丘吉尔的这句话令人触动。 故事3：有位被大家评价为超级聪明的朋友，说自己处在人生的低谷——"左右为难"。他一直认为自己 有较高的"人生均值"，以至于因为傲慢而忽略了"人生波动"。幸运儿和聪明人，总是以为自己可以 被"不确定性"赦免，可事实并非如此， 本文的主题，与上面的三个故事紧密相关。 "在你的两边都有一个悬崖：一个是谨慎的悬崖，一个是过度大胆的悬崖。" 给我的启发是： 先分享三个真实的故事： 故事1：投资人李国飞只选有95%确定性的好公司。有次吃饭，我问他对年化回报率的要求，他说"没 有"。他只看未来五年到十年有数倍的涨幅可能性，但具体是多少，以及哪一年发生，自己"并不知 道"。 故事2：最近接触到一位年轻人，对投资充满了激情，也在当前短暂的牛市中获得了自信。他的话语体 系里都是"板块轮动、捕捉热点、最佳买点、追随故事......"简而言之，仿佛一切都可以预测，每个牛股 都能论天来择时捕获。 我们的人生并不是找到一个完美的、牢不可破的"点"，而是要找到一个"区间"。 一个人生区间，可以由两个关键参数定义：中点c和半径r。 1、中点c代表了你的人生策略的"均值"或"核心"。 是偏向安稳，还是偏向冒险？ 一 ...

Summary of Conference Call Records Industry or Company Involved - The discussion revolves around the Fixed Income + Intelligent Agent (固收+智能体) and the Black-Litterman (BL) model, focusing on asset allocation and investment strategies in the financial sector. Core Points and Arguments - The BL model addresses the sensitivity of traditional asset allocation models to input data, enhancing the stability and accuracy of return predictions [1] - The model calculates market implied asset returns by constructing a market portfolio, using CAPM to compute expected returns, and assuming no Arrow part to derive excess returns [2] - The BL model reflects market risk preferences by translating required return compensation for unit risk exposure into expected returns for each asset [3] - Investor views are integrated into the BL model through absolute and relative perspectives, adjusting posterior returns based on asset correlations and confidence intervals [4] - In China, the BL model requires the use of benchmark portfolios instead of market portfolios, considering contractual constraints and controlling turnover rates [5] - The Fixed Income + Intelligent Agent utilizes a segmented asset model (e.g., GBR model) to predict returns, achieving volatility reduction in portfolios despite limited accuracy for individual assets [6] - Introducing confidence intervals and segmented asset indicators significantly enhances the predictive accuracy of the BL model, indicating substantial development potential for active fixed income strategies [7] Other Important but Possibly Overlooked Content - The Fixed Income + Intelligent Agent consists of two main components: client-facing applications using large models for visualizing investor expectations and a research segment that includes asset selection, performance understanding, return and risk forecasting, combination selection, and trade enhancement [8] - The BL model's application in China necessitates specific adjustments, such as using benchmark combinations and considering contractual constraints on fixed income portfolios [9][10] - The GBR model, while simple, uses price and volume data to predict future returns, achieving an average accuracy of less than 50% for individual assets, but showing reduced volatility in portfolios [11] - Future development of active fixed income strategies relies on stronger and more accurate views of segmented assets, which can lead to better model performance and superior outcomes compared to passive index products [12] - Resources for further research and application of the BL model are available, including Python libraries and pre-existing code for practical implementation [13]