Quantitative Models and Construction Methods Model Name: Residual Volatility Domain Management - **Model Construction Idea**: The model aims to manage excess drawdowns by segmenting stocks based on residual volatility, relaxing constraints on low-volatility stocks while tightening constraints on high-volatility stocks. This approach seeks to optimize the risk-return profile of the portfolio[3][11][44] - **Model Construction Process**: 1. Define residual volatility as the unexplained portion of stock returns after accounting for country, industry, and style factors 2. Use the following formula for stock returns: $$r_{n}=f_{c}+\sum_{i}X_{n i}f_{i}+\sum_{s}X_{n s}f_{s}+u_{n}$$ - \(r_{n}\): Stock excess return - \(f_{c}\): Country factor - \(f_{i}\): Industry factor - \(f_{s}\): Style factor - \(u_{n}\): Residual term[34][36] 3. Optimize portfolio weights using the following formula: $$w_{i}=\lambda^{-1}F_{i}\,/\,\sigma_{i}$$ - \(w_{i}\): Active weight of stock \(i\) - \(F_{i}\): Risk-adjusted signal - \(\sigma_{i}\): Residual volatility of stock \(i\)[45][46] 4. Segment stocks into three groups based on residual volatility (low, medium, high) using the 30% and 70% quantiles 5. Apply different weight constraints for each group: - Low-volatility stocks: [-0.2%, 0.4%] - Medium-volatility stocks: [-0.2%, 0.3%] - High-volatility stocks: [-0.1%, 0.2%][62] - **Model Evaluation**: The model effectively reduces portfolio drawdowns while maintaining or improving excess returns, especially during high-volatility periods[11][44][68] --- Model Backtesting Results Residual Volatility Domain Management Model - **Annualized Excess Return**: 4.66% (compared to 4.30% for the benchmark portfolio) - **Maximum Excess Drawdown**: -6.78% (compared to -10.47% for the benchmark portfolio) - **Information Ratio (IR)**: 1.15 (compared to 0.82 for the benchmark portfolio)[67] --- Quantitative Factors and Construction Methods Factor Name: Alpha Factors (e.g., Growth, Momentum, Surprise) - **Factor Construction Idea**: Alpha factors are designed to predict stock returns by capturing specific characteristics such as growth, momentum, and earnings surprises. These factors are often correlated with style factors like volatility, liquidity, and market capitalization[1][17][18] - **Factor Construction Process**: 1. Use individual factors as signals to generate excess returns while constraining industry, style, and stock deviations 2. Calculate the correlation between alpha factors and style factors to understand their intrinsic relationships 3. Example correlations: - Growth factor positively correlates with momentum (23.9%) and volatility (23.3%) - Surprise factor positively correlates with momentum (35.3%) but negatively correlates with valuation (-9.7%)[17][19] - **Factor Evaluation**: Alpha factors show strong correlations with style factors, but their predictive power for stock returns is relatively weak and unstable, especially during high-volatility periods[20][43][68] --- Factor Backtesting Results Alpha Factors - **IC Mean**: Mostly within ±10%, indicating limited predictive power for stock returns[20][22] - **Correlation with Style Factors**: - Growth factor: Momentum (23.9%), Volatility (23.3%) - Surprise factor: Momentum (35.3%), Valuation (-9.7%)[19] Residual Volatility Factor - **Residual Volatility and Market Cap**: Negative correlation observed, with smaller-cap stocks exhibiting higher residual volatility[38][40] - **Residual Volatility Predictability**: - Residual return predictability: Low (1.9% median correlation with a 1-day lag) - Residual volatility predictability: High (66% median correlation with a 21-day lag)[49][51] --- Key Observations and Insights - Residual volatility plays a critical role in managing excess drawdowns, with high-volatility stocks contributing disproportionately to portfolio risk[3][44][56] - Alpha factors exhibit weak and unstable predictive power for stock returns, particularly during periods of market turbulence[20][43][68] - Segmenting stocks by residual volatility and applying differentiated constraints can effectively balance risk and return, as demonstrated by the improved performance of the optimized portfolio[62][67][68]

Core Insights - The investment approach of index-enhanced quantitative strategies is evolving, with expectations for AI to play a significant role in optimizing trading signals and automating transactions [1][2] - The concept of alpha factors is central to quantitative strategies, which require a combination of factors to perform well across different market conditions [1][2] - The company has a strong track record in the index enhancement field, with a focus on developing effective alpha factors to achieve superior investment results [2][3] Group 1 - The company is exploring the potential of AI in finance, anticipating that advanced models could eventually handle stock selection and trading autonomously [2] - The firm has accumulated significant experience in index enhancement since 2010, leading to a positive reputation and a large client base, with over 610,000 holders in its index-enhanced funds as of June 30 [2] - A new index-enhanced fund based on the CSI 500 index is set to be launched, emphasizing mid-cap growth sectors such as electronics and pharmaceuticals, aligning with economic transformation trends [3] Group 2 - The new fund aims to create a stock portfolio that outperforms the existing index by leveraging strong and stable alpha factors, thereby generating consistent excess returns [3] - The strategy involves constructing a new stock combination that differentiates itself from the index's constituent stocks while maintaining similar characteristics [3] - The company emphasizes the importance of a comprehensive system of factors rather than relying on individual factors, likening successful strategies to a forest rather than a single tree [2]

Core Insights - The domestic index investment has seen significant growth, with investors increasingly seeking clear risk-return characteristics [1] - The company, Xingzheng Global Fund, has rapidly developed a diverse range of index-enhanced products, leveraging its expertise in quantitative investment [1] Group 1: Quantitative Investment Team Development - The quantitative research team has been established over the past two years, developing over 2,000 alpha factors and a modular quantitative management system [2] - The team operates in a collaborative environment that encourages sharing of results and strategies, enhancing overall productivity [2] - The focus is on achieving full automation in the quantitative system, ensuring stable operations and enhancing modularity and fault tolerance [2][3] Group 2: Alpha Factor Exploration - The core focus of the quantitative strategy is on the exploration of alpha factors, which are crucial for generating excess returns while closely tracking index characteristics [4] - The team employs a systematic approach to develop and optimize alpha factors, ensuring their effectiveness is tested over longer periods [4][5] - Continuous iteration and optimization of alpha factors are conducted to adapt to market changes and incorporate the latest machine learning models [4] Group 3: Product Line Expansion - The company has recognized the growth potential in index-enhanced funds, which currently represent only a fraction of the scale of equity ETFs [6] - Recent product launches include various index-enhanced funds, particularly in the Hong Kong market, where the company has developed proprietary risk models and factor libraries [7] - The company aims to build a comprehensive product line that includes various styles such as quality, value, and growth to meet diverse investor needs [8]

Core Viewpoint - The domestic index investment has seen significant growth, with investors increasingly seeking clear risk-return characteristics. Xingzheng Global Fund is leveraging its expertise in index-enhanced investment to build a diverse range of products covering large-cap, mid-cap, and Hong Kong stocks [1]. Group 1: Development of Quantitative Investment Team - Since joining Xingzheng Global Fund over two years ago, the quantitative research team has developed over 2,000 alpha factors and established a modular quantitative management system, supported by ample GPU resources [2]. - The company fosters a collaborative environment where team members share results and strategies, enhancing the overall effectiveness of the quantitative models [2]. - The team has achieved a high level of automation in its quantitative system, from data cleaning to portfolio generation, aided by strong technical support from the IT department [3]. Group 2: Focus on Alpha Factor Exploration - The core focus of the quantitative strategy is on the exploration of alpha factors, which are crucial for generating excess returns while closely tracking index characteristics [4]. - The team employs a systematic approach to develop and optimize alpha factors, including self-research and referencing external factor libraries and academic reports [4]. - Continuous iteration and optimization of alpha factors are essential, with the team integrating the latest machine learning models and conducting in-depth research on sell-side analyst expectations [4][5]. Group 3: Expansion of Index-Enhanced Product Line - Xingzheng Global Fund has identified significant growth potential in index-enhanced funds, currently only a fraction of the size of equity ETFs [7]. - The company has successfully launched several index-enhanced products, including the CSI 500 Index Enhanced strategy, which is noted for its maturity and ability to leverage alpha factors for excess returns [7][8]. - Future plans include expanding the product line to cover various styles such as quality, value, and growth, to meet diverse investor needs [8].

Core Viewpoint - The rapid development of index investment in China has led to a growing demand for clear risk-return characteristics among investors, prompting the company to enhance its index-enhanced investment products across various styles and markets [1][4]. Group 1: Quantitative System Development - The company has established a relatively complete quantitative research team, developing over 2,000 alpha factors and a modular quantitative management system [1][2]. - The quantitative system has achieved a high level of automation, from raw data cleaning to target portfolio generation, supported by the company's strong IT capabilities [2][3]. - The focus is on the exploration of alpha factors, which are crucial for generating excess returns while closely tracking index characteristics [3][4]. Group 2: Product Line Expansion - The company has launched several index-enhanced products, including the CSI 500 index enhancement strategy, which is one of the most mature strategies in operation [4][5]. - There is a significant potential for growth in index-enhanced funds, as their current scale is only about one-tenth of the equity ETF market, which exceeds 3 trillion yuan [3][4]. - The company aims to build a comprehensive product line that includes various styles such as quality, value, and growth strategies to meet diverse investor needs [5].

Core Insights - The article highlights the career journey of Hu Di, who has developed a unique perspective on quantitative investment strategies through her experiences in both international and domestic markets [1][5] - Hu Di emphasizes the importance of continuous innovation in quantitative investment, focusing on refining models and exploring new data sources and algorithms to adapt to changing market conditions [1][2] Investment Strategy - Hu Di leads a team at Morgan Asset Management (China) that focuses on a "Core Beta + Enhanced Alpha" framework, aiming to create a product system that balances efficiency and resilience while pursuing long-term risk premiums and stable excess returns [1][5] - The team has identified around 200 commonly used factors, with 40% being fundamental factors, 40% price-volume factors, and the remaining 20% derived from machine learning and alternative factor systems [2][3] Factor Analysis - The team employs a multi-dimensional approach to factor analysis, enhancing traditional methods to capture excess returns more effectively by considering various dimensions of factors like reversal [3][4] - Machine learning techniques are integrated into the factor generation process, leading to a "logic-driven + data-enhanced" paradigm that spans factor discovery, return prediction, and portfolio optimization [3][4] Market Adaptation - Hu Di notes that the impact of U.S. tariff policies on China has diminished over time, and the focus has shifted to diversifying export markets and mitigating external shocks through policy measures [5][6] - The introduction of the Morgan CSI A500 Enhanced Strategy ETF is positioned as a response to current market conditions, prioritizing leading companies in emerging industries while reducing exposure to traditional sectors [6][7] Risk Management - The investment strategy emphasizes strict control over industry and style risks, ensuring that the sources of returns remain independent and minimizing excessive exposure [4][8] - Hu Di advocates for a "core + satellite" asset allocation approach, where core positions are based on stable beta assets adjusted for volatility, while satellite positions target growth or policy-driven assets [8][9] Product Development - The timing of product launches is critical, with successful ETFs launched in 2023 and 2024 showing significant growth in scale, indicating effective market entry strategies [9] - The company prioritizes investor education alongside product offerings, aiming to provide tailored asset allocation solutions based on individual risk preferences and return expectations [9]