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The paper highlights the process of electric vehicles optimal design as an inverse problem and presents the global constrained optimization as the best way to solve it.

The electric vehicle optimal design is carried out by a new approach. It consists an electric vehicle design model managed by constrained optimization techniques. It includes sizing models for all drive train components and a vehicle dynamic model build in a new “design way” as an energy‐based model using the response surface methodology. The sensitivity of first simple sizing models can be evaluated by the experimental design method, giving information about the most important part of the model that must be improved.

The result shows the superiority of the constrained optimization technique that treats simultaneously the global optimization and the model adjustment. This method of simultaneous resolution is much more powerful than the successive resolution of each subproblem. The proposed “design approach” used for electric vehicle optimal design offer a large potential in the field of the complex systems design.

The electric vehicle design process is treated on a vehicle design model based on a design approach. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all components interactions. Furthermore, considering fine components sizing models, the components can be sized taking into account the whole system behavior in an optimal global design.

The purpose of this paper is to present a low evaluation budget optimization strategy for expensive simulation models, such as 3D finite element models.

A 3D finite element electromagnetic model and a thermal model are developed and coupled in order to simulate the linear induction motor (LIM) to be conceived. Using the 3D finite element coupling model as a simulation model, a multi‐objective optimization with a progressive improvement of a surrogate model is proposed. The proposed surrogate model is progressively improved using an infill set selection strategy which is well‐suited for the parallel evaluation of the 3D finite element coupling model on an eight‐core machine, with a maximum of four models running in parallel.

The proposed strategy allows for a significant gain of optimization time. The 3D Pareto front composed of the finite element model evaluation results is obtained, which provides the designer with a set of optimal trade‐off solutions for him/her to make the final decision for the engineering design.

An infill set selection strategy is proposed, which allows the parallel evaluation of the finite element model, and at the same time guides the progressive construction of an improved surrogate model during the multi‐objective optimization run. The paper may stand as a good reference for researchers/engineering designers who have to deal with optimal design problems implying costly simulation models.

This study aims to present a model for detecting and predicting creative accounting in companies listed on the Tehran Stock Exchange (TSE).

The authors conduct this research in three stages. First, the authors review the literature to determine the dimensions, components, indicators and techniques of creative accounting. Second, the authors conduct semi-structured interviews with experts using the fuzzy Delphi technique to obtain screening and reach a consensus. Finally, the authors develop a model to predict creative accounting by classifying the financial statements of the sample companies into two groups based on the use or non-use of creative accounting techniques, measuring the indicators determined in the previous stage, running various machine learning algorithms and choosing the superior algorithm.

The results indicate the usefulness of accounting information for detecting and predicting creative accounting and the relevance of several financial attributes as important predictors. The results also indicate the superiority of extremely randomized trees over other algorithms in predicting creative accounting and suggest that the primary purpose of creative accounting in Iran is earnings management. Contrary to the political cost hypothesis, large Iranian companies use creative accounting to inflate profits.

The present research also has several limitations that must be considered, and caution must be exercised in interpreting and generalizing the findings as specified in the revised manuscript.

This study’s implications are significant for policymakers, standard-setters and practitioners. By recognizing the detrimental effects of creative accounting on financial transparency within companies, policymakers can address existing gaps in accounting standards to minimize the potential for earnings manipulation. Consequently, strengthening internal and external mechanisms related to a firm’s financial performance becomes achievable. The study provides evidence of the need for audit firms to recognize the importance of creative accounting and consider creative accounting in their audit plans to prevent insufficient or even misleading disclosure by companies that extensively use creative accounting practices in their financial reporting. Moreover, knowledge of creative accounting techniques can help auditors assess audit and detection risks and serve as a valuable guide for reducing audit costs and improving audit quality.

Given that creative accounting practices distort the true or real accounting results, curbing creative accounting practices reduces corporate failures and could lead to the reduction of job losses and other social consequences.

This study uses a unique database in Iran to determine a model for predicting creative accounting using a mixed-method methodology, qualitative and quantitative, to identify creative accounting techniques and run various machine learning algorithms.

This paper studies the profit efficiency of a sample of large U.S. commercial banks and explores how this performance varies with selected measures of bank risk reflecting aspects of credit risk, liquidity risk, and insolvency risk. We use a standard profit function and the stochastic frontier approach, and compare two standard functional forms – Cobb‐Douglas and translog – to assess the tradeoff between precision and parsimony. We find that profit efficiency is sensitive to credit risk and insolvency risk but not to liquidity risk or to the mix of loan products.

This paper examines the cost and profit productivity of European co‐operative banks between 1996 and 2003 using the parametric productivity decomposition suggested by Berger and Mester (2003). We find that over the period co‐operative banks benefited from substantial gains in both profit and cost productivity. Annual profit improvements range between 4% and 8% for the majority of co‐operative banks, with even larger cost productivity gains. These productivity improvements have predominantly been generated by the enhanced performance of best practice banks relative to other banks. This means that the best practice co‐operative banks have moved further away from other banks in terms of increasing profits and reducing costs.

The purpose of this study is to explore and evaluate potential nonmonotonicity in the determinants of profit efficiency, specifically IT and R&D investments in the Indian commercial banking sector.

The study employs an alternative stochastic profit efficiency framework and introduces nonmonotonic effects by parameterizing the location and scale parameters of the inefficiency component on an unbalanced panel data set of 72 commercial banks in the 2008–2019 period. Marginal effects across quartiles are calculated using a bias-corrected and accelerated bootstrap procedure of 500 simulations. The study disaggregates across ownership and size for gauging the impact of structure on the associations between determinants of profit efficiency.

The study partially rejects the productivity paradox as it discovers a negative association of IT and R&D with profit inefficiency. However, the observed nonmonotonicity of IT is of significance for bank managers, as the study concludes that overinvestment in IT is detrimental to a bank’s profit-maximizing interests. Further, bank size, loan default and credit risk depict a nonmonotonic relationship across the sample with large banks, high NPAs and high credit risk associated with reducing profit efficiency. In addition, higher margins and greater diversification are related positively to efficiency, and banks with cost-heavy structures or having high liquidity risk associated negatively with efficiency.

To the best knowledge of the authors, the study is perhaps the first to acknowledge and incorporate nonmonotonic associations of IT investments amidst other exogenous determinants under a stochastic profit efficiency framework.

There is strong excitement during Ponzi schemes and financial bubble periods. This emotion causes investors to turn to “unknown and new investment instruments”. This study, the factors that made “unknown and new investment instruments” preferable to “known and experienced investment instruments” were investigated.

It was taken into account unconscious like phantasy, emotional like emotional intelligence, both affective and cognitive like financial literacy and subjective beliefs like trust and overconfidence. In addition, risk preferences were measured with four different risk variables. In this context, data were collected by online survey method between November 2020 and May 2021 with convenience sampling. First, the data were collected from 832 participants in the pilot study. Additional data were also collected using convenience sampling and online surveys, and a total of 1,692 participants were obtained. Data were analyzed using Statistical Package for the Social Sciences (SPSS) 25 and AMOS 24.

As a result of the analyses made, the variables that lead investors to choose “unknown and new investment instruments” were determined as risky investment intention, phantasy, risk taking/risk avoidance, confidence, risk tolerance and subjective financial literacy. Trust and risk perception have a very weak effect on preferences. However, no effect of emotional intelligence and objective financial literacy was detected. In addition, a moderately positive and significant relationship was found between objective and subjective financial literacy. Subjective financial literacy was found to have a strong and significant relationship with emotional intelligence, confidence, trust, risky investment intention and phantasy.

This study investigates the factors underlying individuals' investment preferences from a broad perspective. We think that this study is unique in this structure and wide variables. We believe that the findings obtained in this manner are unique to both academics and practitioners. We also believe that the findings of the study will make an important contribution to understanding participation behavior in various Ponzi schemes and financial bubbles.

The processes of liberalisation, globalisation and integration have brought new dynamics into banking markets. In an increasingly competitive environment, banks have been forced to refocus their strategies and examine their performance, because their survival in the 21st century will depend on efficiency (Denizer & Tarimcilar, 2001). In recent years, therefore, bank efficiency has received wide attention, and researchers have developed an extensive array of sophisticated methods and tools to estimate efficiency.

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

This paper aims to examine alternative methods for treating nonperforming loans (NPLs) in bank cost-efficiency studies using stochastic frontier analysis (SFA).

The authors consider three methods of treating NPLs in SFA: as an additional control variable, as an environmental factor or as a deduction from total loans. Using data from the Vietnamese banking system (2003-2010), the authors then compare these results with those of the base model (where total loans is used regardless of the NPLs) to see which one is more appropriate for this study.

The authors observed that the first two methods are inappropriate for the analysis: one cannot find the significant relationship between NPLs and the banks’ total cost, and the other cannot account for any inefficiency at all. The authors suggested that the third method of separating NPLs from total loans can provide better insights. Using the proposed method, the authors showed that the cost-efficiency of Vietnamese banks over the period examined was moderate with a slight decreasing trend. When NPLs are separated, the cost-efficiency decreases in state-owned banks and big banks, whereas it increases in small and private banks.

Research is limited to Vietnamese banks during a certain period, and it would be useful to apply the same technique to other data sets.

The paper suggests a new approach to account for NPLs in cost SFA studies in banking.

The paper provides a much more searching analysis of NPLs in banking than has generally been seen in previous research.