8月27日，A股登上3800点，就本轮行情的背后核心逻辑是什么？哪些行业机会确定性较大？信汇泉总 经理孙加滢做客直播间，为投资者深入解析市场脉络>>视频直播 孙加滢认为，我们A股市场，其实不能看得太短，因为它自身有非常鲜明的运行特性。在过去二十多年 里，A股一直表现出一个明显的周期规律，叫做"两年冲顶、两年回归、三年底部震荡"。 那么按照这个规律来看，我们是不是刚刚经历完三年的下跌？然后又有一年上涨，但其中还包含了将近 7个月的横盘震荡，最终才突破了去年10月8号的高点。从时间和形态上来理解，目前我们可以判断市场 正处在"两年冲顶"阶段的初期。 关于当前市场所处的周期位置，我们要认清两个方面：第一，是每个周期阶段具备什么样的特征。关于 这一点，其实在我2018年出版的第一本书《投资思维的边界》中就有提到，当时提出了"牛熊八段论"。 现在明确说，目前市场是处于牛市第二阶段的末期、牛市第三阶段的初期。牛市第三阶段，也就是大家 通常所说的"主升浪"，它是能够持续较长时间的一段行情。 再从市场心态的角度看，心态本身也存在牛市和熊市的周期轮动。关于心态周期的分析，我把它放在了 我的第二本书《中国的繁荣》的后三分之一部分 ...

今日午间收盘，A股三大指数全线翻红，上证指数涨0.11%，距离3900点仅有12点的咫尺之别，差点以为A股强到两天就能拿下3900点了。 幸好没这么疯。 午后上证指数几度尝试反攻失败，最终收跌0.39%，报3868.38点，深证成指仍顽强收红0.26%，成交额2.7万亿元，较上日缩量4671亿元，连续第10个交易 日突破2万亿元。 1 A股加速上涨 事实上，A股这波行情的确在加速。 上证指数从6月23日的3381.58点站上3500点用了14个交易日，再用18个交易日站上3600点，9个交易日站上3700点，4个交易日3800点顺利攻下。 | 序号 | 交易日期 | 收盘价 | 当日涨跌幅% | 开始日累计涨跌幅% | | --- | --- | --- | --- | --- | | 1 | 2025-08-25 | 3883.56 | 1.51 | 15.59 | | 2 | 2025-08-22 | 3825.76 | 1.45 | 13.87 | | 3 | 2025-08-18 | 3728.03 | 0.85 | 10.96 | | 4 | 2025-08-05 | 3617.60 | 0.96 ...

【导读】上周五股票ETF市场净流入资金57亿元 8月22日，沪指涨破3800点，当日股票ETF市场（含跨境ETF，下同）规模站稳4万亿元关口，单日净流入资金约57亿元。其中，宽基ETF净流入居前，涨 幅较大的行业ETF净流出较多。 科创芯片类ETF全线大涨 数据显示，截至8月22日，全市场1179只股票ETF总规模达4.11万亿元，进一步站稳4万亿元关口。 当日涨幅领先的ETF均集中于科创50指数、科创芯片指数、科创信息（300730）指数、科创成长指数以及芯片类指数。 其中，科创50ETF富国单日大涨15.94%，领涨ETF市场。科创芯片类ETF全线爆发，国联安、鹏华旗下产品涨超15%，南方、国泰、博时、华安、嘉实、 汇添富等公司旗下科创芯片ETF均涨超10%。 | | | 8月22日股票ETF价格涨幅 | | | | --- | --- | --- | --- | --- | | 排行 | 证券简称 | 当日涨跌幅 | 最新规模 | 草等晨攝Y | | | | (%) | (亿元) | | | 1 | 科创50ETF富国 | 15.94 | 2.93 | 昌国真寺 | | 2 | 科创芯片设计ETF | ...

【导读】 上周五股票 ETF 市场净流入资金 57 亿元 中国基金报记者 张燕北 8 月 22 日， 沪指涨破 3800 点，当日股票 ETF 市场（含跨境 ETF ，下同）规模站稳 4 万亿元关口，单日净流入资金约 57 亿元。其 中，宽基 ETF 净流入居前，涨幅较大的行业 ETF 净流出较多。 科创芯片类 ETF 全线大涨 Wind 数据显示，截至 8 月 22 日，全市场 1179 只股票 ETF 总规模达 4.11 万亿元，进一步站稳 4 万亿元关口。 当日股票 ETF 成交额合计 2708.11 亿元，与上一日的 2090.25 亿元相比，成交额激增近 618 亿元，增幅近 30% 。其中，科创 50ETF 当日成交额超百亿元，达到 119.86 亿元，较前一日翻倍。 当日涨幅领先的 ETF 均集中于科创 50 指数、科创芯片指数、科创信息指数、科创成长指数以及芯片类指数。 | 排行 | 证券简称 | 资金流向 | 最新规模 | | 基金份额 当日涨跌幅 | 基金管理人 | | --- | --- | --- | --- | --- | --- | --- | | | | (177) | (亿元) ...

（原标题：中证500ETF、港股通互联网ETF、香港证券ETF本周强势吸金，本周资金净流入科创 50ETF、半导体ETF） 周五A股出现单边逼空走势，上证指数一举突破3800点，再创十年新高。 科创50周五大涨8.59%，创三年新高，有资金选择落袋为安。本周，科创50ETF涨幅达14.25%，资金净 流出额96.71亿元。此外，芯片ETF、半导体ETF本周涨幅14%，资金净流出额均超20亿元。 对于当下市场，百亿私募大佬林园认为：大牛市行情还没有开始，现在正朝着这个方向前进，可能还会 加速前进，牛市起点应该在4200点到4500点，牛市就是市场至少有一半的人挣钱了才叫牛市。 东兴证券认为，市场走到目前阶段或远未结束，居民存款搬家的空间仍然十分巨大，经济未来进入新一 轮复苏周期的时间节点可能也不会太远。从短期来看，市场有望剑指4000点整数关口，进而强化中期慢 牛的宏大叙事，同时有望进一步激活场外资金对A股的配置热情，从更长的维度看，相信中国股票市场 会创出新的高度。 光大证券认为，场内资金虽有分歧，但市场的走势显示韧性十足，且场外资金有望延续入市之势，接下 来市场大概率延续震荡上行的走势。 中信证券认为，市场 ...

同壁：近一年涨超110%！中证2000增强ETF午盘再度"吸金"近5000万 金融界、同花顺：杠杠资金加速涌入，投资小盘应该注意些什么？ 午盘三大指数集体收红，沪深两市半日成交额高达1.57万亿，较上个交易日放量591亿。 在高成交额与高杠杆的背景下，资金加速流入小盘的趋势不减。 数据显示，年内宽基ETF涨幅NO.1的中证2000增强ETF（159552），今天上午微调期间再度强势"吸金"将近5000万。最近10个交易日内，ETF有8天保持净 流入，区间强势吸金4.9亿元，年初至今规模增加超11.8亿！ | 中证2000增强ETF | | 159552 | | --- | --- | --- | | 2.015 | -0.010 -0.49% 1 0 + | | | SZSE CNY 11:30:00 不市 | | | | 净值走势 | 招商中证2000增强策略ETF | | | 交生 | 54.64% 120日 34.07% | | | 5日 | 4.78% 250日 109.24% | | | 20日 | 11.88% 52周高 | 2.03 | | 60日 | 29.58% 52周低 | 0.93 | ...

Group 1: ETF Market Growth - As of April 2025, the total scale of major market index ETFs has increased nearly fourfold compared to the end of 2021[8] - The scale of the CSI 300, CSI 500, and CSI 1000 ETFs reached CNY 10,773 billion, CNY 1,441 billion, and CNY 1,409 billion respectively, with increases of CNY 9,274 billion, CNY 659 billion, and CNY 1,382 billion since the end of 2021[8] - The scale of the SSE 50, STAR 50, and ChiNext Index ETFs reached CNY 1,706 billion, CNY 1,664 billion, and CNY 1,156 billion respectively, with increases of CNY 988 billion, CNY 1,234 billion, and CNY 930 billion since the end of 2021[8] Group 2: Index Component Adjustments - The adjustment of index components occurs biannually in May and November, with implementation dates on the second Friday of the following month[15] - The average prediction accuracy for the CSI 300's adjustments is 87% for additions and 91% for deletions, with recent adjustments showing 93% and 91% accuracy respectively[23] - The average coverage rate for the CSI 300's adjustments is 89% for additions and 93% for deletions[23] Group 3: Investment Opportunities - The study identifies significant Alpha return characteristics in the sample combinations of stocks added and removed during index adjustments[25] - Liquidity shock factors significantly affect the performance of stocks during index adjustments, indicating potential investment opportunities[25]

Core Viewpoint - The recent surge in the A-share market is primarily driven by external factors, particularly the favorable CPI data from the US, which has increased expectations for a potential interest rate cut by the Federal Reserve [4][5]. Market Performance - The Shanghai Composite Index closed at 3683.46, up 0.48%, while the Shenzhen Component Index rose by 1.76% and the ChiNext Index increased by 3.62% [2]. - A significant milestone was reached as the trading volume in A-shares exceeded 2 trillion yuan for the first time in 114 trading days [3]. Influencing Factors - The US CPI data showed a month-on-month increase of 0.2% and a year-on-year increase of 2.7%, which was lower than market expectations, indicating no immediate inflation risk [4]. - The anticipation of a rate cut by the Federal Reserve is expected to enhance global liquidity, benefiting various asset classes, including A-shares [5]. Market Dynamics - The current market is characterized as a "slow bull" market, driven by both government support and institutional investment, with a notable absence of significant pullbacks since June [12][14]. - The market is currently trading on liquidity rather than fundamentals, with the focus on indices rather than individual stock performance [15][23]. Investment Strategy - Investors are advised to focus on index investments rather than chasing individual stocks or hot sectors, as the current environment favors a slow and steady upward trend in indices [25]. - The market's behavior resembles that of the Nasdaq, where sustained upward movements are expected despite potential short-term corrections [25].

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The core idea is that "the decline in a shrinking volume market is significantly greater than the rise in a shrinking volume market, so avoiding shrinking volume signals can achieve higher trading odds"[3][22][24] - **Model Construction Process**: 1. Calculate the rolling 60-day average and standard deviation of the turnover and turnover rate of the index or market[23] 2. Standardize the daily turnover data: - If the turnover is within ±2 standard deviations, map the score to -1~+1 - If the turnover exceeds ±2 standard deviations, assign a score of +1/-1 3. Combine the scores of turnover and turnover rate equally[23] 4. Generate signals based on the combined score: - Method 1: Go long if the score > 0, stay out if the score < 0 - Method 2: Use the rolling 5-year or 3-year percentile of the score; go long if above the 50th percentile, stay out if below[23] 5. The report adopts the simpler method of directly judging whether the score is greater than 0[23] - **Model Evaluation**: The model is not a high-win-rate strategy but achieves relatively high odds by avoiding significant market adjustments during shrinking volume periods[24] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of growth and value styles based on macroeconomic cycles, valuation differences, and market sentiment[52][54] - **Model Construction Process**: 1. **Fundamentals**: - Growth is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Value is favored under the opposite conditions[52] 2. **Valuation**: - Growth is favored when the PE and PB valuation differences between growth and value are in the lower percentiles and mean-reverting upward[52] 3. **Sentiment**: - Growth is favored when turnover and volatility differences between growth and value are low[52] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between growth and value[52] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[53][55] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of small-cap and large-cap styles based on macroeconomic cycles, valuation differences, and market sentiment[56][58] - **Model Construction Process**: 1. **Fundamentals**: - Small-cap is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Large-cap is favored under the opposite conditions[56] 2. **Valuation**: - Large-cap is favored when the PE and PB valuation differences between small-cap and large-cap are in the higher percentiles and mean-reverting downward[56] 3. **Sentiment**: - Small-cap is favored when turnover differences are high - Large-cap is favored when volatility differences are mean-reverting downward[56] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between small-cap and large-cap[56] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[57][60] 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: Combines the conclusions of the growth-value and small-cap-large-cap rotation models to allocate across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[61][63] - **Model Construction Process**: 1. Use the growth-value model to determine the allocation between growth and value 2. Use the small-cap-large-cap model to determine the allocation between small-cap and large-cap 3. Combine the two models to allocate across the four styles[61] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance, with consistent outperformance in most years[61][63] --- Model Backtest Results 1. Volume Timing Signal - **Win Rate**: 47.34%[24] - **Odds**: 1.75[24] - **Annualized Excess Return**: 6.87% (based on next-day open price)[34] - **Maximum Drawdown**: 31.40%[34] - **Return-to-Drawdown Ratio**: 0.4634[34] 2. Growth-Value Style Rotation Model - **Annualized Return**: 11.76%[55] - **Annualized Volatility**: 20.77%[55] - **Maximum Drawdown**: 43.07%[55] - **Sharpe Ratio**: 0.5438[55] - **Return-to-Drawdown Ratio**: 0.2731[55] 3. Small-Cap vs. Large-Cap Style Rotation Model - **Annualized Return**: 12.45%[60] - **Annualized Volatility**: 22.65%[60] - **Maximum Drawdown**: 50.65%[60] - **Sharpe Ratio**: 0.5441[60] - **Return-to-Drawdown Ratio**: 0.2459[60] 4. Four-Style Rotation Model - **Annualized Return**: 13.37%[63] - **Annualized Volatility**: 21.51%[63] - **Maximum Drawdown**: 47.91%[63] - **Sharpe Ratio**: 0.5988[63] - **Return-to-Drawdown Ratio**: 0.2790[63]

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: Monitor the crowding level of industries on a daily basis[3] - **Model Construction Process**: The model is built to monitor the crowding level of Shenwan First-Level Industry Indexes daily. It tracks the main fund flows into and out of various industries, identifying those with high and low crowding levels[3] - **Model Evaluation**: The model provides valuable insights into industry crowding levels, helping investors identify potential investment opportunities and risks[3] 2. Model Name: Premium Rate Z-score Model - **Model Construction Idea**: Screen ETF products for potential arbitrage opportunities based on premium rate Z-score[4] - **Model Construction Process**: The model calculates the Z-score of the premium rate for various ETF products on a rolling basis. This helps identify ETFs with potential arbitrage opportunities while also warning of possible pullback risks[4] - **Model Evaluation**: The model is effective in identifying ETFs with potential arbitrage opportunities, but investors should be cautious of the associated risks[4] Model Backtesting Results Industry Crowding Monitoring Model - **Crowding Level**: Military, machinery equipment, coal, and finance showed significant changes in crowding levels[3] - **Main Fund Flows**: Main funds flowed into machinery, automotive, and military industries, while flowing out of pharmaceuticals and communications[3] Premium Rate Z-score Model - **ETF Products**: The model identified several ETFs with significant net inflows and outflows, indicating potential arbitrage opportunities[5][6] Quantitative Factors and Construction Methods 1. Factor Name: Main Fund Flow Factor - **Factor Construction Idea**: Track the main fund flows into and out of various industries over a period of time[3] - **Factor Construction Process**: The factor is constructed by monitoring the net inflows and outflows of main funds into Shenwan First-Level Industry Indexes daily. This helps identify industries with significant changes in fund allocation[3] - **Factor Evaluation**: The factor provides valuable insights into the allocation of main funds, helping investors make informed decisions[3] Factor Backtesting Results Main Fund Flow Factor - **Net Inflows and Outflows**: The factor showed significant net inflows into machinery, automotive, and military industries, and net outflows from pharmaceuticals and communications over the past three days[3][13] ETF Product Signals Premium Rate Z-score Model - **ETF Products to Watch**: The model identified several ETFs with potential arbitrage opportunities, including Medical Equipment ETF, China Concept Technology ETF, VR ETF, and Gold Stock ETF[14] Key Points - Industry crowding monitoring model tracks daily crowding levels of Shenwan First-Level Industry Indexes[3] - Premium rate Z-score model screens ETF products for potential arbitrage opportunities based on premium rate Z-score[4] - Main fund flow factor monitors net inflows and outflows of main funds into various industries[3] - Significant net inflows into machinery, automotive, and military industries, and net outflows from pharmaceuticals and communications[3][13] - ETF products identified for potential arbitrage opportunities include Medical Equipment ETF, China Concept Technology ETF, VR ETF, and Gold Stock ETF[14]