与普通选个股相比，红利低波策略有明显的优势，低波帮助控回撤，红利帮助低买高卖。但 是单纯拿红利低波指数感觉还是风险过大，遇到2018年的行情，红利低波100指数也有16% 的单年度下跌，对于胆小的人来说还是挺吓人的。 如果加入债券，两类资产55开，那么回测 下来年化大概7%左右，但是2018年那种行情下还是会有4个点的亏损。 请教下坛子里的各位前辈，大家持有红利低波都用怎样的姿势？怎么对冲2018年那样的极端 下跌情况？感谢 goodxx 2018年11月14开始 买入: 现价 < 近80日均线 -5% 卖出: 现价 > 近80日均线 5%，买入招商双债 年化 19.07% 最大回撤 -13.47% kkqq999 多简单的事情，对于这种上下波动有限的东西，做一个布林线通道，涨到通道上轨就减仓， 跌到通道下轨就加仓，收益肯定比死拿好。 指锚湾 红利低波，是非常低的回撤了，如果这点回撤都承受不了，那只能降低配置比例了。 我老婆和我闺女的账号我都是 红利低波100和城投债实时再平衡，后面准备等机会加入国证 现金流。 红利低波跌16%，那是历史性的撒钱机会，应该加倍珍惜。 比如：红利低波和债券5:5配置，如果红利回 ...

Quantitative Models and Construction Methods Model Name: Black Swan Index - **Construction Idea**: Measure the extremity of market transactions based on the deviation of style factor returns[24][25] - **Construction Process**: 1. Calculate the daily return deviation of style factors: $$ \sigma_{s,t}=\frac{\bar{r}_{s,t}-\bar{r}_{s}}{\sigma_{s}} $$ where $\bar{r}_{s,t}$ is the daily return of style factor $s$ on day $t$, $\bar{r}_{s}$ is the average daily return of style factor $s$ over the entire sample period, and $\sigma_{s}$ is the standard deviation of daily returns of style factor $s$ over the entire sample period[25] 2. Calculate the Black Swan Index: $$ BlackSwan_{t}=\frac{1}{N}\times\sum_{s\in S}\left|\sigma_{s,t}\right| $$ where $BlackSwan_{t}$ is the Black Swan Index on day $t$, $S$ is the set of all style factors, and $N$ is the number of style factors[25] - **Evaluation**: The Black Swan Index effectively captures the extremity of market transactions, indicating higher probabilities of extreme tail risks[24][25] Model Name: Heuristic Style Classification for Cognitive Risk Control - **Construction Idea**: Address the discrepancy between individual and collective cognition in style classification to control cognitive risk[80][81] - **Construction Process**: 1. Calculate the value and growth factors for each stock based on predefined metrics[85] 2. Construct value and growth portfolios by selecting the top 10% and bottom 10% stocks based on factor scores[82] 3. Perform time-series regression to classify stocks into value, growth, or balanced styles: $$ r_{t,t}\sim\beta_{\mathit{Value}}\cdot r_{\mathit{Value},t}+\beta_{\mathit{Growth}}\cdot r_{\mathit{Growth},t}+\varepsilon_{t} $$ subject to $0\leq\beta_{\mathit{Value}}\leq1$, $0\leq\beta_{\mathit{Growth}}\leq1$, and $\beta_{\mathit{Value}}+\beta_{\mathit{Growth}}=1$[97] 4. Use weighted least squares (WLS) to estimate regression coefficients based on the most differentiated trading days[98] - **Evaluation**: The heuristic style classification method captures market consensus more accurately than traditional factor scoring methods, reducing cognitive risk[80][81] Model Name: Louvain Community Detection for Hidden Risk Control - **Construction Idea**: Cluster stocks based on excess return correlations to identify hidden risks[116][117] - **Construction Process**: 1. Calculate weighted correlation of excess returns between stocks: $$ Corr_{w}(X,Y)=\frac{Cov_{w}(X,Y)}{\sigma_{w,X}\cdot\sigma_{w,Y}}=\frac{\sum_{i=1}^{n}w_{i}(x_{i}-\overline{X_{w}})(y_{i}-\overline{Y_{w}})}{\sqrt{\sum_{i=1}^{n}w_{i}(x_{i}-\overline{X_{w}})^{2}}\cdot\sqrt{\sum_{i=1}^{n}w_{i}(y_{i}-\overline{Y_{w}})^{2}}} $$ where $w_{i}$ is the weight for day $i$, reflecting market volatility[118] 2. Use Louvain algorithm to cluster stocks based on weighted correlation matrix[117] 3. Ensure clusters have at least 20 stocks and remove clusters with fewer stocks[121] - **Evaluation**: The Louvain community detection method effectively identifies hidden risks by clustering stocks with similar return patterns, which traditional risk models may overlook[116][117] Model Name: Dynamic Style Factor Control - **Construction Idea**: Control style factors dynamically based on their volatility clustering effect[128][129] - **Construction Process**: 1. Identify style factors with high volatility or significant volatility increase: $$ \text{High volatility: Rolling 3-month volatility in top 3} $$ $$ \text{Volatility increase: Rolling 3-month volatility > historical mean + 1 standard deviation} $$ 2. Set the exposure of these style factors to zero in the portfolio[136] - **Evaluation**: Dynamic style factor control captures major market risks without significantly affecting portfolio returns, leveraging the predictability of volatility clustering[128][129] Model Name: Adaptive Stock Deviation Control under Target Tracking Error - **Construction Idea**: Adjust stock deviation based on tracking error to control portfolio risk[146][147] - **Construction Process**: 1. Calculate rolling 3-month tracking error for different stock deviation levels[153] 2. Set the maximum stock deviation that keeps tracking error within the target range[153] - **Evaluation**: Adaptive stock deviation control effectively reduces tracking error during high market volatility, maintaining portfolio stability[146][147] Model Backtest Results Traditional CSI 500 Enhanced Index - **Annualized Excess Return**: 18.77%[5][162] - **Maximum Drawdown**: 9.68%[5][162] - **Information Ratio (IR)**: 3.56[5][162] - **Return-to-Drawdown Ratio**: 1.94[5][162] - **Annualized Tracking Error**: 4.88%[5][162] CSI 500 Enhanced Index with Full-Process Risk Control - **Annualized Excess Return**: 16.51%[5][169] - **Maximum Drawdown**: 4.90%[5][169] - **Information Ratio (IR)**: 3.94[5][169] - **Return-to-Drawdown Ratio**: 3.37[5][169] - **Annualized Tracking Error**: 3.98%[5][169]

Core Viewpoint - The banking sector has shown remarkable performance over the past two years, with significant stock price increases, but there are concerns about the divergence between stock prices and fundamental performance [2][5][6]. Group 1: Banking Sector Performance - The stock performance of major banks, such as Industrial and Commercial Bank of China (ICBC), has seen increases of +17.66%, +52.30%, and +5.71% over the past three years [2]. - The China Securities Banking Total Return Index has been reaching historical highs, indicating strong overall sector performance [2][4]. Group 2: Dividend Indices and Bank Weighting - Traditional dividend indices are strongly correlated with the banking sector, with the "low volatility dividend" index having nearly half of its weight in the banking sector [5]. - The performance of city commercial banks has been better than that of state-owned and joint-stock banks, influencing the composition of various dividend indices [5]. Group 3: Concerns Regarding Banking Sector Fundamentals - Despite a 42.90% increase in the China Securities Banking Total Return Index over the past year, banks have shown stagnation in revenue and net profit growth, alongside declining ROE and increasing overdue rates [5][6]. - The ROE for major banks is around 10%, and maintaining this level requires a profit growth rate of 6.80%, which is not being met according to the latest quarterly reports [6]. - The overall dividend yield for the banking sector has decreased significantly, with major banks now yielding less than 4.50%, down from nearly 7% two years ago [7]. Group 4: Market Sentiment and Valuation - Market sentiment towards the banking sector has shifted, with reduced concerns about bad debts and profit growth, leading to a lack of negative commentary in discussions about bank stocks [8]. - The current price-to-book ratio for the China Securities Banking Index is 0.67, indicating that while the sector is not overvalued, the overall investment attractiveness is being questioned [8]. Group 5: Investment Strategy - The current market is characterized by "medium-low valuation" and "low interest rates," suggesting a potential asset allocation of 65% equities and 35% bonds for defensive investors [11]. - The focus for long-term investment remains on dividend-paying stocks and low-cost dividend ETFs, with a strategy to reinvest dividends and new funds into short-term bonds [11].

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。 2.1 什么是红利低波100指数 ？ 红利低波100指数 （ 代码 ： 930955 ） 是中证红利低波动100指数的简称 ， 由中证指数有限公司于 2017年5月26日发布 ， 是一只以 " 高股息和低波动 " 为核心逻辑的指数 。 作者： 王博雅投资 来源：雪球 1 引言 红利低波100指数是仅有的2个五星级红利指数之一 。 它可以看做中证红利的升级版 ， 在保持优秀分散性 的情况下实现了更高的收益和更低的波动 ， 因此在市场上备受关注 。 图1 沪深300 、 中证红利与红利低波指数全收益对比 在之前 ， 我已经对追踪中证红利的基金进行了分析。 今天我将对红利低波100指数进行简介 ， 然后从 费用 、 规模 、 流动性 、 收益与分红频率 等几个关键角 度 ， 对红利低波100ETF进行横评 ， 看看谁最好 ！ 2 红利低波100指数简介 红利低波100指数构成逻辑如下 ： 1 中证全指中过去一年日均成交金额前 80%的公司 。 这是为了避免流动性过差 。 2 过去三年连续现金分红 。 这是为了确保公司具有良好的分红 ...