风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者：上善山水 来源：雪球 " 红利+ " ， 在红利的基础上加了一点东西 ， 主要还是红利 。 长按即可免费加入哦 牛市 ， 红利跑不赢沪深300 ， 没关系 。 如果觉得有关系 ， 应该想一下牛市里是不是找点红利 之外的机会 。 如果找不到机会 ， 安心接受红利 ， 涨得慢 ， 只是相对慢 ， 还是涨 。 红利低波 ， 就是$红利低波(CSIH30269)$ ， 其他红利低波先来后到 。 基金市场上 ， 先有 512890 ， 后有515080 。 在515080之前 ， 有两家场外中证红利指数基金 ， 一个家场内很冷的 LOF 。 中证红利指数ETF ， 512890是先行者 ， 至今 ， 它依然是大哥 ， 规模最大 。 最近 ， 总有人拉着我讨论红利低波 ， 现在回过内味了 。 自由现金流一顿乱棒 ， 515080的投资 者春暖花开 ， 510880的投资者面朝大海 ， 512890的投资者纠葛了 ， 一部分 。 这是典型的 " 聪明 " 因子投资 ， 投资者为了更高收益聪明地踏出一步 ， 当收益能力更强的产品 出现 ...

Core Viewpoint - The article emphasizes the importance of investment factors in selecting stocks and constructing investment strategies, highlighting that understanding these factors can lead to better investment decisions and potential returns [2][20]. Investment Factors Overview - The article introduces eight commonly used investment factors, each with distinct principles, applicable market conditions, and associated risks, which can help investors optimize their investment strategies [4][16]. Factor Summaries 1. Market Capitalization Factor - Focuses on the impact of stock size on returns, with large-cap stocks generally being more stable but less elastic, while small-cap stocks offer higher growth potential but come with increased risk [5][6]. 2. Value Factor - Concentrates on the discrepancy between a company's intrinsic value and market price, aiming to identify undervalued stocks for potential gains when market sentiment improves [8]. 3. Growth Factor - Evaluates a company's earnings growth and future potential, typically performing well in favorable economic conditions but facing higher risks during downturns [9]. 4. Low Volatility Factor - Selects stocks with stable prices and low volatility, providing better risk-adjusted returns, especially during market downturns [11]. 5. Dividend Factor - Targets stocks with stable dividends and high yield, offering defensive characteristics in volatile markets but may lag in strong bull markets [12]. 6. Quality Factor - Based on financial and operational metrics to identify high-quality companies, which may face valuation risks during periods of high market risk appetite [13]. 7. Momentum Factor - Utilizes the trend-following theory, capitalizing on stocks that have shown strong past performance, though it may struggle in volatile markets [14]. 8. Reversal Factor - Exploits price reversal opportunities, performing well in choppy or bearish markets but underperforming in strong trends [15]. Factor Usage Considerations - Investors should choose factors that align with their risk tolerance and investment goals, combining multiple factors to enhance returns while being mindful of market conditions [17][18][19].

Core Viewpoint - The global market has become increasingly complex, with significant challenges arising from weak tech stocks, fluctuating consumer sentiment, and policy uncertainties impacting investor decisions [1][4]. Group 1: Market Dynamics - The "Magnificent 7" tech giants have seen an 8% decline this year, contrasting with a 4% increase in the remaining 493 companies in the S&P 500, highlighting a shift in market dynamics [5]. - Recent weeks have shown a significant rise in market difficulty, with hedge funds experiencing their second-worst five-day performance in nearly two years [6]. - The "momentum" factor has recently contributed significantly to hedge fund returns, but has shown volatility since the U.S. elections, indicating a shift towards new sectors and industries [7]. Group 2: Economic Indicators - U.S. GDP growth rate estimates have dropped from slightly above 3% to below 2% in the past month, reflecting economic uncertainty [9]. - Investor sentiment data from the American Association of Individual Investors (AAII) is nearing historical highs, indicating a rapid shift in investor mood [10]. Group 3: Sector Analysis - The technology sector ETF (XLK) has recently fallen below its 200-day moving average but remains above the upward trend line established since Q4 2022, indicating potential for further movement [14]. - Xiaomi's recent launch of an electric vehicle, with the first batch of 10,000 units selling out in 10 minutes, demonstrates the resurgence of innovation among Chinese private enterprises [16].

一、本周指数增强组合表现 沪深300指数增强组合本周超额收益0.44%，本年超额收益0.80%。 中证500指数增强组合本周超额收益0.44%，本年超额收益1.81%。 中证1000指数增强组合本周超额收益-0.13%，本年超额收益0.50%。 二、本周选股因子表现跟踪 沪深300成分股中三个月换手、股息率、一个月换手等因子表现较好。 中证500成分股中高管薪酬、预期净利润环比、股息率等因子表现较好。 中证1000成分股中预期PEG、SPTTM、一个月波动等因子表现较好。 公募基金重仓股中股息率、预期EPTTM、EPTTM等因子表现较好。 三、本周公募基金指数增强产品表现跟踪 沪深300指数增强产品本周超额收益最高1.67%，最低-2.70%，中位数 0.11%。 中证500指数增强产品本周超额收益最高1.55%，最低-0.45%，中位数 0.38%。 中证1000指数增强产品本周超额收益最高1.59%，最低-0.87%，中位数 0.30%。 主 要 结 论 一 国信金工指数增强组合表现跟踪 二 因子表现监控 沪深300指数增强组合本周超额收益0.44%，本年超额收益0.80%。 中证500指数增强组合本周超 ...

躺师傅的投资世界 . 格施好学徒，佛系收息人~ 以下文章来源于躺师傅的投资世界 ，作者躺师傅 点击图片即可免费加入哦 风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者：躺红利摊转债 来源：雪球 当前市场上的红利类指数投资主要集中在上证红利 、 中证红利 、 红利低波 、 红利低波100等少数热门红利指数 上 ， 但是这几只热门红利指数并不能完全代表整个红利大家族 ， 实际上中证 、 国证 、 恒生以及标普等指数公 司还编有大量不同类型的红利指数 ， 这些红利指数同样得到了指数公司的日常维护 ， 其中的很多长期数据都非 常有参考价值 ， 因此躺师傅打算写一个 " 冷门红利指数研究 " 的长文系列来给大家介绍介绍这些被市场忽略的 " 冷门 " 红利指数 ， 一方面是为了整理相关资料备用 ， 另一方面也是想借此加深对红利指数的研究与认识 。 当然 ， 一些与红利因子强相关的其它因子 （ 如低波 、 价值 、 现金流类 ） 指数也会一并纳入研究 。 今天来聊一只横跨沪港深三市的红利低波指数 —— 中证沪港深红利低波动指数 。 一 、 指数编制概况 | | 中证沪港深红利低波动 ...

[Table_ReportInfo] 《大额买入与资金流向跟踪 （20250210-20250214）》2025.02.17 《风格 50 组合均跑赢均衡组合——风格 Smart beta 组合跟踪周报 （2025.02.10-2025.02.14）》2025.02.17 《下周 A 股的上行趋势不会改变——量 化择时和拥挤度预警周报（20250216）》 2025.02.16 分析师:郑雅斌 Tel:(021)23219395 Email:zhengyb@haitong.com 证书:S0850511040004 分析师:曹君豪 Tel:(021)23185657 Email:cjh13945@haitong.com [Table_MainInfo] 金融工程研究 证券研究报告 [Table_Title] 相关研究 证书:S0850524010001 联系人:付欣郁 Tel:02123183940 Email:fxy15672@haitong.com 金融工程周报 2025 年 02 月 19 日 上周159只固收+产品业绩创历史新高—— 绝 对 收 益 产 品 及 策 略 周 报 （20250210-20 ...

周末闲来无事，回答最近一段时间读者留言比较多的几个问题。 什么是暴跌次日公式？ 其实就是短线超卖后的日内反弹模型。通常发生在连续下跌、暴跌后的下一个交易日，具体走势是开盘 小弹、犹豫、横盘、下跌、加速、暴跌、插针、快速反弹、横盘震荡、继续拉升、收盘，大致是这个样 子，所以插针的那一下是日内值得投机的买点。 前几年经常出现-4%、-5%的暴力长阴，所以第二天买插针通常有3%以上的日内操作机会，这几年类似 的机会变少了，我也就说的不多了。本周五跌完，日线已经形成超卖，公式的前置条件已经具备，下周 一可以看看有没有针。 至于为什么近几年公式套用的机会变少，我也私底下琢磨过，可能和国家队资金维稳，以及量化资金的 介入有一定关系，当然也只是想想，毕竟没数据没证据。 a股行情现在这么差劲是不是都被量化交易给害了，关停量化是不是就解决问题了？ a股的量化 规模 比较靠谱 的数据大概在2万亿左右， a股总体最新市值大概在90万亿左右， 也就 是说量化 头寸占比2.2%左右。 但是量化基金的实际影响力要 比2.2%大的多，因为量化基金的交 易频率远远高于 其它机构和自然人， 如果按照交易 规模的占比来算，熊市的时候 20-30 ...

Quantitative Factors and Construction - **Factor Name**: LongBuy_1p0, LongBuy_1p5, LongBuy_2p0, LongSell_1p0, LongSell_1p5, LongSell_2p0, LongBuySell_1p0, LongBuySell_1p5, LongBuySell_2p0 **Construction Idea**: These factors are based on the completion time of buy or sell orders. Orders with completion times exceeding the mean plus N standard deviations are classified as "long orders," while others are classified as "short orders" [21][23][24] **Construction Process**: - Calculate the mean and standard deviation of order completion times - Define long orders as those exceeding the mean + N standard deviations (N = 1.0, 1.5, 2.0) - Compute the proportion of long buy orders (LongBuy), long sell orders (LongSell), and their combined proportion (LongBuySell) - Formula: $ LongBuy = \frac{\text{Volume of long buy orders}}{\text{Total buy order volume}} $ $ LongSell = \frac{\text{Volume of long sell orders}}{\text{Total sell order volume}} $ $ LongBuySell = LongBuy + LongSell $ **Evaluation**: These factors effectively capture the dynamics of order completion times and provide insights into market behavior [21][24][25] - **Factor Name**: LongBuy_ShortSell_1p0, ShortBuy_LongSell_1p0, ShortBuy_ShortSell_1p0 **Construction Idea**: These factors are derived by decoupling long and short orders into four distinct attributes: long buy-long sell, long buy-short sell, short buy-long sell, and short buy-short sell [33][34][36] **Construction Process**: - Use the same thresholding method (mean + N standard deviations) to classify long and short orders - Combine long/short buy and sell orders into distinct factor categories - Example formula for LongBuy_ShortSell: $ LongBuy\_ShortSell = \frac{\text{Volume of long buy orders and short sell orders}}{\text{Total order volume}} $ **Evaluation**: These factors provide a more granular view of order dynamics by considering both buy and sell directions [33][34][36] Factor Backtesting Results LongBuySell_1p0 Factor (5-day Rebalancing) - **RankIC Mean**: 7.4% - **Win Rate**: 72% - **Long Portfolio Annualized Return**: 22.63% - **Maximum Drawdown**: 15.91% - **Sharpe Ratio**: 1.27 - **Long-Short Portfolio Annualized Return**: 54.76% - **Maximum Drawdown**: 10.76% - **Sharpe Ratio**: 3.80 [24][28][29] LongBuySell_1p0 Factor (20-day Rebalancing) - **RankIC Mean**: 10.4% - **Win Rate**: 77% - **Long Portfolio Annualized Return**: 23.17% - **Maximum Drawdown**: 9.31% - **Sharpe Ratio**: 1.54 - **Long-Short Portfolio Annualized Return**: 37.74% - **Maximum Drawdown**: 9.03% - **Sharpe Ratio**: 2.34 [25][30][32] ShortBuy_ShortSell_1p0 Factor (5-day Rebalancing) - **RankIC Mean**: -7.4% - **Win Rate**: 28% - **Long Portfolio Annualized Return**: 22.53% - **Maximum Drawdown**: 15.73% - **Sharpe Ratio**: 1.27 - **Long-Short Portfolio Annualized Return**: 54.85% - **Maximum Drawdown**: 11.00% - **Sharpe Ratio**: 3.78 [34][37][38] ShortBuy_ShortSell_1p0 Factor (20-day Rebalancing) - **RankIC Mean**: -10.4% - **Win Rate**: 23% - **Long Portfolio Annualized Return**: 23.03% - **Maximum Drawdown**: 9.17% - **Sharpe Ratio**: 1.53 - **Long-Short Portfolio Annualized Return**: 37.73% - **Maximum Drawdown**: 9.19% - **Sharpe Ratio**: 2.31 [36][40][41] Selected Long-Short Factor Portfolio Performance All-Market Portfolio (2021-2023) - **RankIC Mean**: 13.2% - **Win Rate**: 80.5% - **Top-150 Portfolio Annualized Return**: 21.41% - **Maximum Drawdown**: 18.70% - **Sharpe Ratio**: 1.31 - **Benchmark (CSI All-Share Index) Annualized Return**: -8.50% [10][44][49] ChiNext Portfolio (2021-2023) - **RankIC Mean**: 13.2% - **Win Rate**: 80.3% - **Top-150 Portfolio Annualized Return**: 21.52% - **Maximum Drawdown**: 29.49% - **Sharpe Ratio**: 1.07 - **Benchmark (ChiNext Index) Annualized Return**: -7.46% [50][54][82] CSI 300 Portfolio (2021-2023) - **RankIC Mean**: 10.2% - **Win Rate**: 65.9% - **Top-50 Portfolio Annualized Return**: 6.61% - **Maximum Drawdown**: 14.87% - **Sharpe Ratio**: 0.35 - **Benchmark (CSI 300 Index) Annualized Return**: -13.79% [56][58][82] CSI 500 Portfolio (2021-2023) - **RankIC Mean**: 11.1% - **Win Rate**: 65.9% - **Top-50 Portfolio Annualized Return**: 8.18% - **Maximum Drawdown**: 17.63% - **Sharpe Ratio**: 0.44 - **Benchmark (CSI 500 Index) Annualized Return**: -5.98% [61][63][82] CSI 800 Portfolio (2021-2023) - **RankIC Mean**: 11.3% - **Win Rate**: 68.7% - **Top-50 Portfolio Annualized Return**: 7.93% - **Maximum Drawdown**: 16.76% - **Sharpe Ratio**: 0.44 - **Benchmark (CSI 800 Index) Annualized Return**: -12.01% [66][69][82] CSI 1000 Portfolio (2021-2023) - **RankIC Mean**: 10.0% - **Win Rate**: 67.4% - **Top-50 Portfolio Annualized Return**: 10.59% - **Maximum Drawdown**: 20.40% - **Sharpe Ratio**: 0.58 - **Benchmark (CSI 1000 Index) Annualized Return**: -4.70% [73][74][78]

过去 50 年最伟大的金融创新。 文丨 曾梦龙 编辑丨钱杨 据《万亿指数》，截至 2020 年年底，美国公募市场指数基金的规模已经接近 16 万亿美元。除此之外，许多私募基金、大型养老金计划和主权财富基金采用的是也是指数 2017 年，在美国奥马哈举行的伯克希尔·哈撒韦股东大会上，巴菲特向 4 万参与者介绍了约翰·博格。巴菲特说他为美国投资者作的贡献，可能比全美 任何一个人都要多。 "博格，你能站起来吗？" 巴菲特朝人群喊了一声。 在热烈的掌声中，瘦削的博格站了起来，向人群挥了挥手，并向着巴菲特和芒格讲台的方向，躬身致意。 "我估计，博格已经帮助投资者省下了很多很多……随着时间推移，数字将变得更大，至少数千亿美元。周一是博格 88 岁的生日，我只想说，生日 快乐！谢谢你为美国投资者所做的一切。" 巴菲特说。 这一年，巴菲特刚获得一场 10 年赌局的胜利。11 年前，他提出：一只简简单单跟踪美股市场的基金，能够击败任何一位自信满满的对冲基金经理。 普罗蒂杰公司在一年后接受了挑战，赌注为 100 万美元。10 年后，普罗蒂杰挑选的 5 只 FOF （投资超过 100 只对冲基金）平均收益率只有 36%， 而追踪标 ...

Quantitative Models and Factor Construction Multi-Horizon Factor - **Factor Name**: Multi-Horizon Factor - **Construction Idea**: This factor captures short-term reversal, medium-term momentum, and long-term reversal effects by analyzing moving average (MA) data across multiple time horizons [2][14][21] - **Construction Process**: - Calculate moving averages for different time horizons \( L = [3, 5, 10, 20, 30, 60, 90, 120, 180, 240, 270, 300] \) using the formula: \[ A_{j t,L} = \frac{P_{j,\,d-L+1}^{t} + \cdots + P_{j,d}^{t}}{L} \] where \( P_{j,d}^t \) represents the price of stock \( j \) at time \( t \) [21] - Standardize the moving average factor: \[ \tilde{A}_{j t,\,L} = \frac{A_{j t,\,L}}{P_{j}^{t}} \] [22] - Perform cross-sectional regression of stock returns on lagged standardized moving average factors: \[ r_{j,t} = \beta_{0,t} + \Sigma_{i}\beta_{i,t}\tilde{A}_{j t-1,L_{i}} + \epsilon_{j,t} \] [23] - Predict next-period regression coefficients by averaging the past 25 weeks' coefficients: \[ E\left[\beta_{i,\,t+1}\right] = \frac{1}{25}\,\sum_{m=1}^{25}\,\beta_{i,t+1-m} \] [24] - Use predicted coefficients and new factor values to estimate next-period returns: \[ E\left[r_{j,t+1}\right] = \Sigma_{i}\,E\left[\beta_{i,\,t+1}\right]\tilde{A}_{j t,\,L_{i}} \] [25] - Rank stocks by predicted returns and construct long-short portfolios [26] - **Evaluation**: The factor demonstrates strong predictive power for stock returns across different market segments, with positive IC values dominating [30][32] LLT Trend Factor - **Factor Name**: LLT Trend Factor - **Construction Idea**: To address the lagging sensitivity of MA, the LLT (Low-Lag Trendline) indicator is used as a replacement. LLT reduces delay and better captures momentum and reversal effects [14][76] - **Construction Process**: - LLT is calculated using a second-order linear filter with the recursive formula: \[ LLT = \begin{cases} P(T), & T=1,2 \\ (2-2\alpha)LLT(T-1) - (1-\alpha)^2LLT(T-2) + \left(\alpha-\frac{\alpha^2}{4}\right)P(T) \\ + \left(\frac{\alpha^2}{2}\right)P(T-1) - \left(\alpha-\frac{3}{4}\alpha^2\right)P(T-2), & \text{else} \end{cases} \] where \( \alpha = \frac{2}{1+N} \) and \( N \) is the smoothing parameter [76] - Replace MA with LLT in the multi-horizon factor construction process [76] - **Evaluation**: LLT-based factors outperform MA-based factors in terms of IC mean, positive IC ratio, and predictive power for asset returns [82][84] --- Backtesting Results Multi-Horizon Factor - **Annualized Return**: 25.40% [3][48] - **Annualized Volatility**: 14.12% [48] - **Maximum Drawdown**: 13.31% [48] - **IR**: 1.81 [48] LLT Trend Factor - **Annualized Return**: 29.58% [4][103] - **Annualized Volatility**: 10.46% [103] - **Maximum Drawdown**: 11.57% [103] - **IR**: 2.51 [103]