时隔一年半后，管理规模超百亿的主动权益基金经理（下称"百亿基金经理"）人数，重新回到100位以上。 券商中国记者近日从天相投顾基金评价中心（下称"天相投顾"）和理财魔方处获取的数据均显示，截至目前该人数已超过110 名。从阶段性分析来看，该人数历经此前持续下降后，已开始"探底回升"。 "百亿"规模新老有别。一是坚守消费、医药、新能源等赛道的基金经理逐渐淡出"百亿"舞台，新晋百亿基金经理在科技、红 利、量化等赛道崛起，契合了从"传统内需"向"科技创新+安全边际"的行情主线转变。二是新晋的百亿规模，主要是业绩与行 情共振的结果，与过往的"明星效应+大规模新发"形成鲜明对比。通过业绩做大规模固然可喜，但更重要的是避免重蹈覆辙， 通过制度和契约精神来实现规模与业绩的良性循环，并持续夯实循环根基。 "新"百亿基金经理的三大类型 天相投顾对券商中国记者提供的统计数据显示，百亿基金经理人数在2022年一季度是151位，于当年二季度升到163位高点， 平均管理规模超过200亿元。从2022年三季度开始，这一人数开始走低，到2023年末为106人。2024年一季度，百亿基金经理 人数跌破100，到2025年二季度末时只有82人 ...

| | | 主动权益百亿规模基金经理前20(截至11月14日) | | | | --- | --- | --- | --- | --- | | 序号 | 基金经理 | 在管基金数 | 管理总规模(亿元) | 其金公司 | | 1 | 张 坤 | ব | 565.44 | 易方达基金 | | 2 | 谢治宇 | 3 | 453.57 | 兴证专球基金 | | 3 | 葛 兰 | 3 | 435.44 | 中欧基金 | | র | 刘彦春 | 6 | 358.60 | 景顺长城基金 | | 5 | 周蔚文 | 5 | 336.59 | 中欧基金 | | 6 | 刘格松 | 5 | 334.10 | 厂发星金 | | 7 | 赵磊 | 1 | 329.53 | 中欧基金 | | 8 | 胡昕炜 | 8 | 316.96 | 汇漆富基金 | | 9 | 陈 皓 | 8 | 298.87 | 易方达基金 | | 10 | 李晓星 | 10 | 261.45 | 据英语等 | | 11 | 乔 迁 | 2 | 252.38 | 兴证全球基金 | | 12 | 杨世讲 | 3 | 252.23 | 兴证舍球星金 | | 1 ...

Group 1: Report Information - The report is a weekly strategy report from the Dongzheng Derivatives Research Institute, covering stock index futures, treasury bond futures, and commodity CTA strategies [1][38][51] Group 2: Stock Index Futures Market Review - The market declined last week, with the electronics sector contributing the main decline in each index [3] - IH and IF trading volumes increased week-on-week, while IC and IM trading volumes decreased. The basis weakened [4] Strategy Recommendations - For the basis strategy, it is recommended to pay attention to the opportunity of calendar spread arbitrage when the discount narrows due to market sentiment. The rollover strategy recommends going long on the near - term contract and short on the far - term contract [4] - For the arbitrage strategy, in the calendar spread arbitrage, the annualized basis rate, cash - and - carry, and momentum factor strategies were profitable last week, with returns of 0.2%, 0.5%, and 0.2% respectively (6 - times leverage). The cross - variety arbitrage time - series synthesis strategy had a net profit of 0.1% last week, and the latest signal recommends 50% long IF and short IC, 100% long IM and short IC [5] - For the timing strategy, the daily timing strategy was generally profitable last week, with the Shanghai Composite 50, CSI 300, CSI 500, and CSI 1000 having returns of 0.4%, 0.6%, 1.1%, and - 0.7% respectively. The timing model's latest signal shows a significant increase in bullish sentiment [6] Group 3: Treasury Bond Futures Strategy Focus - For the basis and calendar spread, the IRR of treasury bond futures declined this week, and the calendar spread was volatile and strong. The space for cash - and - carry arbitrage is limited, and it is expected to move sideways [40] - For interest rate timing and hedging signals, the interest rate timing signal predicts a decline in interest rates, and it is recommended to choose high - duration varieties for hedging [40][41] - For the futures timing strategy, the multi - factor timing strategy signal is neutral, with the basis factor and high - frequency factor being mainly bullish, and the spread factor and volume - price factor being mainly bearish [40][44] - For the futures cross - variety arbitrage strategy, the latest signals of the treasury bond futures cross - variety arbitrage strategies TS - T and T - TL are neutral [40][47] Group 4: Commodity CTA Factor Performance - Last week, the performance of various commodity varieties in the market varied. Precious metal silver led the rise due to renewed risk - aversion sentiment, and lithium carbonate rose 7%, while coking coal, glass, and red dates led the decline. Most commodity factors had good increases, with the momentum trend and term structure factors with significant long - term logic having an average increase of over 1%, and the value factor rising nearly 2%. The position - related factor had a slight increase, the spot - futures basis factor had a slight decline, and the warehouse receipt factor was basically flat [52][54] Tracking Strategy Performance - Different tracking strategies have different performance indicators such as annualized return, Sharpe ratio, Calmar ratio, and maximum drawdown. For example, the CW FT strategy has an annualized return of 9.4%, a Sharpe ratio of 1.61, and a maximum drawdown of - 8.81% [53]

Core Insights - The article discusses the strategic transformation of Zenlong Asset, a private equity firm, from fixed income investments to technology-driven and quantitative investment strategies, highlighting its commitment to social responsibility [1][2][3]. Group 1: Strategic Transformation - Zenlong Asset was established in 2014 and initially gained recognition in the private equity sector through fixed income investments, being rated among the top 100 bond trading institutions on the Shanghai Stock Exchange for two consecutive years [2]. - The firm began building its stock and quantitative teams in 2021, indicating a proactive approach to transitioning towards technology and innovation investments [2]. - Zenlong Asset currently manages approximately 7.5 billion yuan, with a product line that includes bonds, stocks, and quantitative composite strategies, focusing on sectors such as semiconductors, high-end manufacturing, and AI [2]. Group 2: Social Responsibility - Zenlong Asset integrates social responsibility into its corporate DNA, creating a unique "investment-empowerment-feedback" closed-loop model [3]. - The firm supports the growth of technology enterprises not only through financial investments but also by providing resources and technical interactions, enhancing the efficiency of fund utilization [3]. - Zenlong Asset has been involved in educational initiatives and mental health projects, such as the "Looking Up to the Stars Education Public Welfare Fund," which aims to promote educational equity and mental health support in schools [4]. Group 3: Future Outlook - The firm anticipates significant advancements in AI and quantitative strategies, aiming to enhance investment strategy intelligence through collaborations with technology companies [5]. - Zenlong Asset is expanding its collaboration with brokerage firms, recognizing their role in supporting the scalability of quantitative strategies through improved systems and tools [6]. - The company expects that within the next three years, its quantitative composite strategies and stock products will account for over 50% of its total managed assets, reinforcing its focus on technology finance [6].

Core Viewpoint - The Chinese private equity industry is transitioning from quantitative expansion to qualitative transformation, supported by the "Golden Yangtze" private equity empowerment plan launched by Changjiang Securities, Industrial Bank, and Securities Times, focusing on research, funding, trading, and branding [1][4]. Group 1: Market Overview - As of late October, the Shanghai Composite Index returned to 4000 points after ten years, indicating a significant recovery in market sentiment, with private equity performance and issuance showing remarkable improvement, leading to a rapid increase in the number of billion-yuan private equity firms to 113 [3]. - The current capital market is experiencing a strategic opportunity period, with the A-share market steadily rising and private equity management scale reaching a historical high [4]. Group 2: Economic and Industry Trends - The "14th Five-Year Plan" emphasizes increasing consumer rates and enhancing consumption capacity, with consumption expected to contribute approximately 63% to economic growth during this period [5]. - The next five years are anticipated to see a boom in service and entertainment consumption, driven by a growing middle-income group in China [5]. Group 3: Private Equity Insights - Many private equity institutions remain optimistic about the current market, believing that overall valuations are within a reasonable range, despite recent market fluctuations [6]. - The market has seen a shift in style from value and micro-cap stocks to large-cap growth stocks, influenced by macroeconomic trends and industry dynamics [6]. Group 4: Investment Strategies - Investment strategies are becoming more complex due to increasing market divergence and geopolitical risks, necessitating higher demands for strategy selection and risk management [7]. - Quantitative strategies are gaining traction as tools for both relative and absolute return products, allowing investors to better navigate market volatility [7]. Group 5: Sector Focus - The technology growth sectors, particularly AI and robotics, are expected to remain key market themes, although careful selection and timing will be crucial due to increased competition within these sectors [8]. - Traditional manufacturing sectors are anticipated to gradually see price and profit recovery, presenting new investment opportunities [8]. Group 6: Market Dynamics - The current A-share market exhibits characteristics of strong indices alongside individual stock divergence, with a notable rotation among sectors [10]. - The market is entering a consolidation phase, with historical patterns suggesting that even in bull markets, periods of adjustment are common [12].

Group 1: Report Industry Investment Rating - Not provided in the content Group 2: Core Views of the Report - The report focuses on constructing a convertible bond multi - factor system from a style dimension, aiming to establish a framework covering 115 factors and five types of style factors (valuation, momentum, volatility, liquidity, and volume - price) based on a "behavior - valuation - volatility" three - dimensional logic, and obtain excess returns while keeping the investment portfolio market - neutral through non - linear combination optimization, providing quantitative strategy support for asset allocation [1] - In the environment of low interest rates and asset shortage, the shift of funds to the "fixed income +" strategy drives the structural prosperity of the convertible bond market. The market has entered a stage of "structural differentiation - complex pricing - refined strategies", and the multi - factor system has significant applicability in the convertible bond market [2] - The style factor framework provides a path for convertible bond research. Different convertible bonds can be regarded as recombinations of style factors, and depicting convertible bonds from the style dimension helps understand market structure and rotation rules and provides a framework for constructing a multi - factor bond - selection system [3] Group 3: Summary According to Relevant Catalogs 1. Introduction - In 2025, driven by the equity market, the convertible bond market continued to strengthen, showing characteristics of active trading, stable stock, and structural differentiation. As of November 4, 2025, the average daily trading volume in the convertible bond market was about 66 billion yuan, with a high - volatility and high - central - value feature. The market had 415 convertible bonds in circulation, with a total scale of about 595.7 billion yuan. The price distribution was biased towards the medium - high price range, indicating an increase in the performance of the underlying stocks and market risk appetite [12] - Quantitative methods are more applicable in the convertible bond market. The T + 0 mechanism and high - frequency trading structure provide rich price - volume information, and the stock - bond hybrid characteristics of convertible bonds enable the multi - factor system to be applied in five dimensions: valuation, momentum, volatility, liquidity, and volume - price correlation [13] 2. Recent Expansion of the Convertible Bond Market 2.1 Convertible Bonds: "Hybrid Assets" with Both Stock and Bond Attributes - Convertible bonds can be converted into the issuer's stocks under specific conditions, with both "bond" and "stock" characteristics. Their price is composed of the pure bond value and the option value of conversion. The market has expanded rapidly, and its concentrated and active trading provides a basis for multi - factor model testing [15][16] - Compared with stocks, convertible bonds have bond - based downside protection, stock - based upside potential, medium - level volatility between stocks and bonds, and more flexible trading rules. Quantitative methods are highly applicable in the convertible bond market due to high - frequency data support, effective behavioral factors, Alpha - providing stock - bond linkage factors, and the advantage of trading systems [17][19] 2.2 Necessity of Strategies Driven by the Expansion of "Fixed Income +" under Low Interest Rates - In the environment of low interest rates and asset shortage, the shift of funds to the "fixed income +" strategy drives the prosperity of the convertible bond market, creating a situation of strong demand, tight supply, and a rising pricing center, which provides a long - term foundation for quantitative and systematic strategies [18] - As of Q3 2025, the scale of public funds has increased steadily, with a pattern of "expansion of equity products and contraction of bond funds". The "fixed income +" products, especially secondary bond funds, have expanded significantly. The demand for convertible bond allocation has increased, while the supply has slowed down. The market has formed a pattern of "high valuation - high position - low supply", and convertible bonds have shown stronger resilience in the volatile market [20][21][23] 3. Convertible Bonds and Equities from the Perspective of Style Factors 3.1 Style Factors: Systematic Depiction of the Equity Market from the Barra System - Style factors are core dimensions for depicting the common characteristics and systematic differences of assets in the multi - factor model system. The Barra model decomposes asset returns into style factor returns and idiosyncratic returns, and in the Barra framework, style factors in the equity market include valuation, growth, momentum, volatility, scale, leverage, and liquidity, which jointly form the "style map" of the equity market and provide a path for convertible bond research [28][29][32] 3.2 Style - Based Structure of the Convertible Bond Market: Division into Stock - Oriented, Balanced, and Bond - Oriented Types - Convertible bonds can be divided into stock - oriented, balanced, and bond - oriented types based on style factors. Stock - oriented convertible bonds are dominated by stock characteristics, with high elasticity and large fluctuations; balanced convertible bonds have a balanced risk - return profile, with both stock and bond features; bond - oriented convertible bonds are dominated by bond characteristics, with strong defensive properties. This division provides a basis for factor stratification and strategy construction [33] 3.3 The Stock - Dominant Nature of the Convertible Bond Market under the Slow - Bull Expectation - The convertible bond market has shifted from being bond - dominated to stock - dominant. The high correlation between the convertible bond index and the CSI 1000 and CSI 2000 indices indicates that the market is currently in a stock - driven stage. The reasons include the increase in the concentration of high - priced convertible bonds, the change in the capital structure, and the support of the macro - liquidity and interest - rate environment [35][36][37] 3.4 Introduction to the Multi - Factor Convertible Bond System: From Five Style Factors to the Systematic Back - Testing Framework - A multi - dimensional system covering 115 daily - frequency factors is constructed based on the price - volume characteristics and clause structure of the convertible bond market, including valuation, momentum, volatility, liquidity, and volume - price correlation factors. These factors form a relatively complete convertible bond quantitative framework [41][42] - Daily - frequency data is chosen as the core sample dimension for constructing the convertible bond multi - factor system. It can capture short - term market changes, maintain signal effectiveness, and balance signal sensitivity and execution feasibility [44][45] 4. Convertible Bond Multi - Factor System and Back - Testing Results 4.1 Historical Performance of Five Types of Style Factors - Based on the back - testing results from 2021 to 2025, the five types of style factors can be divided into three categories: the leading group includes momentum and volatility factors with high annualized excess returns; the stable group includes the liquidity factor; the medium group includes the five - factor equally - weighted composite factor, valuation factor, and volume - price correlation factor [47] - The excellent performance of the momentum factor is due to its ability to capture the "trend effect" in the convertible bond market. The volatility factor has high risk - adjusted returns and good risk control, which may be related to risk - pricing compensation and avoiding the "volatility trap" [48] 4.2 Portfolio Optimization Logic - Single - factor investment in convertible bonds has shortcomings such as high return volatility, insufficient factor synergy, significant trading - cost erosion, and style - deviation risk. A non - linear optimization framework is used for portfolio construction, with the goal of maximizing risk - adjusted returns under multiple constraints such as market value, industry, style, and individual bond weights [51][53][54] - Back - testing results show that the liquidity factor performs best under market neutrality since 2021, followed by volume - price and momentum factors. After optimization, the excess returns of most style factors decline significantly, indicating that high returns in the convertible bond market often come from style deviation and high turnover [56] 4.3 Follow - up Optimization Logic - The follow - up optimization should change the way of synthesizing large - category factors from "equally - weighted synthesis" to "weighted synthesis based on historical performance". Specific methods include weighted synthesis based on risk indicators, weighted synthesis based on return indicators, and direct optimization by eliminating ineffective or redundant sub - factors [58][59] 5. Follow - up Strategy Optimization 5.1 Event - Driven: Seizing the Certainty Opportunities in Clause Games - The event - driven strategy uses issuers' active actions such as downward - revision of conversion prices and share repurchases to obtain excess returns. It is necessary to establish a systematic event database and real - time monitoring mechanism [60][61][62] 5.2 Mispricing: Exploiting the Cognitive Bias of Option Value - The mispricing strategy is based on the market's mis - evaluation of the option value of convertible bonds. It involves constructing a theoretical value model, identifying pricing deviations, and constructing a market - neutral portfolio to earn value - regression returns [63]

Core Insights - The private equity industry has seen a significant increase in the number of private equity firms managing over 10 billion yuan, rising from 96 to 113 in just one month, with a net increase of 17 firms [1][2] - The growth is driven by three main factors: concentration of resources towards leading firms, strong performance in the stock market boosting investor sentiment, and overall impressive returns from private equity funds, particularly quantitative strategies [1][2] Group 1: Industry Growth - As of October 31, 2025, the total number of private equity firms managing over 10 billion yuan has reached 113, with 18 new firms joining the ranks [1] - Among the new entrants, 10 are first-time firms surpassing the 10 billion yuan threshold, including notable quantitative and subjective private equity firms [1] - The industry has also seen a return of 8 firms that had previously exited the "10 billion yuan club" [1] Group 2: Investment Strategies - Quantitative private equity firms have emerged as the dominant force in this expansion, with 10 out of the 18 new firms employing quantitative strategies [2] - As of the end of October, quantitative firms make up 48.67% of the 113 firms, while subjective firms account for 41.59% and mixed strategy firms represent 7.96% [2] - The average return for the 74 firms with performance data is 29.57%, with quantitative firms achieving an average return of 32.88%, outperforming subjective and mixed strategy firms [2][3] Group 3: Performance Metrics - Among the 38 firms with average returns exceeding 30%, 28 are quantitative firms, indicating a strong performance concentration in this strategy [3] - Four quantitative firms have reported average returns over 50%, showcasing exceptional performance in the sector [3] - The future of the private equity industry is expected to be diversified, with quantitative firms likely to continue their strong growth, while subjective firms provide valuable insights through in-depth research [3]

Group 1 - The market is experiencing a lack of buying momentum, with today's buying power at 861, which is insufficient compared to the 200 billion trading volume [3] - The index is hovering around 4000 points, indicating a normal market condition despite limited buying power and increasing selling pressure from institutions [3] - The market is characterized by a lack of clear direction, with institutions showing a consistent selling trend while retail investors are cautious about reducing their positions [3] Group 2 - The stock market is seeing a rotation in themes, with ST stocks leading the gains, indicating a trend where main players are focusing on speculative stocks [5] - There is a noticeable split in market performance, with small-cap stocks in sectors like photovoltaic and consumer goods performing well, while larger stocks in the same sectors are declining [5] - The market is currently fragmented, resembling the initial phase of the recent rally, suggesting that investors may need to adopt a strategy of either clustering around certain stocks or using quantitative methods [7]

Group 1 - The core viewpoint is that brokerages are optimistic about the continuation of the A-share bull market into 2026, recommending an overweight position in Chinese stocks and gold, while suggesting a balanced approach to market styles focusing on technology growth and large-cap growth opportunities [2][5][10]. - China’s economic indicators show signs of an upward trend, with brokerages adjusting their asset allocations accordingly, increasing exposure to commodities and maintaining a focus on stocks [2][5]. - The semiconductor industry is experiencing positive developments, with HBM4 prices rising by 51.35% to approximately $560, and AMD receiving export licenses for its AI chips to China, indicating a favorable environment for the sector [14][15]. Group 2 - The storage industry is entering a new upward cycle driven by the increasing demand for memory capacity due to AI model training, with HBM and DDR5 memory shortages impacting the entire storage supply chain [16][18]. - Major storage manufacturers like Samsung and SK Hynix are adjusting prices in response to the heightened demand for storage driven by AI applications, with AI servers requiring significantly more DRAM and NAND capacity compared to standard servers [18][21]. - The domestic storage industry is expected to see significant growth in production capacity, with companies like Yangtze Memory Technologies and Changxin Memory Technologies ramping up output to meet the rising demand [15][16].

Group 1 - Market sentiment score has continued to rise, reaching 3 as of November 7, up from 2.7 the previous week, indicating further recovery in market sentiment and a bullish outlook [7][11][19] - The trading volatility between industries has increased rapidly, breaking through the upper Bollinger Band, suggesting accelerated sector switching and a short-term improvement in sentiment [19][22] - The average daily trading volume for the entire A-share market decreased slightly to 20,123.50 billion yuan, with the highest trading day on November 3 at 21,329.04 billion yuan [14][18] Group 2 - The short-term trend scores for industries such as banking, petrochemicals, light manufacturing, electric equipment, and steel have shown significant upward movement, with utilities currently having the highest short-term score of 100 [38][39] - The crowdedness of capital in sectors like electric equipment, steel, and coal has increased, indicating potential volatility risks due to high valuations and sentiment corrections [40][44] - The model indicates a preference for large-cap and value styles, with signals suggesting that these styles may strengthen in the future [49][56]