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A Meta-Learning Model of Philippine Bank Lending Behaviour

Author

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  • Christian S. de Leon

    (Bangko Sentral ng Pilipinas, San Beda University-Manila)

Abstract

This study develops a meta-learning framework to predict the lending behaviour of Philippine commercial banks using aggregate bank financial ratios and macroeconomic variables. Five baseline machine learning models - Boosting, k‑Nearest Neighbours, Neural Networks, Random Forest, and Support Vector Machine - were employed, with their outputs synthesised through LASSO‑regularised regression. Results demonstrate that the metamodel consistently achieves superior accuracy, lower error levels, and closer proximity to perceived bank lending behaviour. Robustness checks confirmed stability across volatile and low‑variance regimes using Ridge and Elastic Net, while feature importance highlighted profitability and asset quality as key drivers of lending behaviour.

Suggested Citation

  • Christian S. de Leon, 2026. "A Meta-Learning Model of Philippine Bank Lending Behaviour," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 25(2), pages 119-158.
    • Handle: RePEc:mnb:finrev:v:25:y:2026:i:2:p:119-158
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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