“…Input: Management fees ratio: the ratio of the fee charged to investors to mutual fund size (Galagedera et al, ; Pástor, Stambaugh, & Taylor, );Transaction costs ratio: the ratio of the transaction fee incurred to mutual fund size (Cremers & Petajisto, );Buying turnover ratio: the ratio of the total amount of the purchase of stocks to the average mutual fund size for the year (Cuthbertson, Nitzsche, & O'Sullivan, ; Pástor et al, ); andSelling turnover ratio: the ratio of amount of stocks sold minus the redemption fund to the mutual fund size for the year (Cuthbertson et al, ; Pástor et al, ).Output/Input: Sharpe ratio: the average return earned in excess of the risk‐free rate per unit of total risk (Sharpe, );Jensen's alpha: the abnormal return that fund managers provide to the mutual fund (Angelidis, Giamouridis, & Tessaromatis, ; Jensen, ); andNet return change ratio: the 1‐year net asset value change in units of magnitude (Cuthbertson et al, ; Premachandra et al, ).Output: Net flow change ratio: the ratio of the difference between the purchased amount and redeemed amount to the mutual fund size for the year (Cuthbertson et al, ).…”
Section: Methodsmentioning
confidence: 99%
“…They showed that small fund families tend to perform better than their bigger counterparts. Recently, Galagedera et al () developed a multiplier‐based three‐stage DEA model for mutual fund performance evaluation, which further improved its discriminatory power. They exposed the inefficiencies of U.S. equity mutual funds in terms of operational, resource, and portfolio management.…”
Section: Mutual Fund Performance Evaluationmentioning
confidence: 99%
“…This allows certain performance indicators that remain hidden in other tools to be uncovered. In the literature the following studies (e.g., Galagedera, Watson, Premachandra, & Chen, ; Galagedera, Roshdi, Fukuyama, & Zhu, ; Premachandra, Zhu, Watson, & Galagedera, ) have applied DEA in evaluating mutual funds.…”
This study employs a two-stage network data envelopment analysis model to analyze the decision quality and capital magnet efficiencies of 155 mutual funds in Taiwan during the period 2007-2016. The empirical results show that fund managers improved their decision quality; however, their capital magnet efficiency declined.This study also found 10 mutual funds performing in decision quality and capital magnet efficiencies, from which practical suggestions are provided to investors.Finally, this study constructs a market competition matrix to help fund managers (and investors) improve their operating and portfolio performance, plus resource allocation.
“…Input: Management fees ratio: the ratio of the fee charged to investors to mutual fund size (Galagedera et al, ; Pástor, Stambaugh, & Taylor, );Transaction costs ratio: the ratio of the transaction fee incurred to mutual fund size (Cremers & Petajisto, );Buying turnover ratio: the ratio of the total amount of the purchase of stocks to the average mutual fund size for the year (Cuthbertson, Nitzsche, & O'Sullivan, ; Pástor et al, ); andSelling turnover ratio: the ratio of amount of stocks sold minus the redemption fund to the mutual fund size for the year (Cuthbertson et al, ; Pástor et al, ).Output/Input: Sharpe ratio: the average return earned in excess of the risk‐free rate per unit of total risk (Sharpe, );Jensen's alpha: the abnormal return that fund managers provide to the mutual fund (Angelidis, Giamouridis, & Tessaromatis, ; Jensen, ); andNet return change ratio: the 1‐year net asset value change in units of magnitude (Cuthbertson et al, ; Premachandra et al, ).Output: Net flow change ratio: the ratio of the difference between the purchased amount and redeemed amount to the mutual fund size for the year (Cuthbertson et al, ).…”
Section: Methodsmentioning
confidence: 99%
“…They showed that small fund families tend to perform better than their bigger counterparts. Recently, Galagedera et al () developed a multiplier‐based three‐stage DEA model for mutual fund performance evaluation, which further improved its discriminatory power. They exposed the inefficiencies of U.S. equity mutual funds in terms of operational, resource, and portfolio management.…”
Section: Mutual Fund Performance Evaluationmentioning
confidence: 99%
“…This allows certain performance indicators that remain hidden in other tools to be uncovered. In the literature the following studies (e.g., Galagedera, Watson, Premachandra, & Chen, ; Galagedera, Roshdi, Fukuyama, & Zhu, ; Premachandra, Zhu, Watson, & Galagedera, ) have applied DEA in evaluating mutual funds.…”
This study employs a two-stage network data envelopment analysis model to analyze the decision quality and capital magnet efficiencies of 155 mutual funds in Taiwan during the period 2007-2016. The empirical results show that fund managers improved their decision quality; however, their capital magnet efficiency declined.This study also found 10 mutual funds performing in decision quality and capital magnet efficiencies, from which practical suggestions are provided to investors.Finally, this study constructs a market competition matrix to help fund managers (and investors) improve their operating and portfolio performance, plus resource allocation.
“…Previous studies note that two-stage DEA models are more efficient than single-stage ones since their discriminatory power is higher [26,48,49]. The traditional DEA model neglects the connectivity of internal economic activities and cannot express the management messages of those activities.…”
Section: Data Collection and Descriptive Statisticsmentioning
Sustainable development has become the biggest concern of the semiconductor industry, which plays a vital role not only in technology breakthroughs, but also by serving as an enabler for sustainability. This study combines Analytic Hierarchy Process (AHP) and additive network Data Envelopment Analysis (DEA) to measure the sustainable performance which are derived from business growth stage and energy utilization stage through the parametric linear program. Meanwhile, this method makes up the disadvantage of the weighting technique used additive decomposition approach to the two-stage network and avoids biasing toward the second stage. The findings demonstrate that Taiwan’s semiconductor manufacturing sector has exhibited a steady increase in its overall trend of sustainability performance. According to the stage-level performance results, the performance of business growth is better than energy utilization. However, the changing trend of overall sustainability performance is through a steady increase from environmental efficiency and not from economic efficiency.
“…Using a two-stage framework, we are able to open the DMU black box and decompose it into different stages, under a divisional structure with network connections. This method is commonly used to depict the operational structure in many industries [17][18][19][20][21][22]. In this paper, we adopt the basic assumptions of Tone and Tsutsui [16].…”
Sustainable development has always been an important issue for all policy makers, even more so now, as global warming has seriously threatened the whole world. To understand the efficacy of regional sustainable policies, we proposed a dynamic, two-stage, slacks-based measure (SBM) model with carry-over and intermediate variables, highlighting the importance of an electricity portfolio, to measure overall energy performance for the purpose of regional sustainable development. In this unified linear programming framework with intertemporal evaluation, we estimated the effects of a clean electricity supply by the abatement of CO 2 emissions and the gain of economic growth. The results can be used as a reference for decision makers to shape regional sustainable development policies. Using data of 30 provincial administration regions in China for the period of 2012-2017, we postulate that the lower energy performance of the Chinese regional economic system for sustainable development may be attributed to a lower electricity portfolio performance. We then postulate that investment in low-carbon energy infrastructure can combat CO 2 emissions, and is also a major driving force in the regional economic growth.
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