Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviations, and turnover limits. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints [41][42]. - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector [42]. - **Constraints**: - **Style Exposure**: \( X \) is the style factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure [42]. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviations [42]. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviations relative to the benchmark [42]. - **Constituent Weight Control**: \( B_b \) is a binary vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights [42]. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \) [42]. - **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \) [43]. - The MFE portfolio is constructed monthly, and historical returns are backtested with a 0.3% transaction cost applied to both sides of trades [45]. - **Model Evaluation**: The MFE approach is effective in testing factor efficacy under realistic constraints, ensuring that factors deemed "effective" are more likely to contribute to actual portfolio performance [41][42]. --- Factor Construction and Methodology 1. Factor Name: Book-to-Price Ratio (BP) - **Factor Construction Idea**: Measures valuation by comparing a company's book value to its market value [17]. - **Factor Construction Process**: - Formula: \( \text{BP} = \frac{\text{Net Assets}}{\text{Market Value}} \) [17]. 2. Factor Name: Single-Quarter ROE - **Factor Construction Idea**: Evaluates profitability by calculating the return on equity for a single quarter [17]. - **Factor Construction Process**: - Formula: \( \text{ROE} = \frac{\text{Net Profit (Quarterly)} \times 2}{\text{(Beginning Equity + Ending Equity)}} \) [17]. 3. Factor Name: Single-Quarter Revenue Growth (YoY) - **Factor Construction Idea**: Measures growth by comparing quarterly revenue to the same quarter in the previous year [17]. - **Factor Construction Process**: - Formula: \( \text{Revenue Growth (YoY)} = \frac{\text{Revenue (Current Quarter)} - \text{Revenue (Same Quarter Last Year)}}{\text{Revenue (Same Quarter Last Year)}} \) [17]. 4. Factor Name: DELTAROA - **Factor Construction Idea**: Captures changes in return on assets (ROA) compared to the same quarter in the previous year [17]. - **Factor Construction Process**: - Formula: \( \text{DELTAROA} = \text{ROA (Current Quarter)} - \text{ROA (Same Quarter Last Year)} \) [17]. 5. Factor Name: Non-Liquidity Shock - **Factor Construction Idea**: Measures the impact of illiquidity on stock prices over a 20-day period [17]. - **Factor Construction Process**: - Formula: \( \text{Non-Liquidity Shock} = \frac{\text{Absolute Daily Returns}}{\text{Average Trading Volume (20 Days)}} \) [17]. --- Factor Backtesting Results 1. Single-Quarter ROE - **Performance**: - Recent Week: 1.07% - Recent Month: 1.80% - Year-to-Date: 2.56% - Historical Annualized: 5.38% [19]. 2. DELTAROA - **Performance**: - Recent Week: 0.95% - Recent Month: 0.09% - Year-to-Date: 1.15% - Historical Annualized: 4.99% [19]. 3. Single-Quarter Revenue Growth (YoY) - **Performance**: - Recent Week: 0.94% - Recent Month: 0.78% - Year-to-Date: 1.50% - Historical Annualized: 4.65% [19]. 4. Non-Liquidity Shock - **Performance**: - Recent Week: 0.54% - Recent Month: -0.21% - Year-to-Date: -0.49% - Historical Annualized: 0.34% [19]. 5. Book-to-Price Ratio (BP) - **Performance**: - Recent Week: -0.87% - Recent Month: -0.34% - Year-to-Date: -0.49% - Historical Annualized: 2.56% [19]. --- Model Backtesting Results 1. CSI 300 Enhanced Portfolio - Weekly Excess Return: 1.90% - Year-to-Date Excess Return: 5.84% [5][14]. 2. CSI 500 Enhanced Portfolio - Weekly Excess Return: 1.94% - Year-to-Date Excess Return: 3.61% [5][14]. 3. CSI 1000 Enhanced Portfolio - Weekly Excess Return: 1.13% - Year-to-Date Excess Return: 4.68% [5][14]. 4. CSI A500 Enhanced Portfolio - Weekly Excess Return: -0.90% - Year-to-Date Excess Return: 3.71% [5][14].

Core Viewpoint - The article discusses the "Four Quadrant Monthly Industry Rotation Strategy," which utilizes four dimensions: economic conditions, sentiment, technical analysis, and macroeconomic factors to construct investment strategies. The strategy has shown strong performance since its inception in 2018, with annualized excess returns of 13.85% for single-factor multi-strategies and 7.28% for composite factor strategies by the end of 2025 [2]. Summary by Sections Performance Metrics - By 2025, the single-factor multi-strategy portfolio achieved an absolute return of 36%, with an excess return of 12.29% compared to an equal-weight benchmark. The composite factor strategy portfolio had an absolute return of 38.1% and an excess return of 14.38%. Both portfolios had a monthly excess return win rate of 58.3% [2]. Factor Analysis - In 2025, factor effectiveness showed significant differentiation. The macroeconomic factor performed exceptionally well with an annualized excess return of 23.8% and a monthly win rate of 67%. In contrast, the economic conditions and sentiment factors contributed modestly with excess returns of 4.1% and 7.1%, respectively. The technical factor underperformed with an excess return of -1.1%, consistent with historical trends during market uptrends [2]. Market Environment Interaction - The performance of factors is closely linked to market conditions. In rising markets, macroeconomic, economic conditions, and sentiment factors drive industry performance, while the technical factor serves a defensive role in declining markets. Future research aims to incorporate market environment predictions into the strategy to achieve more stable excess returns [3]. ETF Strategy Performance - Since 2014, a strategy portfolio based on ETFs has achieved an annualized excess return of 11.4% relative to the CSI 800 index, with an information ratio of 1.01 [3].

- The valuation factor demonstrated strong performance this month, with its long-short portfolio returns leading across all stock pools, followed by volatility and expected valuation factors[2][3][22] - Small-cap and growth factors showed negative average long-short portfolio returns, with small-cap factors performing well in IC but experiencing significant drawdowns in the CSI 300 stock pool, impacting overall performance[3][22] - Expected valuation factors exhibited robust positive returns across stock pools, while other expectation-related factors showed weaker performance, with the "surprise" factor only achieving positive returns in the CSI 500 stock pool[2][3] - Profitability factors performed well in the CSI 300 and CSI 500 stock pools but experienced drawdowns in other pools, while growth factors showed limited positive returns only in the CSI 500 stock pool[2][3] - Momentum and turnover factors displayed strong positive returns across stock pools, with turnover factors showing significant IC performance in the CSI 300 stock pool[2][3][22] - Defensive factors such as dividend yield and valuation factors are recommended for core portfolio allocation due to their higher certainty and lower risk exposure in the current geopolitical and market environment[1][14]

- The valuation factor has been weak this month, with significant pullbacks at the beginning of the month[2] - Growth, profitability, small-cap, and reversal factors have shown relative strength, presenting positive returns outside the CSI 500 constituent stock pool[2] - The small-cap factor has been leading in terms of average long-short returns this month, mainly due to its advantage in the CSI 300 constituent stock pool[3] - The excess returns of quantitative index-enhanced funds have shown marginal recovery this month, with the CSI 300 index-enhanced funds leading in excess returns[4] - The median returns of CSI 1000 and CSI A500 index-enhanced funds have returned to near the benchmark[4] - The excess returns of CSI 500 index-enhanced funds have slightly recovered from the beginning of the month but still show significant excess pullbacks overall[4] - The average long-short returns of factors such as volatility, turnover rate, and other defensive volume-price factors have been under pressure, showing significant pullbacks on average[3] - The excess returns of public quantitative index-enhanced funds are tracked based on the performance of their net value curves, with the CSI 300 index-enhanced funds showing the most significant excess returns this month[4] - The performance of classic factors such as valuation, growth, profitability, small-cap, reversal, volatility, turnover rate, and expectation factors is tracked in different stock pools, including CSI 300, CSI 500, CSI 1000, and all A-shares[9] - The performance of large category factors and their sub-factors is displayed, with blue-marked large category factors and their sub-factors used to synthesize them[9] - The Rank IC values of factors in the CSI 300 constituent stock pool are tracked monthly to evaluate their effectiveness[10] - The Rank IC values of factors in the CSI 500 constituent stock pool are tracked monthly to evaluate their effectiveness[11] - The Rank IC values of factors in the CSI 1000 constituent stock pool are tracked monthly to evaluate their effectiveness[12] - The Rank IC values of factors in the all A-shares stock pool are tracked monthly to evaluate their effectiveness[13] - The performance of long-short combinations of factors is evaluated by constructing industry-neutral long-short combinations based on the scores of large category factors in different stock pools[14] - The average returns of long-short combinations of factors in different stock pools are tracked monthly[15][16][18][20][21][22] - The excess returns of quantitative index-enhanced funds tracking CSI 300, CSI 500, CSI 1000, and CSI A500 indices are tracked, with the top 10 representative funds presented for each category[23][25][31][37][42] - The excess returns and maximum drawdowns of quantitative index-enhanced funds are tracked and presented in bubble charts, with the size of the bubbles representing the fund size as of Q4 2025[26][32][38][43][46]

- Profitability and turnover rate factors showed positive performance across all stock pools, delivering positive returns this month[1][10] - Valuation factors demonstrated positive returns outside the CSI 300 stock pool, while growth factors performed well in CSI 300 and CSI 500 but experienced pullbacks in other pools[1][10] - Small-cap factors showed mixed results, achieving positive returns in CSI 300 and CSI 1000 stock pools but pulling back in others[1][10] - Expectation-related factors, such as the "exceed expectations" factor, only delivered positive returns in the CSI 300 stock pool, while "expected valuation" and "expected growth rate" factors showed varied performance across different pools[1][10] - Turnover rate factor led the average long-short portfolio returns this month, especially in CSI 1000 and All-A stock pools[2][15] - Expected net profit growth factor ranked second in long-short portfolio returns, followed by profitability and growth factors, which also delivered positive average returns[2][15] - Other factors, including reversal, valuation, and small-cap factors, showed negative average long-short portfolio returns[2][15] - CSI 1000 index-enhanced funds maintained leading excess returns this month, with median performance significantly ahead of other index-enhanced funds[3][25] - CSI 1000 index-enhanced funds also led in excess returns year-to-date, followed by CSI A500 index-enhanced funds[3][25]

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure (MFE) Portfolio - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under realistic constraints, such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures that the factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[38][39]. - **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \begin{array}{ll} max & f^{T}\ w \\ s.t. & s_{l}\leq X(w-w_{b})\leq s_{h} \\ & h_{l}\leq H(w-w_{b})\leq h_{h} \\ & w_{l}\leq w-w_{b}\leq w_{h} \\ & b_{l}\leq B_{b}w\leq b_{h} \\ & \mathbf{0}\leq w\leq l \\ & \mathbf{1}^{T}\ w=1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, \( f^{T}w \) is the weighted exposure, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure[39]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation[39]. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation[39]. 4. **Constituent Weight Control**: \( B_b \) is a 0-1 vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights[39]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights[39]. 6. **Full Investment**: Ensures the portfolio is fully invested (\( \mathbf{1}^{T}w = 1 \))[40]. - **Implementation**: - Constraints are set monthly, and the MFE portfolio is rebalanced accordingly. - Historical returns are calculated, and transaction costs of 0.3% (double-sided) are deducted to evaluate the portfolio's performance relative to the benchmark[42]. - **Model Evaluation**: The MFE portfolio effectively identifies factors that can predict returns under realistic constraints, making it a robust tool for factor validation[38][39]. --- Quantitative Factors and Construction Methods 1. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings, to capture earnings surprises[15]. - **Factor Construction Process**: $ SUE = \frac{\text{Actual Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ - **Parameters**: - Actual Net Profit: Reported quarterly net profit. - Expected Net Profit: Consensus analyst forecast for the quarter. - Standard Deviation: Variability in analyst forecasts[15]. 2. Factor Name: Delta ROA (DELTAROA) - **Factor Construction Idea**: Tracks the change in return on assets (ROA) compared to the same quarter in the previous year to capture profitability trends[15]. - **Factor Construction Process**: $ \Delta ROA = \text{ROA}_{\text{current quarter}} - \text{ROA}_{\text{same quarter last year}} $ - **Parameters**: - ROA: \( \frac{\text{Net Income} \times 2}{\text{Average Total Assets}} \)[15]. 3. Factor Name: Standardized Unexpected Revenue (SUR) - **Factor Construction Idea**: Measures the deviation of actual revenue from expected revenue, standardized by the standard deviation of expected revenue, to capture revenue surprises[15]. - **Factor Construction Process**: $ SUR = \frac{\text{Actual Revenue} - \text{Expected Revenue}}{\text{Standard Deviation of Expected Revenue}} $ - **Parameters**: - Actual Revenue: Reported quarterly revenue. - Expected Revenue: Consensus analyst forecast for the quarter. - Standard Deviation: Variability in analyst forecasts[15]. --- Factor Backtesting Results 1. **Performance in CSI 300 Universe** - **Top-Performing Factors (1 Week)**: Single-quarter ROA (1.09%), Standardized Unexpected Revenue (0.73%), Single-quarter Revenue Growth (0.71%)[17]. - **Underperforming Factors (1 Week)**: Specificity (-0.93%), 3-Month Reversal (-0.53%), 1-Month Volatility (-0.46%)[17]. 2. **Performance in CSI 500 Universe** - **Top-Performing Factors (1 Week)**: Standardized Unexpected Revenue (1.07%), Single-quarter Net Profit Growth (1.00%), Standardized Unexpected Earnings (0.99%)[19]. - **Underperforming Factors (1 Week)**: 3-Month Volatility (-1.08%), BP (-0.28%), 1-Month Volatility (-1.14%)[19]. 3. **Performance in CSI 1000 Universe** - **Top-Performing Factors (1 Week)**: Standardized Unexpected Revenue (1.07%), Standardized Unexpected Earnings (1.00%), Single-quarter Revenue Growth (0.90%)[21]. - **Underperforming Factors (1 Week)**: 1-Month Volatility (-1.14%), 3-Month Volatility (-1.08%), 3-Month Reversal (-1.02%)[21]. 4. **Performance in CSI A500 Universe** - **Top-Performing Factors (1 Week)**: Single-quarter ROA (1.14%), Delta ROA (1.12%), Delta ROE (1.02%)[23]. - **Underperforming Factors (1 Week)**: Specificity (-0.65%), Non-Liquidity Shock (-0.64%), 1-Month Volatility (-0.62%)[23]. 5. **Performance in Public Fund Heavyweight Index** - **Top-Performing Factors (1 Week)**: Delta ROA (1.12%), Expected PEG (0.94%), Standardized Unexpected Earnings (0.99%)[25]. - **Underperforming Factors (1 Week)**: 3-Month Volatility (-0.60%), 1-Month Volatility (-0.62%), 1-Month Reversal (-0.37%)[25].

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, alongside monitoring the performance of common stock selection factors across different stock selection spaces [12][13][16] - Guosen JinGong's index enhancement portfolios are constructed based on three main components: return prediction, risk control, and portfolio optimization. These portfolios are benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500 [13][15] - The MFE (Maximized Factor Exposure) portfolio is used to test the effectiveness of individual factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for style, industry, stock weight deviations, and other constraints. The formula for the optimization model is: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style exposure (`X`), industry exposure (`H`), stock weight deviation (`w`), and component stock weight limits (`B_b`) [40][41][42] - The factor library includes over 30 factors categorized into valuation, reversal, growth, profitability, liquidity, corporate governance, and analyst dimensions. Examples include BP (Net Asset/Market Cap), single-quarter EP (Net Profit/Market Cap), and EPTTM (TTM Net Profit/Market Cap) [17][18] - Factor performance varies across different stock selection spaces. For CSI 300, factors like single-quarter EP, EPTTM, and expected EPTTM performed well recently, while factors like three-month volatility and expected net profit QoQ performed poorly [19][20] - For CSI 500, factors such as single-quarter ROE, DELTAROE, and single-quarter EP showed strong performance recently, whereas factors like one-year momentum and three-month reversal underperformed [21][22] - In the CSI 1000 space, factors like standardized unexpected earnings, EPTTM, and single-quarter EP performed well, while factors like non-liquidity impact and three-month institutional coverage lagged [23][24] - For CSI A500, factors such as expected EPTTM, single-quarter ROE, and expected PEG showed strong performance, while factors like one-year momentum and expected net profit QoQ underperformed [25][26] - In the public fund heavy index space, factors like expected PEG, expected EPTTM, and single-quarter EP performed well recently, while factors like one-month reversal and one-month volatility performed poorly [27][28] - Public fund index enhancement products are tracked for their excess returns relative to benchmarks. For CSI 300 products, the highest weekly excess return was 1.02%, and the lowest was -0.37%, with a median of 0.08% [29][33] - CSI 500 products showed a weekly excess return range of 1.87% to -0.44%, with a median of 0.38% [34][35] - CSI 1000 products had a weekly excess return range of 1.06% to -0.43%, with a median of 0.38% [36][37] - CSI A500 products showed a weekly excess return range of 0.73% to -0.19%, with a median of 0.17% [38][39]