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News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks

Author

Listed:
  • Hamed Mirashk

    (Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 14117-13114, Iran)

  • Amir Albadvi

    (Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 14117-13114, Iran)

  • Mehrdad Kargari

    (Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 14117-13114, Iran)

  • Mohammad Ali Rastegar

    (Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 14117-13114, Iran)

Abstract

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank’s short-term resilience to liquidity shocks. Despite limited prior research, particularly in anticipating upcoming positions of bank liquidity risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the liquidity coverage ratio (LCR) under Basel III reforms, focusing on its direction (up, down, stable) rather than on exact values, thus distinguishing itself from previous studies. The research objectively explores the influence of external signals, particularly news sentiment, on liquidity prediction, through novel data augmentation, supported by empirical research, as qualitative factors to build a model predicting LCR positions using AI techniques such as deep and convolutional neural networks. Focused on a semi-private Islamic bank in Iran incorporating 4,288,829 Persian economic news articles from 2004 to 2020, this study compared various AI algorithms. It revealed that real-time news content offers valuable insights into impending changes in LCR, particularly in Islamic banks with elevated liquidity risks, achieving a predictive accuracy of 88.6%. This discovery underscores the importance of complementing traditional qualitative metrics with contemporary news sentiments as a signal, particularly when traditional measures require time-consuming data preparation, offering a promising avenue for risk managers seeking more robust liquidity risk forecasts.

Suggested Citation

  • Hamed Mirashk & Amir Albadvi & Mehrdad Kargari & Mohammad Ali Rastegar, 2024. "News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks," Risks, MDPI, vol. 12(11), pages 1-32, October.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:11:p:171-:d:1509771
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    References listed on IDEAS

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    1. Paul Ormerod & Rickard Nyman & David Tuckett, 2015. "Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis," Papers 1508.05357, arXiv.org.
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    3. Pham, Xuan T.T. & Ho, Tin H., 2021. "Using boosting algorithms to predict bank failure: An untold story," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 40-54.
    4. Raffaella Calabrese & Paolo Giudici, 2015. "Estimating bank default with generalised extreme value regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1783-1792, November.
    5. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
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