超额（相对）收益与绝对收益真的是不可兼得的鱼和熊掌吗？长期主义者会给出不一样的答案——可持续 的超额（相对）收益本质上是绝对收益的时间积分形态。 物理学家费曼说："量子力学之所以难以理解，是因为我们总用经典世界的语言描述它。"资本市场同样如 此——当投资者用线性思维理解非线性的收益获得机制，必然陷入认知困境，如粒子位置与动量不可同时 精确测定一样。 在投资领域同样存在"风险、收益、可持续性"的不确定性三角 ： ★ 宣称高绝对收益者，往往承受隐性尾部风险（长期资本管理公司的陨落）； ★ 标榜可持续性者，常以牺牲收益锐度为代价（固定收益类产品的平庸回报)； ★ 追求超额收益者，难逃时间维度的均值回归（连续跑赢市场的主动管理基金数量了了）。 对投资者而言，就像一场在迷雾中前行的冒险，收益的测不准性让投资领域充满了悬念与变数，许多投资 者陷入"超额（相对）收益与绝对收益对立"的思维困境。 这并非文字游戏，而是数学规律的必然。当某组合每年以2%的幅度持续跑赢标普500指数，30年后的累计 超额收益将高达81.1%，相当于在美股百年长牛的贝塔地基上，用阿尔法的复利魔法建造出一座收益金字 塔。 历史反复验证：当管理人以长期 ...

Core Viewpoint - The article discusses the evolving landscape of quantitative investment, emphasizing the unique advantages of human decision-making over AI in the investment process, particularly in understanding the "why" and "how" behind investment outcomes [2][3][6][21]. Group 1: Investment Process and Decision-Making - Investment is framed as a game of uncertainty, where the focus should be on the decision-making process rather than short-term results [3][5]. - A good investment process involves understanding three key aspects: what is happening, why it is happening, and how the results are derived [6]. - AI excels in identifying "what" but lacks in understanding "why" and "how," which are critical for effective decision-making [6][10]. Group 2: Creativity and Challenging Consensus - The ability to challenge prevailing consensus is highlighted as a source of excess returns, with unique and scarce value being more valuable in the long term [8][9]. - Human creativity allows for the questioning of established norms, which AI cannot replicate [7][8]. Group 3: Role of Quantitative Techniques - Quantitative techniques are viewed as tools for capturing signals but do not inherently provide advantages in value judgment [10]. - The long-term excess returns from stocks are attributed to the sustainable operational advantages of the companies, rather than merely statistical signals [10][11]. Group 4: Handling Data and Anomalies - Historical data is essential for investment strategies, but anomalies must be carefully managed to avoid skewing results [14][15]. - The approach to handling large-cap stocks as anomalies in data sets is discussed, emphasizing the need for preprocessing to mitigate their impact [14]. Group 5: Differentiation in Investment Strategies - Differences in performance between indices like the CSI 300 and the CSI 500 are attributed to the varying composition of stocks and their respective weights in the indices [16][17]. - The article suggests that personal investors should focus on areas of expertise to identify differentiated investment opportunities [18][19].

Group 1 - The core viewpoint of the article highlights the performance of various index-enhanced portfolios, indicating that the CSI 300 and CSI 500 index-enhanced portfolios achieved positive excess returns, while the CSI 1000 index-enhanced portfolio experienced a slight decline in excess returns this week [1][2][18]. Group 2 - The CSI 300 index-enhanced portfolio recorded an excess return of 0.44% this week and 0.80% year-to-date [1][2]. - The CSI 500 index-enhanced portfolio also achieved an excess return of 0.44% this week and 1.81% year-to-date [1][2]. - The CSI 1000 index-enhanced portfolio saw a decrease of 0.13% in excess return this week, with a year-to-date excess return of 0.50% [1][2]. Group 3 - In the CSI 300 component stocks, factors such as three-month turnover, dividend yield, and one-month turnover performed well [5]. - For the CSI 500 component stocks, factors like executive compensation, expected net profit month-on-month, and dividend yield showed strong performance [6]. - In the CSI 1000 component stocks, factors such as expected PEG, SPTTM, and one-month volatility performed well [10]. Group 4 - The public fund index-enhanced products for the CSI 300 had a maximum excess return of 1.67%, a minimum of -2.70%, and a median of 0.11% this week [18]. - The CSI 500 index-enhanced products had a maximum excess return of 1.55%, a minimum of -0.45%, and a median of 0.38% this week [19]. - The CSI 1000 index-enhanced products recorded a maximum excess return of 1.59%, a minimum of -0.87%, and a median of 0.30% this week [21].

Core Viewpoint - The report tracks the performance of various active quantitative strategies by GuoXin JinGong, focusing on their ability to outperform the median returns of actively managed equity funds, with specific strategies including "Excellent Fund Performance Enhancement Portfolio," "Expected Surprises Selection Portfolio," "Brokerage Golden Stock Performance Enhancement Portfolio," and "Growth Stability Portfolio" [2][3][19]. Group 1: Performance Overview - The "Excellent Fund Performance Enhancement Portfolio" had an absolute return of -3.35% this week and 2.06% year-to-date, ranking in the 60.77 percentile among active equity funds [1][7]. - The "Expected Surprises Selection Portfolio" achieved an absolute return of -1.97% this week and 6.75% year-to-date, ranking in the 27.36 percentile among active equity funds [6][8]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" reported an absolute return of -2.03% this week and 4.12% year-to-date, ranking in the 43.96 percentile among active equity funds [12][10]. - The "Growth Stability Portfolio" had an absolute return of -2.32% this week and 7.11% year-to-date, ranking in the 25.48 percentile among active equity funds [13][14]. Group 2: Strategy Descriptions - The "Excellent Fund Performance Enhancement Portfolio" aims to outperform the median returns of actively managed equity funds by utilizing a quantitative approach based on the holdings of top-performing funds [19][21]. - The "Expected Surprises Selection Portfolio" selects stocks based on expected earnings surprises and analyst upgrades, focusing on both fundamental and technical criteria to build a portfolio [23][24]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" leverages the stock pool identified by brokerage analysts, optimizing the selection to enhance performance relative to the ordinary equity fund index [26][27]. - The "Growth Stability Portfolio" employs a two-dimensional evaluation system for growth stocks, prioritizing those with upcoming earnings announcements to capture potential excess returns [29][30].