Machine Learning for Predicting Deposits Bank Market Shares in Emerging Markets: Evidence from Egypt

Osama Wagdi, Ream N. Kinawy, Ghada Nabil

Resumo


The study investigated machine learning for predicting deposit bank market shares in Egypt as emerging markets. The examination encompasses the years 2014 to 2022, based on the Egyptian banks listing on the Egyptian exchange. The study sampled 11 banks based on artificial neural networks ANN under the economic growth rate, interest spreads, required reserve ratio, capital adequacy requirements, style of bank, number of branches, number of ATMs, number of cards, and number of e-banking services. The study found that artificial neural networks can explain changes in the market shares of Egyptian banks by 99.4% and 96.71%, according to regression analysis and cross-sectional analysis, respectively. But the predicted value was less than the actual values according to the Wilcoxon Signed Ranks Test, which can be explained by the study’s reliance on a sample representing one-third of the study population, which are the banks listed on the Egyptian Exchange only. These banks are under the supervision of shareholders to a greater extent than the rest of the unlisted banks.

Palavras-chave


Banking, Egypt, Artificial Neural Network ANN

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Referências


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DOI: https://doi.org/10.20397/2177-6652/2023.v23i4.2729

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