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The goal of this paper is to examine the effect of high liquidity creation on systemic risk. We use a hand-collected dataset on 94 banks from 16 Western European countries over the 2004–2020 period, including the crisis (2008–2009) period and sound periods (2004–2007 and 2010–2020). We assess banks' systemic risk using two different proxies: banks' systemic risk exposure, measured by the marginal expected shortfall (MES), and banks' systemic risk contribution, measured by the delta conditional value at risk (ΔCoVaR). Based on panel regressions, our results mainly show that, during calm periods, high liquidity creation is associated with high systemic risk exposure. Moreover, we show that the effect of liquidity creation on banks' systemic risk exposure is stronger during turmoil periods. Interestingly, our results show that banks' liquidity creation increases the systemic contribution only during the financial crisis of 2008–2009. Our findings contribute to the literature and the regulatory debate by suggesting that regulators should pay more attention to high liquidity-creating banks as they may cause aggregate financial fragility. [ABSTRACT FROM AUTHOR]

This paper considers the use of loan loss reserves (LLRs) in the banking production process and treats it as one variable with a dual role. We establish a three-stage network Data Envelopment Analysis model to address this issue. Using a sample of 43 Chinese commercial banks over the period 2011–2019, the results show that the banks with the ratio between LLRs and total loans less than 1% have higher level of efficiency compared to the ones holding the ratio greater than 1%. The results show that when excluding LLRs in the production process, the efficiency scores are significantly inflated. We find that small and medium sized banks are more efficient than their big counterparts, however, the results show that big banks hold more than enough amounts of LLRs than the one required by the regulatory authority. When LLRs are excluded from the production process, it shows that big banks perform better than small and medium sized banks. Our findings show that less liquid banks perform better than the ones with higher levels of liquidity no matter in which way LLRs are treated. Finally, we find that lower capitalized banks, compared to the ones with high levels of capitalization, are less efficient. however, it shows that higher capitalized banks consistently keep more than 1% LLRs out of total loans. [ABSTRACT FROM AUTHOR]

This paper analyses the volatility transmission between European Global Systemically Important Banks (GSIBs) and implied stock market volatility. A Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model is applied to determine the dynamic correlation between returns of Europe's GSIBs and the world's most prominent measure of market "fear", the CBOE Volatility Index (VIX). The results identify a higher negative co-relationship between the VIX and GSIB returns during the COVID-19 period compared with the Global Financial Crisis (GFC), with one-day lagged changes in the VIX negatively Granger-causing bank returns. The asymmetric impact of changes in implied volatility is examined by quantile regressions, with the findings showing that in the lower quartile–where extreme negative bank returns are present–jumps in the VIX are highly significant. This effect is more pronounced during COVID-19 than during the GFC. Additional robustness analysis shows that these findings are consistent during the periods of the Swine Flu and Zika virus epidemics. [ABSTRACT FROM AUTHOR]

This study examines whether nonlinear effects of income diversification on bank stability occurs in the European banking system. Based on the framework developed by Tone and Tsutsui (Omega 42(1):124–131, 2014), we propose a novel three-stage (deposit producing, intermediation, and value maximization) dynamic network slacks-based measure model to assess bank stability in a sample of 114 European commercial banks during the post financial crisis period (2010–2019). We use the panel smooth transition regression model (PSTR), as introduced by González et al. (Panel smooth transition regression models, CREATES research paper 2017-36. Department of Economics and Business Economics, Aarhus University, 2017), to investigate the potential regime-switching behavior of the relationship between income diversification and stability. Our findings show that higher levels of income diversification, into and within nontraditional banking generating activities, worsen bank stability. It results that, in the case of European banks, income diversification is sub-optimal as no benefits are found from "over diversification". Important policy implications arise from our findings pertaining to the optimality of income diversification and stability, which could be in conflict with banks' traditional lines of business aiming at promoting lending activities. [ABSTRACT FROM AUTHOR]

The paper examines the sensitivity for the solution of linear programming problems using Bayesian techniques, when samples for the coefficients of the objective function are uncertain. When data is available, we estimate the solution of the linear program and provide statistical measures of uncertainty through the posterior distributions of the solution in the light of the data. When data is not available, these techniques examine the sensitivity of the solution to random variation in the coefficients of the linear problem. The new techniques are based on two posteriors emerging from the inequalities of Karush–Kuhn–Tucker conditions. The first posterior is asymptotic and does not require data. The second posterior is finite-sample-based and is used whenever data is available or if random samples can be drawn from the joint distribution of coefficients. A by-product of our framework is a robust solution. We illustrate the new techniques in two empirical applications to the case of uncertain Data Envelopment Analysis efficiency, involving two large samples, of US commercial banks and a sample of European commercial banks regulated by the Single Supervisory Mechanism. We analyse whether some pre-determined criteria, associated with size and new supervisory framework, can adequately affect the solution of linear program. The results provide evidence of substantial improvements in statistical structure with respect to sensitivities and robustification. Our methodology can serve as a consistency check of the statistical inference for the solution of linear programming problems in efficiency under uncertainty in data. [ABSTRACT FROM AUTHOR]

This paper constructs a dual-channel green supply chain model that includes a well-capitalized retailer and e-commerce platform, as well as a capital-constrained manufacturer. The manufacturer sells two different green quality products through the e-commerce platform and the retailer. There are two portfolio financing strategies for the manufacturer to choose and decide whether to use blockchain. We establish four models of whether to use blockchain under the EP financing strategy (e-commerce platform financing and prepayment financing) and the EB financing strategy (e-commerce platform financing and bank financing). By comparing the optimal solutions under four models, we discover some results. Firstly, we discover that the wholesale price will be higher than the selling price of the online channel when the product greenness difference between the two channels is large, and the optimal expected profit of the retailer is not related to product green differences. Secondly, the EP financing strategy is the optimal financing strategy choice for the manufacturer regardless of whether blockchain is used or not. Thirdly, when the cost per unit of using blockchain is greater than a certain threshold, the manufacturer should choose not to use blockchain. Finally, the manufacturer's acceptable blockchain usage threshold under the EP financing strategy is higher than under the EB financing strategy, and it decreases with the smaller product greenness difference between the two channels under the EP financing strategy. In addition, we also make an extension that verifies our model and findings are robust. [ABSTRACT FROM AUTHOR]

To disentangle the sources of bank inefficiency, this paper presents an extended two-stage network multi-directional efficiency analysis (NMEA) approach by taking the internal structure of the banking system into account. The proposed two-stage NMEA approach extends the conventional "black-box" MEA approach, providing a unique efficiency decomposition and identifying which variables drive the inefficiency for banking systems with a two-stage network structure. An empirical application of Chinese listed banks from 2016 to 2020 during the 13th Five-year Plan reveals that the overall inefficiency of sample banks is primarily sourced from the deposit-generating subsystem. Additionally, different types of banks display differentiated evolution modes over different dimensions, confirming the importance of applying the proposed two-stage NMEA approach. [ABSTRACT FROM AUTHOR]

This paper examines the performance of Chinese commercial banks before, during and after the 2007–2008 global financial crisis and China's 4 trillion renminbi stimulus plan during 2008–2010. Fully nonparametric methods are used to estimate technical efficiencies. Recently-developed statistical results are used to test for changes in efficiencies as well as productivity over time, and to test for changes in technology over time. We also test for differences in efficiency and productivity between big and small banks, and between domestic and foreign banks. We find evidence of non-convexity of banks' production set. Our results suggest that technical efficiency declined at the start of the global financial crisis and again during 2011–2012 after enactment of the stimulus plan. Although efficiency recovered somewhat during 2013–2014, China's banks ended with lower technical efficiency on average in 2014 than in 2007. We find that productivity declined during 2008–2009, increased during 2013–2014, but as with efficiency ended lower on average in 2014 than in 2007. We find compelling evidence that big banks were more efficient than small banks throughout 2007–2014, and in most years also were more productive. Finally, domestic banks were on average more efficient than foreign banks in 2007, but foreign banks were more efficient than domestic banks in 2014. Similarly, foreign banks were on average more productive than domestic banks in 2007, but less productive than domestic banks in 2012–2014. [ABSTRACT FROM AUTHOR]

This paper analyses the performance of non-banking finance companies (NBFC) in the Indian context using data envelopment analysis (DEA). The underlying objective of this study is to fill the void in the domain of NBFC, although a lot of research has been done on the banking industry in the context of the application of DEA, but none on NBFCs. The paper takes the panel data of the last 5 years (2014–2018) to calculate super-efficiencies in the first stage and then regresses the same on exogenous factors in stage-2. Descriptive statistics are used to estimate the efficiency by carrying out the calculations using both the traditional models (OTE, PTE and SE) and super-efficiency model. A comparison is made by categorizing NBFC's based on the size of total assets and using non-parametric statistic tests to find whether the efficiency scores are significantly different across different categories. The second stage DEA analysis uses Tobit regression to find the exogenous factors which affect the model significantly. Based on traditional models, the total number of efficient DMUs are 8 out of 43 while there are 15 after considering the super-efficiency algorithm. Malmquist Indices are used to study the productivity indices of NBFCs over the last 5 years, and it gives us a maximum productivity growth of 8.53%. It was noticed that there is a significant difference in the mean efficiency values of different sized NBFC's which can be explained by the lack of standardization in the NBFC domain and the few companies which are listed on the stock market. The managers should not consider ROE as a significant indicator of efficiency and should instead focus on aspects such as ROA and income diversity. Through the Malmquist analysis, the managers can break down the productivity change into technical and efficiency shifts for further investigation. [ABSTRACT FROM AUTHOR]

Although the business environment is stochastic, deterministic data envelopment analysis (DEA) models are typically used to measure the efficiency of commercial banks for the purpose of simplicity. Bank operations are characterized by a network structure due to the dual role of deposits, which, on the one hand, are the output of the process of borrowing funds from depositors and, on the other hand, are the input of the process of making loans. Since the outputs of the production process of the bank are correlated with its inputs, the model for measuring efficiency in this case is a stochastic program with correlated data. To take the correlation between the inputs and outputs into consideration, in this paper, a standard normal transformation is applied for the correlated data, and a network stochastic model is developed to obtain the distribution of the stochastic efficiency. The model is used to measure the efficiency of twenty-two commercial banks in Taiwan. The results are more reliable, discriminative, and informative than those obtained from the existing models. They also show that the performance of a bank is mainly affected by its loan performance. Different from the stereotype suggesting that private companies usually operate more efficiently than state-owned companies, public banks perform better than private banks in Taiwan. [ABSTRACT FROM AUTHOR]

This paper examines differences in bank efficiency between banks affiliated with single-bank holding companies and those affiliated with multi-bank holding companies by applying a fuzzy multi-objective two-stage data envelopment analysis technique. Using a sample of U.S. commercial banks covering 1994–2018, the results show that banks affiliated with multi-bank holding companies are more efficient than those affiliated with single-bank holding companies, suggesting that the former takes advantage of their parents' resources to enhance their efficiency, consistent with the internal capital market theory. They also show that banks with a powerful CEO exhibit lower efficiency than others. Moreover, there is an inverted U shape relationship between multi-bank holding company structure and bank efficiency, suggesting the presence of an optimal number of multi-bank holding subsidiaries that maximizes efficiency. [ABSTRACT FROM AUTHOR]

Over the past few decades, the banking sectors in Latin America have undergone rapid structural changes to improve the efficiency and resilience of their financial systems. The up-to-date literature shows that all the research studies conducted to analyze the above-mentioned efficiency are based on a deterministic data envelopment analysis (DEA) model or econometric frontier approach. Nevertheless, the deterministic DEA model suffers from a possible lack of statistical power, especially in a small sample. As such, the current research paper develops the technique of satisficing DEA to examine the still less explored case of Peru. We propose a Satisficing DEA model applied to 14 banks operating in Peru to evaluate the bank-level efficiency under a stochastic environment, which is free from any theoretical distributional assumption. The proposed model does not only report the bank efficiency, but also proposes a new framework for peer mining based on the Bayesian analysis and potential improvements with the bias-corrected and accelerated confidence interval. Our study is the first of its kind in the literature to perform a peer analysis based on a probabilistic approach. [ABSTRACT FROM AUTHOR]

During the last decades, the banking industry has been facing multiple challenges, from changes in regulations and legal systems, to the irruption of new technologies. A constant adaptation to the rapidly changing business ecosystem is therefore required. This paper deals with one type of efficiency improvement that is required from traditional banking operations as a result of the current shift in the industry towards more use of technology. That is, we deal with the decision-making process related to the redesign of existing ATM networks, which is currently being considered by many banks worldwide. This redesign is mainly a consequence of an increased use of electronic payment methods and card transactions, which have made the demand for cash decrease significantly. We describe a multi-objective mathematical programming model to be used as a tool when making strategic decisions regarding the bank ATM network redesign. Total costs and the coverage of customers and turnover are considered as conflicting performance attributes and analyzed through several multi-criteria techniques, namely the weighted sum and Tchebycheff methods and Archimedean goal programming. Among other insights, the results on a realistic case study show that the potential for cost reductions is high and that the consideration of both customer coverage and turnover coverage is crucial for the performance of the network. [ABSTRACT FROM AUTHOR]

To create efficient funds appealing to a sector of bank clients, the objective of minimizing downside risk is relevant to managers of funds offered by the banks. In this paper, a case focusing on this objective is developed. More precisely, the scope and purpose of the paper is to apply the mean-semivariance efficient frontier model, which is a recent approach to portfolio selection of stocks when the investor is especially interested in the constrained minimization of downside risk measured by the portfolio semivariance. Concerning the opportunity set and observation period, the mean-semivariance efficient frontier model is applied to an actual case of portfolio choice from Dow Jones stocks with daily prices observed over the period 2005-2009. From these daily prices, time series of returns (capital gains weekly computed) are obtained as a piece of basic information. Diversification constraints are established so that each portfolio weight cannot exceed 5 per cent. The results show significant differences between the portfolios obtained by mean-semivariance efficient frontier model and those portfolios of equal expected returns obtained by classical Markowitz mean-variance efficient frontier model. Precise comparisons between them are made, leading to the conclusion that the results are consistent with the objective of reflecting downside risk. [ABSTRACT FROM AUTHOR]

The paper analyses the effects on the productivity of Greek commercial banks of sector regulatory reforms in the pre-Euro era, using the Global Malmquist Index. In a bootstrap Data Envelopment Analysis framework, we propose an alternative to smoothing that utilises the Pearson system random number generator, offering greater flexibility in the choice of the fitting distribution. In the context of a step-by-step approach, we demonstrate the contribution of deregulatory commercial freedoms to greater productivity and the negative effect of prudential controls. Our findings offer insights into the current state of the Greek banking sector, suggesting that the imposition of additional prudential controls may have a detrimental impact on the productivity of Greek banks, given the adverse business conditions. [ABSTRACT FROM AUTHOR]

An individual investor has to decide how to allocate his/her savings from a retirement perspective. This problem covers a long-term horizon. In this paper we consider a 40-year horizon formulating a multi-criteria multistage program with stochastic dominance constraints in an intermediate stage and in the final stage. As we are dealing with a real problem and we have formulated the model in cooperation with a commercial Italian bank, the intermediate stage corresponds to a possible withdrawal allowed by the Italian pension system. The sources of uncertainty considered are: the financial returns, the interest rate evolution, the investor's salary process and a considerable withdrawal event. We include a set of portfolio constraints according to the pension plan regulation. The objective of the model is to minimize the Average Value at Risk Deviation measure and to satisfy wealth goals. Three different wealth target formulations are considered: a deterministic wealth target (i.e. a comparison between the accumulated average wealth and a fixed threshold) and two stochastic dominance relations-the first order and the second order-introducing a benchmark portfolio and then requiring the optimal portfolio to dominate the benchmark. In particular, we prove that solutions obtained under stochastic dominance constraints ensure a safer allocation while still guaranteeing good returns. Moreover, we show how the withdrawal event affects the solution in terms of allocation in each of the three frameworks. Finally, the sensitivity and convergence of the stochastic solutions and computational issues are investigated. [ABSTRACT FROM AUTHOR]

Owing to the importance of internal structure of decision making units (DMUs) and data uncertainties in real situations, the present paper focuses on multi-component data envelopment analysis (MC-DEA) approach with imprecise data. The undesirable outputs and shared resources are also incorporated in the production process of multi-component DMUs to validate real problems. The interval efficiencies of DMUs and their components in MC-DEA are often challenging with imprecise data. In many practical situations, different set of weights may be resulted into valid efficiency intervals for DMUs but invalid interval efficiencies for their components. Therefore, the present study proposes a new common set of weights methodology, based on interval arithmetic and unified production frontier, to determine unique weights for measuring these interval efficiencies. It is a two-level mathematical programming approach that preserves linearity of DEA and exhibits stronger discrimination power among the DMUs as compared to some existing approaches. Theoretically, the aggregate efficiency interval of each DMU lies between the components' interval efficiencies. Further, the proposed approach is also applied to banks in India for proving its acceptability in practical applications. The performance of each bank is investigated in terms of two components: general business and bancassurance business for the years 2011-2013. The present study emphasized expanding pattern of bancassurance business in current market scenario with more percentage increase as contrasted to general business. [ABSTRACT FROM AUTHOR]

This paper highlights the role of behavioral factors for efficiency measurement in supply networks. To this aim, behavioral issues are investigated among interrelations between decision makers involved in corporate bond service networks. The corporate bond network was considered in three consecutive stages, where each stage represents the relations between two members of the network: issuer-underwriter, underwriter-bank, and bank-investor. Adopting a multi-method approach, we collected behavioral data by conducting semi-structured interviews and applying the critical incident technique. Financial and behavioral data, collected from each stage in 20 corporate bond networks, were analyzed using fuzzy network data envelopment analysis to obtain overall and stage-wise efficiency scores for each network. Sensitivity analyzes of the findings revealed inefficiencies in the relations between underwriters-issuers, banks-underwriters, and banks-investors stemming from certain behavioral factors. The results show that incorporating behavioral factors provides a better means of efficiency measurement in supply networks. [ABSTRACT FROM AUTHOR]

Performance evaluation is an important part in the management of any decision-making unit (DMU) as it identifies sources of managerial inefficiencies and provides a policy for inefficient DMUs to improve their efficiency. The latter is generally affected by environmental variables that are beyond managerial control. Modeling the impact of these environmental variables is a critical issue for both researchers and practitioners. Researchers developed and proposed several methods to deal with this issue in general and in the data envelopment analysis (DEA) literature in particular. However, the available two-stage DEA methods do not account for interdependence between observations and they are of limited use when the number of variables is fairly large. This paper proposes an integrated framework combining DEA, and boosted generalized linear mixed models (GLMMs) that accounts for the interdependence problem when studying the impact of environmental variables on performance. Additionally, the framework carries out variable selection. The framework is illustrated with a sample of 151 commercial banks from Middle East and North African countries. [ABSTRACT FROM AUTHOR]

In this paper. Data Envelopment Analysis (DEA) is developed to analyze the efficiency of a single bank. The inputs are given in terms of cost of personnel, cost of material and expected cost of credit losses. Outputs concern lending, deposits and gross revenues (interest margins and non-interest income). The data covers 48 large Nordic banks during the two years 1992 and 1993. Fourteen banks are from Denmark, thirteen from Finland, twelve from Norway and nine from Sweden. For each of these banks, the DEA method is used to form a ‘reference bank’, which is a convex combination of the best competing banks (those at the efficiency frontier). The three inputs and the three outputs of the reference bank will be used as benchmarks. This procedure implies that one can only say that one single bank is less efficient than its reference bank, not less efficient than another bank. The results show that 4–7 Nordic banks were situated at the efficiency frontier for those two years. These banks should then be used to form reference banks for other banks, and to set benchmarks for them. Such benchmarks would have been slightly different, dependent on the ‘window’ to be used, 1992, 1993 or 1992 + 1993. [ABSTRACT FROM AUTHOR]

We consider the problem faced by a central bank which bails out distressed financial institutions that pose systemic risk to the banking sector. In a structural default model with mutual obligations, the central agent seeks to inject a minimum amount of cash in order to limit defaults to a given proportion of entities. We prove that the value of the central agent's control problem converges as the number of defaultable institutions goes to infinity, and that it satisfies a drift controlled version of the supercooled Stefan problem. We compute optimal strategies in feedback form by solving numerically a regularized version of the corresponding mean field control problem using a policy gradient method. Our simulations show that the central agent's optimal strategy is to subsidise banks whose equity values lie in a non-trivial time-dependent region. [ABSTRACT FROM AUTHOR]

Bank industry plays a critical role in the economic development of China. In this paper, we develop a new two-stage data envelopment analysis approach for measuring the slacks-based efficiency of Chinese commercial banks during years 2008-2012, where the banking operation process of each bank is divided into a deposit-generation stage (division) and a deposit-utilization stage (division). In the approach, the increase of desirable outputs and the decrease of undesirable outputs are simultaneously considered in order to identify the inefficiency of a bank. Three efficiency statuses are first defined for such a system to investigate its input-output performance and divisional performances, and a full efficiency status is then defined based on these statuses. The empirical results show that the improvement of the banks' performances during this period was mainly contributed by the improvement of deposit-utilization stage. Besides, the results also show that our approach can provide a benchmark for the intermediate measures of the two stages of an inefficient bank. [ABSTRACT FROM AUTHOR]

Reverse factoring, a financial scheme in which established retailers facilitate financing for suppliers, is becoming an increasingly important tool in the industry. Normally, an SME supplier, a core retailer and a bank participate in the reverse factoring scheme. A three-level Stackelberg game is proposed in this study to investigate the interaction of the participants. The closed-form equilibria of the retailer's replenishment decision, the supplier's payment term decision and the bank's financing decision are derived from the theoretical model. To our knowledge, this study is the first attempt which takes banks into account and endogenises their interest rates in the modelling of reverse factoring. The reverse factoring scheme is compared with commercial loans and traditional factoring. Compared to commercial loans, the introduction of factoring can lower credit risk, but fraud risk still exists. Reverse factoring solves this fraud problem and further decreases the financing cost for the supplier. Consequently, reverse factoring benefits the retailer through a significantly increased payment extension granted by the supplier. The numerical results also indicate that the utility of the bank significantly improves by 8–50% under varying levels of default risk compared with traditional factoring. Our study provides incentives and guidelines for supply chain participants to adopt such schemes when faced with capital constraints and the credit risk of the supplier. [ABSTRACT FROM AUTHOR]

Past studies about the application of data envelopment analysis (DEA) to banking performance often follow the concept of technical efficiency (TE) and/or the productivity defined by the TE. In this paper, we propose an enhanced DEA model, based on a modification of the directional distance function by simultaneously but disproportionately seeking the maximum expansion of each desirable output and contraction of each undesirable output for efficiency measurement, which allows us to decompose the TE into operating efficiency (OPE) and risk management efficiency (RME). The OPE characterizes the ability of a bank to expand the room for profits through its regular business activities, while the RME describes a bank's ability in risk management activities for sustaining operations. To illustrate the usefulness of the proposed model, a case study of Taiwan's domestic commercial banks is presented. The major findings are that operating inefficiency is the main source of technical inefficiency, although banks with a higher OPE generally also have a higher RME. Banks subordinate to financial holding companies are more efficient in both OPE and RME than stand-alone banks. [ABSTRACT FROM AUTHOR]

The linkage among customer satisfaction, employee evaluation, and business performance data is very important in modern business organizations. Several previous research efforts have studied this linkage, focusing mainly on the financial or business performance in order to analyze the efficiency of an organization. However, recent studies have tried to consider other important performance indicators, which are able to affect business operations and future growth (e.g., external and internal customer satisfaction). In the case of the banking industry, studying the relations among the aforementioned variables is able to give insight in the performance evaluation of bank branches and the viability analysis of the banking organization. This paper presents a real-world study for measuring the relative efficiency of a set of bank branches using a Data Envelopment Analysis (DEA) approach. In particular, a multistage DEA network model is proposed, using a set of performance indicators that combine customer satisfaction, employee evaluation, and business performance indices. The main aim of the presented study is to evaluate the relative efficiency of each customer service delivery step, in the environment of a bank branch. The results are also able to estimate the contribution of the assessed performance indicators to the branch's overall efficiency, and to determine potential improvement actions. [ABSTRACT FROM AUTHOR]

An accurate prediction of financial distress is beneficial to investors and allows banks and other financial institutions to build an early warning system to avoid risk contagion. This study investigated financial distress prediction using textual sentiment extracted from listed firms' annual reports in the Chinese market. The sentiments reflected by the firms' management discussions and analysis (MD&A) sections and audit reports were extracted separately through the application of deep learning algorithms. We found that the sentiment score extracted from MD&A sections was more optimistic compared with that extracted from audit reports. Moreover, the experimental results demonstrated that the modeling performance was significantly improved with the incorporation of textual sentiment scores, and the inclusion of sentiment from audit reports lead to a more significant incremental improvement than that from the MD&A sections. However, when both sentiment scores were included in the modeling input, the improvement in predictive accuracy was insignificant compared to the model using audit report scores only. Our study highlights the predictive power of textual information in annual reports, and shows that the textual sentiment of annual reports should be applied in distress modeling. The results provide implications for the utilization of soft information in credit risk modeling in the context of Chinese market, and such application can be further explored in other areas of operational research studies. [ABSTRACT FROM AUTHOR]

This paper presents two empirical studies of software production conducted at two large Canadian banks. For this purpose, we introduce a new model of software production that considers more outputs than those previously cited in the literature. The first study analyses a group of software development projects and compares the ratio approach lo performance measurement to the results of DEA. It is shown that the main deficiencies of the performance ratio method can be avoided with the latter. Two different approaches are employed to constrain the DEA multipliers with respect to subjective managerial goals. As is further shown, incorporating subjective values into efficiency measures must be done in a careful and rigorous manner, within a framework familiar to management. The second study investigates the effect of quality on software maintenance (enhancement) projects. Quality appears to have a significant impact on the efficiency and cost of software projects in the data set. We further show the problems that may result when quality is excluded from the production models for efficiency assessment. In particular, we show some of the misleading results that can be obtained when the simple, traditional, ratio definition of productivity is used for this purpose. [ABSTRACT FROM AUTHOR]

Short-sighted asset/liability strategies of the seventies left financial intermediaries - banks, insurance and pension fund companies, and government agencies - facing a severe mismatch between the two sides of their balance sheet. A more holistic view was introduced with a generation of portfolio immunization techniques. These techniques have served the financial services community well over the last decade. However, increased interest rate volatilities, and the introduction of complex interest rate contingencies and asset-backed securities during the same period, brought to light the shortcomings of the immunization approach. This paper describes a series of (optimization) models that take a global view of the asset/liability management problem using interest rate contingencies. Portfolios containing mortgage-backed securities provide the typical example of the complexities faced by asset/liability managers in a volatile financial world. We use this class of instruments as examples for introducing the models. Empirical results are used to illustrate the effectiveness of the models, which become increasingly more complex but also afford the manager increasing flexibility. [ABSTRACT FROM AUTHOR]

The measurement of financial risk relies on two factors: determination of riskiness by use of an appropriate risk measure; and the distribution according to which returns are governed. Wrong estimates of either, severely compromise the accuracy of computed risk. We identify the too-big-to-fail banks with the set of 'Global Systemically Important Banks' (G-SIBs) and analyze the equity risk of its equally weighted portfolio by means of the 'Foster-Hart risk measure'-a bankruptcy-proof, reserve based measure of risk, extremely sensitive to tail events. We model banks' stock returns as an ARMA-GARCH process with multivariate 'Normal Tempered Stable' innovations, to capture the skewed and leptokurtotic nature of stock returns. Our union of the Foster-Hart risk modeling with fat-tailed statistical modeling bears fruit, as we are able to measure the equity risk posed by the G-SIBs more accurately than is possible with current techniques. [ABSTRACT FROM AUTHOR]

To shed light on whether and how effective MCDA can be in security screening, as an alternative or complementary modeling procedure, to a real-world business model currently used in the banking industry, we attempt to answer the following question: Does it cost to be traditional? This is the first time that four popular MCDA methods are utilized simultaneously, and an integrated comparative framework is presented, to assess its effectiveness, against an ISO-certified investment management protocol. The portfolios generated, either as the individual output of each MCDA method or as the intersection of results of all the utilized methods, perform better or equally satisfactory as the professional benchmark. This is true, both in-sample and out-of-sample, considering a large universe of European mutual funds, managed by all the well-known investment houses, and spanning the whole spectrum of strategies. Also, the empirical validation analysis provides insights that, the multicriteria techniques exploited, might effectively utilized, towards enhancing the absolute and risk-adjusted portfolio returns and enforcing the underlying managerial transparency and compliance. After all, this study implies that the MCDA paradigm may be effective on the structured appraisal of financial securities, and the corresponding economic stylized facts. [ABSTRACT FROM AUTHOR]

An investor’s decisions affect the way taxes are paid in a general portfolio investment, modifying the net redemption value and the yearly optimal portfolio distribution. We investigate the role of these decisions on multistage mean-variance portfolio allocation model. A number of risky assets grouped in wrappers with special taxation rules is integrated in a multistage financial portfolio optimization problem. The uncertainty on the returns of assets is specified as a scenario tree generated by simulation/clustering based approach. We show the impact of decisions in the yearly reallocation of the investments for three typical cases with an annual fixed withdrawal in a fixed horizon that utilizes completely the option of taper relief offered by banks in UK. Our computational framework can be used as a tool for testing decisions in this context. [ABSTRACT FROM AUTHOR]

Although there is a growing number of research articles investigating the performance in the banking industry, research on Chinese banking efficiency is rather focused on discussing rankings to the detriment of unveiling its productive structure in light of banking competition. This issue is of utmost importance considering the relevant transformations in the Chinese economy over the last decades. This is a development of a two-stage network production process (production and intermediation approaches in banking, respectively) to evaluate the efficiency level of Chinese commercial banks. In the second stage regression analysis, an integrated Multi-Layer Perceptron/Hidden Markov model is used for the first time to unveil endogeneity among banking competition, contextual variables, and efficiency levels of the production and intermediation approaches in banking. The competitive condition in the Chinese banking industry is measured by Panar–Rosse H-statistic and Lerner index under the Ordinary Least Square regression. Findings reveal that productive efficiency appears to be positively impacted by competition and market power. Second, credit risk analysis in older local banks, which focus the province level, would possibly be the fact that jeopardizes the productive efficiency levels of the entire banking industry in China. Thirdly, it is found that a perfect banking competition structure at the province level and a reduced market power of local banks are drivers of a sound banking system. Finally, our findings suggest that concentration of credit in a few banks leads to an increase in bank productivity. [ABSTRACT FROM AUTHOR]

We develop an output-oriented data envelopment analysis framework to examine the efficiency of Chinese banks over the period 2007–2017 and further test the relationship between efficiency and corporate social responsibility (CSR). We are the first piece of research considering the number of employees as one bank input and potential increase in the number of employees as one CSR indicator. Additionally, we innovatively propose another three specific CSR indicators: namely donation, balance of green credits and loans to small and medium sized enterprises. The results show that the gain from improving allocative output-efficiency by reallocating variable inputs is less than the gain attained by improving technical output-efficiency. Evidence from the second-stage regression analysis shows that the overall indirect technical efficiency is significantly and negatively affected by the volumes of green credits, while an increase in the volumes of donations will improve the indirect allocative efficiency. [ABSTRACT FROM AUTHOR]

We propose a clustering method for large dimensional data to classify the 365 largest euro area financial institutions according to their business model. The proposed clustering approach is applied to granular supervisory data on banks' activities and combines also dimensionality reduction and outlier detection. We identify four business models, namely wholesale funded, securities holding, traditional commercial and complex commercial banks while identifying as outliers the banks that follow idiosyncratic business models. Evidence is provided that the sets of banks following the distinct business models differ with respect to performance and risk indicators. [ABSTRACT FROM AUTHOR]

One method for evaluating cost-efficiency of commercial firms is data envelopment analysis. In the real world, data have a time dependency that also affects the cost-efficiency calculation. This article introduces a new model for measuring cost-efficiency under inter-temporal dependency in an assessment window. In the proposed approach, there is a reserve capital for the assessment window that is considered as the input of this period, and at any time of the window, a certain amount of capital is used. For practical application, the proposed model is applied to a real dataset of branches of an Iranian commercial bank to evaluate cost-efficiency under inter-temporal dependency. [ABSTRACT FROM AUTHOR]

This study proposes a two-stage data envelopment analysis model based on the meta-frontier boundary and intermediate output goal setting. Comparing to the traditional models, the proposed model is able not only to consider technology heterogeneity of decision making units, but also to target the intermediate output. The proposed model was applied to an analysis of 28 Chinese commercial banks (CCBs). Empirical analysis has obtained some valuable research results. First, the efficiency of the CCBs' deposit sub-system is not very high, especially in terms of the deposit efficiency of city commercial banks (CBs). Second, in the deposit sub-system, the efficiency gap among state-owned commercial banks (SBs) is higher than the joint stock commercial banks (JBs) and the CBs. Third, in the loan sub-system, the efficiency gap among SBs and CBs is higher than that in the JBs. Fourth, the deposits of more than half of CCBs are not on the frontier of efficiency, showing that the financial resource allocation of CCBs is severely ineffective. Finally, this study divides CCBs into four categories and provides specific recommendations to improve performance and deposit target setting. [ABSTRACT FROM AUTHOR]

This study explored how dramatic changes in ownership structure and operational technologies have influenced Chinese bank efficiency over the past decade. The study included an empirical analysis using 5 years (2011–2015) of operational data for 71 Chinese commercial banks. Two two-stage meta-frontier data envelopment analysis network models and multiple regression models were used to estimate and analyze impacts of variations in bank ownership structure. The main empirical results show that irrespective of deposits or loans efficiency, State-owned Banks (SOBs) have the highest technology and management levels. In contrast, City Commercial Banks should improve both technology and management levels, narrowing the gap with SOBs and Joint-stock Banks. The deposit efficiency of a bank was found to be mainly influenced by the nature of ownership (national shareholding ratio and the shareholding ratio of the domestic legal entities) and ownership concentration. The loan efficiency of a bank was mainly affected by the nature of ownership (the shareholding ratio of the foreign legal entities) and ownership liquidity. [ABSTRACT FROM AUTHOR]

In this study, a data envelopment analysis-based approach is proposed for estimating and decomposing the potential gains from the horizontal integrations of two-stage production systems. In the first tier, the gains of the potential merger are measured by a composite efficiency index. The potential efficiency gains are then decomposed as the product of coordination efficiency and two divisional potential efficiency gains. In the second tier, the divisional potential efficiency gains are decomposed into scope, size, and learning efficiencies. We develop a multiplicative framework for decomposing potential efficiency gain in mergers of the two-stage production systems, and the coordination efficiency is considered. In the case study of commercial banks in mainland China, we develop an algorithm for estimating and decomposing potential efficiency gain for a two-stage bank production system. [ABSTRACT FROM AUTHOR]

In general, the demand rate moving through a product life cycle can be reasonably depicted by a trapezoidal-type pattern: it initially increases during the introduction and growth phases, then remains reasonably constant in the maturity phase, and finally decreases in the decline phase. It is evident that perishable products deteriorate continuously over time and can not be sold after its maximum lifetime. Thus, the deterioration rate of a product is increasing with time and closely related to its maximum lifetime. Furthermore, it has been hard to obtain loans from banks since the global financial meltdown in 2008. Hence, over 80% of firms in the United Kingdom and the United States sell their products on various short-term, interest-free loans (i.e., trade credit) to customers. To incorporate those important facts, we develop an inventory model by (1) assuming the demand pattern is trapezoidal, (2) extending the deterioration rate to 100% as its maximum lifetime is approaching, (3) using discounted cash-flow analysis to calculate all relevant costs considering the effects of upstream and downstream trade credits, and (4) including the costly purchase cost into the total cost, which is omitted in previous studies. Then, the order quantity that maximizes the present value of the profit is uniquely determined. Finally, through numerical examples, managerial insights are provided. [ABSTRACT FROM AUTHOR]

This work extends the contagion model introduced by Nier et al. (J Econ Dyn Control 31(6):2033-2060, 2007. ) to inhomogeneous networks. We preserve the convenient description of a financial system using a sparsely parameterized random graph, but add several relevant inhomogeneities. These include well-connected banks, financial institutions with disproportionately large interbank assets, and big banks that focus on wholesale and retail customers. These extensions significantly enhance the model's generality as they reflect realistic inhomogeneities that have a potentially decisive impact on a system's stability. We find that large and well-connected banks have a surprisingly modest impact. However, institutions with disproportionately large interbank assets significantly increase the risk of contagion in networks. Moreover, these effects can be partly compensated by a redistribution of equity capital, even without increasing the total amount. However, the level of regulatory capital should be defined according to the interbank market position of a bank, and not the size of the bank. [ABSTRACT FROM AUTHOR]

Adopting a profit-based approach to the estimation of the efficiency of South Korean banks over the 2007Q3 to 2011Q2 period, we systematically analyse, within a non-parametric DEA analysis, how the choice of risk management control variable impacts upon such estimates. This is in recognition of previous findings that such estimates are dependent on the choice of risk management control variable and the lack of guidance from such studies on the optimal choice of risk control variable. Using the model of Liu et al. (Ann Operat Res 173:177-194, 2010), we examine the dependency of the estimated efficiency scores on the chosen risk control variables embracing loan loss provisions and equity as good inputs and non-performing loans as a bad output. We duly find that, both for individual banks and banking groups, the mean estimates are indeed model dependent although, for the former, rank correlations do not change much at the extremes. Based on the application of the Simar and Zelenyuk (Econom Rev 25:497-522, 2006) adapted Li (Econom Rev 15: 261-274, 1996) test, we then find that, if only one of the three risk control variables is to be included in such an analysis, then it should be loan loss provisions. We also show, however, that the inclusion of all three risk control variable is to be preferred to just including one, but that the inclusion of two such variables is about as good as including all three. We therefore conclude that the optimal approach is to include (any) two of the three risk control variables identified. The wider implication for research into bank efficiency is that the optimal choice of risk management control variable is likely to be crucial to both the delivery of risk-adjusted estimates of bank efficiency and the specification of the model to be estimated. [ABSTRACT FROM AUTHOR]

We construct and estimate a dynamic network Luenberger productivity indicator for Japanese banks during fiscal years 2006-2012. The network aspect to the model recognizes that banks produce deposits in the first stage of production using inputs such as labor, physical capital, and equity capital and then in the second stage use those deposits to generate a portfolio of loans and securities investments. Because of asymmetric information between borrower and lender and uncertainty about the future state of the economy the second stage of production also generates an undesirable by-product: some loans become nonperforming. The dynamic aspect to the model recognizes that nonperforming loans generated in one period will typically constrain production in a subsequent period. Moreover, bank managers have discretion over when to transform deposits into the portfolio of loans and investments so that when faced with a high risk lending environment managers can choose to save some deposits as excess reserves for use in a subsequent period when they anticipate a more favorable lending environment. [ABSTRACT FROM AUTHOR]

The primary objective of the article is to illustrate how to develop a multiple stakeholder perspective (MSP) on a common set of financial ratios using a cross-country survey and reflect this information in a single comparative bank performance estimate. Data Envelopment Analysis brings together the varying perspectives of five key stakeholders, namely, regulators, shareholders, customers, bank managers and employees. I develop the MSP approach by taking advantage of a recent online survey of stakeholder perceptions on key financial ratios across the major trading partners Australia, China and Japan. Insights gained through MSP can guide regulatory vigor, promotional or public relations activities, raising of equity capital in overseas markets and other cross-border operations such as positioning an institution's international presence in a host country. [ABSTRACT FROM AUTHOR]

This research intends to develop the classifiers for dealing with binary classification problems with interval data whose difficulty to be tackled has been well recognized, regardless of the field. The proposed classifiers involve using the ideas and techniques of both quantiles and data envelopment analysis (DEA), and are thus referred to as quantile-DEA classifiers. That is, the classifiers first use the concept of quantiles to generate a desired number of exact-data sets from a training-data set comprising interval data. Then, the classifiers adopt the concept and technique of an intersection-form production possibility set in the DEA framework to construct acceptance domains with each corresponding to an exact-data set and thus a quantile. Here, an intersection-form acceptance domain is actually represented by a linear inequality system, which enables the quantile-DEA classifiers to efficiently discover the groups to which large volumes of data belong. In addition, the quantile feature enables the proposed classifiers not only to help reveal patterns, but also to tell the user the value or significance of these patterns. [ABSTRACT FROM AUTHOR]

new dynamic Data Envelopment Analysis (DEA) approach is created to provide valuable managerial insights when assessing the merger performance. This new approach allows us to dynamically evaluate the pre-merger firms and the post-merger firm in a multi-period situation. A case study of bank branch merger is conducted to illustrate and validate the proposed approach. Both stochastic frontier analysis and data envelopment analysis are used and compared leading to highly correlated results. The computation show that merger results in an overall efficiency achievement in a banking industry. [ABSTRACT FROM AUTHOR]

We study how the threat of entry affects financial contracting between an incumbent firm and a bank, in a stochastic and dynamic environment. Contracts are short term and public. We determine the effects of the first period financial contract on the first period outputs in face of the threat of entry. Specifically, it is shown that the distance between first period outputs is increased due to potential entry. This is due to two underlying effects: first, the threat of entry reduces the signal dampening effect and thus the surplus left to the low cost incumbent is reduced. Second, learning is more valuable as it decreases the probability of entry. Indeed, experimentation takes on a strategic form, since the bank must take into account the impact of the possible game on its expected profits. This work integrates the agency problem between a firm and its financial intermediary with the issue of entry-deterrence under uncertainty. [ABSTRACT FROM AUTHOR]