Entrusting decisions to the public service pension fund: An integrated predictive model with additive network DEA approach

CCUR > College of Business > Department of Business Administration & Graduate Institute of International Business Administration > periodical articles > Item 987654321/48883

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/48883

Title:	Entrusting decisions to the public service pension fund: An integrated predictive model with additive network DEA approach
Authors:	Lin, SW (Lin, Sheng-Wei) Lu, WM (Lu, Wen-Min) Lin, FY (Lin, Fengyi)
Contributors:	國企系
Keywords:	Decision analysis network DEA forecasting model investment trust corporations
Date:	2021
Issue Date:	2020-12-11 13:26:03 (UTC+8)
Abstract:	A critical issue for ensuring long-term sustainability of pension funds is how the Public Service Pension Fund Management Board (PSPFMB) makes entrusting decisions to choose investment trust corporations (ITCs). To design a more comprehensive performance measure, this study uses the additive network data envelopment analysis (DEA) approach to measure efficiency scores of 34 ITCs, including operating performance, equity fund performance, and bond fund performance. We further propose a method to effectively forecast the future efficiency scores of each ITC by using a trend analysis technique. Our mechanism for ITC evaluation helps improve the deficiency of old decision-making processes that merely look at ITCs' past performances. Our proposed approach offers a theoretical contribution to the additive DEA literature by through its practicality and by integrating measurement and prediction procedures. For a practical implication with additive network DEA, we provide a more feasible method for selecting ITCs that will run the Public Service Pension Fund.
Relation:	JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 卷 72, 期 5, 頁 1015 - 1032 2021
Appears in Collections:	[Department of Business Administration & Graduate Institute of International Business Administration ] periodical articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	134	View/Open

View Licence

Loading...