The academic paper investigates stock price prediction in Korean markets by comparing deep learning models that use only raw OHLCV (open-high-low-close-volume) data against traditional machine learning models utilizing extensive technical indicators. The authors employ triple barrier labeling to generate classification targets for the prediction task, optimizing the parameters to a 29-day window and 9% barriers for a balanced label distribution. A key finding is that a simple Long Short-Term Memory (LSTM) network trained on raw OHLCV data achieves performance comparable to highly optimized traditional models like XGBoost that rely on engineered features. Furthermore, the study identifies that the optimal model performance results from a specific joint optimization of the input window size and the LSTM hidden size, specifically a window of 100 days and a hidden size of eight. This research challenges the conventional emphasis on complex feature engineering in financial forecasting by demonstrating the sufficiency of raw data with appropriate deep learning architectures