Narrow Results

The Vietnam economy has gained lots of achievements after the financial crisis 2007–2011, until it reached a low inflation rate of 0.6% in 2015. This paper measures the volatility of market risk in Vietnam banking industry after this period (2015–2017). The main reason is the vital role of the bank system in Vietnam in the economic development and growth in recent years always goes with risk potential and risk control policies.

This research paper aims to figure out the increase or decrease in the market risk of Vietnam banking firms during the post-low inflation period 2015–2017.

First, by using the quantitative combined with comparative data analysis method, we find out the risk level measured by equity beta mean in the banking industry is acceptable, although it is little higher than ($>$) 1.

Then one of its major findings is the comparison between risk level of banking industry during the financial crisis 2007–2009 compared to those in the post-low inflation time 2015–2017. In fact, the research findings show us market risk level during the post-low inflation time has increased much. We compare beta in two periods because we want to figure out the reason underlying the fact beta has increased. One of the reasons is that the accumulated banking risks during the longer time and criteria to meet Basel 2 have been partially contributing to increasing market risk.

Finally, this paper provides some ideas that could provide companies and government more evidence in establishing their policies in governance. This is the complex task but the research results show us warning that the market risk might be higher during the post-low inflation period 2015–2017. Our conclusion part will recommend some policies and plans to deal with it.

Directors’ monitoring and advising activities as agents were supposed to increase after the Dodd-Frank Act in 2010. The Dodd-Frank Act significantly increases the pressure on the board of directors to be more effective agents of the stockholders even after the Sarbanes-Oxley Act (2002) became effective. Director compensation, especially incentive-based compensation, is intended to align with the interests of shareholders and motivate director behavior. This paper empirically tests how banks respond to the Dodd-Frank Act by redesigning their director compensation plans. Our findings suggest that banks recognize the need for improved board monitoring by highlighting the importance of director workload and qualifications through the design of director compensation packages in the post-Dodd-Frank Act period. We also find that the negative impact of excessive director equity compensation on firm performance was attenuated after the passage of the Dodd-Frank Act. The findings of this study shed light on the rationale of director compensation policies for banking firms.

Recently, the number of consultative documents and research papers that discuss risk integration has grown considerably. This paper presents a comprehensive review of the work done on risk integration in the banking industry. This survey includes: (1) risk integration methods within regulatory frameworks and the banking industry; (2) challenges of risk integration; (3) risk interaction mechanisms; (4) development of risk integration approaches; (5) risk interaction results: diversification versus compounding effects; and (6) some future research topics in risk integration. We seek to understand developments in modeling risk integration, particularly in light of the global financial crisis of 2008 and important future research topics that emerge from both the literature and the banking industry.

Data envelopment analysis (DEA) is a widely used mathematical programming technique for measuring the relative efficiency of decision-making units which consume multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally used in classical DEA models, real-life problems often involve uncertainties characterized by fuzzy and/or random input and output data. We present a new input-oriented dual DEA model with fuzzy and random input and output data and propose a deterministic equivalent model with linear constraints to solve the model. The main contributions of this paper are fourfold: (1) we extend the concept of a normal distribution for fuzzy stochastic variables and propose a DEA model for problems characterized by fuzzy stochastic variables; (2) we transform the proposed DEA model with fuzzy stochastic variables into a deterministic equivalent linear form; (3) the proposed model which is linear and always feasible can overcome the nonlinearity and infeasibility in the existing fuzzy stochastic DEA models; (4) we present a case study in the banking industry to exhibit the applicability of the proposed method and feasibility of the obtained solutions.

Competitive markets and customers’ changing needs in the bank industry necessitate accurately predicting customers who may leave the firm in the near future. Consequently, creating an approach to predict precisely and identify churn-leading causes is a part of retention strategies in customer relationship management. The approach that has been utilized in this research to predict customer churn combines decision tree (DT) and multinomial regression (MR) to classify customers with no limitation of binary classification in the churn prediction context. A customer club dataset of a commercial bank case as a real churn problem is used in this study to benchmark the hybrid forecasting approach against its building blocks. The results showed that the hybrid forecasting approach outperformed DT and MR with an average accuracy of 87.66%, 90.74% micro-average, and 90.44% macro-average of AUC. Further analysis of the model performance per class indicated that the hybrid approach’s misclassification error for the churn class decreased significantly, which is the most costly error in churn problems. Moreover, due to the structure of hybrid forecasting approach, more interoperability is obtained by assessing the impact of features in different segments, resulting in transforming them into actionable insights. The proposed approach is applied to the banking industry to prevent financial loss by detecting leading churn causes. Accordingly, after predicting the risk of customer churn, marketers and managers can determine appropriate actions that will have the most significant retention impact on each customer by applying proactive retention marketing.

This paper discusses the economic growth and technological change of the Thai banking industry in relation to a post financial crisis, based on Schumpeter's economic development theory. It is argued that the structural changes of the Thai banking industry reflect Schumpeter's gales of creative destruction. The circumstance in which Thailand has to let the ailing banks and financial institutions go bankrupt and renew the process of growth through mergers and acquisitions represents an adjustment phase of an economy undergoing technological change. Using Porter's Competitive Forces Model, this paper aims to understand banks' pursuit of strategies to survive and increase competitiveness under the financial liberalization policies. The paper concludes with policy recommendations for the Thai banking industry to manage innovations under a competitive pressure after the financial crisis.

Increasing digitalization and new technological possibilities also entail substantial changes for working methods in the B2B (business-to-business) environment in banking. In this context, the concept of co-creation is critical. Although this concept and the motivation factors behind it have been thoroughly investigated in the B2C (business-to-consumer) sector, only a few research results exist for the B2B context. This study aims to bridge the current knowledge gap and investigate individuals’ motivation to participate in B2B co-creation. By using a case study and qualitative interviews, this study focuses on two aspects: (a) It reveals how a co-creation measure is used in practice in the B2B environment; and (b) it provides information on the motivation factors and outcome from the point of view of the participants in the B2B co-creation project. The paper concludes with an integrative model of the main motivation factors behind B2B co-creation and their effects.

Synopsis

The research problem

We examine whether small audit firms adopt niche strategies to compete in the public company audit market. To that end, we focus on auditors servicing clients from one industry only (which we label as a nationwide single-industry focus) and test its association with audit quality and pricing.

Motivation

Prior research on auditor competition has mainly focused on the large (mainly Big Four) audit firms. While large and small audit firm markets are two distinct markets, little is known about competitive strategies of small audit firms in the public company audit market.

The test hypotheses

Our first hypothesis is that small audit firms adopting a single-industry focused strategy supply higher audit quality compared with audit firms that do not adopt a single-industry focused strategy or a simple industry specialization strategy, ceteris paribus. Our second hypothesis is that small audit firms adopting a single-industry focused strategy engage in discount pricing compared with audit firms that do not adopt a single-industry focused strategy or a simple industry specialization strategy, ceteris paribus.

Target population

The U.S. commercial banking sector covering the period 2004–2020.

Adopted methodology

Multivariate analyses adopting linear and probit models.

Analyses

To test our first hypothesis, we run a bank audit quality model using loan loss provisions as the dependent variable for our main analyses. Subsequent analyses adopt restatement, going concern, and internal control weaknesses models. To test our second hypothesis, we rely on a bank audit fee model. All models include relevant controls established in previous research and those for self-selection.

Findings

The evidence suggests that small audit firms adopting a nationwide single-industry focus are able to provide higher audit quality (compared with other rivals, even industry specialists) while at the same time charging lower audit fees implying cost efficiencies that are passed on to the client due to their focused expertise.

Climate change and technological innovation expose banks to “new risks.” The COVID-19 pandemic has accelerated and amplified these effects. This paper investigates how the traditional tool of the risk map can incorporate these “new risks” for banks. We develop the configuration of the risk map under the influence of the COVID-19 pandemic in the context of economy digitalization for an anonymous Bank X. We show that the risk map is still a useful tool for identifying risks in banks, appreciable for its simplicity and adaptability especially in evolving contexts. The main contribution is the construction of a methodological framework that is useful for operators and provides banking industry decision makers with a supporting tool to adequately respond to the changing environment.

This study is an attempt to evaluate and compare the performance of State-Owned Commercial Banks (SOCBs) and Private Commercial Banks (PCBs) of Bangladesh. CAMEL rating model has been applied to confess where a bank can be successful and where it has weaknesses. Data have been collected from four SOCBs and eight PCBs for the years 2014–2017. Among the selected SOCBs, it is found that Agrani Bank holds “Satisfactory” position where Sonali Bank holds “Fair” position through the year 2014–2017. On the other hand, Janata bank has improved its position from “Fair” to “Satisfactory” for the year 2016 and 2017. Moreover, Rupali Bank holds ‘Satisfactory’ position only for the year 2017 where this position was “Fair” for the year 2014–2016. On the other hand, it is found that all the selected PCBs hold “Satisfactory” position through the year 2014–2017. Though the composite rating for both types of banks (SOCBs and PCBs) is in “Satisfactory level”, Rank-1 is given to PCBs and Rank-2 is given to SOCBs. CAMEL ratio for “Asset quality” for both types of banks (SOCBs and PCBs) are showing “Dissatisfactory level”. “Earning quality” of SOCBs is showing at a “Marginal level”. Therefore, proper attention should be given to manage the “Asset quality” and SOCBs should increase the “Earning quality”.

High liquidity is one of the most significant objectives in the banking industry. To ensure stable liquidity, client prediction is a common method adopted by bank managers. Although there has been some literature discussing the methods to make client predictions, it lacks quantitative comparison between algorithms. This study will focus on the prediction of bank clients and compare the effectiveness of different algorithms in machine learning (neural network, decision tree, logistic regression). It is designed to compare the five metrics (Type I sample f1-score, Type II sample f1-score, accuracy, Area Under Curve, Kolmogorov-Smirnov) to distinguish the feasibility of different algorithms. The higher index represents the better algorithm. As the results, the neural network has the highest AUC (0.85) and highest Type I sample f1-score (0.50), while the logistic regression has the highest accuracy (0.90), KS (0.64), and Type II sample f1-score (0.50). (0.94). According to reality, the neural network is suggested to be the optimal algorithm that needs to be adopted by bank managers for client prediction.