Dr Menelaos Tasiou

Analytic Hierarchy Process (AHP) is a well-founded and popular method in the Multi-Criteria Decision Analysis (MCDA) field. AHPSort, a recently introduced sorting variant, uses crisp class-assignment of alternatives. This can sometimes be misleading, especially for alternatives near the border of two classes. This paper aims at making the class assignment process in AHPSort more flexible by using fuzzy sets theory, which facilitates soft transitions between classes and provides additional information about the membership of alternatives in each class that can be used to fine-tune actions beyond the crisp sorting process. This essentially complements the ordinal information of its crisp variant with cardinal information as to the degree of membership of an alternative to each class. The applicability of the proposed approach is illustrated in a case study that regards the classification of London boroughs according to their safety levels.

In the ongoing context of climate change, there is an increasing need to support decision-making processes in the domain of landscape planning and management. Suitable evaluation techniques are needed to take into account the interests of actors and stakeholders in shared policy decisions. An important methodological contribution to the field is given by the Multicriteria Decision Analysis (MCDA), due to its ability to combine multiple aspects of a decision problem with the values and opinions expressed by different Decision Makers. The present paper develops the "Group Analytic Hierarchy Process Sorting II method" (GAHPSort II), which aims to sort a group of municipalities included in the UNESCO site "Vineyard Landscape of Piedmont: Langhe-Roero, and Monferrato" (Italy) according to the economic attractiveness of the landscape. Extending the previous versions AHPSort I, AHPSort II and GAHPSort, the GAHPSort II optimizes multi-stakeholder evaluations on large databases by reducing the number of comparisons. Moreover, the GAHPSort II method is proposed as a novel spatial decision support system because it combines a set of economic indicators for landscape and GIS methods for aiding the Decision Makers to better understand the case study and to support the definition and localization of policies and strategies of landscape planning and management.

In recent years, accelerator programs experienced substantial growth, becoming an important part of the entrepreneurial ecosystems around the world. New ventures that want to participate in such programs must go through a multi-stage and highly competitive process, with only one out of ten applicants being successful. However, our knowledge with regards to the factors that drive the decisions of accelerator programs is limited, and empirical research on this topic is scarce. We hypothesise that the national culture of the founding team can play an important role as a proxy for the unobservable values and the behaviour of the venture founders, and we examine the impact of cultural diversity on the probability of being admitted into an accelerator program. The results show that diversity enhances the probability of being selected. This finding is robust across several specifications, and while accounting for the potential endogeneity of cultural diversity

Two reduced-form versions of New Keynesian wage Phillips curves based on either sticky nominal wages or real-wage rigidity using monthly US state-level data for the period 1982-2016 are examined, taking account of the endogeneity of unemployment by instrumentation and the use of common correlated effects (CCE) and mean group (MG) methods. This is the first time that this methodology has been applied in this context. These are important issues, as ignoring them may lead to substantial biases. The results show that while the aggregate data do not provide estimates that are consistent with either of the theoretical models examined, the panel methods do. Moreover, use of an appropriate MG CCE estimator leads to economically significant changes in parameters (primarily a steeper Phillips curve) relative to those from inappropriate but widely used panel methods, and in the real-wage rigidity case is required to deliver results that have a theoretically admissible interpretation

Despite serious threats as to their soundness, the adoption of composite indicators is constantly growing alongside their popularity, especially when it comes to their adoption in policy-making exercises. This study presents a robust non-compensatory approach to construct composite indicators that is mainly based, at least with respect to the basic ideas, on the classic Borda scoring procedure. The non compensatory indicators we are proposing can be seen as aggregation of ordinal non-compensatory preferences between considered units supplying a numerical cardinal comprehensive evaluation. For this reason, we define our methodology, the ordinal input for cardinal output non-compensatory approach for composite indicators. To take into account hesitation, imprecision and ill-determination in defining preference relations with respect to the elementary indices, we adopt the PROMETHEE methods, whose net flow score can be seen as an extension to the fuzzy preferences of the Borda score. Moreover, we systematically deal with robustness of the results with respect to weighting and parameters such as indifference and preference thresholds, allowing to define preference relations of elementary indices. In this regard, we couple PROMETHEE methods with the recently proposed sigma - mu it approach, which permits to explore the whole domain of feasible preference parameters mentioned above, giving a synthetic representation of the distribution of the values assumed by the composite indicators in terms of mean, it, and standard deviation, a. it and a are also used to define a comprehensive overall composite indicator. Finally, we enrich the results of this analysis with a set of graphical visualizations based on principal component analysis applied to the PROMETHEE methods with the GAIA technique, providing better understanding of the outcomes of our approach. To illustrate its assets, we provide a case study of inclusive development evaluation, based on the data of the homonymous report produced by the World Economic Forum. (C) 2020 Elsevier B.V. All rights reserved.

This paper introduces an extension of a well-known Multiple Criteria Decision Aiding method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Most of the TOPSIS applications assume that preferences are monotonic for each evaluation criterion and that qualitative scales are converted into quantitative ones before the method is applied. However, both assumptions have been subject of discussion and criticism in the literature. To this solution, this paper introduces a normalization technique based on simulations that permit taking into account non-monotonic preferences as well as qualitative criteria. An additional novelty lies in the integration of the Multiple Criteria Hierarchy Process, which extends the applicability of the method to problems in which criteria are hierarchically structured. To deal with robustness concerns, the Stochastic Multicriteria Acceptability Analysis will be used in the new proposal, giving information in statistical terms on the goodness of the considered alternatives. The new method has been applied to evaluate a set of banks listed in the LSE’s FTSE350 Index.

This paper examines the impact of three culturally endorsed leadership prototypes on bank lending corruption. We bring together studies that approach the corruption of bank lending officers from the perspective of a principal-agent problem and studies from the leadership literature, suggesting leadership as an alternative to contractual solutions to agency problems. We hypothesize, based on these views, that culturally endorsed leadership styles that improve (worsen) the leader-subordinate relationships have a negative (positive) effect on bank lending corruption. Using a sample of around 3,500 firms from 36 countries, we find that the prosocial leadership prototype and the nonautonomous leadership prototype do not matter, whereas the self-serving leadership prototype has a positive and statistically significant effect on bank lending corruption. These findings are robust to the inclusion of various control variables in the regressions, and alternative estimation approaches, including ones that account for endogeneity concerns. Furthermore, we find that the power of bank regulators and the age of the credit information sharing mechanism play a moderating role in the relationship between the self-serving leadership prototype and bank lending corruption.

In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured the attention of the media and policymakers around the globe, and their number of applications has surged ever since. This increase in their popularity has solicited a plethora of methodological contributions in response to the substantial criticism surrounding their underlying framework. In this paper, we put composite indicators under the spotlight, examining the wide variety of methodological approaches in existence. In this way, we offer a more recent outlook on the advances made in this field over the past years. Despite the large sequence of steps required in the construction of composite indicators, we focus particularly on two of them, namely weighting and aggregation. We find that these are where the paramount criticism appears and where a promising future lies. Finally, we review the last step of the robustness analysis that follows their construction, to which less attention has been paid despite its importance. Overall, this study aims to provide both academics and practitioners in the field of composite indices with a synopsis of the choices available alongside their recent advances.

•We consider the distribution of composite indicator values in the weight vector space.•Mean (μ) and standard deviation (σ) synthesize the distribution.•Measurement of local and global efficiency in the σ−μ space is introduced.•Static or dynamic multiple efficient frontiers can be graphically represented.•σ−μ efficiency analysis is applied to the World Happiness Index. We propose a methodology to employ composite indicators for performance analysis of units of interest using and extending the family of Stochastic Multiattribute Acceptability Analysis. We start evaluating each unit by means of weighted sums of their elementary indicators in the whole set of admissible weights. For each unit, we compute the mean, μ, and the standard deviation, σ, of its evaluations. Clearly, the former has to be maximized, while the latter has to be minimized as it denotes instability in the evaluations with respect to the variability of weights. We consider a unit to be Pareto–Koopmans efficient with respect to μ and σ if there is no convex combination of μ and σ of the rest of the units with a value of μ that is not smaller, and a value of σ that is not greater, with at least one strict inequality. The set of all Pareto–Koopmans efficient units constitutes the first Pareto–Koopmans frontier. In the spirit of context-dependent Data Envelopment Analysis, we assign each unit to one of the sequence of Pareto–Koopmans frontiers. We measure the local efficiency of each unit with respect to each frontier, but also its global efficiency taking into account all frontiers in the σ−μ plane, thus enhancing the explicative power of the proposed approach. To illustrate its potential, we present a case study of ‘world happiness’ based on the data of the homonymous report that is annually produced by the United Nations’ Sustainable Development Solutions Network.

Clustering is a long and widely-used technique to group similar objects based on their distance. Recently, it has been found that this grouping exercise can be enhanced if the preference information of a decision-maker is taken into account. Consequently, new multi-criteria clustering methods have been proposed. All proposed algorithms are based on the non-hierarchical clustering approach, in which the number of clusters is known in advance. In this paper, we propose a new hierarchical multi-criteria clustering that is based on PROMETHEE, where the number of clusters does not need to be specified. Because the outcome is dependent on the parameters of PROMETHEE, we take into account uncertainty and imprecision by enhancing our approach making use of the Stochastic Multiobjective Acceptability Analysis (SMAA) and cluster ensemble methods. SMAA is used to generate a large number of solutions by randomly varying the PROMETHEE parameters, followed by the use of ensemble clustering, which reaches a consensus solution. Our new approach is illustrated in a clustering study of the performance evaluation of US banks according to a set of financial and non-financial (environmental, social and corporate governance; ESG) criteria. We find that established banks appear in the overall best-performing clusters, with more contemporary banks following suit. In additional analysis we compare financial and overall (financial and non-financial) performance and find a mixed appreciation of the ESG aspects in this industry in the middle clusters. (C) 2020 Elsevier Ltd. All rights reserved.

We introduce country-month indices of efficiency of government policy in dealing with the COVID-19 pandemic. Our indices cover 81 countries and the period from May 2020 to November 2021. Our framework assumes that governments impose stringent policies (listed in the Oxford COVID-19 Containment and Health Index) with the single goal of saving lives. We find that positive and significant correlates of our new indices are institutions, democratic principles, political stability, trust, high public spending in health, female participation in the workplace, and economic equality. Within the efficient jurisdictions, the most efficient ones are those with cultural characteristics of high patience.

•We propose a holistic evaluation framework for banks to screen corporate entities.•This is based on a composite index of financial, social and environmental attributes.•It permits incorporation of stakeholders preferences in the environment of banks.•Results provide analytical and managerial insights to the banks corporate clientele. We propose a holistic evaluation framework that banks could adopt when screening corporate entities. The framework is based on the development of a composite indicator of social, environmental and financial performance. This integrated view of evaluation is conceptually aligned with ISO standards and empirical proposals of academics and market practitioners in defining inclusive performance. We complement those proposals with a methodological framework that permits incorporation of a plethora of viewpoints in the evaluation process, reflecting the expectations of the various stakeholders in the environment of a bank. We further enhance the evaluation process with analytics of a more detailed hierarchical view of performance, fine-tuning of the evaluation process, and provide implications and suggestions to the senior executive team of a bank’s clients.

The gravity of insurance within the financial sector is constantly increasing. Reasonably, after the events of the recent financial turmoil, the domain of research that examines the factors driving the risk-taking of this industry has been signified. The purpose of the present study is to investigate the interplay between national culture and risk of insurance firms. We quantify the cultural overtones, measuring national culture considering the dimensions outlined by the Hofstede model and risk-taking using the ‘Z-score’. In a sample consisting of 801 life and non-life insurance firms operating across 42 countries over the period 2007–2016, we find a strong and significant relationship among insurance firms' risk-taking and cultural characteristics, such as individualism, uncertainty avoidance and power distance. Results remain robust to a variety of firm and country-specific controls, alternative measures of risk, sample specifications and tests designed to alleviate endogeneity. •The gravity of culture for finance scholarship constantly escalates.•We explore the impact cultural attributes exert on insurers' risk.•Uncertainty avoidance, individualism and power distance index seemingly matter.•Results hold for a wide variety of controls and tests.•We discuss implications related to policy and firm decision-making.

We study the impact of social capital and perceptions about corporate ethical behaviour on the use of collateral in corporate borrowing. Using a dataset of more than 17,500 firms operating in over 100 transition and developing countries, we find evidence that country-level social capital and better perceptions about corporate ethical behaviour are negatively associated with the likelihood to pledge collateral. In addition, these country-level characteristics influence the value of collateral relative to the loan value.

We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank’s judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.

Over the past years, the financial technology industry has gained considerable attention from policy makers and regulators, market participants, as well as the general public. Despite the interest of these stakeholders, academic research on the topic is scarce and we aim to extend the literature by examining the impact of financial leverage on the performance of FinTech firms. Using a sample of 146 U.S. FinTech firms operating in ten market segments over the period 2000-2016, we find that financial leverage has a negative impact on profitability and risk-adjusted performance. We also reveal that the magnitude of the influence of leverage depends on firm age. The results are robust to the use of a cross-country sample, alternative model specifications and estimation approaches.

Building on the upper echelon and signalling theories, we hypothesize that perceptions about the corporate ethical behaviour in the country of origin of a venture's founders may provide an important piece of information to the selection committees of impact-oriented accelerator programmes that serves as a signal for the trustworthiness and opportunistic behaviour of the founding team. In turn, this could have implications for the decision regarding admission into a programme. Using a sample of over 16,000 early-stage ventures from 131 countries that applied to 287 accelerator programmes, we find evidence consistent with this hypothesis. Our results show that better perceptions about the ethical behaviour of the founding team enhance the likelihood of admission into an impact-oriented accelerator programme. The role of perceived ethics appears to be stronger in the case of programmes that guarantee some kind of financing. Further analysis shows that the strength of both formal and informal institutions moderates the relationship between the ethical perceptions and the admission likelihood.

Using a data set of 1,618 firms from 39 countries, we examine the influence of the educational attainment of a firm’s board of directors on its credit rating. We construct a Leadership Education Index that reflects the educational level of the key members of the board. We document, after controlling for firm and country-specific characteristics, that firms in which the key members of the board have a higher educational level are more likely to receive better credit ratings. To ensure robustness in our results, we conduct a number of analyses and tests designed to alleviate endogeneity and correct for sample bias. Our findings highlight the importance of hiring and retaining well-educated board members that are capable to manage firms and obtain better credit ratings.

The literature suggests that trust can influence the behavior of economic agents and improve access to financing for both households and corporations. Subsequently, this might have implications for the consumption of households and the investments of corporations. Therefore, trust could mitigate the negative impact of financial stress on economic growth. To test this hypothesis, we use a sample of EU countries over the period 2002-2020 and examine the interaction of trust with financial stress in shaping GDP growth. The interaction term enters the estimations with a positive and statistically significant coefficient, and it therefore mitigates the negative impact of financial stress on economic growth. Furthermore, by disaggregating the GDP into its four main components, we find that the moderating effect of trust flows through the two main components of GDP mentioned above, namely households' consumption and firms' investments. Additionally, we observe that the interaction effect becomes weaker in countries with a higher economic freedom and is strengthened in centre and left-wing governments compared to right-wing economically oriented ones.

Top managers are responsible for important decisions and their efficient implementation. Therefore, higher ability is more likely to lead to effective practice and favourable firm outcomes. This paper examines the association between managerial ability and corporate greenhouse gas emissions. The results suggest that firms with more able managers have lower greenhouse gas emissions. The disaggregation of total greenhouse gas emissions into Scope 1 emissions and Scope 2 emissions shows that managerial ability is negatively associated with both components. The results hold while controlling for various firm and country-level attributes and econometric specifications mitigating endogeneity concerns.

We propose a holistic evaluation framework that banks could adopt when screening corporate entities. The framework is based on the development of a composite indicator of social, environmental and financial performance. This integrated view of evaluation is conceptually aligned with ISO standards and empirical proposals of academics and market practitioners in defining inclusive performance. We complement those proposals with a methodological framework that permits incorporation of a plethora of viewpoints in the evaluation process, reflecting the expectations of the various stakeholders in the environment of a bank. We further enhance the evaluation process with analytics of a more detailed hierarchical view of performance, fine-tuning of the evaluation process, and provide implications and suggestions to the senior executive team of a bank's clients

Using a sample of EU countries over the period 2002-2018, we find that a national culture of uncertainty avoidance and trust has a conditional role in the interplay between financial stress and economic growth. However, the cultural dimension of individualism does not appear to influence this relationship. By dis-aggregating the GDP into its four main components, we find that the moderating effect of uncertainty avoidance and trust flows through consumption and investments. The results also show that, during the global financial crisis, the moderation effect of trust is weaker in magnitude, whilst that of uncertainty avoidance is reinforced. Finally, by adopting a North-South EU divide perspective, we find that the results are mainly driven by the latter cluster of countries

Based on US state-level data for the period 1982-2016, two reduced-form versions of New Keynesian wage Phillips curves are examined. These are based on either sticky nominal wages or real-wage rigidity. The endogeneity of unemployment is taken into account by instrumentation and the use of common correlated effects (CCE) and mean group (MG) methods. This is the first time that this methodology has been applied in this context. These are important issues, as ignoring them may lead to substantial biases. The results show that while the aggregate data do not provide estimates that are consistent with either of the theoretical models examined, the panel methods do. Moreover, use of an appropriate MG CCE estimator leads to economically significant changes in parameters (primarily a steeper Phillips curve) relative to those from inappropriate but widely used panel methods. In the real-wage rigidity case, this is required to deliver results that have a theoretically admissible interpretation.

??In recent years sustainable finance along with Environmental, Social and Governance (ESG) aspects and their implications for financial institutions have attracted the attention of academics and policy makers. The aim of the book is to bring together chapters that discuss the most recent empirical and theoretical evidence in the field, along with policy making and regulatory initiatives. The book covers topics such as the changing role of banks in the financial system, the differences between sustainable banks and traditional banks, ESG and financial performance, bank social responsibility and customer satisfaction, ESG risk management of financial institutions, the politics of climate finance and policy initiatives, and the role of bank regulators. It will be of interest to academics and policymakers working in banking, risk management, sustainable finance and related fields.

Over the past years, studies shed light on how social norms and perceptions potentially affect loan repayments, with overtones for strategic default. Motivated by this strand of the literature, we incorporate collective social traits in predictive frameworks on credit card delinquencies. We propose the use of a two-stage framework. This allows us to segment a market into homogeneous sub-populations at the regional level in terms of social traits, which may proxy for perceptions and potentially unravelled behaviours. On these formed sub-populations, delinquency prediction models are fitted at a second stage. We apply this framework to a big dataset of 3.3 million credit card holders spread in 12 UK NUTS1 regions during the period 2015-2019. We find that segmentation based on social traits yields efficiency gains in terms of both computational and predictive performance compared to prediction in the overall population. This finding holds and is sustained in the long run for different sub-samples, lag counts, class imbalance correction or alternative clustering solutions based on individual and socio-economic attributes.