Efficiency of well-diversified portfolios: Evidence from data envelopment analysis

Choi, Hyung Yun; Min, Daiki

doi:10.1016/j.omega.2016.12.008

Cited by 17 publications

(16 citation statements)

References 30 publications

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“…However, these two models do not allow to tackle negative inputs and outputs because the factors typically used to evaluate investments, namely returns, can take negative values. To overcome this limitation, Chu et al (2010), Tsolas and Charles ( 2015 ) and Choi and Min ( 2017 ) use DEA models to evaluate the performance of ETFs while considering the presence of negative data. Our study employs the Weighted Russell Directional Distance Model (WRDDM) approach, which is inspired by the Range Directional Model (RDM +) as in Portela et al ( 2004 ), to handle negative data.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the DEA approach enables managers to obtain the benchmarks of inefficient DMUs, providing them with information regarding the best practices to be followed. In finance, this approach has been used to measure the financial performance of ETFs because it allows to overcome the intrinsic limitations of traditional performance measures (Choi & Min, 2017 ; Murthi et al, 1997 ). On the one hand, this tool does not involve any theoretical reference model (e.g., CAPM or APT).…”

Section: Literature Reviewmentioning

confidence: 99%

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Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach

et al. 2022

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This paper proposes a two-step approach to build portfolio models. The first step employs the Data Envelopment Analysis (DEA) to select assets attaining efficient financial performance according to a set of indicators used as inputs and outputs. The second step builds interval multiobjective portfolio models to obtain the optimal composition of efficient portfolios previously identified with respect to investor preferences. The usefulness of this proposed methodology is illustrated through a selected sample of diversified Exchange Traded Funds (ETFs) operating in the US energy sector. Our results with respect to all models and time horizons mainly show that: (i) ETFs related to nuclear energy are more often viewed as efficient according to all DEA models considered; (ii) the efficient portfolios do not contain any ETFs related to the renewable energy sector; and (iii) natural gas and oil are the sectors that have the most ETFs represented in efficient portfolios. Supplementary Information The online version contains supplementary material available at 10.1007/s10479-021-04323-6.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach

et al. 2022

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“…Lim et al [7] further considered the returnrisk factor in the evaluation process and developed a cross-efficiency based portfolio efficiency evaluation model. Choi and Min [8] conducted a portfolio efficiency measure by incorporating the Treynor and Sharpe indices into the DEA portfolio efficiency index after considering the type of risk in the portfolio in their study. Banihashemi and Navid [9] added CVaR to the input indicators and constructed an evaluation model for application in conjunction with the range directional measure.…”

Section: Research On Dea-based Efficiency Evaluationmentioning

confidence: 99%

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Liu

Ding

2021

Sustainability

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Since investors have diverse risk motives for green investments, this paper uses data envelopment analysis (DEA) and simulation to accurately evaluate the efficiency of green portfolios from the perspective of investors’ subjective risks and accordingly provide suitable investment selection strategies. On the one hand, the paper integrates investors’ risk preferences with efficiency evaluation models under the framework of behavioral finance, and then constructs a green portfolio efficiency evaluation model based on cumulative prospect theory on the basis of defining green portfolio efficiency. On the other hand, by bringing realistic Chinese stock data into the evaluation model and solving it with the help of large number iteration and DEA, the trends of frontier movements and selection options of green portfolios under the influence of different risk preferences are obtained and analyzed. The empirical simulation reveals that: (1) if investors’ risk aversion at return rises, it will not only reduce the expected prospective value of the green portfolio, but also shift down and flatten the frontier of the green portfolio; indicating that investors will tend to reduce their risk-tolerant attitude and prefer a conservative strategy under the same value condition. (2) If investors increase their risk-seeking in the case of losses, this will raise the expected prospect value of the green portfolio and lead to an inward and steeper green portfolio frontier; suggesting that, given equal value, investors prefer to increase their risk-taking capacity and use aggressive strategies in the hope of turning the profit around. (3) The efficiency results of green portfolios are very sensitive to changes in investors’ risk preferences, suggesting that investors need to select and match green portfolios with their own risk appetite levels. The above findings enrich and expand the risk types and evaluation models in previous green investment studies from the perspective of investors’ subjective risk.

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“…However, the significance of this study can be emphasised by the limited research in investment management. The only existing literature in this field includes, amongst others, the use of DEA as a portfolio selection criterion (Dia 2009;Edirisinghe & Zhang 2008;Huang et al 2015;Joro & Na 2006;Mashayekhi & Omrani 2016;Pätäri, Leivo & Honkapuro 2010, 2012, to measure portfolio efficiency in a mean-variance framework (Liu et al 2015), to evaluate fund managers' efficiency (Banker, Chen & Klumpes 2016), as a mutual fund performance assessor (Abad, Thore & Laffarga 2004;Alexakis & Tsolas 2011;Basso & Funari 2016;Chen 2008;Kuosmanen 2007), to evaluate portfolio risk in the forex spot market (Amiri et al 2010), to evaluate the link between bank efficiency and share performance (Beccalli, Casu & Girardone 2006;Sufian & Majid 2007), to evaluate the relationship between portfolio diversification and efficiency (Choi & Min 2017), as a complementary share performance tool to the traditional set of fundamental factors (Van Heerden & Heymans 2013), as an investment tool to predict Japanese bank share performance (Avkiran & Morita 2010), and to evaluate an investment fund's or portfolio's performance (Lim, Oh & Zhu 2014;Tarim & Karan 2001).…”

Section: Relative Efficiency and The General Data Envelopment Analysis Modelmentioning

confidence: 99%

Re-examining the ability to explain future South African banking share returns: A data envelopment analysis approach

Heerden

Coetzee

2019

South African Journal of Economic and management Sciences

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Background: Using financial ratios is considered to be important when making informed judgments about investment portfolios. However, the 'ideal' set of ratios is an elusive notion on which the literature has failed to reach any consensus.Aim: This study attempts to identify to what extent so-called 'non-financial' measures can outperform 'traditional' financial and risk-adjusted performance ratios to explain future share returns for South African banks in a momentum investment strategy.Method: A multi-stage data envelopment analysis model was used in the study. Results:The results suggest that non-financial measures are able to explain up to 90% of banking shares' future returns, which is a 30% to 40% improvement on that of traditional financial and risk-adjusted performance ratios. In identifying the 'ideal' set of ratios for the South African banking industry, this study also found that pure technical efficiency, the priceto-earnings ratio and the static omega ratio were able to explain up to an average of 83% of future banking share returns. Conclusion:The study contributes to the field of portfolio management in that both riskadjusted performance ratios and non-financial measures can be used as short-term and longterm investment decision-making tools. Further to this, the ability to explain future returns to some extent implies that the South African banking industry may be time-varying-information efficient.

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Efficiency of well-diversified portfolios: Evidence from data envelopment analysis

Cited by 17 publications

References 30 publications

Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach

Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Re-examining the ability to explain future South African banking share returns: A data envelopment analysis approach

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