## 4 Empirical Study
### Performance Evaluation  
Performance evaluation of various combinations of feature selection algorithms was conducted using preprocessed data. Four types of variable selection algorithms--filter, wrapper, embedded, and ensemble--were employed, incorporating a comprehensive set of \begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Feature** \\ \hline \multirow{7}{*}{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) \\ \hline \multirow{7}{*}{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 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Price Indicators \\ \end{tabular} } & Volume, Daily OHLC (Open, High, Low, Close), Individual/Institution/Foreigner Quantity \\ \hline \multirow{2}{*}{\begin{tabular}{c} Financial Indicators \\ \end{tabular} } & Certificate of Deposit (CD) (3 months), Monetary Stability, Government Bonds, M1 Monthly, M2 \\  & Monthly, Lf Monthly, KOSPI Index Monthly \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Fundamental Indicators \\ \end{tabular} } & 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) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Market Sentiment Indicators \\ \end{tabular} } & Google Trends Search Volume Weekly, News Sentiment Index, \\  & Naver DataLab Company Name Search Index \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features collected in DFSS. 16 variable selection algorithms as detailed in Section 3. Their performances were subsequently compared for evaluation. To determine the contribution of the proposed algorithm to the improvement in prediction performance, the benchmark model, No Feature Selection (NOFS), was utilized. NOFS evaluates prediction performance by extracting non-repeating features from the entire feature set at random based on the feature selection percentile. For each algorithm, we varied the \"feature selection percentile\", \"look-back size\", and \"window size\", measuring the classification performance on the rise or fall of the sector index during the test period using models trained in the training period. We compared \"feature selection percentiles\" at 10%, 20%, 30%, 50%, and 70%. Additionally, we explored \"look-back sizes\" of Lag 1, Lag 5, and Lag 10. The \"window sizes\" for the train-test period included 3 months-1 month, 6 months-2 months, 12 months-4 months, and 18 months-6 months. As a result, performance measurements were conducted for a total of 60 combinations for each of the 16 feature selection algorithms. The \"look-back size\" refers to using data from time t to t-n time points to predict the direction at time t + 1. Lag 1 predicts the direction using data from only the previous day, lag 5 uses data from one week ago, and lag 10 uses data from two weeks ago. The \"window size\" and test period maintained a 3:1 ratio, with window sizes varying from 3 to 18 months for training. A dataset was selected for each \"window size\", with 22 datasets over 3 months, 18 datasets over 6 months, 10 datasets over 12 months, and 2 datasets over 18 months used for evaluation.