - |
Communication Services (Random Selection) | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
Price | 0.375 | Market Sentiment | 5.000 |
Fundamental | 0.250 | Economic | 6.000 |
Market Sentiment | 0.200 | Financial | 6.000 |
Economic | 0.176 | Technical | 6.000 |
Financial | 0.143 | Price | 7.333 |
Technical | 0.136 | Fundamental | 11.000 |
Utilities (Random Selection) | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
Price | 0.375 | Market Sentiment | 5.000 |
Fundamental | 0.250 | Economic | 6.000 |
Market Sentiment | 0.200 | Financial | 6.000 |
Economic | 0.176 | Technical | 6.000 |
Financial | 0.143 | Price | 7.333 |
Technical | 0.136 | Fundamental | 11.000 |
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Type | Feature |
---|---|
Technical Indicators | Moving Average Convergence Divergence (MACD), On-Balance Volume (OBV), Commodity Channel Index (CCI), Relative Strength Index (RSI), Stochastic Oscillator D%/K%, Stochastic Oscillator, Disparity Index, Moving Average (5,20,60,120), Bollinger Band (h, l), Average Directional Index (ADX), Accumulation Distribution Index (ADI), Force Index (FI), Money Flow Index (MI), True Strength Index (TSI), Market Facilitation Index (MFI), Williams %R Awesome Oscillator, Rate of Change (ROC) |
Economic Indicators | Exchange Rate (KRW-USD), Exchange Rate (KRW-EUR), Exchange Rate (KRW-JPY), Exchange Rate (KRW-CNY), International Gold Price Monthly, Economic Sentiment Index Monthly, Oil Prices (Crude, Diesel, Gasoline) Monthly, Gross Domestic Product (GDP)_Yearly, Employed Persons Monthly, Unemployed Persons Monthly, Consumer/Producer Price Index, Import/Export Price Index, Housing Sales Price Index Monthly |
Price Indicators | Volume, Daily OHLC (Open, High, Low, Close), Individual/Institution/Foreigner Quantity |
Financial Indicators | Certificate of Deposit (CD) (3 months), Monetary Stability, Government Bonds, M1 Monthly, M2 Monthly, Lf Monthly, KOSPI Index Monthly |
Fundamental Indicators | Sales per Share (SPS), Operating Profit per Share (OPS), Earnings per Share (EPS), Book Value per Share (BPS), Price-to-Earnings Ratio (PER), Price-to-Book Ratio (PBR), Return on Assets (ROA), Return on Equity (ROE Quarterly) |
Market Sentiment Indicators | Google Trends Search Volume Weekly, News Sentiment Index, Naver DataLab Company Name Search Index |
Model | Method | Rank Mean | Rank STD |
---|---|---|---|
SHAP | Wrapper | 7.222 | 4.3 |
BorutaSHAP | Wrapper | 7.453 | 4.483 |
RFE | Wrapper | 7.468 | 4.407 |
NOFS | Random Selection | 7.608 | 4.678 |
Embedded Random Forest | Embedded | 7.628 | 4.341 |
Borda Count | Ensemble | 8.117 | 4.406 |
Embedded LightGBM | Embedded | 8.137 | 4.591 |
Mutual Information | Filter | 8.368 | 4.913 |
Variance Threshold | Filter | 8.687 | 4.68 |
Permutation Importance | Wrapper | 8.768 | 4.618 |
ANOVA F-value | Filter | 8.878 | 4.654 |
Reciprocal Rank | Ensemble | 9.163 | 4.618 |
MultiSURF | Filter | 9.185 | 4.744 |
Fisher Score | Filter | 9.580 | 4.610 |
Embedded Lasso | Embedded | 9.683 | 4.622 |
Chi2 | Filter | 11.058 | 4.849 |
Boruta | Wrapper | 14.650 | 3.543 |
Sector | Algorithm (Feature Count) | Method | 1st Ranked Feature Class by Percentage (%) | 1st Ranked Feature Class in Feature Importance |
---|---|---|---|---|
Energy | RFE (20) | Wrapper | Price (50) | Economic |
Material | Variance Threshold (34) | Filter | Price (100) | Financial |
Industrial | ANOVA F-value (20) | Filter | Market Sentiment (80) | Market Sentiment |
Consumer Discretionary | Mutual Information (13) | Filter | Financial (28.6) | Market Sentiment |
Consumer Staples | SHAP (13) | Wrapper | Market Sentiment (60) | Price |
Health Care | Embedded Random Forest (7) | Embedded | Price (25) | Economic |
Financial | SHAP (47) | Wrapper | Market Sentiment (100) | Technical |
IT | RFE (7) | Wrapper | Market Sentiment (40) | Price |
Communication Services | NOFS (13) | Random Selection | Price (37.5) | Market Sentiment |
Utilities | NOFS (13) | Random Selection | Price (37.5) | Market Sentiment |
Material | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
Price | 1 | Financial | 10.500 |
Technical | 0.591 | Price | 11.000 |
Financial | 0.571 | Technical | 17.769 |
Fundamental | 0.500 | Economic | 23.800 |
Economic | 0.294 | Fundamental | 28.750 |
Market Sentiment | 0 | Market Sentiment | - |
Consumer Discretionary | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
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 |
Financial | |||
Feature Class | Percentage | Feature Class | Average Importance Ranking of Feature |
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 |
Sector | Win3 | Win6 | Win12 | Win18 | ||||
---|---|---|---|---|---|---|---|---|
1st Algorithm | 1st Score | 1st Algorithm | 1st Score | 1st Algorithm | 1st Score | 1st Algorithm | 1st Score | |
Energy | ERF (47) | 0.4938 | BSHAP (13) | 0.5014 | BC (20) | 0.5428 | ERF (20) | 0.5567 |
Material | MSURF (34) | 0.5114 | BC (7) | 0.6503 | RFE (20) | 0.5188 | RFE (20) | 0.5824 |
Industrial | RFE (20) | 0.4933 | PI (13) | 0.5076 | MI (20) | 0.5182 | ELGBM (20) | 0.5926 |
Consumer Discretionary | VT (7) | 0.4906 | ERF (13) | 0.4858 | BSHAP (20) | 0.5051 | PI (20) | 0.5426 |
Consumer Staples | MSURF (20) | 0.4921 | SHAP (47) | 0.5032 | BSHAP (13) | 0.5349 | MSURF (13) | 0.5524 |
Health Care | ELGBM (7) | 0.4777 | VT (47) | 0.4892 | SHAP (7) | 0.5214 | MI (20) | 0.5434 |
Financial | PI (7) | 0.5403 | BSHAP(20) | 0.5322 | ELASSO (34) | 0.5511 | BSHAP (47) | 0.6153 |
IT | MI (7) | 0.5215 | MI (7) | 0.5220 | VT (13) | 0.5467 | RR (20) | 0.5792 |
Communication Services | BSHAP (20) | 0.4793 | RR (20) | 0.4995 | ELGBM (7) | 0.5467 | RFE (7) | 0.5689 |
Utilities | SHAP (20) | 0.4705 | ELGBM (20) | 0.5107 | MSURF (47) | 0.5467 | ELGBM (47) | 0.5745 |
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Kim, W.; Jeon, J.; Jang, M.; Kim, S.; Lee, H.; Yoo, S.; Ahn, J. Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry Sectors. Appl. Sci. 2024, 14, 7314. https://doi.org/10.3390/app14167314
Kim W, Jeon J, Jang M, Kim S, Lee H, Yoo S, Ahn J. Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry Sectors. Applied Sciences. 2024; 14(16):7314. https://doi.org/10.3390/app14167314
Chicago/Turabian StyleKim, Woojung, Jiyoung Jeon, Minwoo Jang, Sanghoe Kim, Heesoo Lee, Sanghyuk Yoo, and Jaejoon Ahn. 2024. "Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry Sectors" Applied Sciences 14, no. 16: 7314. https://doi.org/10.3390/app14167314