PREDICTING STOCK PRICE DIRECTION OF EUROZONE BANKS: CAN DEEP LEARNING TECHNIQUES OUTPERFORM TRADITIONAL MODELS?

Due to market volatility and complex regulations, forecasting stock price movements within the European banking sector is highly challenging. This study compares the predictive performance of Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) with traditional models - Extreme Gradient Boosting (XGBoost) and Logistic Regression - in predicting the daily stock price direction of the ten largest Eurozone banks by market capitalization. Utilizing a dataset from January 1, 2000, to May 31, 2024, comprising eight financial and macroeconomic indicators, a comparative analysis of these models was conducted. The findings suggest that traditional machine learning models are more effective than advanced deep learning models for predicting stock price direction in the Eurozone banking sector. The underperformance of LSTM and BiLSTM may be attributed to dataset limitations relative to deep learning requirements.