Search

Bayesian Networks are a well-recognized decision support tool for a wide range of situations that involve uncertainty and probabilistic reasoning. Projects are not an exception to these situations. They are usually one-off experiences where many data are incomplete, suffer from imprecision and accuracy, and estimations are conditionally dependent on assumptions that are a major source of uncertainty. This uncertainty is present during the different stages of a project where complex causal relationships are involved between uncertainty and project performance. In this complex and uncertain environment many project managers are faced with the question, will the project finish successfully? This paper introduces an approach, using Bayesian Networks to know the project manager's confidence on the future of its project. With this aim, a Bayesian Network is modelled using four earned value management indexes, three financial ratios, and the technical and financial capability of the contractor responsible for the execution of the project. [ABSTRACT FROM AUTHOR]

• Banking data is used to investigate the impact of the 2008 Global Financial Crisis (GFC) on the efficiency and profitability of small U.S. banks. • The performance of the U.S. small banks changes over time during pre-, during, and post-financial crises. • The overall efficiency of U.S. small banks pre-during-post 2008 GFC has been low. • The 2008 GFC had a significant negative impact on the profitability of small U.S. banks. However, the small U.S. enjoyed high profitability scores pre-2008 GFC. • A strong positive association exists between overall efficiency and technical efficiency measures with the return on assets. This paper uses data from 732 U.S. small banks operating between 2001 and 2021 to examine the cost efficiency, profitability, and the association between cost efficiency and profitability pre-during and post-2008 Global Financial Crisis (2008 GFC). In step 1, using Data Envelopment Analysis (DEA), the cost efficiency of small U.S. banks is estimated. The results indicate that the overall efficiency of U.S. small banks pre-during-post 2008 GFC has been continuously low, and the sources of the low level of overall efficiency have been the low level of technical rather than allocative efficiency. In turn, the basis of the low level of technical efficiency has been pure technical rather than scale efficiency. In step 2, the profitability of U.S. small banks is analyzed using the same data. The results indicate that the 2008 GFC had a significant negative impact on the profitability of U.S. small banks: The small U.S. banks had high profitability scores pre-2008 GFC, a declining trend that started three years before the 2008 GFC, a sharp decline during the 2008 GFC, and profitability recovery began in 2010 and continues until 2021. In step 3, the association between cost efficiency and profitability is investigated, and there is a strong positive association between overall efficiency and technical efficiency measures with the ROA. [ABSTRACT FROM AUTHOR]

Relationships between firms and between firms and financial institutions influence firms' credit risk. Thus, these relationships should be crucial considerations in credit evaluation. This paper constructs a comprehensive SME network, which integrates multiple types of inter-firm associations and considers lender-borrower relationships, and then establish credit evaluation models utilizing network microstructure and machine learning. We find that complex interfirm relationships contained in network-based features can significantly enhance the credit risk evaluation of SMEs and the predictive contribution of different levels of network structural features varies. We further find that specific network microstructures containing lender-borrower relationships tend to be associated with high defaulting probabilities. It suggests that if a SME is closely linked to microlending institutions through multiple relationships, its defaulting probability will increase. • We create an SME network that integrates shareholding, managerial, and lending relationships in microfinance. • Our models predict SME credit risk without financial ratios, showing the potential of complex network approaches. • Network microstructures with lender-borrower links reveal high default probabilities and managerial control impacts. • Models using node topology, microstructure, and community features excel, showing the need for multi-level risk analysis. [ABSTRACT FROM AUTHOR]

• We address systematic and efficient use of a large amount of financial statement data. • The data is typically used for relative valuation. • We use principal component analysis to extract statistical factors. • Our results show that factors have distinct economic interpretation and are relatively stable over time. • This is in particular the case when using multiple time periods of the data to extract factors. In this paper we explore systematic and efficient use of a large amount of financial statement data most often used by financial analysts for the valuation purposes in terms of relative valuation (multiples). We use principal component analysis to analyze financial statement data and/or ratios at a company level. We transform financial statement data into new variables that exhibit distinct economic interpretations and can be considered as value drivers reflecting profitability, growth prospects, and risk. [ABSTRACT FROM AUTHOR]

Abstract: This paper offers a novel methodology using several new ratios and comparison approaches to investigate public software companies’ financial activities and condition. The methodology focuses on time-series data mining, monitoring and analyzing. The dataset is based on 100 U.S. software companies with least ten-year SEC verified income statement, balance sheets and cash flow statement. The contribution of this paper is creating and applying several new financial ratios combined with traditional approach to detect companies’ financial structure changes and accounts manipulation. For cash flow statement operating section, our proposed major account to operating net cash inflow and outflow ratios provide a better visualization of the cash sources and usage, which help analysts to observe major cash flow structure changes and make predication. For investing section, our proposed investing cash flow growth contribution ratio is used to identify irregular investment behavior. Combining with the traditional financial ratio tests, we believe that our approach significantly facilitates early detection on suspicious financial activities and the evaluation of its financial status. [Copyright &y& Elsevier]

Abstract: Microfinance Institutions (MFIs) are special financial institutions of both social and non-profit nature whose performance has been traditionally measured by means of financial ratios. However, performance rankings are usually based on a single criterion, so the performance measure varies according to the criterion used. This paper proposes a multicriterion methodology based on goal programming that simultaneously considers different categories involved in the performance of Microfinance Institutions. The paper is illustrated by a sample of Latin American MFIs. [Copyright &y& Elsevier]

Abstract An important feature of the planetary oceanic dynamics is that the aspect ratio (the ratio of the depth to horizontal width) is very small. As a result, the hydrostatic approximation (balance), derived by performing the formal small aspect ratio limit to the Navier–Stokes equations, is considered as a fundamental component in the primitive equations of the large-scale ocean. In this paper, we justify rigorously the small aspect ratio limit of the Navier–Stokes equations to the primitive equations. Specifically, we prove that the Navier–Stokes equations, after being scaled appropriately by the small aspect ratio parameter of the physical domain, converge strongly to the primitive equations, globally and uniformly in time, and the convergence rate is of the same order as the aspect ratio parameter. This result validates the hydrostatic approximation for the large-scale oceanic dynamics. Notably, only the weak convergence of this small aspect ratio limit was rigorously justified before. [ABSTRACT FROM AUTHOR]

Highlights • In the new gear drive, the number of teeth working at the same time has reached more than 7 which is usually only less than 2 in the traditional involute gear drive. • The bearing capacity of the new gear will be improved significantly. • A mathematical model of the novel internal gear drive is established based on gear meshing theory. • The novel internal gear dive has a higher contact ratio, lower sliding ratio and lower contact and bending stresses in comparison with the traditional involute gear drive. • A Computer-aided manufacturing (CAM) software for generating numerical control (NC) code is written, and a prototype is produced using an NC machining center. Abstract A new internal gear drive with a high contact ratio (HCR) is presented in this paper. In the new gear drive, the number of teeth working at the same time has reached 7, which is usually only less than 2 in the traditional involute gear drive. The bearing capacity of the new gear is improved significantly. To obtain a high contact ratio, the addendum tooth profiles of the pinion and internal gear are deduced by a given path of contact based on gear meshing theory and the dedendum tooth profile is designed as the envelope to the family of addendum tooth profile of the mating gear. The mathematical models, including the equation of the path of contact, the equations of addendum and dedendum tooth profiles and the equation of modification, are established. An example drive in the three-dimensional (3D) model is presented. The characteristics, such as contact ratio, sliding ratio, induced curvature, and the contact and bending stress are evaluated. The results confirm that the novel internal gear drive has a higher contact ratio and lower sliding coefficient, and the contact and bending stresses are reduced in comparison with the involute gear drive. Furthermore, a prototype is produced, which illustrate the feasibility of manufacturing. Graphical abstracts Image, graphical abstract [ABSTRACT FROM AUTHOR]

Abstract: Valuation of ecosystems services has been a challenging issue for economists. There is a growing concern to capture the total and incremental changes in services of different types of ecosystems, mainly, due to perturbations arising from anthropogenic activities. Market-based valuation techniques have long been declared inadequate and a constructed market method such as the contingent valuation method albeit a robust tool does not seem to capture the expanse, nuances, and intricacies of many of the ecosystem services. The paper attempts to address the lacunae in valuation of ecosystem services from a psychological perspective by arguing that the common person''s perception of the ecosystem is quite different from what is conceptualized by conventional economists. The paper shows how the ecological identity of individuals is revealed at various levels of the decision-making hierarchy that is, from local to regional and further onto a global level. The paper builds upon insights from psychoanalytic psychology and environmental-psychology. Further, it outlines recent research findings from experimental psychology to redefine concepts such as ecological identity, self-other dichotomy, and the fostering of identification with nature, as issues that must be embraced in the valuation of ecosystem services. Extending the idea of relational goods and reciprocity, the paper offers a deconstructed view of market forces and furthers the idea of interdisciplinary collaboration and cooperation in the valuation of ecosystem services. In this perspective the dichotomy and schism between markets, missing markets and non-markets, gets renovated and reconstructed beyond a utilitarian discourse. [Copyright &y& Elsevier]

Abstract: This paper presents the results of a Belgian case study on the Index of Sustainable Economic Welfare (ISEW). The index is first compiled using the standard methodology applied in other international studies. Owing to the short period in which Belgium''s ‘traditional’ ISEW has recently declined, it is not possible to draw any solid conclusion regarding Max-Neef''s ‘threshold hypothesis’. After a brief review of the criticism that the ISEW has attracted over the years, the paper then explains and discusses the rationale for omitting the ‘net capital growth’ and ‘change in net international investment position’ items. It is argued that these items do not comply with the theoretical foundation underpinning the ISEW. The paper also puts forward new valuation methods for several items within the index: the escalation factor in the valuation of natural capital depletion is dropped, long-term environmental damage is valued using a stock-based approach and considerably more government expenditures are considered to be non-defensive. The results of the ‘revised’ ISEW show significant improvements in sustainable economic welfare over the entire study period. These findings are not in line with the ‘threshold hypothesis’. Finally, the paper offers some guidelines for communicating about the ISEW — namely that (a) all methodological assumptions should be made explicit and (b) all raw (unadjusted) data used should be included in any final ISEW report so as to enable the reader to make his or her own assessment about the assumptions the researcher has employed. [Copyright &y& Elsevier]

The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings. [ABSTRACT FROM AUTHOR]

Financial crisis has raised concerns for years and its effect on companies influence economies globally. The ability to accurately identify the features responsible for business failure is an important issue in financial decision-making. There is clear need for accurate decision support for both credit granting and monitoring of ongoing health of credit customers. The financial ratios involved provide useful quantitative financial information to both investors and analysts so that they can evaluate the operation of a firm and analyze its position within a sector. This research brings awareness to managers as to which features they have to focus on. All the ratios involved each play a crucial role. In this paper, the Swarm Inspired Projection (SIP) algorithm as a new analysis tool is combined with the Principal Component Analysis (PCA) to determine the weights of the features and to adjust these weights to suit the profitability of these construction companies. The study made use of 1615 effective financial reports from 55 construction companies over the last decade. Based on the 25 ratios used, the PCA incorporating the SIP algorithm gives us an average accuracy rate of 90%. This method provides better reliability in the identification of the principal features in bankruptcy analysis. Corporate financial distress is a major concern to business sectors worldwide; therefore using both clustering and statistical techniques is a better basis in mitigating bankruptcy to both practitioners and researchers. [ABSTRACT FROM AUTHOR]

Improving the assessment of credit risk has become a priority for many central banks after the global financial crisis. Central banks need to decide how much effort to put into developing credit risk assessment tools, taking into account their resources and limitations. This paper proposes a tool that is both pragmatic and conceptually sound, which allows to improve the assessment of credit risk for foreign reserves managers and complements the information produced by the rating agencies. The tool we propose uses three different credit risk models in order to identify the issuers that have a high, moderate, or low probability of having a ratings downgrade below the minimum accepted rating, within the issuers that meet the minimum rating requirements. The signals from the tool are built from market and fundamental information of each issuer. Additionally there is a proposal for a framework to turn the outputs from the model into investment decisions. [ABSTRACT FROM AUTHOR]

The use of non-parametric statistical methods, the development of models geared towards the homogeneous characteristics of corporate sub-populations, and the introduction of non-financial variables, are three main issues analysed in this paper. This study compares the predictive performance of a non-parametric methodology, namelyClassification/Regression Trees (CART), against traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as micro-entities,from the United Kingdom for the period 1999 to 2008 that includes financial, non-financial, and macroeconomic variables. Our findings show that CART outperforms the standard approach in the literature, LR. [ABSTRACT FROM AUTHOR]

In this paper, we present a fraud detection framework for publicly traded firms using an optimization approach integrated with a meta-heuristic algorithm known as Beetle Antennae Search (BAS). Existing techniques include human resources, like financial experts and audit teams, to determine the ambiguities or financial frauds in the companies based on financial and non-financial ratios. It is a laborious task, time-consuming, and prone to errors. We designed an optimization problem to minimize the loss function based on a non-linear decision function combined with the maximization of recall (Sensitivity and Specificity). We solved the optimization problem iteratively using the BAS. It is a nature-inspired algorithm and mimics the beetle's food-searching nature. It includes a single searching particle to find an optimal solution to the optimization problem in n -dimensional space. We used a benchmark dataset collected from SEC's Accounting and Auditing Enforcement Releases (AAERs) for the simulation. It includes 28 raw financial variables and the data collected between 1991–2008. For the comparison, we evaluated the performance of BAS with the recently proposed approach using RUSBoost. We also compared it with some additional algorithms, i.e., Logit and SVM-FK. The results showed that BAS is comparable with these algorithms and outperformed them in time consumption. • Fraud detection in US-Public firms using BAS algorithm. • BAS is computationally and time-wise efficient. • Unbalanced Public firms' dataset is tackled with SMOTE and BAS. • Achieved state-of-the-art accuracy in comparison with other algorithms. • Time efficiency of BAS outperformed other algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Balance-sheet data offer a potentially large number of candidate predictors of corporate financial failure. In this paper we provide a novel predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logit model. We show that a simple logit model with dummy variables created in accordance with the nodes of estimated classification tree outperforms both standard logit model with step-wise-selected financial ratios, and CART itself. On a population of Slovenian companies our method achieves remarkable rates of precision in out-of-sample bankruptcy prediction. Our selection method thus represents an efficient way of introducing non-linear effects of predictor variables on the default probability in standard single-index models like logit. These findings are robust to choice-based sampling of estimation samples. [Copyright &y& Elsevier]

Abstract: In data envelopment analysis (DEA) context financial data/ratios have been used in order to produce a unified measure of performance metric. However, several scholars have indicated that the inclusion of financial ratios create biased efficiency estimates with implications on firms’ and industries’ performance evaluation. By applying bootstrap techniques the paper provides an application of evaluating the performance of 23 Greek manufacturing sectors with the use of financial data. The results reveal that in the first stage of our sensitivity analysis the efficiencies obtained are biased. However, after applying the bootstrap techniques the sensitivity analysis reveals that the efficiency scores have been significantly improved. [Copyright &y& Elsevier]

Abstract: On a wake of Basel II Accord in 2004, banks and financial institutions can build an internal rating system. This work focuses on Italian small firms that are more hard to judge because quite often financial data are not simply available. The aim of this paper is to propose a simulation model for assigning rating judgements to these firms, using poor financial information. The proposed model produces a simulated counterpart of Bureau van Dijk-K Finance (BvD) rating judgements. It is clear that there are problems when small firms must be judged because it is difficult to obtain financial data; indeed in Italy these enterprises must deposit the balance-sheet in reduced form. Suggested methodology is a three-layer process where each of them is formed by, respectively, one, two and four feed-forward artificial neural networks with back-propagation algorithm. The proposed model is a good solution for evaluating small firms with poor financial information but not only: the research underlines and supports the ability of artificial neural networks of learning and reproducing some aspects or some features or behaviours of reality. [Copyright &y& Elsevier]

Abstract: For manufacturing industries, financial performance evaluation is very important in a highly competitive environment. Therefore, an accurate and appropriate performance evaluation is critical. As financial performance indicators reflect the competitiveness of a company, they must be carefully identified in the evaluation process. Generally, traditional accounting-based financial performance (AFP) measures are used for performance evaluation. However, these measures are not sufficient for performance evaluation solely in the modern industry time. So, value-based financial performance (VFP) measures have recently been introduced to express the company value. In this paper, we propose a new financial performance evaluation approach to rank the companies of each sector in the Turkish manufacturing industry. For this purpose, a hierarchical financial performance evaluation model is structured based on the AFP and VFP main-criteria and their sub-criteria. We use fuzzy analytic hierarchy process (FAHP) to determine the weights of the criteria. The companies are ranked according to their own manufacturing sector by using TOPSIS and VIKOR comparatively. The results show that the obtained ranks of the companies by these methods are almost same with respect to their own sectors. [Copyright &y& Elsevier]

Abstract: Due to the important role of financial distress prediction (FDP) for enterprises, it is crucial to improve the accuracy of FDP model. In recent years, classifier ensemble has shown promising advantage over single classifier, but the study on classifier ensemble methods for FDP is still not comprehensive enough and leaves to be further explored. This paper constructs AdaBoost ensemble respectively with single attribute test (SAT) and decision tree (DT) for FDP, and empirically compares them with single DT and support vector machine (SVM). After designing the framework of AdaBoost ensemble method for FDP, the article describes AdaBoost algorithm as well as SAT and DT algorithm in detail, which is followed by the combination mechanism of multiple classifiers. On the initial sample of 692 Chinese listed companies and 41 financial ratios, 30 times of holdout experiments are carried out for FDP respectively one year, two years, and three years in advance. In terms of experimental results, AdaBoost ensemble with SAT outperforms AdaBoost ensemble with DT, single DT classifier and single SVM classifier. As a conclusion, the choice of weak learner is crucial to the performance of AdaBoost ensemble, and AdaBoost ensemble with SAT is more suitable for FDP of Chinese listed companies. [Copyright &y& Elsevier]

Abstract: This paper proposes a hybrid method for effective bankruptcy prediction, based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance with variable weight. Unlike the existing case-based reasoning methods using the Euclidean distance, we introduce the Mahalanobis distance in locating the nearest neighbors, which considers the covariance structure of variables in measuring the closeness. Since hundreds of financial ratio variables are available in analyzing credit management problems, the model performance is also affected by input variable selection strategies. Variables selected by the decision trees induction tend to have an interaction compared to those produced by the regression approaches. The Mahalanobis distance is a more true measure of proximity than the Euclidean distance when variables are correlated with each other. The experimental results indicate that the proposed approach outperforms some currently-in-use techniques. [Copyright &y& Elsevier]

Abstract: Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms. [Copyright &y& Elsevier]

Abstract: Portfolio selection is an important issue for researchers and practitioners. Focusing on equity mutual funds, this paper proposes a basic portfolio selection model in which future return rates and future risks of mutual funds are represented by triangular fuzzy numbers. Firstly, a cluster analysis is proposed to categorize the huge amount of equity mutual funds into several groups based on four evaluation indices: rates of return, standard deviation, turnover rate, and Treynor index, in order to aid investors in making the investment decision. The fuzzy optimization model is proposed to determine the optimal investment proportion of each cluster. The portfolio optimization problem is developed in two ways: to maximize the future expected return subject to the given greatest future risk, and to minimize the future risk subject to a required lowest future expected return. The proposed approaches are demonstrated by Taiwan equity mutual funds. [Copyright &y& Elsevier]

Corporate financial failure prediction is of critical importance for decision making of managers, investors and shareholders. In current financial failure prediction models, various financial ratios are usually selected as prediction variables, which implicates that these financial ratios represent the possible cause of financial failure. It is widely recognized that a main cause of financial failure is poor management, and that business operation efficiency is a good reflection of a firm’s management. In this paper, we propose a financial failure prediction model using efficiency as a predictor variable. In the proposed method, data envelopment analysis (DEA) are employed as a tool to evaluate the input/output efficiency of each corporation. To verify the efficacy of efficiency as a predictor, we use the data of corporations listed in Shanghai stock exchange (SSE), and compare the accuracy of the same prediction method with and without the variable. Experimental results of three main financial failure prediction models, i.e., multiple discriminant approach (MDA), logistic regression, and support vector machines (SVMs), all suggest that efficiency is an effective predictor variable. [Copyright &y& Elsevier]

• The new method of classifier selection for imbalanced data is proposed. • Selection rule uses the dominance in the FPR/FNR space and crowding distance. • Tests on two real datasets confirm that new method improves the prediction. The goal of the paper is to develop a new algorithm for predicting whether the company will go bankrupt on the base of unbalanced data. To do it, we propose to consider the classification as a multi-objective optimization problem and construct a prediction model as an ensemble while minimizing the parameters FPR (False Positive Rate) and FNR (False Negative Rate) at the same time. To create the ensemble, the proposed algorithm of a Multi-Objective Classifier Selection (MOCS) selects only classifiers that belong to the Pareto-optimal set in FPR/FNR space; that is, there is no dominance between them, and they satisfy some additional conditions. In the general case, MOCS is determined by three parameters: two threshold values that limit false rates (FNR and FPR), and the crowding distance, which defines the uniqueness of the classifier's results. We tested the proposed algorithm on data collected from 2457 Russian companies, 456 of which went bankrupt, and 5910 Polish companies, 410 of which received bankruptcy status. Datasets contain features such as financial ratios and business environment factors. In the testing, we used more than 70 combinations of under-sampling, over-sampling, and no sampling methods with static and dynamic classification models. Final ensembles include seven classifiers for the Russian dataset and four classifiers for the Polish dataset combined by soft voting rule. In both cases, the proposed algorithm produces a significant improvement of prediction results as in terms of standard metrics (geometric mean, the area under the ROC curve) and in the visual representation in the FNR/FPR space, namely in the shift from a Pareto-optimal set of classifiers. [ABSTRACT FROM AUTHOR]

This paper proposes a DEA-based approach to credit scoring. Compared with conventional models such as multiple discriminant analysis, logistic regression analysis, and neural networks for business failure prediction, which require extra a priori information, this new approach solely requires ex-post information to calculate credit scores. For the empirical evidence, this methodology was applied to current financial data of external audited 1061 manufacturing firms comprising the credit portfolio of one of the largest credit guarantee organizations in Korea. Using financial ratios, the methodology could synthesize a firm’s overall performance into a single financial credibility score. The empirical results were also validated by supporting analyses (regression analysis and discriminant analysis) and by testing the model’s discriminatory power using actual bankruptcy cases of 103 firms. In addition, we propose a practical credit rating method using the predicted DEA scores. [Copyright &y& Elsevier]

Accounting frauds have continuously happened all over the world. This leads to the need of predicting business failures. Statistical methods and machine learning techniques have been widely used to deal with this issue. In general, financial ratios are one of the main inputs to develop the prediction models. This paper presents a hybrid financial analysis model including static and trend analysis models to construct and train a back-propagation neural network (BPN) model. Further, the experiments employ four datasets of Taiwan enterprises which support that the proposed model not only provides a high predication rate but also outperforms other models including discriminant analysis, decision trees, and the back-propagation neural network alone. [Copyright &y& Elsevier]

Abstract: Many previous researches concerning the performance of airlines usually focus on operation. Financial performance, which would influence the survival of an airline, is often ignored. To evaluate financial performance, financial ratios obtained form balance sheet, income statement and cash flow must be partitioned into several clusters and found the representative indicators from these clusters to be criteria. In this paper, we utilize grey relation analysis to cluster financial ratios and find representative indicators. Then we apply a fuzzy multi-criteria decision-making (FMCDM) method to evaluate financial performance of airlines. Finally, an empirical study of financial performance of three domestic airlines in Taiwan is illustrated. [Copyright &y& Elsevier]

Abstract: Credit scoring models often use linear or logistic regression to investigate the relation between observed characteristics and credit ratings. The basic relation is, however, a form of Bayes’ theorem. This paper proposes a model in which estimation techniques from hidden Markov models are adapted to evaluate the parameters of a risk profile. The risk being estimated might be financial, as in credit scoring, or alternatively whether an observed member of a population might represent some terrorist threat. [Copyright &y& Elsevier]

Abstract: This paper shows that the surplus consumption ratio, specified by Campbell and Cochrane [1999. Journal of Political Economy 107, 205–251], is a good predictor of excess returns at long horizons. We also provide empirical evidence that this variable captures a component of expected returns, not explained by the proxies for the consumption to wealth ratio, cay and cdy, proposed by Lettau and Ludvigson [2001a. Journal of Finance 56, 815–849; 2001b. Journal of Political Economy 109, 1238–1286; 2005. Journal of Financial Economics 76, 583–626]. Moreover, used as a conditioning information for the Consumption based Asset Pricing Model (C)CAPM, the resulting linear model helps to explain for the variation in average returns across the Fama–French (25) portfolios sorted by size and book-to-market characteristics. [Copyright &y& Elsevier]

Abstract: This paper presents a soft computing based bank performance prediction system. It is an ensemble system whose constituent models are a multi-layered feed forward neural network trained with backpropagation (MLFF-BP), a probabilistic neural network (PNN) and a radial basis function neural network (RBFN), support vector machine (SVM), classification and regression trees (CART) and a fuzzy rule based classifier. Further, principal component analysis (PCA) based hybrid neural networks, viz. PCA-MLFF-BP, PCA-PNN and PCA-RBF are also included as constituents of the ensemble. Moreover, GRNN and PNN were trained with a genetic algorithm to optimize the smoothing factors. Two ensembles (i) simple majority voting based and (ii) weightage based are implemented. This system predicts the performance of a bank in the coming financial year based on its previous 2-years’ financial data. Ten-fold cross-validation is performed in the training sessions and results are validated with an independent production set. It is demonstrated that the ensemble is able to yield lower Type I and Type II errors compared to its constituent models. Further, the ensemble also outperformed an earlier study [P.G. Swicegood, Predicting poor bank profitability: a comparison of neural network, discriminant analysis and professional human judgement, Ph.D. Thesis, Department of Finance, Florida State University, 1998] that used multivariate discriminant analysis (MDA), MLFF-BP and human judgment. [Copyright &y& Elsevier]

Abstract: In this paper we have combined fundamental analysis and contingent claim analysis into a hybrid model of credit risk measurement. We have extended the standard Merton approach to estimate a new risk neutral distance to default metric, assuming a more complex capital structure, adjusting for dividend payments, introducing randomness to the default point and allowing a fractional recovery when default occurs. Then, using financial ratios, other accounting based measures and the risk neutral distance to default metric from our structural model as explanatory variables we estimate the hybrid model with an ordered probit regression method. Using the same econometric method, we estimate a model using financial ratios and accounting variables as explanatory variables and a model using our risk neutral distance to default metric as unique explanatory variable. We have found that by enriching the risk neutral distance to default metric with financial ratios and accounting variables into the hybrid model, we can improve both in-sample fit of credit ratings and out-of-sample predictability of defaults. Our main conclusion is that financial ratios and accounting variables contain significant and incremental information; thus the risk neutral distance to default metric does not reflect all available information regarding the credit quality of a firm. [Copyright &y& Elsevier]

Abstract: The country credit rating is a key covariate of the cost and availability of international financing for an economy. This paper models ratings as a function of expected repayment capacity, derives empirical implications, and tests them using the most comprehensive consistent series of sovereign credit ratings. These are the Institutional Investor ratings which have been widely used in the international finance literature. We report several stylized facts: volatility clustering, asymmetric adjustments, and serial correlation in credit revisions, especially in Emerging countries. These features are consistent with rational behavior of credit rating teams and have important implications in assessing the long term risk of international investments. [Copyright &y& Elsevier]

A modern view of stakeholder theory considers that ethical behaviour and profit are not contradictory. Under this framework and in the Merger and Acquisitions context we would like to investigate whether companies with well-developed Corporate Social Responsibility engage stakeholders in a better manner and, thus, benefit from more effective acquisition processes and, as a result, better financial performance in the post-deal periods. Within this context, our paper aims to investigate the effect of Corporate Social Responsibility measured in terms of popular Environmental, Social and Governance scores on Mergers and Acquisitions performance. Based on more than 6500 initial deals, we disentangle the short- and long-term effects related to post-merger performance, by considering several econometric approaches, including event study and pooled regression. Our results indicate that sustainability factors in Mergers and Acquisitions are significantly correlated with long-term performance, and we observe evident improvements of financial ratios in the long run. Given the complexity of Mergers and Acquisitions operations and the challenges of Corporate Social Responsibility integration into the corporate culture of the acquirer, we realise that the short-term effects related to Corporate Social Responsibility component are irrelevant. After the completion of Mergers and Acquisitions deals, we also confirm improvement in terms of Environmental, Social and Governance scores for companies involved in the deal. In terms of policy implications, we assess that to obtain the best final outcome of the acquisition, managers should consider that the value for shareholders in terms both of financial and sustainability performance in the post-merger period is higher if the bidder company starts with a high sustainability level. • Sustainability factors matters in long-term post merger performance. • Sustainability factors are irrelevant in short-term post merger performance. • Shareholders value increases in higher sustainability firms. • Social score is the most relevant sustainability index: on average, it counts twice the Environmental and the Governance score. [ABSTRACT FROM AUTHOR]

• Two-phase training algorithm able to boost the Machine Learning accuracy. • Accurate bankruptcy prediction up to 60 months by a Machine Learning algorithm. • Integration of the Machine Learning algorithm in a DSS for policy making. • Test of the DSS on the Italian SMEs, with a focus on the COVID-19 effects. In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to 60 months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a 60 month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making. [ABSTRACT FROM AUTHOR]