Core Viewpoint - The financing balance of the two markets has surpassed 2.3 trillion yuan, marking a historical high since 2015, indicating a significant increase in liquidity and investor risk appetite during the current bull market [2][3]. Group 1: Exposure of Micro-Cap Stocks - There is a noticeable differentiation in the exposure of quantitative long products to micro-cap stocks this year, with micro-cap indices significantly outperforming mid and large-cap stocks [4][5]. - The weighted discount rate of IC/IM stock index futures has remained high, suggesting an increased exposure of quantitative managers to micro-cap stocks [7]. - In the first quarter, the proportion of holdings in stocks below the 2000 index was about 20-40%, which may rise to over 50% in the third quarter [8]. Group 2: Reasons and Risks of Exposure to Micro-Cap Stocks - Historically, small-cap stocks have shown higher average annualized beta returns compared to large-cap stocks, attracting speculative interest from retail investors [9]. - The lower coverage of small micro-cap stocks by large institutional investors leads to higher mispricing probabilities, providing opportunities for quantitative models to identify undervalued targets [9]. - The current market liquidity favors micro-cap stocks, pushing their prices higher, especially during periods of weak economic data [9]. Group 3: Investor Strategies to Mitigate Risks - As long as micro-cap stocks maintain a strong market position, the likelihood of high exposure in quantitative long products remains significant [10]. - New investors may have concerns, but the current bull market is relatively rare, and any adjustments are expected to manifest as fluctuations rather than sharp declines [10]. - Quantitative long strategies differ from simple micro-cap strategies, focusing on identifying strong stocks and increasing exposure based on market conditions [10].

Quantitative Models and Construction - **Model Name**: Volume Model **Construction Idea**: This model uses trading volume as a key indicator to predict market trends in the short term[12][65] **Construction Process**: The model evaluates the trading volume of broad-based indices to generate buy or sell signals. A higher trading volume relative to historical averages indicates a "bullish" signal, while lower volumes may indicate neutrality or bearishness[12][65] **Evaluation**: The model is effective in capturing short-term market momentum and is widely applicable across broad indices[12][65] - **Model Name**: Low Volatility Model **Construction Idea**: This model focuses on the volatility of indices to assess market stability and predict trends[12][65] **Construction Process**: The model calculates the historical volatility of indices over a defined period. If the volatility is low, the model remains neutral, indicating a stable market environment[12][65] **Evaluation**: The model is useful for identifying periods of market stability but may lack predictive power during high-volatility phases[12][65] - **Model Name**: Institutional Feature Model (Top Trader) **Construction Idea**: This model analyzes institutional trading patterns to predict market movements[12][65] **Construction Process**: The model tracks the trading activity of institutional investors, particularly their buying and selling patterns. A high level of institutional selling generates a "bearish" signal[12][65] **Evaluation**: The model provides insights into institutional sentiment but may be less effective in retail-dominated markets[12][65] - **Model Name**: Momentum Model **Construction Idea**: This model leverages price momentum to predict long-term market trends[14][67] **Construction Process**: The model calculates the rate of price change over a long-term horizon. Positive momentum generates a "bullish" signal, while negative momentum indicates bearishness[14][67] **Evaluation**: The model is effective in identifying long-term trends but may lag during sudden market reversals[14][67] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This is a composite model that integrates multiple signals across different time horizons[15][68] **Construction Process**: The model combines short-term, medium-term, and long-term signals from various sub-models (e.g., volume, momentum, institutional features) to generate an overall market outlook[15][68] **Evaluation**: The model balances short-term and long-term perspectives, making it robust for comprehensive market analysis[15][68] - **Model Name**: Hang Seng Turnover-to-Volatility Model **Construction Idea**: This model uses the ratio of turnover to volatility to predict medium-term trends in the Hong Kong market[16][69] **Construction Process**: The model calculates the turnover-to-volatility ratio for the Hang Seng Index. A higher ratio indicates a "bullish" signal, suggesting strong market participation relative to risk[16][69] **Evaluation**: The model is effective in capturing medium-term trends but may be less responsive to short-term fluctuations[16][69] Model Backtesting Results - **Volume Model**: All broad-based indices showed "bullish" signals in the short term[12][65] - **Low Volatility Model**: Neutral signals were observed, indicating stable market conditions[12][65] - **Institutional Feature Model**: Bearish signals were generated due to high institutional selling activity[12][65] - **Momentum Model**: Long-term "bullish" signals were observed, indicating positive price momentum[14][67] - **A-Share Comprehensive Weapon V3 Model**: Overall "bullish" signals were generated, reflecting a positive market outlook[15][68] - **Hang Seng Turnover-to-Volatility Model**: "Bullish" signals were observed, suggesting optimism in the Hong Kong market[16][69]

Quantitative Models and Construction Methods - **Model Name**: GAN_GRU **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for processing volume-price time-series features and Gated Recurrent Unit (GRU) for encoding time-series features to create a stock selection factor[4][13][41] **Model Construction Process**: 1. **GRU Component**: - Input features include 18 volume-price features such as closing price, opening price, turnover, and turnover rate[14][17][19] - Training data consists of the past 400 days of these features, sampled every 5 trading days, forming a 40x18 matrix to predict cumulative returns over the next 20 trading days[18] - Data preprocessing includes outlier removal and normalization at both time-series and cross-sectional levels[18] - Model architecture: Two GRU layers (128, 128) followed by an MLP (256, 64, 64), with the final output being the predicted return (pRet), which serves as the stock selection factor[22] - Training method: Semi-annual rolling training, with training conducted on June 30 and December 31 each year[18] - Optimization: Adam optimizer, learning rate of 1e-4, IC loss function, early stopping after 10 epochs, and a maximum of 50 training epochs[18] 2. **GAN Component**: - GAN consists of a generator (G) and a discriminator (D)[23] - Generator: Uses LSTM to preserve the time-series nature of the input features, transforming random noise into realistic data samples[33][37] - Loss function: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability[24][25] - Discriminator: Uses CNN to process the two-dimensional volume-price time-series features, distinguishing between real and generated data[33][37] - Loss function: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output for real data, and \( D(G(z)) \) is the output for generated data[27][29] - Training: Alternating updates of the generator and discriminator parameters until convergence[30] **Model Evaluation**: The GAN_GRU model effectively captures both time-series and cross-sectional features, leveraging the strengths of GAN and GRU for stock selection[4][13][41] --- Model Backtesting Results - **GAN_GRU Model**: - **IC Mean**: 11.36%[41][42] - **ICIR (Non-Annualized)**: 0.88[42] - **Turnover Rate**: 0.83[42] - **Recent IC**: -2.56%[41][42] - **1-Year IC Mean**: 8.94%[41][42] - **Annualized Return**: 38.09%[42] - **Annualized Volatility**: 23.68%[42] - **IR**: 1.61[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 23.52%[41][42] --- Quantitative Factors and Construction Methods - **Factor Name**: GAN_GRU Factor **Factor Construction Idea**: Derived from the GAN_GRU model, this factor encodes volume-price time-series features to predict stock returns[4][13][41] **Factor Construction Process**: - The factor is generated using the output of the GAN_GRU model, which combines GAN-based feature generation and GRU-based time-series encoding[4][13][41] - The factor undergoes industry and market capitalization neutralization, as well as standardization, before being used for testing[22] **Factor Evaluation**: The GAN_GRU factor demonstrates strong predictive power across various industries, with consistent outperformance in recent years[4][13][41] --- Factor Backtesting Results - **GAN_GRU Factor**: - **IC Mean**: 11.36%[41][42] - **ICIR (Non-Annualized)**: 0.88[42] - **Turnover Rate**: 0.83[42] - **Recent IC**: -2.56%[41][42] - **1-Year IC Mean**: 8.94%[41][42] - **Annualized Return**: 38.09%[42] - **Annualized Volatility**: 23.68%[42] - **IR**: 1.61[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 23.52%[41][42]

- The short-term volume model indicates a bullish outlook for most broad-based indices[2][12][70] - The low volatility model is neutral in the short term[2][12][70] - The institutional model based on the characteristics of the Dragon and Tiger list is bearish in the short term[2][12][70] - The characteristic volume model is bullish in the short term[2][12][70] - The intelligent algorithm models for the CSI 300 and CSI 500 indices are bullish in the short term[2][12][70] - The mid-term limit-up and limit-down model is bullish[2][13][71] - The mid-term calendar effect model is neutral[2][13][71] - The long-term momentum model is bullish[2][14][72] - The comprehensive A-share models, including the A-share Comprehensive Weapon V3 model and the A-share Comprehensive Guozheng 2000 model, are bullish[2][15][73] - The mid-term turnover rate inverse volatility model for Hong Kong stocks is bullish[2][16][74]

在发展方向上，团队聚焦量化选股与资产配置两大领域：量化选股方面，采用人工 + 智能模式，打造 稳定的增强工具，涵盖传统多因子策略、基本面量化策略、AI 策略等，并持续优化因子评价、选择、 迭代机制；资产配置方面，打造高夏普、工程化解决方案，从自上而下视角出发，以强逻辑基本面框架 为支撑，涉及股票风格配置与行业配置、债券量化配置、大类资产配置等。 8月18日，南方基金旗下南方中证A500指数增强型证券投资基金(基金简称：南方中证A500指数增强； 基金代码：A类024375，C类024376)正式发售。该基金锚定新一代大盘宽基指数——中证 A500 指数， 依托南方基金数量化投资团队的积淀与量化多策略赋能，在跟踪指数 Beta 收益的基础上力争超额收 益，为投资者提供布局 A 股核心资产的优质工具。 凝聚团队力量 打造α增强引擎 据了解，南方中证 A500 指数增强的第一大亮点，在于其背后团队的强力支撑。据了解，南方基金数量 化投资团队由 13 位成员组成的专业团队，成员涵盖数学、金融工程、信息技术等复合背景，平均从业 时间超 8 年，投资团队平均从业时间逾 10 年，具备深厚的专业功底和丰富的实战经验。 近年 ...

Group 1 - The core viewpoint is that the private equity market is experiencing a significant recovery, with over 90% of large private equity firms achieving positive returns this year, driven by structural market opportunities and active trading [2][3] - As of the end of July, the average return for large private equity firms was reported at 16.6%, with 54 out of 55 firms showing positive returns, indicating a strong performance in the sector [2] - The number of large private equity firms has increased to 90, reflecting the expansion of the industry amid favorable market conditions [1][2] Group 2 - The issuance market for private equity has notably improved, with a total of 1,298 private equity securities investment funds registered in July, marking an 18% increase from the previous month [3] - Large private equity firms dominated the new fund registrations in July, with significant numbers of new funds being launched, particularly in index-enhanced strategies [3] - Investor sentiment has improved, with institutional investors increasing their participation and shifting their preferences towards long-biased strategies, while individual investors are also showing signs of renewed interest [3] Group 3 - Large private equity firms are maintaining aggressive positions and actively adjusting their portfolios to capitalize on structural opportunities in the market [4][5] - The current investment focus includes sectors such as technology, innovative pharmaceuticals, non-bank financials, and cyclical stocks, with a high portfolio allocation of over 80% [4] - There is an expectation of profit-taking in popular sectors due to recent gains, particularly during the busy earnings reporting period in August, leading to potential adjustments in investment strategies [5]

Quantitative Models and Construction - **Model Name**: Volume Model **Construction Idea**: This model evaluates market trends based on trading volume changes over time [12][72] **Construction Process**: The model analyzes the trading volume of broad-based indices to determine short-term market sentiment. It transitions between "bullish," "neutral," and "bearish" signals based on volume dynamics [12][72] **Evaluation**: The model is effective in capturing short-term market sentiment but may require integration with other indicators for comprehensive analysis [12][72] - **Model Name**: Low Volatility Model **Construction Idea**: This model assesses market conditions by analyzing the volatility of indices [12][72] **Construction Process**: The model calculates the historical volatility of indices and assigns a "neutral" signal when volatility remains within a predefined range [12][72] **Evaluation**: The model provides a stable perspective on market conditions but may lag in highly volatile environments [12][72] - **Model Name**: Intelligent Algorithm Model (CSI 300 and CSI 500) **Construction Idea**: This model uses machine learning algorithms to predict market trends for specific indices [12][72] **Construction Process**: The model applies advanced algorithms to historical price and volume data, generating "bullish" signals for the CSI 300 and CSI 500 indices [12][72] **Evaluation**: The model demonstrates strong predictive capabilities for these indices, particularly in short-term scenarios [12][72] - **Model Name**: Limit-Up/Limit-Down Model **Construction Idea**: This model evaluates market sentiment based on the frequency of limit-up and limit-down events [13][73] **Construction Process**: The model tracks the number of stocks hitting daily price limits and assigns a "neutral" signal when no significant trend is observed [13][73] **Evaluation**: The model is useful for identifying extreme market conditions but may not capture subtle trends [13][73] - **Model Name**: Long-Term Momentum Model **Construction Idea**: This model identifies long-term trends by analyzing momentum indicators [14][74] **Construction Process**: The model calculates momentum metrics for indices like the SSE 50, which recently transitioned to a "bullish" signal [14][74] **Evaluation**: The model is effective for long-term trend analysis but may miss short-term fluctuations [14][74] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This composite model integrates multiple signals to provide an overall market outlook [15][75] **Construction Process**: The model aggregates signals from various short-term, medium-term, and long-term models, currently indicating a "bearish" outlook [15][75] **Evaluation**: The model offers a holistic view but may dilute the impact of individual signals [15][75] - **Model Name**: HK Stock Turnover-to-Volatility Model **Construction Idea**: This model evaluates the Hong Kong market by analyzing turnover relative to volatility [16][76] **Construction Process**: The model calculates the ratio of turnover to volatility, currently signaling a "bullish" outlook for the Hang Seng Index [16][76] **Evaluation**: The model is effective for medium-term analysis but may require additional factors for short-term predictions [16][76] Model Backtesting Results - **Volume Model**: Short-term signal transitioned to "neutral" for most broad-based indices [12][72] - **Low Volatility Model**: Maintains a "neutral" signal [12][72] - **Intelligent Algorithm Model**: "Bullish" signals for CSI 300 and CSI 500 indices [12][72] - **Limit-Up/Limit-Down Model**: "Neutral" signal for medium-term analysis [13][73] - **Long-Term Momentum Model**: SSE 50 transitioned to "bullish" [14][74] - **A-Share Comprehensive Weapon V3 Model**: Overall "bearish" signal [15][75] - **HK Stock Turnover-to-Volatility Model**: "Bullish" signal for the Hang Seng Index [16][76]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Strategy - **Model Construction Idea**: The core idea is to identify expectation gaps in the market and enhance portfolio returns by incorporating surprise expectation factors (e.g., SUE, ROE YoY growth) [31] - **Model Construction Process**: - Based on the PB-ROE pricing model derived by Wilcox (1984), stocks with significant expectation gaps are selected to form a pool - From this pool, 50 stocks are selected using factors such as standardized unexpected earnings (SUE) and ROE YoY growth to construct the PB-ROE-50 portfolio [31] - **Model Evaluation**: The strategy achieved positive excess returns across different stock pools, demonstrating its effectiveness in capturing market expectation gaps [31] 2. Model Name: Institutional Research Strategy - **Model Construction Idea**: This strategy leverages public and private institutional research data to extract alpha by analyzing the frequency of company visits and stock performance relative to benchmarks before the visits [39] - **Model Construction Process**: - Public Research Selection: Stocks are selected based on the number of visits by public institutions and their relative performance to the CSI 800 index - Private Research Tracking: Stocks are selected based on the number of visits by well-known private institutions and their relative performance to the CSI 800 index [39] - **Model Evaluation**: Both public and private research strategies generated significant positive excess returns, indicating the value of institutional research data in stock selection [39] --- Model Backtesting Results 1. PB-ROE-50 Strategy - **Excess Return (YTD)**: - CSI 500: 3.62% - CSI 800: 9.73% - All Market: 10.36% [35] - **Excess Return (Last Month)**: - CSI 500: 0.59% - CSI 800: 2.91% - All Market: 2.34% [35] - **Absolute Return (YTD)**: - CSI 500: 12.68% - CSI 800: 15.10% - All Market: 20.07% [35] - **Absolute Return (Last Month)**: - CSI 500: 5.88% - CSI 800: 7.02% - All Market: 6.77% [35] 2. Institutional Research Strategy - **Excess Return (YTD)**: - Public Research: 7.03% - Private Research: 18.00% [42] - **Excess Return (Last Month)**: - Public Research: 3.66% - Private Research: 5.58% [42] - **Absolute Return (YTD)**: - Public Research: 12.26% - Private Research: 23.77% [42] - **Absolute Return (Last Month)**: - Public Research: 7.80% - Private Research: 9.80% [42] --- Quantitative Factors and Construction Methods 1. Factor Name: Percentage of Advancing Stocks (Market Sentiment Indicator) - **Factor Construction Idea**: Strong-performing stocks often exhibit a demonstration effect, and the percentage of advancing stocks can reflect market sentiment. A higher percentage indicates optimism, while an overly high percentage may signal overheating [12] - **Factor Construction Process**: - Formula: $ \text{Percentage of Advancing Stocks (N days)} = \frac{\text{Number of CSI 300 stocks with positive returns over N days}}{\text{Total number of CSI 300 stocks}} $ - The indicator is smoothed using two moving averages (N1 = 50, N2 = 35). When the short-term average (fast line) exceeds the long-term average (slow line), it signals a bullish market sentiment [12][13][15] - **Factor Evaluation**: The indicator effectively captures upward opportunities but struggles to avoid risks in declining markets. It may also miss gains during prolonged market exuberance [12] 2. Factor Name: Moving Average Sentiment Indicator - **Factor Construction Idea**: This factor uses an eight-moving-average system to assess the trend state of the CSI 300 index. By assigning values to different ranges of the moving average, the relationship between indicator states and index trends becomes clearer [20] - **Factor Construction Process**: - Calculate the eight moving averages of the CSI 300 closing price (parameters: 8, 13, 21, 34, 55, 89, 144, 233) - Assign values based on the range of the moving averages: - Range 1/2/3: -1 - Range 4/5/6: 0 - Range 7/8/9: 1 - A bullish signal is generated when the number of moving averages below the current price exceeds 5 [20][26] - **Factor Evaluation**: The indicator provides a clear relationship between sentiment states and index trends, aiding in market timing [20] 3. Factor Name: Leverage Ratios (Debt Indicators) - **Factor Construction Idea**: High leverage ratios indicate greater debt pressure and liquidity risks. Three calculation methods (traditional, strict, and relaxed) are used to assess leverage comprehensively [44] - **Factor Construction Process**: - Traditional Leverage Ratio: $ \text{Traditional Leverage Ratio} = \frac{\text{Short-term Debt + Long-term Debt + Bonds Payable}}{\text{Total Assets}} $ - Strict Leverage Ratio: $ \text{Strict Leverage Ratio} = \frac{\text{Short-term Debt + Interest Payable + Financial Liabilities + Short-term Bonds + Lease Liabilities + Long-term Debt + Bonds Payable + Long-term Payables}}{\text{Total Assets}} $ - Relaxed Leverage Ratio: $ \text{Relaxed Leverage Ratio} = \frac{\text{Strict Leverage Components + Other Current Liabilities + Liabilities Held for Sale + Non-current Liabilities Due Within One Year}}{\text{Total Assets}} $ [44] - **Factor Evaluation**: The relaxed leverage ratio provides more opportunities for short positions compared to traditional metrics [44] 4. Factor Name: Financial Cost Burden Ratio - **Factor Construction Idea**: This factor measures the pressure of interest payments on companies by isolating interest expenses from financial costs, providing a clearer view of financial burdens [48] - **Factor Construction Process**: - Formula: $ \text{Financial Cost Burden Ratio} = \frac{\text{Interest Expenses}}{\text{EBIT}} $ [48] - **Factor Evaluation**: The factor effectively highlights companies with high financial stress, aiding in risk identification [48] --- Factor Backtesting Results 1. Percentage of Advancing Stocks - **Latest Value**: Above 70% as of July 31, 2025, indicating high market sentiment [12] 2. Moving Average Sentiment Indicator - **Latest State**: CSI 300 index is in a sentiment boom zone as of July 31, 2025 [20] 3. Leverage Ratios - **Top Stocks by Relaxed Leverage Ratio**: - Example: Dizhiyiyao-U (64.10%), Shenzhouxibao (64.06%), Zhongyida (59.68%) [45] 4. Financial Cost Burden Ratio - **Top Stocks by Financial Cost Burden**: - Example: Liaoning Chengda (241084.42), Yinbaoshanxin (2314.41), Ashichuang (69.43) [49]

- The report includes multiple quantitative models for A-share market timing, such as the "Volume Model," "Low Volatility Model," "Feature Institutional Model," "Feature Volume Model," "Smart Algorithm Model," and "Long-term Momentum Model" [12][13][14][76] - The "Volume Model" indicates a bullish signal for most broad-based indices in the short term [12][76] - The "Low Volatility Model" provides a neutral signal for the short term [12][76] - The "Feature Institutional Model" shows a bearish signal for the short term [12][76] - The "Feature Volume Model" indicates a bullish signal for the short term [12][76] - The "Smart Algorithm Model" shows bullish signals for the CSI 300 and CSI 500 indices in the short term [12][76] - The "Long-term Momentum Model" flips to bullish for the SSE 50 index in the long term [14][78] - The "Comprehensive Weapon V3 Model" and "Comprehensive Guozheng 2000 Model" indicate bullish signals for the A-share market [15][79] - For the Hong Kong market, the "Turnover-to-Volatility Model" provides a bullish signal for the mid-term [16][80] - Backtesting results for the "Double Bottom Pattern" show a weekly return of 1.73%, outperforming the SSE Composite Index by 0.05% [46][53] - Backtesting results for the "Cup-and-Handle Pattern" show a weekly return of 2.87%, outperforming the SSE Composite Index by 1.2% [46][47]

Core Insights - Quantitative private equity has shown significant performance differentiation in the market this year, with small and mid-cap strategies outperforming large-cap strategies, reflecting structural changes in the market that deeply impact different investment strategies [1] Group 1: Performance of Quantitative Strategies - As of July 11, the Quantitative 1000 index enhancement strategy has performed the best, with Lingjun Investment leading at a 36.79% year-to-date return, while other institutions like Xinhong Tianhe, Longqi, and Qilin also surpassed the 30% mark [3] - The Quantitative 500 index enhancement strategy also performed well, with Xinhong Tianhe and Abama's related products achieving over 30% year-to-date returns [3] - In contrast, the Quantitative 300 index enhancement strategy lagged, with the highest year-to-date return at only 19.13% [3] - The Quantitative stock selection strategy demonstrated the strongest profitability, with Xiaoyong's strategy leading the market at 46.26% year-to-date return, and other institutions like Ruishengming and Ziwuyou also exceeding 40% [3] Group 2: Market Trends and Structural Changes - The market this year has clearly favored small and mid-cap stocks, providing abundant sources of excess returns for related quantitative strategies [4] - The CSI 1000 index, primarily composed of small and mid-cap stocks, has significantly outperformed the CSI 300 index, benefiting from policies favoring specialized and innovative enterprises [4] - The lower research coverage of small and mid-cap stocks leads to more pricing discrepancies, creating opportunities for quantitative strategies to capture excess returns [4] - Increased market volatility has also created a favorable environment for quantitative strategies, as small and mid-cap stocks typically exhibit higher volatility, allowing strategies to profit from capturing liquidity premiums [4] Group 3: Scale Effects and Strategy Differentiation - Billion-yuan private equity firms exhibit clear scale advantages in index enhancement strategies, dominating the top 20 in both the Quantitative 1000 and 500 index enhancement strategies [5] - Large institutions, with assets under management exceeding 5 billion, achieved an average return of 18.30% in their index enhancement products, with a staggering 99.25% of products generating positive excess returns [5] - Medium-sized private equity firms had an average return of 17.30%, while small firms saw their average return drop to 16.41% [5] - The performance differentiation among quantitative private equity firms is increasingly evident, with over a 15 percentage point difference between the highest and the 20th return in the Quantitative 1000 index enhancement strategy [5]