Core Viewpoint - The report provides a comprehensive analysis of market performance, highlighting the performance of various factors and strategies across different stock pools, indicating potential investment opportunities and trends in the market [4][5][6][7][8][9][10]. Factor Performance - In the large-cap factor performance for the week of December 8-12, 2025, the size factor, beta factor, and non-linear market cap factor achieved positive returns of 1.18%, 0.91%, and 0.82% respectively, while the BP factor and liquidity factor recorded negative returns of -0.55% and -0.38% [4]. - In the CSI 300 stock pool, the best-performing factors included total asset growth rate (2.05%), quarterly ROA (1.71%), and turnover rate relative volatility (1.59%), while the worst-performing factors were logarithmic market cap (-1.00%), downside volatility ratio (-1.10%), and large net inflow (-1.14%) [5]. - In the CSI 500 stock pool, the top factors were quarterly EPS (1.61%), total asset growth rate (1.39%), and momentum spring factor (1.22%), with the worst being price-to-sales ratio TTM inverse (-2.49%), downside volatility ratio (-2.55%), and price-to-book ratio (-3.06%) [5]. - In the liquidity 1500 stock pool, the best factors were total asset growth rate (2.25%), quarterly revenue growth rate (2.05%), and quarterly ROA year-on-year (1.92%), while the worst were price-to-earnings ratio (-0.90%), downside volatility ratio (-0.95%), and price-to-book ratio (-0.97%) [5]. Industry Factor Performance - The net asset growth rate factor performed well in the telecommunications, comprehensive, and coal industries, while the net profit growth rate factor excelled in the telecommunications sector [6]. - The earnings per share factor showed strong performance in the telecommunications industry, and the residual volatility factor performed well in telecommunications and commercial trade sectors [6]. Strategy Performance - The PB-ROE-50 combination achieved significant excess returns across stock pools, with the CSI 500 stock pool gaining an excess return of 0.30%, the CSI 800 stock pool gaining 1.60%, and the overall market stock pool gaining 1.59% [7]. - Public fund research selection strategies and private fund research tracking strategies both yielded positive excess returns, with public fund strategies outperforming the CSI 800 by 1.79% and private fund strategies outperforming by 2.77% [8]. - The block trading combination experienced a relative excess return drawdown against the CSI All Index, with an excess return of -0.95% [9]. - The directed issuance combination also faced a relative excess return drawdown against the CSI All Index, with an excess return of -1.50% [10].

Group 1: Factor Performance Tracking - The large-cap style dominates the market, with significant positive returns from size, beta, and non-linear market capitalization factors, yielding 1.18%, 0.91%, and 0.82% respectively, while BP and liquidity factors posted negative returns of -0.55% and -0.38% [20][21] - In the CSI 300 stock pool, the best-performing factors include total asset growth rate (2.05%), quarterly ROA (1.71%), and turnover rate relative volatility (1.59%), while the worst-performing factors are logarithmic market cap (-1.00%), downside volatility ratio (-1.10%), and large order net inflow (-1.14%) [12][13] - In the CSI 500 stock pool, the top factors are quarterly EPS (1.61%), total asset growth rate (1.39%), and momentum spring factor (1.22%), with the poorest performers being the inverse of price-to-sales ratio (-2.49%), downside volatility ratio (-2.55%), and price-to-book ratio (-3.06%) [14][15] Group 2: Industry Factor Performance - The net asset growth rate factor performed well in the telecommunications, comprehensive, and coal industries, while the net profit growth rate factor excelled in the telecommunications sector [22] - The price-to-earnings (EP) factor showed strong performance in the telecommunications industry, while the BP factor underperformed across most sectors [22] - The logarithmic market cap factor performed well in the comprehensive, telecommunications, agriculture, forestry, animal husbandry, and electronics sectors, while the residual volatility factor excelled in telecommunications and commercial trade [22] Group 3: Combination Tracking - The PB-ROE-50 combination achieved significant excess returns across various stock pools, with excess returns of 0.30% in the CSI 500 stock pool, 1.60% in the CSI 800 stock pool, and 1.59% in the overall market stock pool [24] - The public fund research stock selection strategy and private equity research tracking strategy both generated positive excess returns, with the public fund strategy yielding 1.79% and the private equity strategy yielding 2.77% relative to the CSI 800 [3] - The block trading combination experienced a relative excess return drawdown of -0.95% compared to the CSI All Share Index, while the targeted issuance combination also faced a drawdown of -1.50% [3]

- The report constructs 10 style factors based on the BARRA model, including valuation factor, growth factor, profitability factor, size factor, Beta factor, momentum factor, liquidity factor, volatility factor, non-linear size factor, and leverage factor[16][17][19] - The construction process for style factors involves detailed formulas, such as the valuation factor (BP = Book to Price = Shareholder equity/Market capitalization), growth factor (SGRO = Sales growth rate derived from regression of past five fiscal years' revenue), profitability factor (ETOP = Earnings-to-price ratio = Net profit TTM/Market capitalization), and others[16][17] - The style factors are tested using weekly rebalancing on the CSI All Share Index (000985.SH) with no transaction fees considered[16][17] - Beta factor, momentum factor, and volatility factor showed strong performance recently, with weekly long-short returns of 4.54%, 4.34%, and 3.81%, respectively[19] - The report tracks 53 stock selection factors across valuation, growth, quality, size, reversal, momentum, liquidity, volatility, dividend, corporate governance, and technical categories[21][22] - Examples of stock selection factors include BP (Book to Price = Shareholder equity/Market capitalization), single-quarter EP (Net profit/Market capitalization), and 240-day momentum (cumulative return excluding the last 20 days)[22] - The construction of single-factor portfolios uses a neutral constraint method to maximize factor exposure while maintaining neutrality in industry and style exposures[62][64][65] - Single-quarter ROE, single-quarter ROA, and single-quarter net profit margin factors performed well across multiple stock pools, such as CSI 300, CSI 500, CSI 800, and CSI 1000[24][28][33][38] - The report evaluates index-enhanced portfolios for CSI 300, CSI 500, CSI 800, CSI 1000, and CSI 300 ESG stock pools using composite factors constructed via rolling 1-year Rank ICIR weighting[56][59][61] - CSI 300 enhanced portfolio achieved weekly excess returns of 0.33%, monthly excess returns of 1.05%, and annual excess returns of 13.02%[59][60] - CSI 1000 enhanced portfolio showed the highest annual excess returns of 15.68% among all portfolios[60] - The ESG-enhanced portfolio under CSI 300 stock pool achieved weekly excess returns of 0.59%, monthly excess returns of 1.09%, and annual excess returns of 7.35%[60] - The optimization model for portfolio construction maximizes exposure to target factors while maintaining neutrality in industry and style exposures, with constraints on stock weights, short selling, and full investment[62][64][65] - The model uses the following formula: $Max$$w^{\prime}$$X_{target}$ $s.t.$$(w-w_{b})^{\prime}X_{ind}=0$ $(w-w_{b})^{\prime}$$X_{Beta}=0$ $|w-w_{b}|\leq1\%$ $w\geq0$ $w^{\prime}B=1$ $w^{\prime}1=1$[62][63][64]

Group 1: Core Insights - 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 [1] - As of October 2025, the private fund scale reached 22.05 trillion yuan, marking a significant increase of 1.31 trillion yuan from September, surpassing the previous historical peak of 20.81 trillion yuan in September 2023 [3] - The surge in private equity is primarily driven by private securities investment funds, which saw a record high of 7.01 trillion yuan, with a month-on-month increase of 1.04 trillion yuan, reflecting a growth rate of 17.42% [3] Group 2: Market Trends and Opportunities - The A-share market has experienced significant growth this year, with various sectors, including precious metals and industrial metals, performing well, creating a favorable environment for private equity [3] - The "Golden Yangtze" initiative aims to provide comprehensive services to private fund managers, facilitating connections between long-term capital and quality managers [6] - The commodity market is viewed as having strategic allocation opportunities due to factors such as resource scarcity, low supply growth, and a recovering global economy, which are expected to drive demand for commodities [9][10] Group 3: Investment Strategies and Insights - Factor investing is entering a new era, with a noticeable decline in factor momentum and a divergence in factors across different market segments [7][8] - Private fund managers emphasize the importance of focusing on undervalued sectors and long-term value creation, suggesting a shift towards low-valuation areas for better returns [11][12] - The ongoing technological revolution, particularly in AI, is seen as a critical opportunity for China's economic growth, with a focus on innovation and investment in leading companies [13]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: The PB-ROE-50 combination is designed to capture excess returns by selecting stocks with favorable Price-to-Book (PB) and Return on Equity (ROE) characteristics[25] - **Model Construction Process**: The model selects stocks based on PB and ROE metrics, with adjustments for market capitalization and rebalancing on a periodic basis. The detailed construction process is referenced in a prior report[25][26] - **Model Evaluation**: The model demonstrated significant excess returns across different stock pools during the analyzed period, indicating its effectiveness in identifying profitable investment opportunities[25][26] 2. Model Name: Institutional Research Combination - **Model Construction Idea**: This model leverages the insights from public and private institutional research to identify stocks with potential for excess returns[28] - **Model Construction Process**: The model uses a strategy that tracks stocks selected by public and private institutional research, with adjustments made relative to the CSI 800 index. The methodology is detailed in prior reports[28] - **Model Evaluation**: The model showed strong performance, achieving positive excess returns, which highlights the value of institutional research in stock selection[28][29] 3. Model Name: Block Trade Combination - **Model Construction Idea**: This model identifies stocks with high block trade activity and low volatility, which are likely to outperform[32] - **Model Construction Process**: The model is constructed based on the "high transaction, low volatility" principle, using monthly rebalancing. The detailed methodology is outlined in a prior report[32] - **Model Evaluation**: The model effectively captures excess returns, demonstrating the informational value of block trade activity[32][33] 4. Model Name: Private Placement Combination - **Model Construction Idea**: This model focuses on stocks involved in private placement events, aiming to capture event-driven excess returns[38] - **Model Construction Process**: The model is constructed by considering factors such as market capitalization, rebalancing cycles, and position control, with the private placement announcement date as the key event. The detailed methodology is provided in a prior report[38] - **Model Evaluation**: The model showed a slight negative excess return during the analyzed period, suggesting limited effectiveness in the current market environment[38][39] --- Model Backtesting Results 1. PB-ROE-50 Combination - Weekly excess return: CSI 500: 0.47%, CSI 800: 1.54%, All-market: 1.59%[25][26] - Year-to-date excess return: CSI 500: 2.06%, CSI 800: 15.14%, All-market: 18.32%[26] 2. Institutional Research Combination - Weekly excess return: Public research: 3.63%, Private research: 3.32%[28][29] - Year-to-date excess return: Public research: 16.50%, Private research: 15.79%[29] 3. Block Trade Combination - Weekly excess return: 2.93%[32][33] - Year-to-date excess return: 39.24%[33] 4. Private Placement Combination - Weekly excess return: -0.01%[38][39] - Year-to-date excess return: -3.70%[39] --- Quantitative Factors and Construction Methods 1. Factor Name: Momentum Spring Factor - **Factor Construction Idea**: Captures momentum effects by identifying stocks with strong recent performance trends[12][13] - **Factor Construction Process**: The factor is calculated based on recent price movements, adjusted for market and sector effects. The exact formula is not provided in the report[12][13] - **Factor Evaluation**: Demonstrated strong positive returns across multiple stock pools, indicating its effectiveness in capturing momentum effects[12][13] 2. Factor Name: Early Morning Return Factor - **Factor Construction Idea**: Measures the return performance of stocks during the early trading hours[12][13] - **Factor Construction Process**: The factor is calculated by isolating returns during the early trading hours and adjusting for market and sector influences[12][13] - **Factor Evaluation**: Showed consistent positive returns, highlighting its potential as a predictive factor[12][13] 3. Factor Name: Single Quarter ROA - **Factor Construction Idea**: Focuses on the return on assets (ROA) for a single quarter to identify efficient companies[12][13] - **Factor Construction Process**: The factor is derived from quarterly financial reports, specifically the ratio of net income to total assets, adjusted for market and sector effects[12][13] - **Factor Evaluation**: Demonstrated positive returns, indicating its utility in identifying fundamentally strong companies[12][13] --- Factor Backtesting Results 1. Momentum Spring Factor - Weekly return: CSI 300: 2.26%, CSI 500: 2.56%, Liquidity 1500: 3.25%[12][13][19] - Monthly return: CSI 300: 3.00%, CSI 500: 0.55%, Liquidity 1500: -0.79%[13][15][19] - Year-to-date return: CSI 300: 8.96%, CSI 500: 5.35%, Liquidity 1500: 9.79%[13][15][19] 2. Early Morning Return Factor - Weekly return: CSI 300: 1.88%, CSI 500: 2.66%, Liquidity 1500: 3.21%[12][13][19] - Monthly return: CSI 300: 1.48%, CSI 500: 1.81%, Liquidity 1500: 0.66%[13][15][19] - Year-to-date return: CSI 300: 4.90%, CSI 500: 4.18%, Liquidity 1500: 11.40%[13][15][19] 3. Single Quarter ROA - Weekly return: CSI 300: 1.90%, CSI 500: -0.90%, Liquidity 1500: 0.68%[12][13][19] - Monthly return: CSI 300: 1.19%, CSI 500: 0.47%, Liquidity 1500: -1.20%[13][15][19] - Year-to-date return: CSI 300: 20.46%, CSI 500: -3.19%, Liquidity 1500: 10.47%[13][15][19]

- The report tracks the performance of various style factors, including market capitalization, non-linear market capitalization, profitability, momentum, volatility, and beta factors[2] - The construction process involves creating long-short portfolios at the end of each month, going long on the top 10% of stocks with the highest factor values and shorting the bottom 10% with the lowest factor values, with equal weighting[16] - The recent performance shows strong long positions in market capitalization, non-linear market capitalization, and profitability factors, while momentum, volatility, and beta factors had strong short positions[16] Factor Performance Tracking - The fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[3][4][5] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[3][4][5] - GRU factors had weak long-short performance, with the barra1d model showing some pullback, while other models had insignificant returns[3][4][5] CSI 300 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with growth and surprise growth factors performing negatively, while static financial factors performed positively[4] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[4] - GRU factors had mixed long-short performance, with the barra1d model showing significant pullback, while the barra5d and close1d models performed strongly[4] CSI 500 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[5] - Technical factors had negative long-short returns, with short-term factors showing more significant performance, favoring low volatility and low momentum stocks[5] - GRU factors had good long-short performance, with the open1d and barra1d models showing slight pullback, while the close1d and barra5d models performed strongly[5] CSI 1000 Component Stocks Factor Performance - Fundamental factors showed similar long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[6] - Technical factors had negative long-short returns, favoring low volatility and low momentum stocks[6] - GRU factors had strong long-short performance, with the barra1d model showing some pullback, while the close1d and open1d models performed strongly[6] Long-Only Portfolio Performance - The GRU long-only portfolio showed weak performance, with various models underperforming the CSI 1000 index by 0.54% to 1.12%[7] - The barra5d model performed strongly year-to-date, outperforming the CSI 1000 index by 8.55%[7] - The multi-factor portfolio showed weak performance, underperforming the CSI 1000 index by 0.47%[7] Factor Performance Metrics - Momentum factor: -1.93% (one week), -8.36% (one month), -24.78% (six months), 19.89% (year-to-date), 17.64% (three-year annualized), 17.58% (five-year annualized)[17] - Volatility factor: 1.82% (one week), -2.33% (one month), 16.17% (six months), 6.56% (year-to-date), 7.58% (three-year annualized), -11.09% (five-year annualized)[17] - Beta factor: -1.54% (one week), 5.68% (one month), 0.60% (six months), 19.29% (year-to-date), 7.50% (three-year annualized), 8.99% (five-year annualized)[17] - Liquidity factor: 0.91% (one week), 42.89% (one month), 9.98% (six months), 12.24% (year-to-date), -20.32% (three-year annualized), -24.87% (five-year annualized)[17] - Valuation factor: 0.82% (one week), 0.46% (one month), 0.14% (six months), 3.77% (year-to-date), 14.92% (three-year annualized), 5.46% (five-year annualized)[17] - Growth factor: 0.71% (one week), 2.28% (one month), 2.34% (six months), 3.16% (year-to-date), 49.33% (three-year annualized), -4.78% (five-year annualized)[17] - Leverage factor: 0.35% (one week), 2.37% (one month), 3.68% (six months), 15.17% (year-to-date), 6.40% (three-year annualized), 1.98% (five-year annualized)[17] - Profitability factor: 0.49% (one week), -0.64% (one month), 7.01% (six months), 14.10% (year-to-date), 3.12% (three-year annualized), 0.51% (five-year annualized)[17] - Non-linear market capitalization factor: 4.22% (one week), 0.44% (one month), 3.16% (six months), -32.83% (year-to-date), -38.38% (three-year annualized), -30.29% (five-year annualized)[17] - Market capitalization factor: 5.39% (one week), 0.59% (one month), 2.18% (six months), -37.92% (year-to-date), -40.48% (three-year annualized), -34.25% (five-year annualized)[17]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 - **Model Construction Idea**: This model aims to combine the Price-to-Book (PB) ratio and Return on Equity (ROE) to create a portfolio of 50 stocks[23] - **Model Construction Process**: The model selects stocks based on their PB and ROE values, aiming to balance valuation and profitability. The portfolio is rebalanced periodically to maintain the desired characteristics[23] - **Model Evaluation**: The model's performance is tracked across different stock pools, showing its effectiveness in various market conditions[23] - **Model Test Results**: - **CSI 500**: Weekly excess return -1.30%, YTD excess return 1.58%, weekly absolute return -7.01%, YTD absolute return 20.95%[24] - **CSI 800**: Weekly excess return -2.09%, YTD excess return 13.40%, weekly absolute return -6.31%, YTD absolute return 30.05%[24] - **All Market**: Weekly excess return -1.46%, YTD excess return 16.48%, weekly absolute return -6.44%, YTD absolute return 36.70%[24] 2. Model Name: Institutional Research Portfolio - **Model Construction Idea**: This model tracks the stock selection strategies of public and private institutional research[25] - **Model Construction Process**: The model is constructed based on the stock picks of institutional investors, adjusting the portfolio based on their research and investment decisions[25] - **Model Evaluation**: The model's performance is evaluated by comparing its returns to the CSI 800 index[25] - **Model Test Results**: - **Public Research Stock Selection**: Weekly excess return -1.91%, YTD excess return 12.42%, weekly absolute return -6.14%, YTD absolute return 28.92%[26] - **Private Research Tracking**: Weekly excess return -3.65%, YTD excess return 12.06%, weekly absolute return -7.80%, YTD absolute return 28.51%[26] 3. Model Name: Block Trade Portfolio - **Model Construction Idea**: This model leverages the information from block trades, focusing on stocks with high transaction amounts and low volatility[29] - **Model Construction Process**: The portfolio is constructed based on the "high transaction, low volatility" principle, with monthly rebalancing[29] - **Model Evaluation**: The model's performance is tracked relative to the CSI All Share Index[29] - **Model Test Results**: - **Weekly excess return**: -2.84%[30] - **YTD excess return**: 35.29%[30] - **Weekly absolute return**: -7.75%[30] - **YTD absolute return**: 58.77%[30] 4. Model Name: Private Placement Portfolio - **Model Construction Idea**: This model analyzes the event effects of private placements to identify investment opportunities[35] - **Model Construction Process**: The portfolio is constructed around the announcement dates of private placements, considering factors like market capitalization and rebalancing cycles[35] - **Model Evaluation**: The model's performance is evaluated relative to the CSI All Share Index[35] - **Model Test Results**: - **Weekly excess return**: -1.42%[36] - **YTD excess return**: -3.89%[36] - **Weekly absolute return**: -6.40%[36] - **YTD absolute return**: 12.80%[36] Quantitative Factors and Construction Methods 1. Factor Name: Intraday Volatility and Trading Volume Correlation - **Factor Construction Idea**: This factor measures the correlation between intraday volatility and trading volume[12] - **Factor Construction Process**: The factor is calculated by correlating the intraday price volatility with the trading volume over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.23%[13] - **Monthly return**: 3.14%[13] - **Annual return**: -2.31%[13] - **10-year return**: 22.87%[13] 2. Factor Name: ROE Stability - **Factor Construction Idea**: This factor measures the stability of a company's Return on Equity over time[12] - **Factor Construction Process**: The factor is calculated by assessing the variance in ROE over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.14%[13] - **Monthly return**: 1.82%[13] - **Annual return**: 0.95%[13] - **10-year return**: 3.68%[13] 3. Factor Name: Downside Volatility Proportion - **Factor Construction Idea**: This factor measures the proportion of downside volatility in the total volatility of a stock[12] - **Factor Construction Process**: The factor is calculated by dividing the downside volatility by the total volatility over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.13%[13] - **Monthly return**: 2.09%[13] - **Annual return**: -6.82%[13] - **10-year return**: 30.09%[13] 4. Factor Name: Single Quarter Total Asset Gross Profit Margin - **Factor Construction Idea**: This factor measures the gross profit margin of a company's total assets for a single quarter[14] - **Factor Construction Process**: The factor is calculated by dividing the gross profit by the total assets for a single quarter[14] - **Factor Evaluation**: The factor shows positive returns in the CSI 500 stock pool[14] - **Factor Test Results**: - **Weekly return**: 1.82%[15] - **Monthly return**: -0.84%[15] - **Annual return**: 6.56%[15] - **10-year return**: 82.05%[15] 5. Factor Name: Net Profit Margin TTM - **Factor Construction Idea**: This factor measures the trailing twelve months (TTM) net profit margin of a company[16] - **Factor Construction Process**: The factor is calculated by dividing the net profit by the total revenue for the trailing twelve months[16] - **Factor Evaluation**: The factor shows positive returns in the Liquidity 1500 stock pool[16] - **Factor Test Results**: - **Weekly return**: 1.82%[17] - **Monthly return**: -0.58%[17] - **Annual return**: 1.94%[17] - **10-year return**: -17.46%[17] Factor Backtest Results CSI 300 Stock Pool - **Intraday Volatility and Trading Volume Correlation**: Weekly return 1.23%, monthly return 3.14%, annual return -2.31%, 10-year return 22.87%[13] - **ROE Stability**: Weekly return 1.14%, monthly return 1.82%, annual return 0.95%, 10-year return 3.68%[13] - **Downside Volatility Proportion**: Weekly return 1.13%, monthly return 2.09%, annual return -6.82%, 10-year return 30.09%[13] CSI 500 Stock Pool - **Single Quarter Total Asset Gross Profit Margin**: Weekly return 1.82%, monthly return -0.84%, annual return 6.56%, 10-year return 82.05%[15] Liquidity 1500 Stock Pool - **Net Profit Margin TTM**: Weekly return 1.82%, monthly return -0.58%, annual return 1.94%, 10-year return -17.46%[17]

Quantitative Models and Construction - **Model Name**: FIFAA (Flexible Indeterminate Factor Asset Allocation) **Model Construction Idea**: Combines quantitative academic rigor with subjective forward-looking flexibility, using historical data (ex-post) and subjective views (ex-ante) to derive asset-factor exposure and optimize portfolio allocation[2][15][74] **Model Construction Process**: 1. **Factor Selection**: Select tradable, low-correlation macroeconomic factors with clear economic logic. Factors include global equities (economic growth), U.S. Treasuries (interest rate/defensive), credit, inflation protection, and currency protection[15][16][20] 2. **Asset-Factor Mapping**: Use LASSO regression to calculate historical beta exposure, then adjust using subjective views derived from professional investor interviews. Subjective single-factor beta is converted into multi-factor beta using matrix transformations[16][35][39] - Formula for historical beta regression: $$y\,=\,X W\,=\,w_{1}x_{1}+\cdots+w_{n}x_{n}$$[32] Loss function for Ridge regression: $$L(w)\,=\,\sum_{i=1}^{n}(y_{i}-\sum w_{j}x_{i j})+\lambda\sum w_{j}^{2}$$[33] Subjective beta transformation: $$\beta_{f}^{*}\,=\,(1\quad F_{f})\,{\binom{\beta_{f}}{\beta_{!f}}}$$[35] $$\beta=F^{-1}\beta^{*}$$[39] 3. **Factor Exposure Optimization**: Optimize factor exposure based on subjective risk/reward judgment or quantitative methods[17] 4. **Portfolio Optimization**: Maximize expected returns while minimizing factor exposure differences. Constraints include absolute exposure differences ≤ 10% of the larger exposure value[44] - Optimization formula: $$m a x(w^{T}r)$$ $$s.\,t.\,w^{T}I\;=\;1$$ $$a b s(w^{T}\beta_{i}-w^{T}\beta_{j})<0.1*m a x(a b s(w^{T}\beta_{i}),a b s(w^{T}\beta_{j}))$$[44] 5. **Rebalancing**: Allow slight deviations in factor exposure to reduce transaction costs and frequency[18] **Model Evaluation**: Provides higher returns and risk-adjusted performance compared to equal-weighted portfolios. Simplified implementation demonstrates practical feasibility[2][74] Model Backtesting Results - **Default Parameters**: - Historical beta optimization: Annualized return 13.63%, annualized volatility 11.47%, max drawdown -18.97%[49][50] - Adjusted beta optimization: Annualized return 15.43%, annualized volatility 16.46%, max drawdown -33.86%[49][50] - Equal-weight portfolio: Annualized return 10.32%, annualized volatility 11.91%, max drawdown -25.27%[49][50] - **Different Adjustment Coefficients**: - Coefficient range (0.1-0.5): Annualized return varies between 15.16%-15.43%, annualized volatility between 15.73%-16.46%, max drawdown between -30.51% to -37.50%[57][59] - **Different Expected Returns**: - Neutral expected return scenarios (5%, 10%, 15%): Annualized return ranges from 13.63%-15.90%, annualized volatility from 11.47%-16.45%, max drawdown from -18.97% to -36.67%[69][70][71][72] Quantitative Factors and Construction - **Factor Name**: Macroeconomic Factors (Economic Growth, Interest Rate, Inflation) **Factor Construction Idea**: Represent macroeconomic trends using tradable indices to ensure simplicity and reduce calculation errors[15][20][30] **Factor Construction Process**: - Economic growth: Represented by stock indices (e.g., Wind All A Index, S&P 500)[30] - Interest rate: Represented by bond indices (e.g., China Bond Treasury Wealth Index)[30] - Inflation: Composite of commodity indices (e.g., Nanhua Industrial, Agricultural, Energy, and Black Metal indices)[20][30] **Factor Evaluation**: Tradable and low-correlation factors ensure practical applicability and reduce subjective judgment uncertainty[15][16][20] Factor Backtesting Results - **Macroeconomic Factor Correlation Matrix**: - Wind All A vs. S&P 500: 0.15 - Wind All A vs. China Bond Treasury: -0.12 - Wind All A vs. Commodity Composite: 0.30[28][30] - **Factor Performance**: - Economic growth factor (Wind All A): Annualized return 13.63%-15.43% depending on optimization method[49][50][69] - Inflation factor (Commodity Composite): Adjusted beta optimization shows higher returns during inflationary periods[49][50][69]

Group 1: Core Insights - FTSE Russell anticipates that European sovereign wealth funds and pension funds may increase their allocation to China in the next 6 to 12 months, viewing China as an independent asset class rather than just part of emerging markets [1][6] - Global investors are shifting from defensive cash and short-duration bonds to risk assets, including developed and emerging market equities and bonds, with a notable flow of new funds into Chinese and Greater China assets [2][6] - The upgrade of Vietnam's market from frontier to secondary emerging market status by FTSE Russell is expected to facilitate easier access for global investors, although the impact on other emerging markets is minimal [1][8] Group 2: European Investor Concerns - European institutional investors are facing a complex environment shaped by structural, macroeconomic, and regulatory challenges, including high stock valuations, interest rate uncertainty, and geopolitical tensions [4] - There is a growing interest among European asset managers in diversifying their portfolios away from overexposed positions in the US and Europe, with a focus on China's leadership in technology and artificial intelligence [6][4] Group 3: Investment Trends - The demand for Chinese indices, particularly those focused on technology, artificial intelligence, and electric vehicles, is increasing among global investors, with significant inflows into products like the Invesco China Technology ETF [7] - The transition of Vietnam to a secondary emerging market is expected to attract approximately $1 to $1.5 billion in passive fund inflows, with active management inflows projected to be 4 to 5 times that amount [9][8] Group 4: ESG Investment Evolution - There is a notable shift in investor behavior towards more integrated and thematic approaches to ESG investing, with a focus on understanding how ESG factors impact investment returns [11] - Regulatory frameworks in Europe, such as the Corporate Sustainability Reporting Directive (CSRD), are enhancing corporate disclosure standards, which is crucial for ESG investment transparency [11]

- The report constructs an ETF hotspot trend strategy based on the highest and lowest price trends of ETFs, selecting those with both highest and lowest prices in an upward trend. Further, it constructs a support-resistance factor based on the relative steepness of the regression coefficients of the highest and lowest prices over the past 20 days, and selects the top 10 ETFs with the highest turnover rate in the past 5 days/20 days to construct a risk parity portfolio[27][30] - The report tracks the performance of various style factors, noting that the value factor recorded a positive return of 2.36%, the leverage factor recorded a positive return of 1.08%, and the volatility factor slightly rebounded with a return of 0.19%[41][42] - The report evaluates the performance of different alpha factors, highlighting that the quick ratio factor had the best performance with a weekly excess return of 1.32%, followed by the debt-asset ratio factor with a weekly excess return of 1.21%, and the earnings variability over 5 years factor with a weekly excess return of 1.04%[44][46][47] - The ETF hotspot trend strategy recorded a cumulative excess return over the CSI 300 index since the beginning of the year[28][29] - The value factor achieved a weekly return of 2.36%, the leverage factor achieved a weekly return of 1.08%, and the volatility factor achieved a weekly return of 0.19%[41][42] - The quick ratio factor achieved a weekly excess return of 1.32%, the debt-asset ratio factor achieved a weekly excess return of 1.21%, and the earnings variability over 5 years factor achieved a weekly excess return of 1.04%[44][46][47]