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Short-term prediction of bank deposit flows: do textual features matter?

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Abstract

Motivated by the successful usage of machine learning around computer science and its wide acceptance from the finance literature, we utilize monthly data spanning the period 2008–2018 for the Euro area peripheral countries, in order to embark on a two-fold mission. 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. From the variety of models tested, we find that Random Forest models including both textual features and macroeconomic variables outperform models including 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.

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Data availability

Data are not publicly available due to ethical reasons, though the data may be made available on request from the corresponding author.

Notes

  1. We have collected the speeches directly from the website of the ECB. In our sample, there are merely those speeches that are in English language. We acknowledge that there are other potential sources of country-specific macroeconomic news that could be considered. However, we believe that the speeches of the ECB president are the most relevant and influential source of information for the Euro area peripheral countries. The ECB is the primary monetary authority for these countries, and their policies and communications have a significant impact on the overall economic environment. Moreover, the ECB president’s speeches are carefully crafted to communicate the central bank’s policies and intentions, making them an essential source of information for forecasting bank deposit flows. Additionally, the ECB’s commitment to transparency and accountability ensures that market participants closely monitor and analyze these speeches.

  2. As for the computing environment we use, the algorithms were run through Google Colab, a product of Google Research, which allows anyone to write and run arbitrary Python code. Notably, the machine learning algorithms are implemented via the scikit-learn library.

  3. Feed-forward neural networks are networks with fully-connected layers. Namely, each neuron is linked to all of the neurons in the next layer.

  4. The networks with two or more layers of hidden neurons are known as deep networks, thus leading to the terminology of deep learning (Goldberg, 2017). According to Sun et al. (2017), the existence of many hidden layers benefits us with higher learning capacity.

  5. This set is practically used to assess the out-of-sample and out-of-time performance of our classifiers.

  6. The macro average approach just sums the measure scores for inflows and outflows, and finally, it divides the result by two.

  7. As an alternative to ensemble modeling, we examine the XG boosting model. In untabulated results, we find that RF achieves better scores in our task.

  8. Anastasiou and Drakos (2021b) found that depositors have lower confidence in the peripheral countries’ banking systems, making the latter suffer from larger deposit outflows (especially in crisis periods), leading to more frequent panics in bank deposits and thus financial instability in the periphery. All these, in turn, further deteriorate agents’ trust in the domestic banking system, which may lead them to rely more on sentiment than macro-financial factors (fundamentals).

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Authors and Affiliations

  1. Department of Accounting and Finance, Athens University of Economics and Business, Athens, Greece

    Apostolos G. Katsafados & Dimitris Anastasiou

  2. Economic Research Division, Alpha Bank, Athens, Greece

    Dimitris Anastasiou

  3. Department of Statistics, Bank of Greece, Athens, Greece

    Apostolos G. Katsafados

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  1. Apostolos G. Katsafados
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  2. Dimitris Anastasiou
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Correspondence to Apostolos G. Katsafados.

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Appendix

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Table A1 Summary of literature on bank deposit flow forecasting and determinants
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Katsafados, A.G., Anastasiou, D. Short-term prediction of bank deposit flows: do textual features matter?. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-06048-8

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