IDEAS home Printed from https://ideas.repec.org/a/vls/finstu/v28y2024i4p29-42.html
   My bibliography  Save this article

Predicting Stock Price Direction Of Eurozone Banks: Can Deep Learning Techniques Outperform Traditional Models?

Author

Listed:
  • ANGHEL, Bogdan Ionuț

    (Faculty of International Business and Economics, Bucharest University of Economic Studies, Bucharest, Romania.)

Abstract

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.

Suggested Citation

  • ANGHEL, Bogdan Ionuț, 2024. "Predicting Stock Price Direction Of Eurozone Banks: Can Deep Learning Techniques Outperform Traditional Models?," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 28(4), pages 29-42, December.
    • Handle: RePEc:vls:finstu:v:28:y:2024:i:4:p:29-42
    as

    Download full text from publisher

    File URL: http://www.icfm.ro/RePEc/vls/vls_pdf/vol28i4p29-42.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Financial Market; European Banking Sector; Time Series; Prediction;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vls:finstu:v:28:y:2024:i:4:p:29-42. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Daniel Mateescu (email available below). General contact details of provider: https://edirc.repec.org/data/cfiarro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.