Core Insights - The report investigates the performance of active equity funds, specifically whether they can outperform the broader A-share market, represented by a market capitalization-weighted portfolio of most A-share stocks [11][12] - The analysis reveals that while most active fund portfolios can outperform the market, the excess returns are not statistically significant, with annualized excess returns ranging from 0.216% to 3.05% across various fund sizes [1][2] Fund Performance Analysis - Active funds show a preference for large-cap stocks and high-valuation stocks, with significant positive exposure to high-valuation stocks impacting performance negatively when considering size and value factors [2][3] - Under a three-factor model, all fund portfolios exhibit positive excess returns, ranging from 1.54% to 6.37%, with small and mid-cap funds showing significant excess returns [2][3] - The growth factor demonstrates a high annualized return of 9.91%, with excess returns reaching 10.5% to 12.5%, indicating a strong correlation between short-term earnings growth and future stock returns [3][4] Factor Contributions - The report highlights that aside from the market factor, the value factor has a significant negative contribution, while the growth factor contributes positively to performance [3][4] - In a five-factor model, the market factor contributes between 6.03% and 6.48%, while the value factor contributes negatively between -3.16% and -2.83%, and the growth factor contributes positively between 1.98% and 2.49% [3][4] Fund Composition and Trends - The report notes a structural shift in the composition of active equity funds, with a significant increase in the number of mixed equity funds since 2015, reflecting a change in regulatory requirements [20][21] - The number of active equity funds has grown from 207 at the end of 2008 to 5508 by the end of 2024, with a compound annual growth rate of 22.8% [15][21] - The report also discusses the impact of market conditions on fund performance, noting that the active equity fund's net asset value reached a peak in 2007 and has only recently surpassed that level [22][21]

Core Insights - Quantitative stock investment is expected to perform exceptionally well in 2025 after years of stagnation, driven by models based on factors and machine learning that have shown strong performance amid market volatility and political noise [1][6] Group 1: Strategy Recovery - Traditional long/short factor models and newer machine learning-based strategies are experiencing a revival, with the global bottom-up stock factor strategy rising over 9% this year, successfully navigating market volatility [2][3] - The top-down factor indices covering regions like Europe, the US, and Japan have also shown robust growth, particularly value and momentum strategies outside the US [2] Group 2: Regional and Strategy Performance - Europe has been the leading region for factor performance in 2025, with value strategies achieving the best relative and absolute returns, although valuation gaps have narrowed significantly [3] - Machine learning models from Société Générale have performed strongly, with a newly launched mean-reversion strategy yielding a return of 4.1%, outperforming basic reversal models [3] Group 3: Investment Themes and Strategy Outlook - Société Générale is optimistic about defensive stock income strategies, focusing on companies with strong balance sheets and high dividend yields, particularly in utilities, telecom, and energy sectors [4] - The US small-cap value strategy, excluding distressed stocks, has outperformed benchmark indices, emphasizing the importance of balance sheet strength as credit conditions tighten [4] - The "strong balance sheet" trade is supported as an alternative hedging strategy against high-yield credit risk, maintaining positive growth in 2025 [4] Group 4: Outlook for the Second Half of 2025 - Despite the strong performance of European value strategies, a cautious outlook is held for the second half of 2025 due to rising market volatility and valuation spreads nearing historical norms [5] - The easy gains from European value stocks may be over, influenced by geopolitical uncertainties and increasing earnings risks [5]

吴欣 榜样投资 创始人及CEO 金焰 友山基金 联合 首席投资官 汪大海 彭博大中华区 总裁 Kumar Gautam 彭博行业研究 股票量化策略师 在机遇与挑战并存的背景下， 彭博将分别在上海、深圳、北京、杭州多地举办2025年私募投资策 略闭门交流会系列活动。 行业领袖将与彭博经济学家、彭博行业专家共同讲述对当前市场走势的 洞察与研判，探讨业界普遍面临的挑战与破局之道，并分享科技助力投资决策更加明智的实践经 在市场波动中进行明智的资产配置 因子模型在A股的交易策略及海外延申 2025年彭博私募投资策略 闭门交流会（上海场） 2025年5月22日（星期四） | 15:30 - 18:00 上海 （详细活动地址将在确认函中提供） 2025年第一季度已落下帷幕，我们见证了美国关税政策、地缘政治博弈等因素频频导致全球宏观 格局震荡，引发市场避险情绪。而中国市场凭借经济复苏、融资需求高涨而备受投资者关注，境 内外多元化资产配置机遇显现，吸引多家国际对冲基金在此落子布局。与此同时，人工智能 （AI）等前沿科技的蓬勃发展不仅使科技股表现亮眼、拉动股指上涨，还为量化投研高效赋能， 成为私募机构在不确定性中破局取胜的关键工 ...

Core Insights - The article explores the underlying logic of stock selection ability Alpha, focusing on its purity, confidence, and heterogeneity, and proposes various improvement strategies to enhance its sustainability and predictive power [1][3]. Group 1: Characteristics of Traditional Time-Series Regression Alpha - Historical data shows that the proportion of equity funds with Alpha acquisition capability across different factor models fluctuates between 40% and 80%, significantly decreasing when requiring a significant p-value [3]. - Compared to cumulative return indicators, Alpha exhibits better sustainability [3]. - Long-term, constructing long positions with Alpha can yield returns exceeding market averages, but the presence of mixed components obscures the true fund capability, leading to unstable excess returns [3]. Group 2: Improving Alpha Purity through Regression Models - Cross-sectional regression is employed to reassess factor premiums, which helps mitigate information bias and omissions [5]. - Backtesting results indicate that cross-sectional regression Alpha shows significant improvements over time-series regression, with the IC mean for FF3 Alpha increasing from 4.52% to 6.30% [5]. - Key performance indicators such as annualized return and maximum drawdown for FF3 Alpha have improved, with tracking error decreasing from 4.8% to 2.5% [5]. Group 3: Incorporating Potential Factors to Purify Stock Selection Alpha - Incorporating different numbers of potential factors generally enhances the predictive performance of cross-sectional regression [6]. - For FF3, adding 1 to 3 potential factors increases the information ratio from 0.84 to 1.02, 1.00, and 1.24 respectively [6][8]. Group 4: Confidence of Alpha through P-Value Information - By integrating estimated standard error information, p-values can provide a more accurate assessment of estimation precision and stability [9][10]. - The annualized volatility decreases from 22.7% to 20.9% when using p-values to filter funds for constructing long positions, while tracking error and relative drawdown also improve significantly [10]. Group 5: Addressing Beta Anomalies - The average Alpha decreases significantly with increased exposure to SMB and HML Betas, indicating that traditional factor model-derived Alpha may not accurately reflect fund capabilities [15]. - Adjusting Alpha for Beta using various methods shows that fund regression Beta adjustments yield the best results, enhancing risk-adjusted returns [16][17].