The Effect of the Launch of Bitcoin Futures on the Cryptocurrency Market: An Economic Efficiency Approach

We analyze the economic efficiency of the cryptocurrency market after the launch of Bitcoin futures by means of the Data Envelopment Analysis and Malmquist Indexes. Our results show that the introduction of Bitcoin futures did not affect the economic efficiency of the cryptocurrency market. However, we observe that Bitcoin obtained the highest risk-return trade-off due to its liquidity compared to the rest of cryptocurrencies. Therefore, our paper underlines the support of investors on Bitcoin to the detriment of the rest of cryptocurrencies

Resampling DEA estimates of investment fund performance

Data envelopment analysis (DEA) is attractive for comparing investment funds because it handles different characteristics of fund distribution and gives a way to rank funds. There is substantial literature applying DEA to funds, based on the time series of funds' returns. This article looks at the issue of uncertainty in the resulting DEA efficiency estimates, investigating consistency and bias. It uses the bootstrap to develop stochastic DEA models for funds, derive confidence intervals and develop techniques to compare and rank funds and represent the ranking. It investigates how to deal with autocorrelation in the time series and considers models that deal with correlation in the funds' returns. © 2012 Elsevier B.V. All rights reserved

Data envelopment analysis models of investment funds

This paper develops theory missing in the sizable literature that uses data envelopment analysis to construct return-risk ratios for investment funds. It explores the production possibility set of the investment funds to identify an appropriate form of returns to scale. It discusses what risk and return measures can justifiably be combined and how to deal with negative risks, and identifies suitable sets of measures. It identifies the problems of failing to deal with diversification and develops an iterative approximation procedure to deal with it. It identifies relationships between diversification, coherent measures of risk and stochastic dominance. It shows how the iterative procedure makes a practical difference using monthly returns of 30 hedge funds over the same time period. It discusses possible shortcomings of the procedure and offers directions for future research. © 2011 Elsevier B.V. All rights reserved

Application of DEA in international market selection for the export of goods

This article proposes a methodology to support decision-making to select an international market. To do so, an output-oriented data envelopment analysis (DEA) model is used. This methodology takes into account multiple variables such as import tariffs, logistics costs, the ease of doing business, cultural gaps, the value of imports, GDP per capita, and logistics performance, among others, which are validated with a correlation analysis. The methodology is applied to frozen beef exported from Colombia, and it assesses the efficiency of possible destination countries. Finally, this study concludes that DEA provides easy to apply robust models identifying countries and regions that generate higher benefits to access international markets.Este artículo propone una metodología para apoyar la toma de decisiones en la selección de mercados internacionales. Para esto, se utiliza un modelo de análisis envolvente de datos (DEA) orientado a salidas. La metodología utilizada tiene en cuenta múltiples variables como el arancel de importación, costos logísticos, facilidad de hacer negocios, diferencias culturales, valor de importaciones, PIB per cápita, desempeño logístico, entre otros, a los cuales se les aplica un análisis de correlación para su validación. Se aplica la metodología para la carne de res congelada exportada desde Colombia, y se evalúa la eficiencia de los posibles países destinos en los cuales podría comercializarse el producto. Se concluye que DEA ofrece modelos robustos y fáciles a aplicar para identificar los países y regiones que generan mayores beneficios en el acceso a mercados internacionales

Ranking intervals for two-stage production systems

Traditional Data Envelopment Analysis (DEA) models find the most desirable weights for each Decision Making Unit (DMU) in order to estimate the highest efficiency score as possible. Usually, decision-makers are using these efficiency scores for ranking the DMUs. The main drawback in this process is that the ranking based on weights obtained from the standard DEA models ignore other feasible weights, this is due to the fact that DEA may have multiple solutions for each DMU. To overcome this problem, Salo and Punkka (2011) deemed each DMU as a “Black box” and developed a mix-integer model to obtain the ranking intervals for each DMU over sets of all its feasible weights. In many real-world applications, there are DMUs that have a two-stage production system. In this paper, we extend the Salo and Punkka (2011)’s model to more common and practical applications considering the two-stage production structure. The proposed approach calculates each DMU’s ranking interval for the overall system as well as for each subsystem/sub-stage. An application for non-life insurance companies is given to illustrate the applicability of the proposed approach. A real application in Chinese commercial banks shows how this approach can be used by policy makers

Resampling DEA estimates of investment fund performance

This article was submitted for publication in the serial European Journal of Operational Research [© Elsevier]. The definitive version is available at:http://dx.doi.org/10.1016/j.ejor.2012.07.015Data envelopment analysis (DEA) is attractive for comparing investment funds because it handles different characteristics of fund distribution and gives a way to rank funds. There is substantial literature applying DEA to funds, based on the time series of funds' returns. This article looks at the issue of uncertainty in the resulting DEA efficiency estimates, investigating consistency and bias. It uses the bootstrap to develop stochastic DEA models for funds, derive confidence intervals and develop techniques to compare and rank funds and represent the ranking. It investigates how to deal with autocorrelation in the time series and considers models that deal with correlation in the funds' returns. © 2012 Elsevier B.V. All rights reserved

The efficiency evaluation of mutual fund managers based on DARA, CARA, IARA

We evaluate the efficiency of mutual fund managers of 20 different classes of management styles to identify the most efficient strategies and to propose an optimal pattern in selecting the funds by investors. We collect monthly data of 17,686 US mutual funds for a five-year period 2005–2010 to minimize the impact of survivorship bias and use Data Envelopment Analysis (DEA) model to evaluate the mutual fund performance. The set of considered inputs comprised “variance”, representing the mutual fund risk, and “turnover, expense ratio and loads indicators”, reflecting the mutual fund costs and fees. Two kinds of outputs are taken into account by our DEA model, “portfolio return” and “stochastic dominance indicators”. As a unique contribution, we state the benefits of the DEA approach in the DARA, CARA, and IARA framework, and evaluate the efficiency of mutual funds based on fund strategies as well as the performance of best mutual funds among their group. The evidence shows that the efficiency scores of technical, management, and scale are respectively 0.81, 0.921, and 0.874 for the DARA model, while the efficiency scores of two models of CARA and IARA are negligible. Also, we rank each management strategy in any model based on two methods – the number of referencing and the weighted value so that the managers of inefficient strategies must pattern the managers’ ability of reference (efficient) strategies to improve their efficiency on the fund market in future

Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for the calculation of the risk factor, among which the most important one is the Conditional Value at Risk (CVaR). On the other hand, Data Envelopment Analysis (DEA) can be used to form the optimal portfolio and evaluate its efficiency. In these models, the optimal portfolio is created by stocks or companies with high efficiency. Since the search space is vast in actual markets and there are limitations such as the number of assets and their weight, the optimization problem becomes difficult. Evolutionary algorithms are a powerful tool to deal with these difficulties. The automotive industry in Iran involves international automotive manufacturers. Hence, it is essential to investigate the market related to this industry and invest in it. Therefore, in this study we examined this market based on the price index of the automotive group, then optimized a portfolio of automotive companies using two methods. In the first method, the CVaR measurement was modeled by means of DEA, then Particle Swarm Optimization (PSO) and the Imperial Competitive Algorithm (ICA) were used to solve the proposed model. In the second method, PSO and ICA were applied to solve the CVaR model, and the efficiency of the portfolios of the automotive companies was analyzed. Then, these methods were compared with the classic Mean-CVaR model. The results showed that the automotive price index was skewed to the right, and there was a possibility of an increase in return. Most companies showed favorable efficiency. This was displayed the return of the portfolio produced using the DEA-Mean-CVaR model increased because the investment proposal was basedon the stock with the highest expected return and was effective at three risk levels. It was found that when solving the Mean-CVaR model with evolutionary algorithms, the risk decreased. The efficient boundary of the PSO algorithm was higher than that of the ICA algorithm, and it displayed more efficient portfolios.Therefore, this algorithm was more successful in optimizing the portfolio

Using VaR to measure the relationship between return and risk of mutual funds in China

1 online resource (v, 39 p.)Includes abstract and appendices.Includes bibliographical references (p. 28-30).This paper uses VaR to measure the risk of mutual funds in China and to determine the relationship between the returns. A sample of ten Chinese mutual funds over a three-year period, from 2010-2012 was examined for the significance of the continuity in funds’ performances. The proposed models also indicate whether psst risk level still has an influence on the future mutual fund returns, and how long this influence will last. From the models, conclude that past VaR of one-week lag reflects the risk level of the mutual fund. The mutual fund manager can reduce potential losses without changing asset allocation

Dualidade entre green e black funds: análise de performance e eficiência

A procura por instrumentos financeiros sustentáveis tem atingido proporções maiores, à medida que o tempo avança. Neste sentido, este trabalho procura perceber a viabilidade de uma transição de um investimento não sustentável para um investimento sustentável. Para isso, foram constituídas três amostras – uma de green funds, outra de black funds e a última, considerada a amostra global, inclui as duas primeiras amostras. Foram usados períodos de 1, 3 e 5 anos, com o objetivo de comparar a performance e eficiência entre os dois tipos de fundos e averiguar não só quais os fundos de investimento classificados como eficientes, como também os fatores escolhidos (inputs e outputs) que comprovam a referida eficiência em diferentes momentos no tempo. O Value-Based DEA (Data Envelopment Analysis) foi a metodologia utilizada, visto que supera o problema das escalas associado ao modelo DEA aditivo, na medida em que todos os fatores são convertidos em escalas de valor, bem como o problema de fatores com dados negativos ou nulos, que se traduz numa limitação para os modelos DEA clássicos (modelos radiais). Os resultados método obtidos com a utilização desta metodologia indicam que os green funds nos períodos de 3 e 5 anos evidenciam uma melhor eficiência e performance que os black funds. Todavia, no período de 1 ano os black funds revelam melhor eficiência que os green funds, devido à rendibilidade exibida. Pelos resultados obtidos é possível deduzir que a transição de um investimento focado em energias não renováveis para um investimento ligado às energias renováveis pode trazer bons desempenhos e ao mesmo tempo auxiliar na descarbonização de algumas indústrias, incrementando os valores da sustentabilidade