文 | 银行螺丝钉 （转载请注明出处） 在主动优选、指数增强这两个组合上， 自动止盈功能上线啦。 一般是到3点几星，止盈机会会多一些，不过自动止盈功能可以先上线，方便大家开通。 投顾组合，是如何止盈的？ 像 主动优选 （点击查看介绍） 、 指数增强 等组合止盈，一般两种方式。 （1）当市场部分高估：组合自动调仓 当一部分品种高估，一部分低估，投顾组合会自动调仓，会帮助大家， 止盈 高估品种， 加仓 低估品种。 投顾组合自动完成，不需要大家手动操作，更加省心省力。 例如2021年初，主动优选止盈了组合中成长风格的基金，当时止盈收益也达到120%。 刚好深度价值风格当时也有低估品种。成长风格基金止盈后，主动优选也调仓买入了深度价值风格品种。 （2）当市场整体高估：组合发车 止盈 如果市场整体不便宜，没有低估品种，会对组合整体做止盈。 螺丝钉会给出「发车 止盈 」的信号，提示大家将 主动优选 、 指数增强投顾组合 ，分批转换成 365天 等稳健类投顾组合，实现组合的止盈。 什么是组合发车止盈？ • 当市场处于低估阶段，例如4星级-5星级，组合会给出「发车买入」的信号。 • 当市场走出低估阶段以后，例如3星级-1星级 ...

文 | 银行螺丝钉 （转载请注明出处） 今天A股港股比较强势，整体上涨。 截止到收盘，还在4.2星，距离4.1星不远。 大中小盘股都上涨，小盘股上涨更多。 昨天价值风格强势，今天变成成长风格强势。 科创、创业板领涨。 港股也整体上涨。 • 港股恒生指数，上涨43%。港股科技股上涨更多，恒生科技上涨66%。 人民币资产最近一年涨幅排在全球前列。 原因也是因为，一年前估值实在太低了。 当时A股港股在5.9星。 港股科技股上涨超2%。 1. 今天刚好是9月24日一周年。 在一年前，9月中旬，A股港股都在5.9星上下。 过去一年里，A股港股大幅上涨，从5.9星的历史极端低估，上涨到现在的4.2星上下。 过去一年里，截止到9月23日收盘： • 全球股票市场，上涨17%。 很多指数的估值创下历史新低。 同时A股港股整体估值，比全球市场平均估值，低50%。 另一方面，去年9月美联储开启了首次降息，之后美元利率、汇率下降。 全球市场流动性变得充裕。 同时去年9月24日，咱们也推出了一篮子的利好政策。 加上国家队买入上万亿的300、500等指数基金，让A股港股逐渐从底部提升起来。 低估，是最大的利好。 如果再来一轮5点几星，相 ...

Core Viewpoint - The article discusses the current market trends, emphasizing the performance of growth versus value styles in investment, and highlights the importance of timing and strategy in achieving investment returns. Group 1: Market Performance - The overall market opened with a slight increase but closed with minimal fluctuations, maintaining a rating of 4.2 stars [1] - Large-cap stocks showed slight gains while small-cap stocks declined [2] - Growth styles, such as those represented by the ChiNext board, experienced an increase, whereas value styles remained relatively weak [3] Group 2: Investment Strategy - As growth styles have risen this year, valuations have gradually increased, prompting some fund managers to reduce their growth allocations and increase value style allocations [4] - The current trend of strong growth and weak value styles may be influenced by various factors [5] - Long-term investment strategies will continue to favor lower valuation styles for portfolio allocation [6] - Historical trends indicate that during last year's growth style decline, portfolios increased their growth style allocations, which is beneficial for long-term returns [7] Group 3: Investor Behavior - There is a notable disparity in returns among investors in the same asset class, influenced by their entry timing and purchase costs [10] - Investors entering during bull markets tend to have higher initial costs compared to those entering at market lows [15] - A significant portion of A-share accounts were opened during the major bull markets of 2007 and 2015, indicating a tendency for investors to enter the market during rising phases [18] - Strategies such as dollar-cost averaging during market downturns can help lower investment costs and lead to profitability without needing the market to return to previous highs [20][21] Group 4: Learning and Experience - The first round of investing through bear and bull markets is primarily about gaining experience, and investors should not overly focus on initial returns [28] - Historical market cycles provide valuable lessons, and understanding these cycles can help investors make informed decisions in future investments [30][33] - The article suggests that over the next 30 years, investors will likely experience multiple cycles of bull and bear markets, providing ample opportunities for undervalued purchases and overvalued sell-offs [38] Group 5: Product Features - The company has introduced an "automatic profit-taking" feature for its actively selected and index-enhanced portfolios, which will trigger profit-taking signals as the market moves out of undervaluation [40] - A live session is scheduled to discuss the financial performance of listed companies in A-shares and Hong Kong stocks, focusing on profit recovery in the first two quarters of the year [42]

Core Viewpoint - The article discusses the construction and performance of a "limited attention" factor in stock selection, highlighting how investor attention is a scarce resource that influences market behavior and stock performance [1][2]. Limited Attention Factor Construction - The traditional efficient market hypothesis assumes that investors can instantaneously process all new information, but in reality, investors have limited attention, leading them to focus on high-attention stocks [1]. - The article constructs the limited attention factor using four indicators: abnormal turnover rate, abnormal trading volume, extreme returns, and whether a stock has appeared on the "Dragon and Tiger List" [2][3]. - The abnormal turnover rate is calculated by comparing a stock's daily turnover to its average turnover over the past 252 trading days [2]. - The abnormal trading volume is similarly calculated by comparing a stock's daily volume to its average volume over the past 252 trading days [2]. - Extreme returns are determined by measuring the deviation of a stock's daily return from the average return of all stocks on that day [2]. - The final limited attention factor is constructed using both linear combination and random forest methods, with the latter providing better predictive power for returns [3][4]. Factor Performance - The performance of the limited attention factor is evaluated based on its ability to capture retail investor interest, with higher attention stocks showing a significant increase in shareholder accounts [5][7]. - The random forest method shows higher information coefficient (IC) and win rate compared to the linear combination method, indicating better performance in distinguishing between high and low attention stocks [8][12]. - The factor performs best in smaller stock pools, where retail investors' trading behavior can significantly impact stock prices [13]. Implications of Limited Attention Factor - The limited attention factor can indicate "money-making effects" in the market, with its performance varying across different market conditions [14][17]. - The factor's IC showed a notable decline from 2019 to mid-2021, suggesting that high attention stocks were profitable during that period, while subsequent periods indicated poor performance for these stocks [17][19]. - The article explores the use of the limited attention factor for market timing and size rotation strategies, achieving a monthly win rate exceeding 70% [19][22]. Application of Limited Attention Factor - The limited attention factor can be directly used in index enhancement strategies, either by adding it to existing predictive factors or by excluding high attention stocks from the selection process [25][29]. - The results indicate that directly adding the limited attention factor improves performance in the CSI 300 and CSI 500 indices, while excluding high attention stocks does not yield better results [30][31]. - The article also examines the performance of other factors within different limited attention domains, finding that growth and low volatility factors perform better in high attention stocks, while profitability and value factors show decreased effectiveness [32][33]. Conclusion - The limited attention factor effectively represents the degree of retail investor interest in stocks, with its average IC being negative, indicating that high attention stocks may face greater pullbacks due to lack of long-term support [41][42]. - The factor's performance can reflect market "money-making effects," and its application in stock selection can enhance portfolio performance, particularly in simpler selection methods [41][42].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.86% this week and 10.78% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of 0.16% this week and 11.24% year-to-date [1][6] - The CSI 1000 index enhanced portfolio experienced an excess return of -0.29% this week but has a year-to-date excess return of 15.73% [1][6] - The CSI A500 index enhanced portfolio had an excess return of 0.29% this week and 11.42% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as DELTAROE, expected PEG, and expected EPTTM performed well [1][7] - In the CSI 500 component stocks, factors like one-year momentum, expected net profit month-on-month, and one-month reversal showed strong performance [1][7] - For the CSI 1000 component stocks, factors such as DELTAROA, single-quarter net profit year-on-year growth rate, and single-quarter surprise magnitude performed well [1][7] - In the CSI A500 index component stocks, factors like expected PEG, DELTAROE, and expected EPTTM showed good performance [1][7] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 0.82%, a minimum of -0.24%, and a median of 0.26% this week [1][18] - The CSI 500 index enhanced products achieved a maximum excess return of 0.95%, a minimum of -0.73%, and a median of 0.14% this week [1][22] - The CSI 1000 index enhanced products recorded a maximum excess return of 0.69%, a minimum of -0.64%, and a median of -0.02% this week [1][25] - The CSI A500 index enhanced products had a maximum excess return of 0.85%, a minimum of -0.33%, and a median of 0.34% this week [1][24]