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

Katsafados, Apostolos and Anastasiou, Dimitris (2022): Short-term Prediction of Bank Deposit Flows: Do Textual Features matter?

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Bank deposit flows; European banks; textual analysis; short-term prediction; machine learning
• Subjects: C - Mathematical and Quantitative Methods > C0 - General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C54 - Quantitative Policy Modeling; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications; G - Financial Economics > G1 - General Financial Markets > G10 - General

References

Ambrose, B.W., and Megginson, W.L., (1992). “The role of asset structure, ownership structure, and takeover defenses in determining acquisition likelihood”. Journal of Financial and Quantitative Analysis, 27, 575-589.

Anastasiou, D., and Drakos, K., (2021a). “Nowcasting the Greek (semi‐) deposit run: Hidden uncertainty about the future currency in a Google search”. International Journal of Finance and Economics, 26, 1133-1150.

Anastasiou, D., Kapopoulos, P., and Zekente, KM., (2022a), “Sentimental Shocks and House Prices”. Journal of Real Estate Finance and Economics.

Anastasiou, D., Kallandranis, C., and Drakos, K., (2022b), “Borrower Discouragement Prevalence for Eurozone SMEs: Investigating the Impact of Economic Sentiment”. Journal of Economic Behavior and Organization, 194, 161-171.

Anastasiou, D., and Drakos, K., (2021b). “European depositors’ behavior and crisis sentiment”. Journal of Economic Behavior and Organization, 184, 117-136.

Anastasiou, D., and Katsafados, A., (2020). “Bank deposits flows and textual sentiment: When an ECB president’s speech is not just a speech”. MPRA Working Paper No. 99729.

Anastasiou, D., and Petralias, A., (2021). “On the construction of a leading indicator based on news headlines for predicting Greek deposit outflows”, MPRA Working Paper No. 107602.

Anastasiou, D., Louri, H., and Tsionas, M., (2019). “Non-performing loan in the Euro area: Are Core-Periphery Banking Markets Fragmented?”. International Journal of Finance and Economics, 24, 97-112.

Angelopoulou, E., Balfoussia, H., and Gibson, H.D., (2014). “Building a financial conditions index for the euro area and selected euro area countries: What does it tell us about the crisis?”. Economic Modelling, 38, 392-403.

Balakrishnan R., Qiu X.Y., and Srinivasan P., (2010). “On the predictive ability of narrative disclosures in annual reports”. European Journal of Operational Research, 202, 789-801.

Bijsterbosch, Μ., and Falagiarda, Μ., (2015). “The macroeconomic impact of financial fragmentation in the euro area: Which role for credit supply?”. Journal of International Money and Finance, 54, 93-115.

Bodnaruk, A., Loughran, T., and McDonald, B., (2015). “Using 10-K text to gauge financial constraints”. Journal of Financial and Quantitative Analysis, 50, 623-646.

Boehm, T.P., and DeGennaro, R.P., (2011). “A discrete choice model of dividend reinvestment plans: Classification and prediction”. Managerial and Decision Economics, 32, 215-229.

Breiman, L., (1996). “Bagging predictors”. Machine Learning, 24, 123-140.

Brown, S.V, and Tucker, J.W., (2011). “Large-sample evidence on firms’ firms’ year-over-year MD&A modifications”. Journal of Accounting Research, 49, 309-346.

Cao, L., (2003). “Support vector machines experts for time series forecasting”. Neurocomputing, 51, 321-339.

Demirguç-Kunt, A., and Detragiache, E., (1998). “The determinants of banking crises in developing and developed countries”. International Monetary Fund Staff Papers, 45, 81-109.

Dosdogru, A.T., (2019). “Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market”. Managerial and Decision Economics, 40, 460-471.

Doumpos, M., Andriosopoulos, K., Galariotis, E., Makridou, G., and Zopounidis, C., (2017). “Corporate failure prediction in the European energy sector: a multicriteria approach and the effect of country characteristics”. European Journal of Operational Research, 262, 347-360.

Espahbodi, H., and Espahbodi, P., (2003). “Binary choice models for corporate takeover”. Journal of Banking and Finance, 27, 549-574.

Finger, M.H., and Hesse, H., (2009). “Lebanon-determinants of commercial bank deposits in a regional financial center”. International Monetary Fund, No. 9-195.

Gaganis, C., Pasiouras, F., and Tzanetoulakos, A., (2005). “A comparison and integration of classification techniques for the prediction of small UK firms failure”. Journal of Financial Decision Making, 1, 55-69.

Galariotis, E.C., Makrichoriti, P., & Spyrou, S., (2016). “Sovereign CDS spread determinants and spill-over effects during financial crisis: A panel VAR approach”. Journal of Financial Stability, 26, 62-77.

Gandhi, P., Loughran, T., and McDonald, B., (2019). “Using annual report sentiment as a proxy for financial distress in U.S. banks”. Journal of Behavioral Finance, 20, 424-436.

Geng, R., Bose, I., and Chen, X., (2015). “Prediction of financial distress: An empirical study of listed Chinese companies using data mining”. European Journal of Operational Research, 241, 236-247.

Goldberg, Y., (2017). “Neural network methods for natural language processing”. Morgan & Claypool Publishers.

Gómez-Puig, M., Sosvilla-Rivero, S., and del Carmen Ramos-Herrera, M., (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, 30, 133-153.

Gorton, G., (1988). “Bank panics and business cycles”. Oxford Economic Papers, 40, 751-781. Hagenau, M., Liebmann, M., and Neumann, D., (2013). “Automated news reading: Stock price prediction based on financial news using context-capturing features”. Decision Support Systems, 55, 685-697.

Hondroyiannis, G., (2004). “Estimating private savings behaviour in Greece”. Journal of Economic Studies, 31, 457-476.

Huang, W., Nakamori, Y., and Wang, S.Y., (2005). “Forecasting stock market movement direction with support vector machine”. Computers and Operations Research, 32, 2513-2522.

Huo, D., and Chaudhry, H.R., (2021). Using machine learning for evaluating global expansion location decisions: An analysis of Chinese manufacturing sector. Technological Forecasting and Social Change, 163, 120436.

Iworiso, J., and Vrontos, S., (2020). “On the directional predictability of equity premium using machine learning techniques”. Journal of Forecasting, 39, 449-469.

Kamble, S.S., Gunasekaran, A., Kumar, V., Belhadi, A., and Foropon, C., (2021). “A machine learning based approach for predicting blockchain adoption in supply Chain”. Technological Forecasting and Social Change, 163, 120465.

Katsafados, A.G., Androutsopoulos, I., Chalkidis, I., Fergadiotis, E., Leledakis, G.N., and Pyrgiotakis, E.G., (2020). “Textual information and IPO underpricing: A machine learning approach”. Working Paper SSRN: ssrn.com/sol3/papers.cfm?abstract_id=3720213.

Katsafados, A.G., Androutsopoulos, I., Chalkidis, I., Fergadiotis, E., Leledakis, G.N., and Pyrgiotakis, E.G., (2021). “Using textual analysis to identify merger participants: Evidence from U.S. banking industry”. Finance Research Letters, forthcoming.

Kearney, C., and Liu, S., (2014). “Textual sentiment in finance: A survey of methods and models”. International Review of Financial Analysis, 33, 171-185.

Kumar R.B., Kumar B.S., and Prasad C.S.S., (2012). “Financial news classification using SVM”. International Journal of Scientific and Research Publications, 2, 2250-3153.

Kumar, B.S., and Ravi, V., (2016). “A survey of the applications of text mining in financial domain”. Knowledge-Based Systems, 114, 128-147.

Li, J.P., Mirza, N., Rahat, B., and Xiong, D., (2020). “Machine learning and credit ratings prediction in the age of fourth industrial revolution”. Technological Forecasting and Social Change, 161, 120309.

Loughran, T., and McDonald, B., (2016). “Textual analysis in accounting and finance: A survey”. Journal of Accounting Research, 54, 1187-1230.

Loughran, T., and McDonald, Β., (2013). “IPO first-day returns, offer price revisions, volatility, and form S-1 language). Journal of Financial Economics, 109, 307-326.

Mai, F., Tian, S., Lee, C., and Ma, L., (2019). “Deep learning models for bankruptcy prediction using textual disclosures”. European Journal of Operational Research, 274, 743-758.

Martinez-Peria, M.S., and Schmukler, S.L., (2001). “Do depositors punish banks for bad behavior? Market discipline, deposit insurance, and banking crisis”. Journal of Finance, 56, 1029-1051.

Min, J.H., and Lee, Y.C., (2005). “Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters”. Expert Systems with Applications, 28, 603-614.

Moniz, A., and Jong, F.D., (2014). “Classifying the influence of negative affect expressed by the financial media on investor behavior”. 5th Information Interaction in Context Symposium (IIiX), 275-278.

Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., and Ling Ngo, D.C., (2014). “Text mining for market prediction: A systematic review”. Expert Systems with Applications, 41, 7653-7670.

Nys, E., Tarazi, A., and Trinugroho, I., (2015). “Political connections, bank deposits, and formal deposit insurance”. Journal of Financial Stability, 19, 83-104.

Oliveira, R., Schiozer, R.F. and Barros L., (2014). “Depositors’ perception of too-big-to-fail”. Review of Finance, 18, 1-37.

Pai, P.F., and Lin, C.S., (2005). “A hybrid ARIMA and support vector machines model in stock price forecasting”. Omega, 33, 497-505.

Palepu, K.G., (1986). “Predicting takeover targets: A methodological and empirical analysis”. Journal of Accounting and Economics, 8, 3-35.

Papoulias, C., and Theodossiou, P., (1992). “Analysis and modeling of recent business failures in Greece”. Managerial and Decision Economics, 13, 163-169.

Pasiouras, F., and Tanna, S., (2010). “The prediction of bank acquisition targets with discriminant and logit analyses: methodological issues and empirical evidence”. Research in International Business and Finance, 24, 39-61.

Pasiouras, F., Gaganis, C., Tanna, S., and Zopounidis, C., (2008). “An application of support vector machines in the prediction of acquisition targets: Evidence from the EU banking sector). In: Zopounidis, C., Doumpos, M., Pardalos, P. (Eds.), Handbook of Financial Engineering. Springer.

Pasiouras, F., Gaganis, S., and Zopounidis, C., (2010). “Multicriteria classification models for the identification of targets and acquirers in the Asian banking sector”. European Journal of Operational Research, 204, 328-335.

Pasiouras, F., Tanna, S., and Zopounidis, C., (2007). “The identification of acquisition targets in the EU banking industry: An application of multicriteria approaches”. International Review of Financial Analysis, 16, 262-281.

Pestov, V., (2013). “Is the k-NN classifier in high dimensions affected by the curse of dimensionality?”. Computers and Mathematics with Applications, 65, 1427-1437.

Petropoulos, A., Vlachogiannakis, N. E., and Mylonas, D., (2018). “Forecasting private sector bank deposits in Greece: determinants for trend and shock effects”. International Journal of Banking, Accounting and Finance, 9, 141-169.

Piscopo, G., (2010). “Italian deposits time series forecasting via functional data analysis”. Banks and Bank Systems, 5, 12-19.

Routledge, B.R., Sacchetto, S., and Smith, N.A., (2017). “Predicting merger targets and acquirers from text”. Working Paper, Carnegie Mellon University.

Shin, K.S., Lee, T.S., and Kim, H., (2005). “An application of support vector machines in bankruptcy prediction model”. Expert Systems with Applications, 28, 127-135.

Sun, S., Luo, C., and Chen, J., (2017). “A review of natural language processing techniques for opinion mining systems”. Information Fusion, 36, 10-25.

Tang, X., Li, S., Tan, M., and Shi, W., (2020). “Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods”. Journal of Forecasting, 39, 769-787.

Vapnik, V.N., and Vapnik, V., (1998). “Statistical learning theory: 1”. New York: Wiley. Veganzones, D., and Severin, E., (2018). “An investigation of bankruptcy prediction in imbalanced datasets”. Decision Support Systems, 112, 111-124.

Wu, C.H., Tzeng, G.H., Goo, Y.J., and Fang, W.C., (2007). “A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy”. Expert Systems with Applications, 32, 397-408.