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Short-term Prediction of Bank Deposit Flows: Do Textual Features matter?

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  • Katsafados, Apostolos
  • Anastasiou, Dimitris

Abstract

The purpose of this study is twofold. First, to construct short-term prediction models for bank deposit flows in the Euro area peripheral countries, employing machine learning techniques. Second, to examine whether textual features enhance the predictive ability of our models. We find that Random Forest models including both textual features and macroeconomic variables outperform those that include only macro factors or textual features. Monetary policy authorities or macroprudential regulators could adopt our approach to timely predict potential excessive bank deposit outflows and assess the resilience of the whole banking sector in the Euro area peripheral countries.

Suggested Citation

  • Katsafados, Apostolos & Anastasiou, Dimitris, 2022. "Short-term Prediction of Bank Deposit Flows: Do Textual Features matter?," MPRA Paper 111418, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:111418
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    as
    1. Gómez-Puig, Marta & Sosvilla-Rivero, Simón & Ramos-Herrera, María del Carmen, 2014. "An update on EMU sovereign yield spread drivers in times of crisis: A panel data analysis," The North American Journal of Economics and Finance, Elsevier, vol. 30(C), pages 133-153.
    2. Priyank Gandhi & Tim Loughran & Bill McDonald, 2019. "Using Annual Report Sentiment as a Proxy for Financial Distress in U.S. Banks," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(4), pages 424-436, October.
    3. Thomas P. Boehm & Ramon P. DeGennaro, 2011. "A discrete choice model of dividend reinvestment plans: classification and prediction," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 32(4), pages 215-229, June.
    4. Anastasiou, Dimitrios & Katsafados, Apostolos G., 2020. "Bank Deposits Flows and Textual Sentiment: When an ECB President's speech is not just a speech," MPRA Paper 99729, University Library of Munich, Germany.
    5. Spyros Spyrou & Emilios Galariotis & Panagiota Makrichoriti, 2016. "Sovereign CDS Spread Determinants and Spill-Over Effects," Proceedings of International Academic Conferences 3606062, International Institute of Social and Economic Sciences.
    6. Pasiouras, Fotios & Gaganis, Chrysovalantis & Zopounidis, Constantin, 2010. "Multicriteria classification models for the identification of targets and acquirers in the Asian banking sector," European Journal of Operational Research, Elsevier, vol. 204(2), pages 328-335, July.
    7. Galariotis, Emilios C. & Makrichoriti, Panagiota & Spyrou, Spyros, 2016. "Sovereign CDS spread determinants and spill-over effects during financial crisis: A panel VAR approach," Journal of Financial Stability, Elsevier, vol. 26(C), pages 62-77.
    8. Anastasiou, Dimitrios & Drakos, Konstantinos, 2021. "European depositors’ behavior and crisis sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 117-136.
    9. Pasiouras, Fotios & Tanna, Sailesh, 2010. "The prediction of bank acquisition targets with discriminant and logit analyses: Methodological issues and empirical evidence," Research in International Business and Finance, Elsevier, vol. 24(1), pages 39-61, January.
    10. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
    11. Dimitris Anastasiou & Athanassios Petralias, 2021. "On the Construction of a Leading Indicator Based on News Headlines for Predicting Greek Deposit Outflows," International Journal of Business Management and Finance Research, Academia Publishing Group, vol. 4(1), pages 1-11.
    12. Doumpos, Michalis & Andriosopoulos, Kostas & Galariotis, Emilios & Makridou, Georgia & Zopounidis, Constantin, 2017. "Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics," European Journal of Operational Research, Elsevier, vol. 262(1), pages 347-360.
    13. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Emmanouel & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2021. "Using textual analysis to identify merger participants: Evidence from the U.S. banking industry," Finance Research Letters, Elsevier, vol. 42(C).
    14. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    15. Anastasios Petropoulos & Nikolaos E. Vlachogiannakis & Dionysios Mylonas, 2018. "Forecasting private sector bank deposits in Greece: determinants for trend and shock effects," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(2), pages 141-169.
    16. Mr. Harald Finger & Mr. Heiko Hesse, 2009. "Lebanon-Determinants of Commercial Bank Deposits in a Regional Financial Center," IMF Working Papers 2009/195, International Monetary Fund.
    17. Bijsterbosch, Martin & Falagiarda, Matteo, 2015. "The macroeconomic impact of financial fragmentation in the euro area: Which role for credit supply?," Journal of International Money and Finance, Elsevier, vol. 54(C), pages 93-115.
    18. Bodnaruk, Andriy & Loughran, Tim & McDonald, Bill, 2015. "Using 10-K Text to Gauge Financial Constraints," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(4), pages 623-646, August.
    19. Loughran, Tim & McDonald, Bill, 2013. "IPO first-day returns, offer price revisions, volatility, and form S-1 language," Journal of Financial Economics, Elsevier, vol. 109(2), pages 307-326.
    20. Anastasiou, Dimitris & Kallandranis, Christos & Drakos, Konstantinos, 2022. "Borrower discouragement prevalence for Eurozone SMEs: Investigating the impact of economic sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 161-171.
    21. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    22. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    23. Michael Doumpos & Kostas Andriosopoulos & Emilios C. C Galariotis & Georgia Makridou & Constantin Zopounidis, 2017. "Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics," Post-Print hal-02879853, HAL.
    24. María Soledad Martínez-Peria & Sergio Schmukler, 2002. "Do Depositors Punish Banks for Bad Behavior? Market Discipline, Deposit Insurance, and Banking Crises," Central Banking, Analysis, and Economic Policies Book Series, in: Leonardo Hernández & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Se (ed.),Banking, Financial Integration, and International Crises, edition 1, volume 3, chapter 5, pages 143-174, Central Bank of Chile.
    25. Espahbodi, Hassan & Espahbodi, Pouran, 2003. "Binary choice models and corporate takeover," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 549-574, April.
    26. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    27. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    28. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    29. Pasiouras, Fotios & Tanna, Sailesh & Zopounidis, Constantin, 2007. "The identification of acquisition targets in the EU banking industry: An application of multicriteria approaches," International Review of Financial Analysis, Elsevier, vol. 16(3), pages 262-281.
    30. Michalis Doumpos & Kostas Andriosopoulos & Emilios Galariotis & Georgia Makridou & Constantin Zopounidis, 2017. "Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics," Post-Print hal-01578092, HAL.
    31. Fotios Pasiouras & Chrysovalantis Gaganis & Sailesh Tanna & Constantin Zopounidis, 2008. "An Application of Support Vector Machines in the Prediction of Acquisition Targets: Evidence from the EU Banking Sector," Springer Optimization and Its Applications, in: Constantin Zopounidis & Michael Doumpos & Panos M. Pardalos (ed.), Handbook of Financial Engineering, pages 431-456, Springer.
    32. Dimitrios Anastasiou & Konstantinos Drakos, 2021. "Nowcasting the Greek (semi‐) deposit run: Hidden uncertainty about the future currency in a Google search," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1133-1150, January.
    33. Dimitrios Anastasiou & Helen Louri & Mike Tsionas, 2019. "Nonperforming loans in the euro area: Are core–periphery banking markets fragmented?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 97-112, January.
    34. Palepu, Krishna G., 1986. "Predicting takeover targets : A methodological and empirical analysis," Journal of Accounting and Economics, Elsevier, vol. 8(1), pages 3-35, March.
    35. Asli Demirgüç-Kunt & Enrica Detragiache, 1998. "The Determinants of Banking Crises in Developing and Developed Countries," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 81-109, March.
    36. Balakrishnan, Ramji & Qiu, Xin Ying & Srinivasan, Padmini, 2010. "On the predictive ability of narrative disclosures in annual reports," European Journal of Operational Research, Elsevier, vol. 202(3), pages 789-801, May.
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    More about this item

    Keywords

    Bank deposit flows; European banks; textual analysis; short-term prediction; machine learning;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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