| 表1：科技50策略指数中选股因子的定义 | | | --- | --- | | 因子名称 | 细分指标 | | 成长 | 修正利润增长、修正营收增长、利润超预期增长、营收超预期增长 | | 研发创新 | 研发支出占比、研发支出增长 | | 一致预期 | 一致预期净利润上调、一致预期评级上调 | | 动量 | 过去11个月动量、过去11个月Alpha | | 价值 | 市净率、市盈率、市销率、企业价值 | | 低波 | 残差波动率、过去12个月每月最高日收益率平均 | | 盈利 | 毛利润率、主营业务利润率 | | 偿债能力 | 短期债务覆盖率、速动比率 | | 运营效率 | 净营运资产收益率、净营运资产周转率、净营运资产周转率增长 | | 财务稳健 | 应计盈余、净营运资产占比 | | 投资稳健 | 利润分配、股息分配 | 资料来源：Wind，申万宏源研究 计算待选样本的自由流通市值占比，作为基础得分，并将基础得分与上述所有因子倾斜得分相加得到待选样本的调整后得分；将待选样本按照调整后得分由高到低排名，选取排名前 50 的 证券作为指数样本。 科技50策略指数采用调整后得分加权，且单个样本权重不超过10% ...

证监会于2025年12月31日发布《公开募集证券投资基金销售费用管理规定》（以下简称"《公募销售新规》"），自2026年1月1日正式实施，不符合规定 的老产品在12个月以内予以调整。《公募销售新规》共有6章29项，一方面从产品类型、持有人类型、销售渠道等维度区分四大类销售费用（认申购费、 销售服务费、赎回费、客户维护费）的差异，另一方面也明确了基金销售费用的规范措施。从多个维度进一步降低了基金投资者投资成本，规范了公募基 金销售市场秩序，保护了基金投资者合法权益。 本文将围绕《公募销售新规》中的具体举措，结合公募基金过往的发展情况以及客观数据，分析可能对公募基金行业带来的影响，为投资者提供参考。 1. 《公募销售新规》对不同公募产品的影响 1.1 四大类费用的变化：根据产品类型、持有人类型、销售渠道等差异化设置 1、认申购费率：相对现有费率水平有明显下降，指数基金与债券基金适用同一费率。 （1）主动偏股基金的最高费率为0.8%，其他混合基金的最高费率 为0.5%，债券型与指数型基金的最高费率为0.3%，其他基金可以参照上述费率水平约定收取标准；（2）按照产品类型的划分，指数增强基金属于指数 型基金，认申购费率 ...

证券研究报告 2025.12.14 主要内容 SWS = SWS 1. 轮动框架介绍:基本面 -- 基于一致预期与财报 2. 轮动框架介绍：资金面 -- 基于投资者资金流向 3. 轮动框架介绍:技术面 -- 基于量价表现 4. 多因子合成与因子分域的进一步改造 5. 风险收益提示 1.1 业绩预期:一致预期变化率更能反映分析师观点 因子分域下的行业轮动框架 申万行业轮动框架介绍 证券分析师:方思齐 A0230525090002 邓虎 A0230520070003 沈思逸 A0230521070001 以FY1、FY2过去三个月一致预期净利润的变化来构建行业选择的组合比较: ■ 图1:组合收益比较(FY1-致预期3M变化) 图2: 组合收益比较(FY2-致预期3M变化) www.swsresearch.com 证券研究报告 子: | 表1:指标IC表现 -- 致预期 | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 因子名称 | | Rank IC IC IR IC>0 比例 | | | | 因子五分组年化收益 ...

Core Viewpoint - The article discusses the shift in investment styles driven by fundamental factors, indicating a transition from growth to value investing as economic indicators improve and market trends are confirmed [3][5][67]. Group 1: Factor Performance - Growth factors have shown strong performance this year, with cumulative returns of 37.93% in the CSI 300 index, while momentum and dividend factors have underperformed [8][11]. - Low volatility factors have performed poorly in the CSI 300, reflecting the high volatility characteristics of the market this year [10][12]. - The performance of long-term momentum factors has been weak, indicating rapid rotation among industries and sectors [10][14]. Group 2: Macro Quantitative Framework - The macroeconomic cycle has been switching more frequently in the past three years compared to before 2020, with economic indicators suggesting a downturn in the first half of 2025 followed by a recovery towards the end of the year [32][38]. - The liquidity indicators have shown a weak overall trend, with market trading rates rising, indicating a correction in liquidity expected in the second half of 2025 [40][46]. - Credit indicators have shown a preference for expansion in the first half of 2025, aligning with social financing, but are expected to shift towards contraction in the second half [53][48]. Group 3: 2026 Equity Quantitative Outlook - The investment strategy for 2026 is expected to be driven by fundamental factors, with a focus on value before growth as economic conditions improve [5][54]. - The market is currently in a consolidation phase, with a trend confirmation expected to benefit value and long-term momentum factors, while growth factors are anticipated to perform better in a volatile environment [75][80]. - Industry rotation speed has slowed down, indicating potential for the formation of main lines in the market, with a focus on industries with low crowding and emerging trends [82][85].

Core Insights - The article discusses the integration of macro quantification and factor momentum to identify resonant factors for investment strategies, emphasizing the importance of economic, liquidity, and credit indicators in shaping market expectations [1][3]. Group 1: Factor Scores and Market Indicators - The macroeconomic indicators show signs of recovery, with economic growth expected to improve, while liquidity is slightly weak and credit conditions are tightening [3][4]. - Value factors have seen a significant increase in scores, becoming resonant factors in the CSI 300 index, while growth factors have declined [4][6]. - The article presents a table of factor scores across different indices, indicating a preference for value and low volatility factors in the current market environment [4]. Group 2: Economic Outlook and Leading Indicators - The economic leading indicators model suggests that the economy is in a rising cycle since September 2025, with a slight upward trend expected in the coming months [6][9]. - Specific indicators such as PMI and fixed asset investment are analyzed, showing a mixed outlook with some indicators in a rising phase while others are nearing a peak [11][12]. - The article highlights the importance of monitoring leading indicators to anticipate future economic cycles and potential downturns [9][10]. Group 3: Liquidity and Credit Conditions - The liquidity environment is assessed as slightly loose despite some tightening in interest rates, with a focus on the net monetary supply and excess reserve rates [12][16]. - Credit indicators show a mixed picture, with overall credit volume and structure remaining low, but some signs of recovery are noted [17][18]. - The article suggests a cautious approach to credit-sensitive investments due to the ongoing tightening in credit conditions [17]. Group 4: Asset Allocation and Market Focus - The asset allocation strategy is adjusted to reflect a neutral to positive stance on A-shares, while reducing exposure to gold and bonds due to changing market dynamics [18]. - The focus on PPI and liquidity as key market drivers indicates a shift in investor sentiment towards these macroeconomic variables [19]. - The article emphasizes the importance of selecting industries that are sensitive to economic changes but less affected by credit conditions, with a preference for sectors like utilities and coal [21].

Group 1: Overseas Quantitative Dynamics - The trend of integration between high-frequency trading and quantitative alpha management is emerging in the U.S. private equity market, particularly after a market pullback in 2025 due to a rebound in "junk stocks" [1][2] - High-frequency trading has evolved significantly over the past 20 years, with firms like Citadel and Jane Street facing intense competition, leading them to adopt short-cycle alpha prediction strategies to mitigate pure speed competition [1][2] - Traditional quantitative alpha strategies, which began in the 1980s, have longer holding periods and larger average exposure compared to high-frequency trading, which is now increasingly overlapping with traditional strategies [2][3] Group 2: Market Performance - In the first half of 2025, large quantitative managers like Citadel underperformed smaller managers such as Balyasny and ExodusPoint, with Citadel achieving only 2.5% returns compared to over 7% for smaller firms, primarily due to increased strategy drawdowns from frequent tariff changes [4] - Citadel and Point72's performance improved due to their focus on fundamental, concentrated portfolios, which outperformed their flagship strategies this year [4] Group 3: Regulatory Issues - Jane Street faced regulatory scrutiny in India, with accusations of manipulating market prices on options expiration dates, leading to a suspension of trading privileges and potential penalties [5] Group 4: Overseas Quantitative Perspectives - Machine learning is gaining traction in macro investment, with firms like BlackRock exploring its application to enhance traditional models and extract investment signals from complex macro data [7][10] - AQR's research highlights biases in subjective versus objective stock return predictions, noting that subjective forecasts tend to be overly optimistic, especially following bull markets [15][16] - Invesco's global quantitative survey indicates a rising trend in the use of quantitative methods across multi-asset portfolio management, with a notable increase in the flexibility of factor adjustments [19][22][23] Group 5: Performance Tracking of Quantitative Products - Factor rotation products, such as those from BlackRock and Invesco, have shown varying performance, with BlackRock's products outperforming benchmarks in recent months [28][30] - Machine learning-based stock selection strategies have demonstrated better performance compared to traditional methods, with products like QRFT outperforming AIEQ [43] - The Bridgewater All Weather ETF has shown resilience, recovering quickly from market pullbacks and achieving over 15% cumulative returns since its inception [44][46]

Group 1: ETF Innovations and Trends - Goldman Sachs launched a new global private equity tracking ETF that aims to reflect the performance of the MSCI World Private Equity Return Tracker index using publicly listed stocks, which may provide a closer alignment to private equity trends compared to traditional stock indices [1][2] - The focus on single-stock ETFs has increased, with 19 new products incorporating various strategies such as leverage and options, indicating a trend towards more specialized investment vehicles [2] Group 2: ETF Fund Flows - Over the past week, U.S. ETFs saw inflows exceeding $30 billion, with the Vanguard S&P 500 ETF leading the inflows, while gold ETFs experienced a slight outflow of approximately $400 million [3][5] - The top inflow products included the Vanguard S&P 500 ETF with $5.659 billion, while the SPDR S&P 500 ETF Trust saw an outflow of $7.380 billion [5] Group 3: Performance Analysis - Leveraged ETFs have shown significant volatility decay, with the ProShares UltraPro QQQ (3x) only achieving a cumulative gain of 40.65% this year, which is less than half of the Invesco QQQ Trust's 21.16% gain [10] - The correlation between gold ETF inflows and performance has been noted, with a correlation coefficient of approximately 0.2 since 2020, indicating that inflows tend to occur during price increases and outflows during price declines [7]

Group 1 - The article discusses the classification of market states into "trend" and "range" based on historical data, emphasizing the importance of recognizing these states for investment strategies [1][4] - In a trending market, momentum strategies like "buy high, sell higher" yield greater returns, while in a ranging market, mean-reversion strategies perform better [1][4] - A two-phase algorithm is developed to label historical trends and ranges in the Shanghai Composite Index, enhancing the accuracy of market state identification [2][3] Group 2 - The backtesting period is set from January 2020 to August 2025, reflecting a shift in market behavior post-2020, with increased frequency of state changes [7] - A feature variable system is constructed to identify market states, focusing on price, volume, and volatility, rather than traditional indicators [8][15] - The model training shows that all six feature indicators have an accuracy above 50%, with the volume feature achieving the highest accuracy of 63.48% [22][23] Group 3 - The decision tree model outperforms other models in predicting market states, achieving an accuracy of 80.10% in the test set [36][39] - The strategy based on the decision tree model yields a total return of 77.20%, significantly outperforming the benchmark [63] - The research highlights the potential of combining strategic signals for long-term market state identification with tactical signals for short-term changes to enhance strategy performance [64]

Group 1: New ETF Products in the US - A total of 22 new ETF products were launched in the US last week, including various types such as downside protection, leverage, thematic, allocation, and rotation products [1][2] - Seven new downside protection products were introduced, including Calamos' structured products linked to Bitcoin, which offer varying levels of protection (80%, 90%, 100%) [1] - Arrow Funds launched a Bitcoin strategy product that adjusts its allocation between Bitcoin and gold or cash based on market risk appetite [1] Group 2: ETF Fund Flows - US ETFs experienced significant inflows of nearly $50 billion last week, with domestic equities attracting over $25 billion, and commodity ETFs, particularly gold, also seeing substantial inflows [3][5] - The SPDR Gold Trust (GLD) was the second highest inflow product, with $40.81 billion, while the Invesco QQQ Trust (QQQ) led with $58.82 billion [5] Group 3: ETF Performance - Precious metal stocks have outperformed precious metal ETFs this year, with several mining-related ETFs showing gains around 150% [6][7] - The SPDR Gold Shares (GLD) and iShares Gold Trust (IAU) have both seen returns of approximately 63.73% and 63.91% respectively this year [7] Group 4: Mutual Fund Flows - As of August 2025, the total amount of non-money market mutual funds in the US reached $22.98 trillion, reflecting an increase of $0.41 trillion from July 2025 [8] - Domestic equity funds experienced outflows of around $20 billion, while bond products saw stable inflows exceeding $10 billion [8]

Group 1: ETF Innovation in the US - In the past three weeks, 91 new ETF products were launched in the US, indicating an accelerated pace of issuance [1][2][4] - Notable new products include Amplify Ethereum and various leveraged inverse ETFs targeting sectors like energy and technology [1][4] - The diversity of cryptocurrency-related products has increased, with regulatory attitudes becoming more favorable [4][5] Group 2: Fund Flows and Performance - In the last month, US ETFs saw inflows of nearly $180 billion, with stock products accounting for over $100 billion of this total [9][12] - iShares S&P 500 ETF IVV experienced significant inflows, surpassing $40 billion, while Bitcoin and gold products also saw notable inflows [12][13] - Aerospace and defense ETFs have performed exceptionally well, with some products showing returns exceeding 80% this year [14][15] Group 3: Recent Fund Launches - New fund managers like Liberty One have launched dividend-focused ETFs, while Global X introduced semiconductor and quantum computing ETFs [8][9] - ARK has entered the buffer ETF market with its DIET series, which offers unique exposure to market downturns [5][7] - Federated Hermes launched a market-neutral ETF utilizing a quantitative approach to optimize its portfolio [7]