Search

Additive manufacturing (AM) suggests promising manufacturing technologies, which complement traditional manufacturing in multiple areas, such as biomedical, aerospace, defense, and automotive industries. This paper addresses the production planning problem in multi-machine AM systems. We consider all relevant physical and technological parameters of the machines and the produced parts, for using direct metal laser sintering (DMLS) technology. In DMLS technology, each machine produces jobs, where each job consists of several parts arranged horizontally on the build tray. Starting a new job requires a setup operation. We address the simultaneous assignment of parts to jobs and jobs to the machines, while considering the cost and makespan objectives. A unified mixed-integer linear-programming (MILP) formulation that can minimize the above objectives separately and simultaneously is suggested, along with analytical bounds and valid inequalities. Experimentation demonstrates the effectiveness of the proposed formulation with single objectives versus similar formulations from the literature. An efficient frontier approach is applied to the multi-objective problem while generating a diverse set of exact non-dominated solutions. The trade-off between the objectives is analyzed via experimentation. Results show that when identical machines are used, the trade-off is relatively small, and hence the decision-maker can use any of the single objectives. However, when non-identical machines are used, it is important to consider both objectives simultaneously. Moreover, the trade-off increases with the number of machines and heterogeneity of the system, with respect to the size and settings of the machines.

The recent growth in economic and financial markets has brought the focus on energy derivatives as an alternative investment class for investors, financial analysts, and portfolio managers. The financial modeling and risk management of portfolios using the energy derivatives instrument is a requirement and challenge for researchers in the field. The energy and other commodity futures force the expert investors to investigate the broader investment spectrum and consequently diversify their portfolios using the futures instruments. Going beyond the conventional portfolios and developing out-of-the-box strategies that comply with the changing financial and economic advancements are the keys to long-term sustainability in the financial world. This study investigates the impact of diversification with five energy futures from January 2011 to July 2020 on three traditional commodity futures portfolios. The results show that diversification increased the returns while simultaneously reducing the portfolio volatility in all portfolios. The diversified portfolios provided higher returns than the traditional portfolios for the same level of risk. This study also revealed that the results might improve when a short position in the futures contracts is allowed. Moreover, we conclude that adding multiple energy futures in a portfolio provides enhanced diversification results, whereas the WTI crude oil futures fail to diversify any portfolio considered in the study.

In general portfolio, optimization is a technique for selecting the proportion of assets to make a better portfolio by maximizing the expected return while also minimizing the risk. In this research, the k-means clustering method is used to classify stocks are listed on the LQ45 Index and select stocks whose has prices tend to be increased. Then the Markowitz approach is used to analyze the performance of optimization portfolio models that have a minimum variance in expected return and risk. After understanding the performance of this portfolio optimization, future works will be able to apply this model in cloud computing or artificial intelligence. In addition, investors will develop a better view of the latest performance of the stocks are listed in the LQ45 index and support them decide which stocks should be included in their portfolios, thus prevent wrong decisions.

This paper mainly studies how investors invest in funds to obtain high returns while avoiding risks. Firstly, from the perspective of portfolio investment, this paper introduces the traditional Markowitz mean-variance model and capital asset pricing model (CAPM), then selects four funds from different industries by MATLAB program in Sina Finance and Economics Network for application analysis from which the optimal portfolio point can be obtained under the combination of efficient frontier and capital allocation line. Subsequently, by analyzing the returns of long-term holdings and short-term operations of Noan Growth Hybrid Fund, it is confirmed that long-term holding funds can better cope with the changing market so as to obtain more stable returns. Finally, this paper discusses the dynamic adjustments of asset portfolio. Resident investors are supposed to take into account the market situation and the changes of the fund itself to adjust the holding fund portfolio. Based on the research in this paper, resident investors ought to combine investment funds to diversify risk allocation and make long-term holding plans according to their risk tolerance. At the same time, they should also make appropriate dynamic adjustments when the external environment changes to ensure long-term benefits.

Socially responsible investing is becoming more popular among people because people are becoming more concerned about the environment and society. Socially responsible investors screen the company by considering the ESG factors. The question raced is whether socially responsible investing improves the portfolio performance and how the funds perform during uncertain times like the Covid-19 pandemic. Since many critics of ESG funds say that the ESG funds’ performance highly depends on Software and Service company stocks, so the relevance of Software and Service companies in the fund has been analyzed in this research. The portfolios have been formed by using the Markowitz mean-variance portfolio model, and the performance of the minimum variance portfolio has been studied. The fund performance has been analyzed using the Sharpe ratio, and the result concludes that the ESG fund performance with minimum variance has an abnormally high Sharpe Ratio of 10.8. A similar type of performance was identified during the Covid-19 pandemic. The abnormally high Sharpe ratio will encourage investors to move towards socially responsible investing.

This paper examines the operational efficiency of equity Real Estate Investment Trusts (REITs) with respect to external advisement and management. We employ data envelopment analysis (DEA), a non-parametric statistical procedure that tests whether decision-making units are operating on their efficient frontier, to measure the relative performance of REITs before, during, and after the 2008–2010 financial crisis. Annual observations of both advising and management status of each REIT allow us to parse efficiency by these groups in various combinations. Our evidence suggests the inefficiency of externally-advised REITs has diminished in recent years, and the structure is no longer strictly inferior. External management of property operations, however, remains less efficient than self-management. General and administrative expenses, external advisory fees and property management fees are the main sources of inefficiency over the study period. In a difference-in-difference specification we find industry-wide operational efficiency was higher in the post-crisis than the pre-crisis period, indicating efficiency gains following the crisis.

The measurement of efficiency in higher education has gained a growing interest in recent years, especially due to the expansion of the university system. This paper provides a review of the literature on efficiency in higher education institutions by covering empirical articles which applied frontier efficiency measurement techniques from 1997 to 2019. We review the methodological approaches used, both parametric and non-parametric techniques, such as Data Envelopment Analysis, Malmquist index and Stochastic Frontier Analysis. Secondly, we list the applied inputs, input prices, outputs, quality, and environment variables and based on the overview, we discuss the advantages and drawbacks of the different empirical proxy variables used. We address the importance of characterizing students and research funding as raw materials of both the teaching and research services, respectively, and we provide suggestions on how to deal with them empirically. We also discuss the difference between quality and environmental variables, and we give some practical indications to distinguish them in doubtful cases.

This study uses a dynamic slacks‐based measure (SBM) model in a data envelopment analysis (DEA) framework with surplus reserve and accounts receivable as desirable and undesirable indicators respectively, based on inter‐connectivity between consecutive periods to estimate the operating efficiency of 110 listed Chinese real‐estate companies in the first step analysis. The results show that 35 listed real‐estate companies in China operated on the efficient frontier by maintaining sustainable growth throughout the entire period. The analysis presents the projection of indicators to suggest a feasible solution to improve the inefficient terms. At the second step analysis, using ordinary least square (OLS) estimation, authors find a curve‐linear relationship between real‐estate company size and efficiency to suggest that operating efficiency increases as size also increases due to economies of scale but only up to an optimum point. Profitability and higher education are also identified as the other significant determinants of operating efficiency. The findings of this research present evidence that average efficiency scores vary over time and generally comove with China's economic growth rate and the average stock returns in the real‐estate sector. By providing a new framework for evaluating input, output and carry‐over indicators of real‐estate companies, the results of this study equip real‐estate companies to have an accurate perspective of their operating position and simultaneously provide investors with an avenue to evaluate real‐estate companies' development before any form of investment.

The Internet is one of the most important innovations of humanity and has brought many of our social and cultural changes. The web pages have great importance because they are the opening door for which users can interact directly with each other and interact with corporations. A bad performance may result in a significant loss from millions of users. This work aims to evaluate the performance of the Internet web pages through Data Envelopment Analysis (DEA), and provide a list with 82 from the 100 most-accessed addresses in the world ranked by their page load time (output) projection on the size and number of objects (inputs). The decomposition of the technical efficiency is made from an inverse frontier perspective; this way, outputs are contracted and inputs expanded. As a result, the pages reaching the efficiency frontier are explored as benchmarks for better practices. The slacks for the page loading time, size and objects are provided for the inefficient units. We discuss the results and the interesting prospects on the Computing and Web-Communication.

The next wave of ETF innovation will expand the investment opportunity set, reduce investment costs, and nudge the efficient frontier northwest. We can see this future in the rearview mirror. It is the history of the fund industry. The authors review a framework for evaluating investment innovation and assessing its contribution to “investor welfare,” a measure of investors’ ability to build portfolios that reflect their preferences. The authors also discuss opportunities for innovation and the technological and regulatory developments that could bring them to the marketplace. TOPICS:Exchange-traded funds and applications, mutual funds/passive investing/indexing, mutual fund performance Key Findings • The mutual and ETF industry’s history is its roadmap to the future. • As in the past, the industry will use advances in investment theory and new technologies to nudge the efficient frontier northwest, improving risk-adjusted returns. • These innovations will give investors the ability to build portfolios that more accurately reflect their preferences, enhancing “investor welfare.”

PurposeThe paper analyzes the relative performance of provincial gas distribution companies with different types of inputs and outputs. A data envelopment analysis (DEA)-based model is developed to construct an equilibrium efficient frontier in the presence of multi-type input/output variables.Design/methodology/approachA DEA-based model is developed to construct an equilibrium efficient frontier in the presence of multi-type input/output variables.FindingsIt has been shown that using this single equilibrium efficient frontier, a trade-off is made between efficient and inefficient companies so as to make all companies as efficient.Originality/valueThe study analyzes the relative performance of provincial gas distribution companies with different types of inputs and outputs. A DEA-based model is developed to construct an equilibrium efficient frontier in the presence of multi-type input/output variables. It has been shown that using this single equilibrium efficient frontier, a trade-off is made between efficient and inefficient companies so as to make all companies as efficient.

The objective of this paper is to identify the most efficient healthcare systems in a sample of 17 EU Member States. According to the health system financing schemes, the selected countries belong to two main groups, Beveridge and Bismarck. The research includes five input variables describing the financial and human resources, the level of health infrastructure, the medical technology and the healthcare utilization. On the output side we analysed four measures that reflect the overall health status of the population and the effectiveness of prevention and emergency care. Using the Data Envelopment Analysis (DEA) method, the most efficient healthcare systems are found in Sweden, the UK and Romania. The constraints applied for all the indicators and scenarios lead to higher or lower inefficiency scores, the Beveridge group being on average more efficient than the Bismarck one.

The development of the pallet rental industry is moving at a rapid pace. However, there is no literature on the performance evaluation of pallet rental companies except our previous study. This paper contributes to extending our previous super-efficiency integer-valued DEA (data envelope analysis) model to a group of integer-valued DEA models with generalized reference sets (G-IDEA). According to the proposed approach, the reference sets for decision-making units (DMUs) are composed of proper efficient sample units instead of efficient DMUs. These sample units, which may or may not be the same as DMUs, can be selected by decision-makers according to their demand. The advantages of the G-IDEA models are as follows: (1) they can be applied to find the projections of pallet rental companies with real-valued and integer-valued variables on the efficient frontier; (2) they are able to help the decision-makers in inefficient pallet rental companies make a step by step efficiency improvement scheme. Particularly, we develop a non-oriented integer-valued DEA model with generalized reference sets (NG-IDEA). The NG-IDEA model can be used to figure out how to improve the performance of inefficient pallet rental companies by simultaneously decreasing inputs and increasing outputs. The proposed models are applied to the performance evaluation of ten pallet rental companies. The results of the case study prove the effectiveness of our models. Furthermore, some valuable suggestions on how to improve the efficiency of inefficient pallet rental companies are presented based on the results.

Essentially, this study asks: Does the business risk audit (BRA) approach increase audit production efficiency? To answer this question empirically, direct and indirect tests are employed using proprietary, working paper data from the larger clients of a major Australian public sector audit provider and an efficiency frontier analytic methodology, data envelopment analysis (DEA). Results based on this proprietary, audit hours data for audit engagements carried out just after BRA approach implementation show that they have high levels of production efficiency and are risk‐adjusted, with no significant difference in production efficiency between higher and lower business risk audit engagements. Results based on audit fees data for audit engagements carried out shortly before and after BRA approach implementation show that overall production efficiency significantly improves. Importantly, while this improvement is significant for lower‐risk audit engagements, there is no significant improvement for higher‐risk audit engagements. In the context of this study's research site, this is consistent with the BRA approach addressing inefficiencies created when lower‐risk audit engagements are being over‐audited. That is, the BRA approach can result in both risk‐adjusted and more efficiently produced audits. With the re‐emergence of the BRA approach in the literature and in practice, this study provides empirical evidence to support the claim that this audit approach can lead to 'creating auditing efficiencies' (Bell et al., 1997, p. 1). [ABSTRACT FROM AUTHOR]

This paper aims to provide a methodological framework for the comparative evaluation of driving safety efficiency based on Data Envelopment Analysis (DEA). The analysis considers each driver as a Decision Making Unit (DMU) and aims to provide a relative safety efficiency measure to compare different drivers based on their driving performance. The last is defined based on a set of driving analytics (e.g. distance travelled, speed, accelerations, braking, cornering and smartphone usage) collected using an innovative data collection scheme, which is based on the continuous recording of driving behavior analytics in real time, using smartphone device sensors. Safety efficiency is examined in terms of speed limit violation, driving distraction, aggressiveness and safety on urban, rural and highway road and in an overall model. DEA models are identifying the most efficient drivers that lie on the efficiency frontier and act as peers for the rest of the non-efficient drivers. The proposed methodological framework is tested on data from fifty-six (56) drivers during a 7-months period. Findings help distinguish the most efficient drivers from those that are less efficient. Moreover, the efficient level of inputs and outputs to switch from non-efficiency to the efficiency frontier is identified. Results also provide a potential for classification of the driving sample based on drivers’ comparative safety efficiency. The main characteristics of the most and less efficient drivers are consequently analyzed and presented. Most common inefficient driving practices are identified (aggressive, risky driving, etc.) and driving behavior is comparatively evaluated and analyzed.

Government legislatures require highway agencies to render account for their stewardship of the highway infrastructure by regularly assessing their highway performance outcomes against resource inputs (expenditures). This can be done by comparing the expenditures and resulting performance in each of multiple highway agencies (jurisdictions) and for a specific type of infrastructure. In addressing this research question, the paper first carries out adjustments to account for differences in attributes (inventory size, measurement bias, infrastructure age, climate severities, and traffic intensities) across the jurisdictions. The methodology develops an efficiency frontier using optimization, identifies frontier located jurisdictions (FLJs), removes the FLJs and re-develops the next frontier, and continues this cycle until all jurisdictions have been removed. The methodology is applied to interstate highway bridges in the various state agencies in the USA. The results yield an overall ranking of the agencies' efficiencies. Highway oversight agencies can use the paper's methodology to monitor the expenditure and performance stewardship of highway agencies in various jurisdictions that report to the oversight agency, and to rank the agencies in terms of their respective levels of efficiency. Also, agencies in the individual jurisdictions can use the methodology to identify which other agencies they could "learn" from, in order to move up to the efficiency frontier, and to estimate the anticipated benefits of moving up.

Despite the evidence of links between health expenditure and health care efficiency, it is still unclear why countries with similar levels of health expenditures experience different outputs in terms of life expectancy at birth. Health care system efficiency might shed some light on the question. Using output-oriented data envelopment analysis, we compared the health systems of 140 countries in terms of attained life expectancy. Efficiency is determined by the distance from the closest country on the best practice frontier, which identifies the highest attainable life expectancy observed for any given level of health care spending. By using national data form the Human Development Data, we built the efficiency frontier and computed the potential life expectancy increase for each country. The potential improvement was, on average, 5.47 years [95%CI: 4.71–6.27 years]. The least efficient countries (10th percentile of the efficiency score) could improve by 11.78 years, while the most efficient countries (90th percentile of the efficiency score) could only improve by 0.83 years. We then analyzed, with regression analysis stratified by average education level, and by the role of health-related variables in differentiating efficient and inefficient countries from each other. The results suggest that, among countries with lower levels of education, decreasing unemployment and income inequality increases average life expectancy, without increasing health expenditure levels.

The WHO-CHOICE (World Health Organization CHOosing Interventions that are Cost-Effective) approach is unique in the global health landscape, as it takes a "generalized" approach to cost-effectiveness analysis (CEA) that can be seen as a quantitative assessment of current and future efficiency within a health system. CEA is a critical contribution to the process of priority setting and decision-making in healthcare, contributing to deliberative dialogue processes to select services to be funded. WHO-CHOICE provides regional level estimates of cost-effectiveness, along with tools to support country level analyses. This series provides an update to the methodological approach used in WHO-CHOICE and presents updated cost-effectiveness estimates for 479 interventions. Five papers are presented, the first focusing on methodological updates, followed by three results papers on maternal, newborn and child health; HIV, tuberculosis and malaria; and non-communicable diseases and mental health. The final paper presents a set of example universal health coverage (UHC) benefit packages selected through only a value for money lens, showing that all disease areas have interventions which can fall on the efficiency frontier. Critical for all countries is institutionalizing decision-making processes. A UHC benefit package should not be static, as the countries needs and ability to pay change over time. Decisions will need to be continually revised and new interventions added to health benefit packages. This is a vital component of progressive realization, as the package is expanded over time. Developing an institutionalized process ensures this can be done consistently, fairly, and transparently, to ensure an equitable path to UHC.

Purpose This paper aims to perform an analytical analysis on portfolio allocation when a tracking error volatility (TEV) constraint holds, drawing specific attention to the portfolio efficiency issue. Indeed, it is well known that investors can assign part of their funds to asset managers who are given the task of beating a benchmark portfolio. However, the risk management office often imposes a TEV constraint to the asset managers’ activity to maintain the portfolio risk near to the risk of the benchmark. This situation could lead asset managers to select non efficient portfolios in the total return and absolute risk perspective. However, the risk management office can impose further constraints, such as on maximum variance or maximum value at risk (VaR) to maintain the overall portfolio risk under control. Design/methodology/approach First the authors define the TEV constrained-efficient frontier (ECTF), a set of TEV constrained portfolios that are mean–variance efficient. Second, they define two new portfolio frontiers analyzing how the imposition of a maximum variance or maximum VaR restriction can reduce the ECTF. Third, they investigate the feasibility of such portfolio frontiers and their relationships. Findings The authors find that variance or VaR constraint can force asset managers to pursue portfolio efficiency. Originality/value This is a practically important issue given that asset managers often receive a constraint on TEV from the risk management office, but the risk management office does not ask them to minimize the TEV as often assumed in the optimizations performed in the literature on this topic.

To better assist decision-makers (e.g., enterprise executives) in selecting the most desirable IT portfolio, this study proposes a new IT Portfolio Efficient Frontier model that incorporates the decision-maker's risk tolerance levels. The proposed model, built on portfolio optimization along with experimental design and simulation data, considers three IT portfolio scenarios: even distribution-based IT portfolios, uneven distribution-based IT portfolios, and dominant IT portfolios. Our findings show that the IT portfolio efficient frontiers derived from both an even distribution-based IT portfolio and an uneven distribution-based IT portfolio have a relatively positive relationship between IT portfolio risk and return. Our findings also indicate that if IT investments are part of a dominant IT portfolio, an inflection point of the IT portfolio efficient frontier appears under the decision-maker's medium risk tolerance level, and the most desirable IT portfolio is generated when a decision maker's risk tolerance level is medium or higher.

The purpose of this research is to determine the composition of an efficient portfolio in the financing of ten Islamic banks. The theory of efficient portfolio by Markowitz is a modern portfolio theory used for analyzing the combination of various investment instruments to form efficient portfolio points at efficient frontier lines. The efficient composition portfolio measurement of Islamic bank in this study uses return, standard deviation, variance-covariance, correlation coefficient, and variation coefficient of investment instruments between 2011 and 2015. This study uses quantitative research achieved using Microsoft Excel. The result of this research shows that the average composition of an efficient portfolio of each Islamic bank is as follows: 48.62% for Mudharabah-Musyarakah, 41.63% for Murabahah, 8.03% for Ijarah, and 8.31% for Istishna. It can be seen that Mudharabah-Musyarakah and Murabahah are more dominant than the other financing types.

The objective of this paper is to investigate the notion of “doing well while doing good” through examining the performance of Islamic, sustainability, and Islamic sustainability equity indices and comparing against the global equity market benchmark. Specifically, we address three key issues that are of concern to most investors: (i) how different are the global portfolio's efficient frontiers that comprise the four types of equities?; (ii) what are the driving factors behind these index performance differences?; and (iii) do the performance and volatilities of these four indices vary across time-periods and regimes? Overall, our findings reveal that investors do not have to pay a price for investing in Islamic or sustainable equity indices. In fact, combining Islamic and sustainability investing strategies are more rewarding, particularly during the economic boom, bullish equity markets and subprime crisis periods. Policy implications are provided. Keywords: Islamic equity, Sustainability investing, Wavelet analysis, Efficient frontier, Bayesian model averaging, Markov switching

In order to respond to climate change, China has committed to reduce national carbon intensity by 40–45% in 2020 and 60–65% in 2030, relative to 2005. Given that energy-intensive industries represent ~80% of total CO2 emissions in China and that China is a large and diverse country, this paper aims to investigate the potential contribution of regional convergence in energy-intensive industries to CO2 emissions reduction and to meeting China's emissions goals. To the best of our knowledge this matter has never been explored before. Using panel data from 2001 to 2015, we build three scenarios of future carbon intensities: business as usual (BAU), frontier (based on the directional distance function, in which all regions reach the efficiency frontier) and best available technology (BAT, in which all regions adopt the lowest-emitting technology). The frontier and BAT scenarios represent a weak and a strong form of regional convergence, respectively, and the BAU assumes that it develops following historical patterns. We then use the Kaya identity to estimate CO2 emissions up to 2030 under the three scenarios. Our results are as follows: (1) Under BAU, the CO2 emissions of energy-intensive industries increase from 7382.8 Mt in 2015 to 8127.6 Mt in 2030. Under the frontier scenario the emissions in 2030 are 44.23% lower than under business as usual, while under the BAT scenario this value becomes 84.81%. Electricity and ferrous metals are the sectors that most contribute to the reduction potential. (2) Even under BAU the carbon intensity of energy-intensive industries as a whole and all of its constituent sub-sectors except for electricity will decrease by more than the nationally-mandated averages. (3) Regional convergence could help the energy-intensive industries peak its CO2 emissions before 2030, while under BAU the absolute emissions of the energy-intensive industries keep increasing.

The industry dynamic creates a difficult problem that must be solved if we are to address climate change. How can we lower greenhouse gas (GHG) emissions while simultaneously increasing production? This paper develops a new inverse data envelopment analysis (DEA) model for optimizing GHG emissions. The inverse DEA model minimizes the overall GHG emissions generated by a set of decision making units (DMUs) for producing a certain level of outputs, given that the DMUs maintain at least their existing performance status. The usefulness of the proposed method in this paper is demonstrated through an application in the oil and gas industry. We find that roughly 57% of our sample DMUs are inefficient. Ample room exists within the current efficiency frontier to lower GHG emissions in the oil and gas sector. We recommend that environmental regulators look into targeting regulations at these inefficiencies that exist within the industry as low bearing fruit for potential GHG emissions reductions.

Research Question: The investigation of the optimal allocation of current assets. Motivation: Current assets investment is a decision process which affects firm value. In this paper, we develop a framework that encompasses these decisions by taking into consideration the trade-off between risk and return. Idea: We build up a model implemented in two stages. In the first stage, using random coefficient modeling on panel data, we obtain the estimates of the expected returns and standard deviations for cash holdings, inventories and receivables along with the correlations between them. Having these estimates on hand we move on to the second stage to determine the optimal allocation of current assets portfolio and construct the efficient frontier of the possible combinations of the current assets’ elements. Data: For the purposes of our study we use financial data from Greek manufacturing firms, drawn from their annual income statements and balance sheets. Firms are classified into the manufacturing industry for the years 2003 to 2014. Tools: In the first stage we use random coefficient modeling on panel data while in the second stage mean-variance analysis is employed. Findings: By applying the model in the Greek manufacturing sector we find that the minimum-variance portfolio of the average firm of our data set has an expected return of 10.00% with a 6.14% standard deviation (risk) and consists of 13% cash and cash equivalents, 29% inventories and 58% receivables. Contribution: Our model would be useful to assess and monitor firms’ current assets investments and may be used in the formulation of sound current assets policies and procedures.

Previous bank efficiency studies assumed that the same efficient frontier system exists in different types of banks, but the conventional radial data envelopment analysis (DEA) model slacks are not counted in efficiency scores, and nonradial DEA models do not consider radial features. This paper develops an Epsilou‐based measure meta‐DEA approach for measuring the efficiencies and technology gaps of different bank types. The results are as the follows: The average efficiency and technology gaps of nonfinancial holding banks are better than those of financial holding banks. The nonfinancial holding banks are more efficient than financial holding banks in investment and other income.

This paper contrasts high-risk, hedge fund trading, with low-risk, mutual fund trading, in terms of their differing utility functions. We envision hedge funds, led by informed traders who use information to seek out investment opportunities, timing market conditions, with the expectation that prices will move in their favor. Directional hedge funds act to influence prices, while non-directional hedge funds do not act to influence prices. We present utility functions based on steeply-sloping Laplace distributions and hyperbolic cosine distributions, to describe the actions of directional hedge fund traders. Less steeply-sloping lognormal distributions, Coulomb wave functions, quadratic utility functions, and Bessel utility functions are used to describe the investing style of non-directional hedge fund traders. Flatter Legendre utility functions and inverse sine utility functions describe the modest profit-making aspirations of mutual fund traders. The paper’s chief contribution is to develop optimal prices quantitatively, by intersecting utility functions with price distributions. Price distributions for directional hedge fund returns are portrayed as sharp increases and decreases, in the form of jumps, in a discrete arrival Poisson-distributed process. Separate equations are developed for directional hedge fund strategies, including event-driven arbitrage, and global macro strategies. Non-directional strategies include commodity trading, risk-neutral arbitrage, and convertible arbitrage, with primarily lognormal pricing distributions, and some Poisson jumps. Mutual funds are perceived to be Markowitz portfolios, lying on the Capital Market Line, or the International Capital Market Line, tangent to the Efficient Frontier of minimum variance-maximum return portfolios.

In many countries, bus operators are private companies whose service has been leased by government agencies. These agencies develop service compliance indices or measures to keep track of factors such as passenger satisfaction, frequency, and regularity but do not necessarily include the objectives of the operators in the assessment. In this paper, we used slack-based measure data envelopment analysis (SBM) to investigate whether it is possible for a bus operator to be efficient (from a private perspective) and match required standards of frequency and regularity. In doing so, data collected from two major bus operators in Santiago, Chile has been used comprising 99 services. The results show that when private objectives, namely revenues, are included in the analysis, bus operators do not necessarily seek to improve the regularity of their service. Moreover, it was found that some bus services are on the efficient frontier while keeping low performance measure standards. Using the shadow prices of the models, it was also found that improving the performance measures will be hard for many bus services unless there is a significant change in factors that are not under control of the operators (i.e., number of stops, length of the route, etc.). This shows the difficulty of correctly aligning the private objectives of operators with agencies’ objectives.

Abstract In this study, we introduce Efficiency Frontier, an economic assessment framework for business operations. The proposed framework emphasizes efficiency and sustainability. It aims to help organizations translate a complex metric system into a value index, conduct performance evaluation, and determine efficient resource planning strategies. Unlike the traditional methods that majorly focus on the achievement of performance targets, the proposed framework also accounts for the amount of resource that is invested in the achievement, which encourages managers to implement sustainable strategies that aim for long-term success. To validate the new framework, we systematically study the case of the City of Chicago’s 311 contact center with a base case analysis and a sensitivity analysis. In the base case, an Efficiency Frontier architected by a set of efficient solutions from 2013 to 2016 is used to identify outperforming scenarios given resource constraints. In the sensitivity analysis, we demonstrate the application of Efficiency Frontier in determining optimal resource planning strategies in conservative, moderate, and aggressive business investment scenarios. The base case Efficiency Frontier reveals that the marginal return on investment in improving the performance may diminish as investment increases. The sensitivity study suggests that optimal resource planning strategies should be adjusted according to value propositions. This work builds a framework for business leaders to economically assess their operational decision-making. Article highlights Efficient strategies can be identified by exploring the maximum value increase per unit of resource given cost scenarios The fund of a strategy should be reduced when the marginal return of the strategy begins to diminish. The framework’s ability to suggest cost-saving strategies is especially informative in resource-limited settings.

Newbuilding dry-docking is a part of the shipbuilding manufacturing process, common for vessels built on slipways. The subject of this research is steel-built vessels intended for non-restricted sea-going navigation. Based on former experience, the necessity of the dry-docking projects measurement has been noted as a managerial tool for performance estimation and project comparison. The dry-docking project is a complex task which includes the first self-propelled sea passage and the transfer of the manufacturing process to a remote place. The dry-docking result is a surveyed and coated vessel ready for sea trials and five-year service until the next dry-docking. This paper deals with a model which enables process measurement using the analytic hierarchy process (AHP) method for qualitative data related to the dry-docking places and data envelopment analysis (DEA) for quantitative data related to the vessels’ technical and cost data. The modelled data are collected from the completed dry-dockings, and the twenty-nine studied vessels represent the decision-making units (DMU) used in two-step process measurement calculations. The obtained results can distinguish the efficient DMUs, which create an efficient frontier as benchmarks or “the best practice units” in the given DMU set. For the non-efficient DMUs, the efficiency score and rate of improvements needed to reach the efficient frontier will be calculated, and the sources of inefficiency will be recognized.

© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.Two essential topics of Sustainable Development Goals (SDGs) are accessible which are clean energy (SDG-7) and climate change action (SDG-13). Developments and innovations in energy technologies play an essential role in achieving these goals. Therefore, any country should use energy R&D expenditures, which are the primary source of energy innovation, most optimally. This paper aims to investigate the environmental efficiency of R&D expenditures for energy efficiency, renewable energy, hydro and fuel cells, fossil energy, nuclear energy, and other power and storage technologies in OECD countries using data envelopment analysis (DEA) and bootstrap DEA. Estimation findings indicate that only the USA ensures the environmental efficiency in energy R&D expenditures among OECD countries. Although Japan, Canada, France, Germany, and Italy cannot provide environmental efficiency in energy R&D, their scores are very close to the efficiency frontier. Portugal, Hungary, and Slovak Republic are the countries with the lowest environmental efficiency in energy R&D expenditures. At the end of the investigation, this paper also provides an empirical estimation of the extent to which inefficient countries should change their R&D spending to achieve efficiency.

This paper introduces Portfolio Theory, a tool in finance, as a solution to power allocation transmitters in coordinated multi-point (CoMP) systems to combat the link loss and variation of directional millimeter-wave (MMW) signals. With this theory, the mean-variance performance of the receiving power in CoMP transmission can be optimized by choosing portfolios along the efficient frontier. The method is experimentally demonstrated in a 2x1 CoMP transmission system operating at 28 GHz, in which the average receiving power is improved by 20% and the power deviation is improved by 16% by using the proposed method.

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost. Supplementary Information The online version contains supplementary material available at 10.1007/s10729-021-09560-6.

This paper presents a probabilistic modeling approach to the generation expansion problem, where the energy manager of a firm aims at minimizing the investment costs of an energy park subject to the stochastic supply-demand balance constraint. We consider a reliability-based framework, where the energy manager determines robust generation expansion by imposing a probabilistic guarantee on the demand coverage distribution, specifying the required system reliability. We compare two solution methodologies to solve the probabilistically constrained generation expansion problem, i.e. (i) the sample approach and (ii) the sample average approximation. Applicability of the model is demonstrated in the use case, where the two renewable energy sources wind and solar technology are considered. We recover the energy manager's efficient frontier characterizing the optimal level of investment in renewable energy technologies as a function of the ex-ante imposed level of reliability, which quantifies the substitution rate of investment and reliability. The portfolio selection depends on the required reliability and is shifted towards a higher share in solar power when required reliability increases. Within the probabilistically constrained framework we demonstrate the potential of Demand Side Management in reducing capital expenditures. With increasing participation in DSM, the energy manager is able to reduce the necessary level of investment to obtain required reliability, thereby mitigating investment risks.

Many countries are facing the increasing cost of healthcare services and the low efficiency of public hospitals. These issues are also evident in China. This paper offers a comprehensive assessment of the efficiency of public hospitals operating in China&rsquo, s 31 regions. The impact of the third round of reform of the health system in 2009 is assessed based on the three-stage data envelopment analysis procedure. The time period from 2011 to 2018 is covered in this study. Due to different functions performed by the public hospitals and other ones, the number of patients with infectious diseases is incorporated as an output variable reflecting the social function. The outpatient visits and inpatient visits are considered to reflect the outputs related to the private functions. The results imply an increase in the mean efficiency of public hospitals from 0.927 to 0.981 after taking the impact of environmental variables and statistic noise into account. These results indicate that the efficiency of public hospitals is dependent in the operational environment. There are 11 regions whose hospitals operate on the efficiency frontier during the whole period covered. Therefore, the Chinese government should reasonably design and apply the regulatory tools to improve the efficiency of public hospitals.

The economic crisis has its roots in the financial services industry, but it certainly impacted the higher education in a way that has far-reaching implications for the colleges and universities in the United States. With unemployment rates of 8% and above, it made it difficult for families to send their kids to colleges and as a result colleges faced decline in enrollments and pressure to cut costs. Discount rates at almost all universities with an average size of 8,000 or less went up significantly. Academic departments at various universities came under pressure to get leaner and perform better with fewer resources. In this study, we benchmark the financial performance of public universities and private universities against each other as well as against themselves over the years by using data envelopment analysis model. The study also compares universities, public and private, with less than 3,000 students and more than 3,000 students against each other as well as over a period of time. The study is important as it will help university policy makers identify their strengths and weaknesses so that they can capitalize on their strong academic programs and make changes to fix weaker academic programs.

We propose an integrated approach to active exploration by exploiting the Cartographer method as the base SLAM module for submap creation and performing efficient frontier detection in the geometrically co-aligned submaps induced by graph optimization. We also carry out analysis on the reachability of frontiers and their clusters to ensure that the detected frontier can be reached by robot. Our method is tested on a mobile robot in real indoor scene to demonstrate the effectiveness and efficiency of our approach.

Large online services employ thousands of people to label content for applications such as video understanding, natural language processing, and content policy enforcement. While labelers typically reach their decisions by following a well-defined "protocol'', humans may still make mistakes. A common countermeasure is to have multiple people review the same content; however, this process is often time-intensive and requires accurate aggregation of potentially noisy decisions. In this paper, we present CLARA (Confidence of Labels and Raters), a system developed and deployed at Facebook for aggregating reviewer decisions and estimating their uncertainty. We perform extensive validations and describe the deployment of CLARA for measuring the base rate of policy violations, quantifying reviewers' performance, and improving their efficiency. In our experiments, we found that CLARA (a) provides an unbiased estimator of violation rates that is robust to changes in reviewer quality, with accurate confidence intervals, (b) provides an accurate assessment of reviewers' performance, and (c) improves efficiency by reducing the number of reviews based on the review certainty, and enables the operational selection of a threshold on the cost/accuracy efficiency frontier.

The theft of electrical energy is one of the main problems faced by electricity distribution utilities, especially in developing countries. Aware of the difficulties in combating non-technical losses (NTLs) in Brazil, the National Electric Energy Agency (ANEEL) established tolerable limits for the percentage of non-technical losses to each Brazilian distribution utility. Despite the notable progress made by ANEEL, when comparing public utility NTLs and their regulatory targets in the last decade, it was observed that the goals defined by this agency were not able to lead to a general reduction in NTLs in the country. Thus, the search for alternative methodologies to deal with the topic is necessary. A more attractive alternative to the ANEEL’s model is an efficient frontier model. This paper describes a stochastic frontier cost model for panel data whose equation is specified to provide the tolerable limits for the percentage of NTLs. The proposed model was applied to a panel of data containing annual observations, over 10 years, of 41 distribution utilities in the Brazilian electrical system.

This research aims at finding risk management efficiency and its determinants of non-life insurers operating in Pakistan. This study applies Data Envelopment Analysis (DEA) to determine the efficiency scores for risk management practices of firms over the period 2009-2018. Tobit model is applied to identify factors influencing risk management efficiency of firms. DEA results obtained indicate that the overall average efficiency score increases from 68% in 2009 to 76% in 2018. Tobit estimates show that the age and firm size has a significantly positive relationship with risk management efficiency of firms. The study suggests that firms at lower efficiency frontier could improve their efficiency level by performing better in terms of underwriting and investment activities as well as enhancing the value-added of the firms. The study also recommends that non-life insurers should adopt a multifaceted approach in managing the risks to derive greater benefits from their businesses.

The transportation sector represents an important barrier to decarbonising economies. The introduction of electric vehicles seems to be a promising solution; however, the intensive use of such vehicles remains a challenge for economies. By using the two-stage Data Envelopment Analysis (DEA) method, this paper aims to provide useful insights to enlarge Battery Electric Vehicles (BEV) market share. In the first stage, it calculates the efficiency scores for 20 European countries for both BEV adoption and policies supporting electric mobility, considering an output-oriented DEA method with constant returns to scale, and using annual data from 2010 to 2018. It is a non-parametric method, which makes it possible to determine the technical efficiency of the countries under study, i.e., the ability of these countries to transform their inputs into outputs. It calculates the efficiency frontier and determines if the countries are (or not) on this frontier. In the second stage, it examines the role of some determinants of electric mobility using the efficiency scores previously calculated by applying a fractional regression model. The main findings show that few countries are performing on the efficiency frontier. Additionally, renewable electricity generation increases a countries’ DEA score and contributes to bringing the inefficient countries closer to the efficiency frontier. Contrary, the existence of peak periods of electricity consumption decreases the DEA score and moves the inefficient countries further away from the frontier. This paper highlights the need to design transport and electricity policies jointly in order to ensure that the intensive use of BEV contributes towards renewables accommodation.

A previous paper (“The Probability Frontier or, Covariance Crunch: A New Paradigm for Mean-Variance Optimization”) introduced the concept of a Directional Covariance matrix, which reduces market returns to pure directionality (+1 or -1), treating the magnitudes as noise. This is intended to produce better-diversified portfolios with more robust out-of-sample performance than traditional optimization. That paper showed the Directional Covariance matrix did indeed produce more diversified portfolios and better performance than a traditional optimizer in a 13-year back-test simulation for a seven-asset portfolio. We now revisit this test with one full year of new out-of-sample data, and compare its “live” performance against the hedge fund industry. While the traditional optimizer happened to do very well over the last year, the Directional Covariance technique produced a more diverse portfolio and outperformed the HFRI Fund-Weighted Composite index on an absolute basis and by nearly 2:1 on a risk-adjusted basis, with a beta of 0.53, an annual alpha of +2.6%, and R2 of 78%. Having a hedge fund-like risk profile and being fully liquid, this particular strategy is thus well-suited as a holding vehicle for committed (but uncalled) Private Equity capital, to avoid the dreaded “cash drag” effect. Don’t just shrink that covariance matrix; crush it!

There is an increasingly growth of China’s social security expenditure(SSE) during the past decade. Regarding to the great responsibility and impact on citizens’ welfare and economic development, the efficiency of social security expenditure has inevitably become the focus of growing attention. Based on Chinese provincial panel data over the period 2007–2016, a three-stage DEA model was conducted and found that the efficiency level of 29 provinces/municipalities did not reach the efficiency frontier. Environmental factors and statistical noises have a significant impact on the efficiency of SSE, if environmental factors and statistical noises are not considered, the efficiency of SSE in China is likely to be underestimated. The regional differences in the efficiency of SSE were significant and ranked by descending order as follows: central region, eastern region and western region.

We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the mean–variance efficient frontier out-of-sample. Our results, therefore, shed important light on the appeal of machine learning into portfolio selection under estimation risk.

We characterize the long-run stable maturity distribution induced by a fixed issuance policy, defined as the maturity mix of new issues, thereby providing a method to link issuance policies with their long-run consequences. We derive closed-form expressions for a new class of forward-looking stable metrics, including per-period refinancing need, debt service cost, and average maturity — an indicator of the supply of long-term bonds. We use these metrics to provide a normative analysis of the classical debt-management trade-off between refinancing risk and debt service cost. Our results indicate that the US Treasury could move closer to the “efficient frontier” by tilting its issuance towards notes.

In power systems, the electricity distribution sector requires proper regulation to guarantee power supply security, electricity tariffs modicity and universal service to customers. Generally, electric utilities operate differently and are distinguished in terms of costs, quality of supply, market and network size, and other aspects, that affect their efficiency. In this context, the Data Envelopment Analysis has been used in electricity distribution regulation to define efficiency scores and compare practices. The Data Envelopment Analysis application sometimes comes with weight restrictions and negative variables that modify the original methodology which affects the efficiency scores. The main goal of this paper is to evaluate weights restrictions influence on efficiencies results and to perform a sensitivity analysis of efficiency scores using additional benchmarking techniques. We apply the Cross-Efficiency Analysis and the Ratio-based Efficiency Analysis benchmarking methods, in order to provide relevant quantitative information to compute relative efficiency scores and perform peer evaluations among utilities even if they are outside of the efficient frontier. The Brazilian electricity distribution system is selected as study case. Brazil has strong diversity in terms of economic development, climate and geography, and the current procedure adopted by the regulator determine efficiency metrics for all distribution companies based on their operation cost. Results from our analysis show that the diversity of concession areas significantly influence the stability of efficiency scores. Moreover, considering the approach proposed here it is possible to identify an efficiency relationship among all the distribution companies and not only using the ones that are in the efficiency frontier.

This paper proposes a framework to analyze hedge funds fee arrangements in which the portfolio construction is determined by the hedge fund manager and the fees are determined via an optimal equilibrium between the manager and the investor. In this setting, fees include management fees ( $$\alpha$$ ) and performance fees ( $$\beta$$ ). We select the paradigm of Expected Utility Theory to determine the managers optimal strategy. Benefiting from the dependence of the optimal terminal payoff on the fee structure, we explore two criteria producing an equilibrium fee mutually satisfying the investor and the manager, one based on Pareto Optimality and the second on negotiable regions. The former also leads to a Pareto efficient frontier of fee structures. We obtain evidence that the popular fee structure of $$(\alpha , \beta )=(2\%, 20\%)$$ is not an equilibrium fee between manager and investor. Although such equilibrium heavily depends on risk aversion levels and market conditions, the pair $$(\alpha , \beta )=(0.5\%, 30.7\%)$$ stands out as a fair choice. Moreover, the expected utility of the investor is not monotone in the performance fee for many market conditions. In other words, we prove that fee arrangements which include performance fees are usually beneficial for the investor.

Corporate governance crises that occur in the banking sector normally cripple economies and bring many hardships to individuals, corporate entities, communities, and the nation at large. In this study, we sought to examine the level of technical efficiency and productivity growth of rural and community banks (RCBs) and the impact of corporate governance indicators on the RCBs' efficiency performance in Ghana. A sample of 70 out of 140 RCBs was selected based on the ARB Apex Bank's performance ratings and data availability. Data envelopment analysis (DEA) was used to determine the technical efficiency scores of the selected RCBs. In the second stage of the analysis, these computed efficiency scores were regressed on the corporate governance variables to assess the effects of the latter. The findings from the DEA approach show that 11% to 20% of the sampled RCBs in Ghana operate close to the efficiency frontier, whereas the majority - about 65% to 81% - underperformed within the study period of 2007 to 2013. The study further established that the number of board members, frequency of board meetings, and corporate social responsibility have significant influence on RCB efficiency.

Data envelopment analysis (DEA) has been a widely used methodology for evaluating the relative performance of portfolios. Extensive work has appeared for realizing the role of DEA in the aspect of assessing portfolio performance and deriving the relative rankings. However, the role of providing realizable benchmarks is not fully explored in the existing literatures. Most importantly, the DEA-based portfolio performance evaluation methods developed in the existing literatures can hardly provide support for fund managers in pursuing sustainability performance. In this paper, we first propose a DEA frontier improvement approach under the mean-variance framework. This approach provides investor with a rebalancing strategy as well as an improved DEA frontier which approximates the portfolio efficient frontier better than the traditional DEA model does. Then, this approach is extended to a general return-risk framework. Our in-sample simulation results verify the effectiveness of the proposed approach for a wide range of applications. In addition, the out-of-sample tests show that the rebalancing investment strategies can achieve higher Sharpe ratios and Sortino ratios than those of the original ones. Finally, we apply the proposed approach to evaluate mutual fund performance in China with the consideration of sustainability information disclosure. We construct a disclosure index to indicate the extent of sustainable information disclosure of each mutual fund. The results show that the proposed approach provides not only investment recommendations, but also references for constructing sustainable green funds.