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 mode...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
“Victor Slăvescu” Centre for Financial and Monetary Research
2024-12-01
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| Series: | Financial Studies |
| Subjects: | |
| Online Access: | http://fs.icfm.ro/Paper02.FS4.2024.pdf |
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| Summary: | 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. |
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| ISSN: | 2066-6071 |