Efficiency in BRICS banking under data vagueness: A two-stage fuzzy approach

Abstract

This study analyzes the efficiency levels of the banking industry in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 2010 to 2014, using an integrated two-stage fuzzy approach. Very often the reliability of data collected from BRICS is questionable. In this research, we first use fuzzy TOPSIS to capture vagueness in the relative efficiency of BRICS banking over time. In the second stage, we adopt fuzzy regressions based on different rule-based systems to enhance the power of significant socioeconomic, regulatory, and demographic variables to predict banking efficiency. These variables are previously identified by using bootstrapped truncated regressions with conditional α-levels, as proposed by Wanke, Barros, and Emrouznejad (2015a). The results reveal that efficiency in the banking industry is positively associated with country gross savings and the GINI index ratio, but negatively associated with relatively high inflation ratios. Fuzzy regressions proved far more accurate than bootstrapped truncated regressions with conditional α-levels. We derive policy implications.

Digitalisation and Big Data Mining in Banking

“…Other advanced supports [76][77][78][79][80][81][82][83][84] classification (NN, DT, SVM), k-mean clustering Nigeria [77], Turkey [78,81], Canada [80], ASEAN [82], Islamic banks [83], BRICS [84], US [79] Branch strategy, bank efficiency evaluation, deposit pricing, early warning of failing bank.…”

“…These studies assisted the regulatory bodies with early signals of banks/branches that require immediate attention; they also contributed to achieving strategic expansion design. Similarly, Wanke et al [82][83][84] have conducted a series of research studies into banking efficiency evaluation by adopting NN techniques. Their applications have covered banking sectors from ASEAN, Islamic and BRICS countries.…”

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

“…Finally, focusing on the analyses of banks, the existing literature shows that the introduction of AI techniques represents a breakthrough in the analysis of several areas, such as risk management. Using big data to increase banks' knowledge of their customers, to provide loans according to certain credit scores, to measure their efficiency, to use advanced computational systems to balance their ALCO portfolios and to optimize the capital structure and liquidity and funding needs, banks have come to rely heavily on these advanced methodologies [77][78][79][80][81][82]. The growing importance of fuzzy logic in this type of analysis is demonstrated by the finding that approximately 20% of the portfolios focused on corporate finance are concerned with bank analysis.…”

Efficiency in banking of developing countries with the same cultural background

“…Conclusions are often taken based on regulatory barriers or cultural aspects of the local market (Wanke and Barros, 2014;Chortareas et al, 2012;Ariff and Can, 2008;Girardone et al, 2004;Garza-García, 2012;Zhang and Matthews, 2012). In the second one, a multi-country analysis is conducted to explore differences in banking performance based not only in terms of specific country regulations (Wanke, Barros and Macanda, 2016;Wanke, Azad and Emrouznejad, 2018;Wanke, Barros and Emrouznejad, 2018), but also in terms of religious aspects, such as those involved in Islamic banking (Wanke, Azad, Barros and Hadi-Vencheh, 2016;Wanke et al, 2017).…”

“…Therefore, the banking activity in development countries is still a relatively underresearched topic. Specifically, previous research on Mozambican and Angolan banks includes Wanke, Barros and Macanda (2016), Wanke, Barros, Azad and Constantino (2016), Wanke, Emrouznejad (2016, 2018), Wanke, Azad and Emrouznejad (2018) and , who used non-parametric models such as DEA and most discussions were focused on efficiency rankings, although some papers also explored the impact of contextual variables on efficiency levels. On the other hand, research on Brazilian banks includes Silva (2001), who analyzed Brazilian bank efficiency with a stochastic frontier model in the period after the Real Plan.…”