Group 1 - The core investment strategy in A-shares is often described as "painting a pie" rather than traditional methods like snowballing or industry rotation [1][6] - The success of the "salesman" in the movie "The Big Short" illustrates the importance of confidence and engaging with clients to validate investment opportunities [2][3] - The concept of "painting a pie" suggests that the driving force behind stock price increases can be both the intrinsic value of a company and the willingness of more people to buy [7][9] Group 2 - The article discusses the difference between being the first to identify an investment opportunity and "cheating" by presenting a common opportunity as rare [19][21] - It emphasizes that the key to successful investing is to buy at a reasonable price, aligning with the early-stage investment opportunities [26][28] - The notion of "painting a pie" can apply to both early identification of opportunities and the act of repackaging common opportunities as unique, with the main distinction being the purchase price [25][27] Group 3 - The article highlights that most investors do not have the chance to be the first and often rely on others' narratives, which can lead to buying inflated opportunities [29][30] - It warns against the pitfalls of investing in opportunities that have already been fully priced in, suggesting that waiting for validation through price increases can lead to poor investment decisions [30][31] - The importance of having one's own investment thesis rather than relying solely on others' ideas is emphasized, advocating for personal conviction in investment choices [31]

Quantitative Models and Construction 1. Model Name: Odds + Win Rate Strategy - **Model Construction Idea**: This strategy combines the risk budget of the odds-based strategy and the win-rate-based strategy to create a comprehensive scoring system for asset allocation[3][48][54] - **Model Construction Process**: 1. The odds-based strategy allocates more to high-odds assets and less to low-odds assets under a target volatility constraint[48] 2. The win-rate-based strategy derives macro win-rate scores from five factors: monetary, credit, growth, inflation, and overseas, and allocates accordingly[51] 3. The combined strategy sums the risk budgets of the two strategies to form a unified allocation model[54] - **Model Evaluation**: The model demonstrates stable performance with low drawdowns and consistent returns over different time periods[54] 2. Model Name: Industry Rotation Strategy - **Model Construction Idea**: This strategy evaluates industries based on three dimensions: momentum/trend, turnover/volatility/beta (crowding), and IR (information ratio) over the past 12 months[43] - **Model Construction Process**: 1. Momentum and trend are measured using the IR of industries over the past 12 months[43] 2. Crowding is assessed using turnover ratio, volatility ratio, and beta ratio[43] 3. The strategy ranks industries based on these metrics and allocates to those with strong trends, low crowding, and high IR[43] - **Model Evaluation**: The strategy has shown strong excess returns and low tracking errors, making it a robust framework for industry allocation[43] --- Model Backtesting Results 1. Odds + Win Rate Strategy - **Annualized Return**: - 2011 onwards: 7.0% - 2014 onwards: 7.6% - 2019 onwards: 7.2%[54] - **Maximum Drawdown**: - 2011 onwards: 2.8% - 2014 onwards: 2.7% - 2019 onwards: 2.8%[54] - **Sharpe Ratio**: - 2011 onwards: 2.86 - 2014 onwards: 3.26 - 2019 onwards: 2.85[56] 2. Industry Rotation Strategy - **Excess Return**: - 2011 onwards: 13.1% - 2014 onwards: 13.0% - 2019 onwards: 10.8%[44] - **Tracking Error**: - 2011 onwards: 11.0% - 2014 onwards: 12.0% - 2019 onwards: 10.7%[44] - **IR**: - 2011 onwards: 1.18 - 2014 onwards: 1.08 - 2019 onwards: 1.02[44] --- Quantitative Factors and Construction 1. Factor Name: Value Factor - **Factor Construction Idea**: Measures stocks with strong trends, low crowding, and moderate odds[27] - **Factor Construction Process**: 1. Trend is measured at zero standard deviation[27] 2. Odds are at 0.3 standard deviation[27] 3. Crowding is at -1.3 standard deviation[27] - **Factor Evaluation**: The factor ranks highest among all style factors, making it a key focus for allocation[27] 2. Factor Name: Quality Factor - **Factor Construction Idea**: Focuses on high odds, weak trends, and low crowding, with potential for future trend confirmation[29] - **Factor Construction Process**: 1. Odds are at 1.7 standard deviation[29] 2. Trend is at -1.4 standard deviation[29] 3. Crowding is at -0.8 standard deviation[29] - **Factor Evaluation**: The factor shows left-side buy signals but requires trend confirmation for stronger allocation[29] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Represents high odds, moderate trends, and moderate crowding, suitable for standard allocation[32] - **Factor Construction Process**: 1. Odds are at 0.9 standard deviation[32] 2. Trend is at -0.2 standard deviation[32] 3. Crowding is at 0.1 standard deviation[32] - **Factor Evaluation**: The factor is recommended for standard allocation due to its balanced characteristics[32] 4. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Characterized by low odds, strong trends, and high crowding, with high uncertainty[35] - **Factor Construction Process**: 1. Odds are at -0.7 standard deviation[35] 2. Trend is at 1.6 standard deviation[35] 3. Crowding is at 0.6 standard deviation[35] - **Factor Evaluation**: The factor is not recommended due to its high uncertainty and crowding[35] --- Factor Backtesting Results 1. Value Factor - **Odds**: 0.3 standard deviation - **Trend**: 0 standard deviation - **Crowding**: -1.3 standard deviation[27] 2. Quality Factor - **Odds**: 1.7 standard deviation - **Trend**: -1.4 standard deviation - **Crowding**: -0.8 standard deviation[29] 3. Growth Factor - **Odds**: 0.9 standard deviation - **Trend**: -0.2 standard deviation - **Crowding**: 0.1 standard deviation[32] 4. Small-Cap Factor - **Odds**: -0.7 standard deviation - **Trend**: 1.6 standard deviation - **Crowding**: 0.6 standard deviation[35]

导读：黄金，在中国文化中有着特殊的地位。早在4000年之前的商周时期，黄金就是身份的标识。作为曾经的四大文 明古国，中国人以黄金作为货币的时间比纸币更长久。对黄金的投资，也是刻入大家骨子里的。 去年11月的一个周末，我带着儿子参加了一次同学聚会。有一个同学的妈妈知道我是做金融的，就来和我讨论怎么做投 资。那时候美国大选已经开始正式投票，特朗普当选的呼声很高。大家普遍的观点是，一旦特朗普当选会让世界格局重 新回到混沌的状态。 许多人都有自己投资黄金的故事，其中最精彩的或许是押注黄金15年的对冲基金大佬：约翰·保尔森（John Alfred Paulson）。。。。。。 史上最伟大的一笔交易者 虽然这些家长不是做金融的，但是对政治和历史也颇有研究。那天一个下午，我们从特朗普当选后的地缘政治冲突，一 直聊到美国债务问题如何解决，最后又谈到了2008年的金融危机。那一年无论是股票还是房子，都出现了暴跌。唯独 黄金是涨的。 约翰·保尔森这个名字，对于华尔街的"老人"应该并不陌生（注：和美国前财政部长亨利·保尔森是两个人）。他父母都 是美国移民，在纽约皇后区长大，本科去了纽约大学。一直到2005年之前，约翰·保尔森在投资 ...