减少“拍脑袋”式决策公募基金雕琢多元配置业绩比较基准

Core Viewpoint - The public fund industry is increasingly focusing on the role of performance benchmarks in investment constraints, leading to a more detailed approach in setting benchmarks for multi-asset portfolio products [1][4]. Group 1: Benchmark Composition - Recent multi-asset products have significantly enhanced the richness of their performance benchmarks, incorporating various asset classes such as U.S. stocks, Hong Kong stocks, commodities, and deposits [1][3]. - The performance benchmark for the newly launched Yongying Yuan Ying Stable Multi-Asset 90-Day Holding product consists of six components, including 70% domestic bonds, 10% A-shares, 5% U.S. stocks, 5% Hong Kong stocks, 5% commodities, and 5% deposits [2][3]. Group 2: Investment Strategy and Transparency - The detailed benchmarks reflect a shift in investment philosophy from simple stock-bond combinations to a more diversified and global asset allocation approach, especially in a low-interest-rate environment [1][6]. - A clear and detailed performance benchmark enhances product transparency, helping investors better understand risk sources and return drivers, thereby establishing reasonable expectations [4][5]. Group 3: Communication and Decision-Making - The refined performance benchmarks serve as a communication bridge between investors and fund managers, clarifying investment strategies and measuring product performance [5][6]. - The trend towards detailed benchmarks indicates a cognitive evolution in asset allocation concepts within the asset management industry, with a focus on transitioning from "selection experts" to "multi-asset allocation experts" [6]. Group 4: Future Implications - The introduction of multi-asset strategies is expected to transform performance benchmarks from passive references to active guides, becoming integral to the entire product lifecycle management [6]. - The detailed benchmarks will help delineate product risk characteristics, shifting investment goals from return-oriented to risk-adjusted matching [6].