博道基金杨梦： 打造公募量化"指数+" 特色矩阵 ◎记者 聂林浩 指数化投资大浪潮下，公募量化赛道逐步获得市场青睐，一些中小型公募基金公司则凭借先发优势在量 化管理规模上"弯道超车"。数据显示，截至三季度末，博道基金主动量化管理规模超270亿元，在这一 领域的行业排名跃居前三。 事实上，早在2013年，还是私募形态的博道已开启量化实盘，其量化投资的发展历程与国内量化行业的 成熟过程高度契合。如今是博道基金量化投资总监的杨梦，2011年从浙江大学毕业后即进入公募体系从 事量化研究，早期便因对编程与数理的兴趣而建立起模型化思维，这段经历为她后来主导博道量化体系 建设打下基础。 "我热爱这份工作，投身于市场和量化投研，每次攻克难题和模型迭代，对我来说都充满乐趣。"杨梦 说。 传统多因子与AI全流程"双框架并行" 杨梦于2014年加入博道投资，当年12月，银行、券商等市场权重股集体大涨，这被视作中国量化史上首 次大级别"黑天鹅"。彼时，一些量化策略依靠小市值暴露获取超额收益，在极端行情中遭遇巨大压力。 而博道量化在早期即采用基于Barra风险模型的多因子组合体系，风险暴露更为均衡，其核心的市场中 性产品经受住考验，并 ...

Quantitative Models and Construction A-Share Market Timing Model - **Model Name**: A-Share Multi-Dimensional Timing Model [10] - **Construction Idea**: The model integrates valuation, sentiment, capital, and technical dimensions to assess the directional outlook of the A-share market [10][12][16] - **Construction Process**: - Signals are generated daily for each dimension, with values of 0, ±1 representing neutral, bullish, and bearish views respectively [10] - **Valuation Dimension**: Uses equity risk premium (ERP) to capture mean-reversion characteristics [12][16] - **Sentiment Dimension**: Includes option put-call ratio, implied volatility, and futures member position ratio to reflect market sentiment [12][16] - **Capital Dimension**: Tracks financing purchase amounts to identify market trends [12][16] - **Technical Dimension**: Employs Bollinger Bands and individual stock turnover ratio differences to capture trend continuation [12][16] - The final market view is determined by the sum of scores across all dimensions [10] - **Evaluation**: The model effectively combines mean-reversion and trend-following strategies, balancing risk avoidance and opportunity capture [10] Style Timing Model - **Model Name**: Dividend Style Timing Model [18] - **Construction Idea**: Targets the relative performance of the CSI Dividend Index against the CSI All Index using trend-based indicators [18][22] - **Construction Process**: - Three indicators are used to generate daily signals (0, ±1 for neutral, bullish, bearish views) [18] - **Relative Momentum**: Positive indicator for dividend style [22] - **10Y-1Y Term Spread**: Negative indicator for dividend style, as wider spreads favor growth assets [22] - **Interbank Repo Volume**: Positive indicator for dividend style, reflecting asset scarcity [22] - Signals are aggregated to determine the overall view on dividend style [18] - **Evaluation**: The model captures dividend style trends effectively, leveraging macroeconomic and liquidity factors [18] - **Model Name**: Large-Cap vs Small-Cap Style Timing Model [23] - **Construction Idea**: Differentiates between macro-driven trends in low congestion and fund-driven reversals in high congestion [23][25] - **Construction Process**: - **Momentum Difference**: Calculates the difference in momentum between the Wind Micro-Cap Index and CSI 300 Index across multiple windows, averaging the top/bottom results for small/large-cap scores [27] - **Turnover Ratio**: Similar calculation for turnover ratio differences across windows, averaged for small/large-cap scores [27] - **Congestion Score**: Combines momentum and turnover scores to determine congestion levels (high congestion >90% for small-cap, <10% for large-cap) [27] - **Trend Model**: Uses small/large parameter double moving average models based on congestion levels [25] - **Evaluation**: The model adapts to market conditions, balancing long-term trends and short-term reversals [23][25] Sector Rotation Model - **Model Name**: Genetic Programming Sector Rotation Model [30] - **Construction Idea**: Directly mines factors from sector index data using genetic programming without relying on predefined scoring rules [30][33] - **Construction Process**: - **Factor Mining**: Utilizes NSGA-II algorithm to optimize for monotonicity and top-group performance simultaneously [33][34] - **Factor Combination**: Combines factors with weak collinearity using greedy strategy and variance inflation coefficient [34] - **Weekly Rebalancing**: Selects top five sectors based on multi-factor scores for equal-weight allocation [30] - **Example Factor**: Calculates covariance between standardized weekly low prices and monthly open prices over 25 days, adjusted by standardized weekly high prices over 15 days [38] - **Evaluation**: The model enhances factor diversity and reduces overfitting risks, achieving robust sector rotation performance [33][34] All-Weather Enhanced Portfolio - **Model Name**: China All-Weather Enhanced Portfolio [39] - **Construction Idea**: Implements macro factor risk parity to diversify risks across underlying macro drivers rather than assets [39][42] - **Construction Process**: - **Macro Quadrant Division**: Divides growth and inflation dimensions into four quadrants based on whether they exceed or fall short of expectations [42] - **Quadrant Portfolio Construction**: Constructs sub-portfolios within each quadrant, focusing on downside risk [42] - **Risk Budgeting**: Adjusts quadrant weights monthly based on macro momentum indicators combining buy-side and sell-side expectations [42] - **Evaluation**: The strategy demonstrates strong defensive attributes during market downturns while maintaining consistent returns [40][43] --- Backtesting Results A-Share Market Timing Model - **Annualized Return**: 24.94% [15] - **Maximum Drawdown**: -28.46% [15] - **Sharpe Ratio**: 1.16 [15] - **Calmar Ratio**: 0.88 [15] - **YTD Return**: 43.84% [15] - **Weekly Return**: 5.28% [15] Dividend Style Timing Model - **Annualized Return**: 15.67% [21] - **Maximum Drawdown**: -25.52% [21] - **Sharpe Ratio**: -0.26 [21] - **Calmar Ratio**: 0.85 [21] - **YTD Return**: 20.86% [21] - **Weekly Return**: -3.63% [21] Large-Cap vs Small-Cap Style Timing Model - **Annualized Return**: 27.04% [28] - **Maximum Drawdown**: -32.05% [28] - **Sharpe Ratio**: 1.13 [28] - **Calmar Ratio**: 0.84 [28] - **YTD Return**: 71.14% [28] - **Weekly Return**: -7.80% [28] Sector Rotation Model - **Annualized Return**: 30.83% [33] - **Annualized Volatility**: 17.74% [33] - **Sharpe Ratio**: 1.74 [33] - **Maximum Drawdown**: -19.63% [33] - **Calmar Ratio**: 1.57 [33] - **YTD Return**: 35.44% [33] - **Weekly Return**: -4.39% [33] All-Weather Enhanced Portfolio - **Annualized Return**: 11.51% [43] - **Annualized Volatility**: 6.18% [43] - **Sharpe Ratio**: 1.86 [43] - **Maximum Drawdown**: -6.30% [43] - **Calmar Ratio**: 1.83 [43] - **YTD Return**: 10.75% [43] - **Weekly Return**: -1.53% [43]

Core Viewpoint - The article provides an in-depth analysis of Huanfang Quantitative, a leading quantitative private equity firm in China, highlighting its performance, investment strategies, and the background of its founder, Liang Wenfeng [2][8]. Company Overview - Huanfang Quantitative was established in 2015 and is controlled by Liang Wenfeng, who is also the founder of DeepSeek. The firm manages two private equity companies: Ningbo Huanfang Quantitative and JiuZhang Asset [2][8]. - As of October 2025, Huanfang Quantitative ranked second in the private equity sector for quantitative returns, maintaining its position from the previous month, with an average return of ***% across 11 products, all of which reached historical highs in October [2][3]. Performance Metrics - Huanfang Quantitative's management scale has grown to between 70 billion and 80 billion, placing it in the "first tier" of domestic quantitative private equity firms [3]. - The firm has consistently ranked in the top ten for returns over various time frames, including the first half of 2025, the past year, and the past three years [8][14]. Investment Strategies - The firm employs a multi-strategy approach, focusing on quantitative investment driven by artificial intelligence (AI) technology. It has been utilizing machine learning since 2008 and fully integrated deep learning into its trading strategies by 2017 [43][44]. - Huanfang Quantitative's investment philosophy emphasizes long-term value creation through continuous investment in technology and team development [14][43]. Core Team - Liang Wenfeng, the founder, has a notable background in quantitative trading and has received multiple awards, including the Golden Bull Award [18][20]. - The core team includes experts from various fields, such as mathematics, physics, and AI, contributing to the firm's innovative strategies [33][34]. Company Development History - The firm has achieved significant milestones, including surpassing 100 billion in assets under management in 2019 and reaching over 1 trillion in 2021 before adjusting its scale to approximately 600 billion for better risk management [8][14]. - Recent developments include the establishment of a general artificial intelligence laboratory and the launch of the DeepSeek platform, which has gained significant attention for its cost-effective AI solutions [14][20]. Awards and Recognition - Huanfang Quantitative has received numerous accolades, including being listed among the top 50 private equity funds in China and winning the Golden Bull Award multiple times [48][49]. - The firm has also engaged in philanthropic efforts, donating over 2.2 billion to charitable causes [50].

Core Insights - The overall market showed a significant positive return from the market capitalization factor at 0.99%, while other factors like leverage, liquidity, residual volatility, and valuation factors yielded negative returns of -0.41%, -0.43%, -0.50%, and -0.68% respectively [4] Factor Performance - In the CSI 300 stock pool, the best-performing factors included the correlation between intraday volatility and trading volume (1.23%), ROE stability (1.14%), and the proportion of downside volatility (1.13%). Conversely, the worst-performing factors were early morning return factor (-2.46%), momentum spring factor (-2.21%), and net profit gap (-1.72%) [5] - In the CSI 500 stock pool, the top factors were quarterly gross margin on total assets (1.82%), momentum-adjusted large orders (1.66%), and TTM gross margin on total assets (1.63%). The underperforming factors included year-on-year quarterly ROA (-0.66%), year-on-year quarterly ROE (-0.55%), and ROIC enhancement factor (-0.53%) [5] - In the liquidity 1500 stock pool, the leading factors were TTM net profit margin (1.82%), TTM operating profit margin (1.44%), and ROA stability (1.38%). The lagging factors were inverse TTM price-to-sales ratio (-1.31%), logarithmic market capitalization factor (-1.07%), and net profit gap (-0.95%) [5] Industry Factor Performance - Fundamental factors showed varied performance across industries, with net asset growth rate, net profit growth rate, earnings per share, and TTM operating profit per share yielding consistent positive returns in the textile and apparel, and steel industries. The EP factor performed well among valuation factors, showing significant positive returns in coal, beauty care, and textile and apparel industries. Residual volatility and liquidity factors also showed notable positive returns in the media industry [6] PB-ROE-50 Combination Tracking - The PB-ROE-50 combination recorded negative excess returns across all stock pools, with the CSI 500 pool showing an excess return of -1.30%, the CSI 800 pool at -2.09%, and the overall market stock pool at -1.46% [7] Institutional Research Combination Tracking - Both public and private fund research selection strategies yielded negative excess returns, with the public fund strategy showing an excess return of -1.91% relative to the CSI 800, and the private fund strategy at -3.65% [8] Block Trade Combination Tracking - The block trade combination recorded negative excess returns relative to the CSI All Index, with an excess return of -2.84% [9] Directed Issuance Combination Tracking - The directed issuance combination also showed negative excess returns relative to the CSI All Index, with an excess return of -1.42% [10]

Core Insights - The report highlights that during the week of November 17-21, 2025, public quantitative funds showed resilience with positive excess returns across various indices, particularly the CSI 500 index which had an average excess return of 0.35% and a 80.82% positive return rate among funds [1][2][3] - For the month of November 2025, the average excess return for the CSI 500 index was 0.77%, with 81.69% of funds achieving positive returns, indicating a strong performance in the quantitative fund sector [2][3] - Year-to-date performance as of November 21, 2025, shows that the CSI 1000 index had the highest average excess return of 6.69%, with 89.13% of funds generating positive returns, suggesting a favorable environment for this index [3] Group 1: Weekly Performance Statistics - The average excess return for the public quantitative funds tracking the CSI 300 index was 0.22% for the week, with 72.00% of funds achieving positive returns [1] - The average excess return for the public quantitative funds tracking the CSI A500 index was 0.20%, with 70.31% of funds achieving positive returns [1] - The average return for public actively managed quantitative funds was -4.65%, with only 0.49% of funds generating positive returns, indicating challenges in this segment [1] Group 2: Monthly Performance Statistics - For November 2025, the average excess return for the public quantitative funds tracking the CSI 300 index was 0.15%, with 66.22% of funds achieving positive returns [2] - The average excess return for the public quantitative funds tracking the CSI A500 index was 0.19%, with 64.91% of funds achieving positive returns [2] - The average return for public actively managed quantitative funds was -4.49%, with only 4.96% of funds generating positive returns, reflecting ongoing difficulties in this area [2] Group 3: Year-to-Date Performance Statistics - Year-to-date as of November 21, 2025, the average excess return for the public quantitative funds tracking the CSI 300 index was -0.75%, with only 34.43% of funds achieving positive returns [3] - The public quantitative funds tracking the CSI A500 index had an average excess return of 1.18%, with 75.00% of funds achieving positive returns, indicating a strong performance relative to other indices [3] - The public actively managed quantitative funds had an impressive average return of 22.14%, with 97.80% of funds generating positive returns, showcasing the effectiveness of active management strategies in the current market [3]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 - **Model Construction Idea**: This model aims to combine the Price-to-Book (PB) ratio and Return on Equity (ROE) to create a portfolio of 50 stocks[23] - **Model Construction Process**: The model selects stocks based on their PB and ROE values, aiming to balance valuation and profitability. The portfolio is rebalanced periodically to maintain the desired characteristics[23] - **Model Evaluation**: The model's performance is tracked across different stock pools, showing its effectiveness in various market conditions[23] - **Model Test Results**: - **CSI 500**: Weekly excess return -1.30%, YTD excess return 1.58%, weekly absolute return -7.01%, YTD absolute return 20.95%[24] - **CSI 800**: Weekly excess return -2.09%, YTD excess return 13.40%, weekly absolute return -6.31%, YTD absolute return 30.05%[24] - **All Market**: Weekly excess return -1.46%, YTD excess return 16.48%, weekly absolute return -6.44%, YTD absolute return 36.70%[24] 2. Model Name: Institutional Research Portfolio - **Model Construction Idea**: This model tracks the stock selection strategies of public and private institutional research[25] - **Model Construction Process**: The model is constructed based on the stock picks of institutional investors, adjusting the portfolio based on their research and investment decisions[25] - **Model Evaluation**: The model's performance is evaluated by comparing its returns to the CSI 800 index[25] - **Model Test Results**: - **Public Research Stock Selection**: Weekly excess return -1.91%, YTD excess return 12.42%, weekly absolute return -6.14%, YTD absolute return 28.92%[26] - **Private Research Tracking**: Weekly excess return -3.65%, YTD excess return 12.06%, weekly absolute return -7.80%, YTD absolute return 28.51%[26] 3. Model Name: Block Trade Portfolio - **Model Construction Idea**: This model leverages the information from block trades, focusing on stocks with high transaction amounts and low volatility[29] - **Model Construction Process**: The portfolio is constructed based on the "high transaction, low volatility" principle, with monthly rebalancing[29] - **Model Evaluation**: The model's performance is tracked relative to the CSI All Share Index[29] - **Model Test Results**: - **Weekly excess return**: -2.84%[30] - **YTD excess return**: 35.29%[30] - **Weekly absolute return**: -7.75%[30] - **YTD absolute return**: 58.77%[30] 4. Model Name: Private Placement Portfolio - **Model Construction Idea**: This model analyzes the event effects of private placements to identify investment opportunities[35] - **Model Construction Process**: The portfolio is constructed around the announcement dates of private placements, considering factors like market capitalization and rebalancing cycles[35] - **Model Evaluation**: The model's performance is evaluated relative to the CSI All Share Index[35] - **Model Test Results**: - **Weekly excess return**: -1.42%[36] - **YTD excess return**: -3.89%[36] - **Weekly absolute return**: -6.40%[36] - **YTD absolute return**: 12.80%[36] Quantitative Factors and Construction Methods 1. Factor Name: Intraday Volatility and Trading Volume Correlation - **Factor Construction Idea**: This factor measures the correlation between intraday volatility and trading volume[12] - **Factor Construction Process**: The factor is calculated by correlating the intraday price volatility with the trading volume over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.23%[13] - **Monthly return**: 3.14%[13] - **Annual return**: -2.31%[13] - **10-year return**: 22.87%[13] 2. Factor Name: ROE Stability - **Factor Construction Idea**: This factor measures the stability of a company's Return on Equity over time[12] - **Factor Construction Process**: The factor is calculated by assessing the variance in ROE over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.14%[13] - **Monthly return**: 1.82%[13] - **Annual return**: 0.95%[13] - **10-year return**: 3.68%[13] 3. Factor Name: Downside Volatility Proportion - **Factor Construction Idea**: This factor measures the proportion of downside volatility in the total volatility of a stock[12] - **Factor Construction Process**: The factor is calculated by dividing the downside volatility by the total volatility over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.13%[13] - **Monthly return**: 2.09%[13] - **Annual return**: -6.82%[13] - **10-year return**: 30.09%[13] 4. Factor Name: Single Quarter Total Asset Gross Profit Margin - **Factor Construction Idea**: This factor measures the gross profit margin of a company's total assets for a single quarter[14] - **Factor Construction Process**: The factor is calculated by dividing the gross profit by the total assets for a single quarter[14] - **Factor Evaluation**: The factor shows positive returns in the CSI 500 stock pool[14] - **Factor Test Results**: - **Weekly return**: 1.82%[15] - **Monthly return**: -0.84%[15] - **Annual return**: 6.56%[15] - **10-year return**: 82.05%[15] 5. Factor Name: Net Profit Margin TTM - **Factor Construction Idea**: This factor measures the trailing twelve months (TTM) net profit margin of a company[16] - **Factor Construction Process**: The factor is calculated by dividing the net profit by the total revenue for the trailing twelve months[16] - **Factor Evaluation**: The factor shows positive returns in the Liquidity 1500 stock pool[16] - **Factor Test Results**: - **Weekly return**: 1.82%[17] - **Monthly return**: -0.58%[17] - **Annual return**: 1.94%[17] - **10-year return**: -17.46%[17] Factor Backtest Results CSI 300 Stock Pool - **Intraday Volatility and Trading Volume Correlation**: Weekly return 1.23%, monthly return 3.14%, annual return -2.31%, 10-year return 22.87%[13] - **ROE Stability**: Weekly return 1.14%, monthly return 1.82%, annual return 0.95%, 10-year return 3.68%[13] - **Downside Volatility Proportion**: Weekly return 1.13%, monthly return 2.09%, annual return -6.82%, 10-year return 30.09%[13] CSI 500 Stock Pool - **Single Quarter Total Asset Gross Profit Margin**: Weekly return 1.82%, monthly return -0.84%, annual return 6.56%, 10-year return 82.05%[15] Liquidity 1500 Stock Pool - **Net Profit Margin TTM**: Weekly return 1.82%, monthly return -0.58%, annual return 1.94%, 10-year return -17.46%[17]

Core Viewpoint - Quantitative investment is a systematic investment approach based on mathematical models, algorithms, and computer technology, which does not rely on subjective judgment but rather on rigorous data analysis and model construction to identify market patterns and make investment decisions [2] Market Performance - Over the past three years (November 2022 to October 2025), major stock indices in A-shares, Hong Kong, and the US have shown impressive performance, with the Hong Kong and US markets being particularly strong [2] - A-shares transitioned from a bear market to a bull market around September 2024, with cumulative gains as follows: - Shanghai Composite Index: 36.68% - Shenzhen Component Index: 28.67% - ChiNext Index: 40.73% - CSI 300: 32.26% - CSI 500: 26.24% - CSI 1000: 19.29% - CSI 2000: 39.41% - Hang Seng Index: 76.39% - Hang Seng Tech Index: 107.11% - Dow Jones Industrial Average: 45.31% - Nasdaq: 115.91% - S&P 500: 76.66% [3] Quantitative Private Equity Performance - As of October 2025, there are 103 quantitative private equity firms with at least three products displayed on the platform, achieving an average return of approximately 59.52% and a median return of about 53.04%, outperforming major A-share indices [3][6] Top Performing Quantitative Private Equity Firms - The top three firms in the billion-dollar category are: - Maoyuan Quantitative - Tianyan Capital - Century Frontier - Maoyuan Quantitative has five products that reached historical highs in October 2025, with an average return exceeding ***% [5][11] - Ningbo Huanfang Quantitative, with 11 products, also achieved historical highs in October 2025, with an average return exceeding ***% [11][20] Performance by Firm Size - For firms with assets under management between 10 billion and 50 billion, the top five are: - Guangzhou Shouzheng Yongqi - Oak Asset Management - Zhixin Rongke - Yanhe Investment - Wuliang Capital [16][22] - For firms with assets between 50 billion and 100 billion, the top performers include: - Dayan Capital - Yunqi Quantitative - Anzi Fund [12][14] - For firms below 10 billion, the top five are: - Shanghai Zijie Private Equity - Quancheng Fund - Huacheng Private Equity [22][24]

Investment Rating - The industry investment rating is "Recommended," indicating an expected increase in the industry index by more than 5% over the next 3-6 months compared to the benchmark index [18]. Core Insights - The report highlights that private equity funds continue to show positive excess returns, while small micro-cap stocks have shown some strength in excess returns [3]. - The average returns for various enhanced strategies since the beginning of the year are as follows: - 300 Enhanced Strategy: +28.5% - 500 Enhanced Strategy: +38.5% - A500 Enhanced Strategy: +29.9% - 1000 Enhanced Strategy: +44.8% - Air Index Enhanced Strategy: +39.5% - Market Neutral Strategy: +14.6% [3]. - The report also provides insights into the average daily trading volumes for major indices, with the following figures since the beginning of the year: - CSI 300: 3,473 billion CNY - CSI 500: 2,382 billion CNY - CSI 1000: 3,521 billion CNY - CSI 2000: 4,376 billion CNY - Micro-cap stocks: 246 billion CNY [5]. Summary by Sections Performance Metrics - The average weekly/monthly/year-to-date returns for the 300 Enhanced Strategy are -0.1%/+2.2%/+28.5% with excess returns of +0.9%/+1.8%/+7.7% [3]. - The average daily trading volume for the CSI 300 has seen a year-to-date increase of +115% [5]. Relative Performance - The relative excess returns of the CSI 300 compared to the CSI 500 are +0%/-6.2%/0% for the week/month/year-to-date [4]. - The relative excess returns of micro-cap stocks compared to the CSI 500 are +3.2%/+8.7%/0% for the week/month/year-to-date [4].

Core Viewpoint - The article discusses the retirement of investment legends like Warren Buffett and the emergence of new investment philosophies, highlighting the shift in investment paradigms and the need for self-reliance among investors as traditional masters fade away [2][3][10]. Group 1: Retirement of Investment Legends - Warren Buffett announced his retirement as CEO of Berkshire Hathaway, emphasizing the importance of seizing the moment and not waiting for regret [3][5]. - The article notes the passing of other investment icons, including Wang Guobin and Charlie Munger, marking a significant transition in the investment landscape [10]. Group 2: Investment Strategies and Philosophies - The article highlights the influence of investment masters like Buffett, Munger, and Soros on contemporary investors, with their philosophies shaping investment strategies [14][16]. - It discusses the importance of understanding high-quality companies and the value of patience in investment, as exemplified by figures like Zhang Kexing and Wang Yongqing [15][38]. Group 3: The Changing Investment Landscape - The article points out that financial investments are increasingly favored over real estate, with high-risk assets in China projected to rise from 9% to 15% by Q3 2025 [9]. - It mentions the growing role of artificial intelligence in trading, with 80% of trading volume in the US market being executed by machines [20]. Group 4: Future of Investment Masters - The article speculates on the characteristics of future investment leaders, emphasizing the need for interdisciplinary knowledge, data-driven decision-making, and a deep understanding of corporate structures [30][32]. - It suggests that future investment heroes may emerge from diverse backgrounds, including those who can navigate the complexities of AI and geopolitical changes [40][43].

基金经理何翔:在整个三季度,A股市场主要是大盘科技成长风格占优的阶段,并且带动了大盘指数连续上 行,这个阶段小微盘股是相对弱势一些的。我们看到的市场始终是结构性牛市,不同阶段不同风格轮换表 现是正常现象。也正是这些大盘成长股的持续大幅上涨,伴随小微盘股的暂缓休憩,让小微盘股进入了性 价比更优的阶段,也进一步打开了小微盘股后续的上涨空间。 基金经理何翔:渤海汇金公募量化权益部总经理,兼公募量化权益部量化投资团队负责人、量化投资总监, 二十一年量化投研从业经历,现任渤海汇金新动能主题混合型基金基金经理。 大家好,我是渤海汇金霄霄,有这么一只小微盘策略基金,渤海汇金新动能主题混合型基金(基金代 码:010584),今年以来的收益已经超过40%,近一年的收益也有近70%(均截止2025年8月1日)①。那么这只 基金究竟有什么独到之处呢,之前的访谈系列中,霄霄代大家采访了渤海汇金新动能的基金经理何翔老师, 今天我们将继续和基金经理对话,帮助大家更好的了解这只小微盘策略基金。 渤海汇金霄霄:首先祝贺您管理的渤海汇金新动能主题混合型基金荣获中国证券报颁发的三年期积极混 合型金牛奖②,对于能够再次荣获金牛奖,您有什么感想吗? ...