主动量化

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主动量化基金发行回暖 单只基金募集14亿元创2024年以来新高
news flash· 2025-07-18 09:42
Group 1 - The issuance of actively managed quantitative funds is recovering, with the Morgan Huizhi Preferred Mixed Fund raising 1.4 billion yuan, marking the largest initial fundraising for an active quantitative fund in 2024 [1] - A total of 62 new active quantitative funds have been established in 2024, with an average fundraising size of 290 million yuan [1] - The overall market volatility has decreased this year, which has positively impacted the performance of low-frequency public quantitative strategies [1]
2025上半年量化基金10强揭晓!小盘指增包揽前10!
Sou Hu Cai Jing· 2025-07-03 11:05
Core Viewpoint - In the first half of 2025, the popularity of quantitative trading continues to rise amid increased activity in small-cap stocks and market volatility, with a significant number of quantitative funds showing positive returns [1][3]. Group 1: Performance of Quantitative Funds - As of June 30, 2025, there are 1,258 quantitative funds with an average return of 4.72% and a median return of 3.74%, with 86.15% of these funds achieving positive returns [1]. - Among the three categories of public quantitative funds, active quantitative funds have the highest returns, with average and median returns of 7.5% and 5.91% respectively [1]. - Index-enhanced funds, while slightly lower in returns, have the highest proportion of positive returns at 92.09% [1]. Group 2: Top Performing Funds - The threshold for the top 10 index-enhanced quantitative funds is set at 18.77%, with all top 10 funds tracking small-cap stock indices [3]. - The top three funds in the index-enhanced category are managed by 创金合信基金, 招商基金, and 长盛基金 [3]. - The top-performing index-enhanced fund, 创金合信北证50成份指数增强A, achieved a return of 37.17% in the first half of 2025 [5]. Group 3: Active Quantitative Funds - The threshold for the top 10 active quantitative funds is the highest at 24.64%, with the top three funds managed by 诺安基金, 中加基金, and 汇安基金 [8]. - The leading active quantitative fund, 诺安多策略A, recorded a return of 40.62% [10]. - The second-ranked fund, 中加专精特新量化选股A, achieved a return of 35.55% [11]. Group 4: Quantitative Hedge Funds - The threshold for the top 10 quantitative hedge funds is 0.82%, with 中邮基金, 富国基金, and 申万菱信基金 managing the top three funds [12]. - 工银瑞信基金 has two funds listed among the top 10 [12].
摩根汇智优选混合型基金7月7日起正式发行
Zheng Quan Ri Bao Wang· 2025-07-03 04:15
Group 1 - The core viewpoint of the news is that the A-share market is experiencing a growth trend, driven by sectors such as AI, semiconductors, and innovative pharmaceuticals, with the launch of the Morgan Wisdom Preferred Mixed Fund aimed at capturing growth investment opportunities [1] - The Morgan Wisdom Preferred Mixed Fund will officially launch on July 7, featuring a growth-oriented active quantitative investment strategy, with 60%-95% of its assets allocated to stocks [1] - The fund's investment strategy involves three steps: constructing a dynamic stock pool using factor models, selecting high-quality stocks with long-term growth potential through quantitative models, and continuously tracking and optimizing the investment portfolio to achieve returns that exceed the CSI A500 index [1] Group 2 - The fund will be managed by Hu Di, the director of the Index and Quantitative Investment Department at Morgan Asset Management, and emerging fund manager Han Xiu, with Hu Di having over 17 years of experience in the securities industry, including more than 8 years in investment management [2] - Hu Di anticipates significant breakthroughs in domestic AI model technology by early 2025, which could create new growth opportunities across various industries, supported by national policies encouraging the development of new productive forces [2] - The outlook for the A-share market is optimistic, with expectations for a growth trend in the future [2]
6 月中旬:边际乐观,逢低建仓——主动量化周报
ZHESHANG SECURITIES· 2025-06-08 13:15
Quantitative Models and Construction Methods 1. Model Name: Annualized Discount Model for CSI 500 Futures - **Model Construction Idea**: The model identifies optimal entry points for building positions based on historical performance when the annualized discount of CSI 500 futures exceeds a certain threshold, indicating market pessimism. [1][11] - **Model Construction Process**: - The model uses the annualized discount rate of the next-month contract of CSI 500 index futures as the key metric. - Historical data from 2017 onwards is analyzed to determine the relationship between the discount rate and subsequent returns. - Key findings: - When the annualized discount exceeds 15%, holding the index for more than 12 trading days results in average cumulative returns trending upward. - Holding for over 33 trading days yields a probability of positive cumulative returns exceeding 50%. - Holding for over 50 trading days increases the probability of positive returns to approximately 60%. - Formula: $ \text{Annualized Discount} = \frac{\text{Spot Price} - \text{Futures Price}}{\text{Futures Price}} \times \frac{365}{\text{Days to Maturity}} $ - Spot Price: Current index level - Futures Price: Price of the futures contract - Days to Maturity: Remaining days until the futures contract expires [11] - **Model Evaluation**: The model effectively captures market pessimism and identifies potential rebound opportunities, making it a useful tool for timing market entry. [11] --- Model Backtesting Results 1. Annualized Discount Model for CSI 500 Futures - **Key Metrics**: - Holding for 12 trading days: Average cumulative returns trend upward. - Holding for 33 trading days: Positive return probability > 50%. - Holding for 50 trading days: Positive return probability ~60%. [1][11] --- Quantitative Factors and Construction Methods 1. Factor Name: Proprietary Active Trader Activity Indicator - **Factor Construction Idea**: This factor measures the activity level of speculative funds (e.g., proprietary traders) to gauge market sentiment and risk appetite. [3][13] - **Factor Construction Process**: - Data Source: Derived from "Dragon and Tiger List" (龙虎榜) data. - The indicator tracks the marginal changes in active trader participation over time. - Observations: - From late April, the indicator showed a consistent decline, reflecting reduced risk appetite and cautious market sentiment. - Recently, the indicator has shown marginal improvement, suggesting a potential rebound in risk appetite. [3][13] - **Factor Evaluation**: The factor provides timely insights into the behavior of speculative funds, which can serve as a leading indicator for shifts in market sentiment. [3][13] 2. Factor Name: BARRA Style Factors - **Factor Construction Idea**: These factors assess the performance of various style attributes (e.g., momentum, volatility, size) to understand market preferences. [23][24] - **Factor Construction Process**: - Data Source: BARRA factor model. - Key Observations for the Week: - Fundamental factors (e.g., profitability) showed significant positive excess returns. - Stocks with high short-term momentum and high volatility outperformed. - Size-related factors (e.g., market capitalization) continued to underperform, indicating a preference for mid- to small-cap stocks. - Formula: Factor returns are calculated as the weighted average of stock returns within each style category. [23][24] - **Factor Evaluation**: The factors effectively capture shifts in market preferences, providing actionable insights for portfolio adjustments. [23][24] --- Factor Backtesting Results 1. Proprietary Active Trader Activity Indicator - **Key Metrics**: - Indicator showed consistent decline from late April, reflecting reduced risk appetite. - Recent marginal improvement suggests a potential rebound in speculative activity. [3][13] 2. BARRA Style Factors - **Key Metrics**: - Momentum: +0.2% weekly return. - Volatility: +0.2% weekly return. - Profitability: +0.3% weekly return. - Size: -0.5% weekly return. - Nonlinear Size: -0.3% weekly return. [23][24]