华玉 本 第 wabao WP F 华宝基金主动权益团队成长工具箱 AI 金面覆盖科技产业链 苏T 南语原 机器 te pr 创新药 新材料 港股互联网 *权益风口研判 2025年10月30日中美元首会晤达成关税共识,中美经贸关 系呈现阶段性缓和。展望后市,风险偏好提升与全球流动 性改善双轮驱动. A股"慢牛"格局可期。 市场主线或将回归长期产业逻辑:科技领域关注人工智 能、半导体设备、工业软件等优质供给创造的新需求;资源 品在能源转型背景下应享稀缺性定价溢价:光伏、化工等 过剩产能行业在"反内卷"政策落地后有望迎来估值修复。 随着国内产业政策持续催化与增量资金入市. 市场结构性 机会将更加凸显。建议关注科技创新与产业升级双主线, 把握能源革命与国产替代带来的长期机遇。 风险提示: 权益投资观点更新时间为2025.10.30, 仅代表当时观点, 今后可能发生改变, 仅供参考, 算力自强·「卷」存高远 产业周期为锚 · 精选AI硬件与新能源方向龙头 华宝核心优势 x* 002152 c* 016461 85.26% 95.65% 即2% (6/413) 近1年超额收益 近1年净值增长率 沂1年同类业绩 品等提名 ...

Core Insights - The A-share market has shown significant recovery in 2025, with the Shanghai Composite Index reaching a 10-year high of 4025.70 points by the end of October, leading to a strong performance of public equity funds and the emergence of numerous "doubling funds" [1][3] Group 1: Fund Performance - The average net value growth rate of actively managed equity funds for the first ten months reached 27.48%, with the best-performing funds exceeding 200% [3][5] - Over 98% of actively managed equity funds reported positive net value growth rates, with 705 funds achieving over 50% growth, and 34 funds surpassing 100% [7][5] - The top-performing fund, Yongying Technology Smart Selection A, achieved a net value growth rate of 200.63%, capitalizing on opportunities in the cloud computing market [9][8] Group 2: Index and Sector Performance - Major indices such as the ChiNext Index and the Science and Technology Innovation 50 Index saw annual growth rates exceeding 50%, with the ChiNext Index at 48.84% [1][4] - The communication equipment sector emerged as a significant winner, with related index funds showing remarkable performance, including the Guotai CSI All-Index Communication Equipment ETF, which had a growth rate of 98.87% [12][13] Group 3: Investment Themes and Manager Insights - Fund managers are focusing on structural opportunities in sectors like AI, innovative drugs, and robotics, which have shown strong performance [7][14] - Investment strategies include a focus on domestic semiconductor equipment and energy storage, with managers highlighting the increasing production capacity of domestic storage chips and the growing demand for energy storage solutions [15][14]

1. Report Industry Investment Rating No information provided regarding the report's industry investment rating. 2. Core View of the Report - Academic research shows that the experience level of fund managers significantly impacts investment decision - making characteristics, and the investment behavior mapping based on experience affects fund performance to some extent. The report aims to dig for excess returns from the perspective of behavioral finance in the areas of fund managers' investment experience and decision - making behavior [1][54]. - Domestic public fund managers are less affected by negative psychology, and their response methods when facing losses are relatively balanced. Active equity fund heavy - position stocks have a lower win - rate but higher odds compared to their industry returns. Fund managers tend to hold stocks when losses are low and reduce positions when losses are high. Those who reduce positions and then re - heavy - position stocks may be able to learn and improve from past experiences [1][18][54]. - By constructing "mistake correction" and "iteration efficiency" factors and combining them, funds that can iterate and improve from negative feedback experiences can be found. A "fund experience iteration" portfolio strategy is constructed, which can outperform the benchmark in the long - term with stable excess returns mainly relying on stock - selection ability and balanced industry allocation [2][3][55][56]. 3. Summary According to the Directory 3.1 Investment Experience's Impact on Investment Decision - Making Analysis 3.1.1 Historical Research Conclusions - Different academic papers have different views on the relationship between fund managers' experience and investment behavior. One paper finds that inexperienced fund managers are more likely to take higher risks and get higher returns, and herd behavior decreases with experience [8]. - Another paper shows that more experienced fund managers are over - confident due to their experience, which distorts performance evaluation and makes them less likely to change investment decisions when facing negative performance feedback, leading to poorer future fund performance [9]. 3.1.2 Behavioral Finance Perspective Analysis - When facing losses, fund managers may show "loss aversion" (avoiding buying or holding stocks that have caused losses even if fundamentals improve) and "over - confidence" (refusing to sell losing stocks). These psychological phenomena may negatively affect fund performance, and the report aims to find product portfolios that can reduce the impact of negative psychology and iterate and improve from past experiences [14][17]. 3.2 Which Funds Can Benefit from Past Experiences? 3.2.1 Analysis of Fund Managers' Heavy - Position Loss Experiences - Active equity fund heavy - position stocks have an average excess return of - 2% compared to their industries in the next quarter, with a win - rate of about 41.75% and odds of about 1.02. The probability of heavy - position losses is between 30% - 50%, and the average under - performance is higher when there are strong - rising industries in the market [18]. - Fund managers tend to hold stocks when losses are low and reduce positions when losses are high. For those who reduce positions, if they re - heavy - position stocks, it helps to find funds that can improve from past experiences. Repeated losses of re - heavy - positioned stocks often occur in leading stocks with an interval of 2 - 5 quarters [20][23]. - Domestic public fund managers are less affected by negative psychology, and the probabilities of different investment decisions when facing losses are relatively balanced. The probability of turning losses into profits for stocks held after losses is relatively high [27]. 3.2.2 Construction of the "Mistake Correction" Factor - The "mistake correction" factor is constructed to measure whether fund managers can create higher alpha in the same sub - industry after heavy - position stock negative feedback. The factor's initial grouping has good monotonicity, and its effectiveness mainly comes from learning and improvement from past experiences [32][33]. 3.2.3 Construction of the "Iteration Efficiency" Factor - Considering different learning efficiencies of fund managers from past experiences, the "iteration efficiency" factor is constructed based on the improvement of the stability of the fund's actual excess return. The overall effectiveness of this factor is relatively weak due to the influence of luck. By double - sorting the "mistake correction" and "iteration efficiency" factors, funds that can actively correct and improve strategy efficiency can be selected [34][36][38]. 3.3 Construction of the Fund Experience Iteration Portfolio Strategy - Based on the double - sorting results of the "mistake correction" and "iteration efficiency" factors, funds with a scale of more than 100 million yuan and an average heavy - position exposure of less than 50% in a single sector in the past year are selected. The top 10 or 20 funds with the highest "mistake correction" factor values are further selected to construct the fund experience iteration portfolio [43]. - The portfolio has a high annual win - rate, stable excess returns, mainly relying on stock - selection ability. It has balanced industry allocation, with relatively balanced market - capitalization styles and high momentum, liquidity, and profitability of held stocks [44][47].