Quantitative Models and Construction GRU Model - **Model Name**: GRU (Generalized Rotation Unit) Model - **Model Construction Idea**: The GRU model is designed to capture industry rotation trends and optimize stock selection by leveraging short-term and long-term market dynamics[3][4][5] - **Model Construction Process**: - The GRU model is applied across different stock pools (e.g., CSI 300, CSI 500, CSI 1000) to evaluate multi-factor performance - It incorporates multiple sub-models such as `barra1d`, `barra5d`, `open1d`, and `close1d` to assess short-term and long-term factor returns - The model evaluates the long-short performance of stocks by ranking them based on factor scores and constructing portfolios with the top 10% (long) and bottom 10% (short) stocks[3][4][5] - **Model Evaluation**: The GRU model demonstrates strong performance in capturing positive long-short returns, particularly in the `barra5d` and `close1d` sub-models, which show consistent strength across different stock pools[4][5][6] --- Quantitative Factors and Construction Style Factors (Barra Factors) - **Factor Names**: Beta, Size, Momentum, Volatility, Non-linear Size, Valuation, Liquidity, Profitability, Growth, Leverage[14][15] - **Factor Construction Ideas**: - These factors are designed to capture specific market characteristics such as risk, size, valuation, and growth potential - They are derived from historical price data, financial metrics, and analyst forecasts - **Factor Construction Process**: - **Beta**: Historical beta - **Size**: Natural logarithm of total market capitalization - **Momentum**: Mean of historical excess return series - **Volatility**: Weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility - **Non-linear Size**: Cubic transformation of size - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted combination of monthly, quarterly, and annual turnover rates - **Profitability**: Weighted combination of analyst forecasted earnings-to-price ratio, inverse of price-to-cash flow ratio, inverse of trailing twelve-month price-to-earnings ratio, and forecasted growth rates - **Growth**: Weighted combination of earnings growth rate and revenue growth rate - **Leverage**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio[15] - **Factor Evaluation**: Valuation, leverage, and volatility factors showed strong long performance, while beta, momentum, and liquidity factors performed well on the short side during the week[16] Fundamental Factors - **Factor Names**: Static Financial Indicators, Growth Indicators, Surprise Growth Indicators - **Factor Construction Ideas**: These factors are derived from financial statements and are designed to capture company fundamentals such as profitability, growth, and operational efficiency - **Factor Construction Process**: - Financial indicators are calculated using trailing twelve-month (TTM) data - Growth indicators include metrics like revenue growth and earnings growth - Surprise growth indicators measure deviations from analyst expectations[19][20][22] - **Factor Evaluation**: Static financial indicators showed significant negative returns, while growth and surprise growth indicators had mixed performance. Low-growth stocks outperformed across all stock pools[20][22][27] Technical Factors - **Factor Names**: Short-term Momentum, Short-term Volatility, Medium-term Momentum, Medium-term Volatility, Median Absolute Deviation - **Factor Construction Ideas**: These factors are derived from historical price data and are designed to capture price trends and volatility - **Factor Construction Process**: - Momentum factors are calculated as the average excess return over specific time windows (e.g., 20-day, 60-day, 120-day) - Volatility factors are calculated as the standard deviation of returns over specific time windows - Median absolute deviation measures the dispersion of returns around the median[20][24][26] - **Factor Evaluation**: Short-term momentum and volatility factors showed positive returns, while medium-term factors generally underperformed. Low-volatility and low-momentum stocks were favored[20][24][26] --- Model Backtesting Results GRU Model - **Open1d**: Weekly excess return of 1.10%, monthly return of 1.55%, and YTD return of 7.19%[34] - **Close1d**: Weekly excess return of 1.84%, monthly return of 3.56%, and YTD return of 6.24%[34] - **Barra1d**: Weekly excess return of 0.45%, monthly return of -0.26%, and YTD return of 5.19%[34] - **Barra5d**: Weekly excess return of 1.77%, monthly return of 4.51%, and YTD return of 9.23%[34] - **Multi-factor Portfolio**: Weekly excess return of 0.75%, monthly return of 1.16%, and YTD return of 1.65%[34] Style Factors - **Beta**: Weekly return of -5.67%, monthly return of -10.16%, and YTD return of 21.16%[17] - **Momentum**: Weekly return of 4.04%, monthly return of -9.28%, and YTD return of 18.32%[17] - **Liquidity**: Weekly return of -2.91%, monthly return of 6.35%, and YTD return of 11.43%[17] - **Size**: Weekly return of 2.67%, monthly return of 18.45%, and YTD return of 41.09%[17] - **Non-linear Size**: Weekly return of 1.80%, monthly return of -7.67%, and YTD return of 35.55%[17] - **Growth**: Weekly return of 1.59%, monthly return of -2.69%, and YTD return of 2.47%[17] - **Profitability**: Weekly return of 1.13%, monthly return of 0.03%, and YTD return of 15.36%[17] - **Volatility**: Weekly return of 1.35%, monthly return of -1.31%, and YTD return of 4.82%[17] - **Leverage**: Weekly return of 1.36%, monthly return of 3.48%, and YTD return of 15.46%[17] - **Valuation**: Weekly return of 1.45%, monthly return of 4.88%, and YTD return of 2.98%[17] Fundamental Factors - **Net Profit Surprise Growth**: Weekly return of 3.62%, monthly return of -2.63%, and YTD return of 37.43%[23] - **Operating Profit Margin Surprise Growth**: Weekly return of 1.77%, monthly return of 1.65%, and YTD return of 2.46%[23] - **ROA Surprise Growth**: Weekly return of 0.73%, monthly return of 0.19%, and YTD return of 11.22%[23] - **ROA Growth**: Weekly return of -0.70%, monthly return of 0.10%, and YTD return of 25.43%[23] Technical Factors - **20-day Momentum**: Weekly return of 1.88%, monthly return of 5.00%, and YTD return of -5.82%[21][24] - **60-day Momentum**: Weekly return of -10.66%, monthly return of -0.05%, and YTD return of -5.73%[21][24] - **120-day Momentum**: Weekly return of 0.13%, monthly return of 0.13%, and YTD return of -3.53%[21][24] - **20-day Volatility**: Weekly return of 0.08%, monthly return of -0.79%, and YTD return of 11.01%[21][24] - **60-day Volatility**: Weekly return of -0.67%, monthly return of -3.35%, and YTD return of 7.37%[21][24] - **120-day Volatility**: Weekly return of 0.50%, monthly return of -4.08%, and YTD return of 11.22%[21][24]

Investment Strategy Overview - The report emphasizes a fundamental-driven style factor switch, awaiting confirmation of trend movements for 2026 [1][4][8] Factor Performance - Growth factors have shown strong performance, while low volatility and momentum factors have retreated, indicating a rapid rotation among market sectors and themes this year [4][10][12] - Year-to-date performance of various factors in different indices shows growth at 37.93% in CSI 300, while low volatility and liquidity factors have negative returns [10][12] Macro Quantitative Framework - The macroeconomic cycle has shifted more frequently in the past three years, with leading indicators suggesting a downturn in the first half of 2025, followed by a recovery signal towards the end of the year [4][38][43] - The liquidity indicators have shown a weak overall trend, with market trading rates rising, indicating a correction in liquidity for the second half of 2025 [50][54][60] - Credit indicators have shown a preference for expansion in the first half of 2025, transitioning to contraction by November [65][66] 2026 Equity Quantitative Outlook - The report anticipates a fundamental-driven style switch, with a focus on economic fundamentals becoming the key driver, transitioning from liquidity concerns to economic and inflation factors [4][86][91] - Market trends indicate a shift to a consolidation phase since August, with an increasing probability of trend confirmation from late October [92][97] - Emotional indicators have shown a supportive trend since July, with overall sentiment remaining warm and moderate [102][105] Industry Rotation and Focus - The speed of industry rotation has slowed down in 2025, with potential for a main trend to form, particularly in sectors with lower crowding and emerging trends [106][112] - Key sectors to watch include electronics and computing, which have shown lower crowding and are in a trend initiation phase [113][116]

Group 1: Report Industry Investment Rating - Not provided in the content Group 2: Core Views of the Report - The report focuses on constructing a convertible bond multi - factor system from a style dimension, aiming to establish a framework covering 115 factors and five types of style factors (valuation, momentum, volatility, liquidity, and volume - price) based on a "behavior - valuation - volatility" three - dimensional logic, and obtain excess returns while keeping the investment portfolio market - neutral through non - linear combination optimization, providing quantitative strategy support for asset allocation [1] - In the environment of low interest rates and asset shortage, the shift of funds to the "fixed income +" strategy drives the structural prosperity of the convertible bond market. The market has entered a stage of "structural differentiation - complex pricing - refined strategies", and the multi - factor system has significant applicability in the convertible bond market [2] - The style factor framework provides a path for convertible bond research. Different convertible bonds can be regarded as recombinations of style factors, and depicting convertible bonds from the style dimension helps understand market structure and rotation rules and provides a framework for constructing a multi - factor bond - selection system [3] Group 3: Summary According to Relevant Catalogs 1. Introduction - In 2025, driven by the equity market, the convertible bond market continued to strengthen, showing characteristics of active trading, stable stock, and structural differentiation. As of November 4, 2025, the average daily trading volume in the convertible bond market was about 66 billion yuan, with a high - volatility and high - central - value feature. The market had 415 convertible bonds in circulation, with a total scale of about 595.7 billion yuan. The price distribution was biased towards the medium - high price range, indicating an increase in the performance of the underlying stocks and market risk appetite [12] - Quantitative methods are more applicable in the convertible bond market. The T + 0 mechanism and high - frequency trading structure provide rich price - volume information, and the stock - bond hybrid characteristics of convertible bonds enable the multi - factor system to be applied in five dimensions: valuation, momentum, volatility, liquidity, and volume - price correlation [13] 2. Recent Expansion of the Convertible Bond Market 2.1 Convertible Bonds: "Hybrid Assets" with Both Stock and Bond Attributes - Convertible bonds can be converted into the issuer's stocks under specific conditions, with both "bond" and "stock" characteristics. Their price is composed of the pure bond value and the option value of conversion. The market has expanded rapidly, and its concentrated and active trading provides a basis for multi - factor model testing [15][16] - Compared with stocks, convertible bonds have bond - based downside protection, stock - based upside potential, medium - level volatility between stocks and bonds, and more flexible trading rules. Quantitative methods are highly applicable in the convertible bond market due to high - frequency data support, effective behavioral factors, Alpha - providing stock - bond linkage factors, and the advantage of trading systems [17][19] 2.2 Necessity of Strategies Driven by the Expansion of "Fixed Income +" under Low Interest Rates - In the environment of low interest rates and asset shortage, the shift of funds to the "fixed income +" strategy drives the prosperity of the convertible bond market, creating a situation of strong demand, tight supply, and a rising pricing center, which provides a long - term foundation for quantitative and systematic strategies [18] - As of Q3 2025, the scale of public funds has increased steadily, with a pattern of "expansion of equity products and contraction of bond funds". The "fixed income +" products, especially secondary bond funds, have expanded significantly. The demand for convertible bond allocation has increased, while the supply has slowed down. The market has formed a pattern of "high valuation - high position - low supply", and convertible bonds have shown stronger resilience in the volatile market [20][21][23] 3. Convertible Bonds and Equities from the Perspective of Style Factors 3.1 Style Factors: Systematic Depiction of the Equity Market from the Barra System - Style factors are core dimensions for depicting the common characteristics and systematic differences of assets in the multi - factor model system. The Barra model decomposes asset returns into style factor returns and idiosyncratic returns, and in the Barra framework, style factors in the equity market include valuation, growth, momentum, volatility, scale, leverage, and liquidity, which jointly form the "style map" of the equity market and provide a path for convertible bond research [28][29][32] 3.2 Style - Based Structure of the Convertible Bond Market: Division into Stock - Oriented, Balanced, and Bond - Oriented Types - Convertible bonds can be divided into stock - oriented, balanced, and bond - oriented types based on style factors. Stock - oriented convertible bonds are dominated by stock characteristics, with high elasticity and large fluctuations; balanced convertible bonds have a balanced risk - return profile, with both stock and bond features; bond - oriented convertible bonds are dominated by bond characteristics, with strong defensive properties. This division provides a basis for factor stratification and strategy construction [33] 3.3 The Stock - Dominant Nature of the Convertible Bond Market under the Slow - Bull Expectation - The convertible bond market has shifted from being bond - dominated to stock - dominant. The high correlation between the convertible bond index and the CSI 1000 and CSI 2000 indices indicates that the market is currently in a stock - driven stage. The reasons include the increase in the concentration of high - priced convertible bonds, the change in the capital structure, and the support of the macro - liquidity and interest - rate environment [35][36][37] 3.4 Introduction to the Multi - Factor Convertible Bond System: From Five Style Factors to the Systematic Back - Testing Framework - A multi - dimensional system covering 115 daily - frequency factors is constructed based on the price - volume characteristics and clause structure of the convertible bond market, including valuation, momentum, volatility, liquidity, and volume - price correlation factors. These factors form a relatively complete convertible bond quantitative framework [41][42] - Daily - frequency data is chosen as the core sample dimension for constructing the convertible bond multi - factor system. It can capture short - term market changes, maintain signal effectiveness, and balance signal sensitivity and execution feasibility [44][45] 4. Convertible Bond Multi - Factor System and Back - Testing Results 4.1 Historical Performance of Five Types of Style Factors - Based on the back - testing results from 2021 to 2025, the five types of style factors can be divided into three categories: the leading group includes momentum and volatility factors with high annualized excess returns; the stable group includes the liquidity factor; the medium group includes the five - factor equally - weighted composite factor, valuation factor, and volume - price correlation factor [47] - The excellent performance of the momentum factor is due to its ability to capture the "trend effect" in the convertible bond market. The volatility factor has high risk - adjusted returns and good risk control, which may be related to risk - pricing compensation and avoiding the "volatility trap" [48] 4.2 Portfolio Optimization Logic - Single - factor investment in convertible bonds has shortcomings such as high return volatility, insufficient factor synergy, significant trading - cost erosion, and style - deviation risk. A non - linear optimization framework is used for portfolio construction, with the goal of maximizing risk - adjusted returns under multiple constraints such as market value, industry, style, and individual bond weights [51][53][54] - Back - testing results show that the liquidity factor performs best under market neutrality since 2021, followed by volume - price and momentum factors. After optimization, the excess returns of most style factors decline significantly, indicating that high returns in the convertible bond market often come from style deviation and high turnover [56] 4.3 Follow - up Optimization Logic - The follow - up optimization should change the way of synthesizing large - category factors from "equally - weighted synthesis" to "weighted synthesis based on historical performance". Specific methods include weighted synthesis based on risk indicators, weighted synthesis based on return indicators, and direct optimization by eliminating ineffective or redundant sub - factors [58][59] 5. Follow - up Strategy Optimization 5.1 Event - Driven: Seizing the Certainty Opportunities in Clause Games - The event - driven strategy uses issuers' active actions such as downward - revision of conversion prices and share repurchases to obtain excess returns. It is necessary to establish a systematic event database and real - time monitoring mechanism [60][61][62] 5.2 Mispricing: Exploiting the Cognitive Bias of Option Value - The mispricing strategy is based on the market's mis - evaluation of the option value of convertible bonds. It involves constructing a theoretical value model, identifying pricing deviations, and constructing a market - neutral portfolio to earn value - regression returns [63]

Quantitative Models and Construction 1. Model Name: Barra Style Factors - **Model Construction Idea**: The Barra style factors are designed to capture various market characteristics such as valuation, momentum, volatility, and growth, among others, to explain stock returns[14][15] - **Model Construction Process**: - The factors are calculated based on specific financial and market metrics. For example: - **Beta**: Historical beta - **Size**: Natural logarithm of total market capitalization - **Momentum**: Weighted average of historical excess return series - **Volatility**: Weighted average of historical residual return volatility - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted average of turnover ratios (monthly, quarterly, yearly) - **Profitability**: Weighted average of various profitability metrics such as analyst forecasted earnings-to-price ratio, inverse of price-to-cash flow ratio, and inverse of trailing twelve-month price-to-earnings ratio - **Growth**: Weighted average of earnings growth rate and revenue growth rate - **Leverage**: Weighted average of market leverage, book leverage, and debt-to-asset ratio[15] - **Model Evaluation**: The model is widely used in the industry to capture systematic risk factors and explain stock returns. It is considered robust and comprehensive in its approach to factor construction[14][15] 2. Model Name: GRU (Generalized Risk Utility) Model - **Model Construction Idea**: GRU models are used to capture complex relationships in stock returns by leveraging advanced statistical and machine learning techniques. They are designed to identify patterns in historical data and predict future performance[4][6][8] - **Model Construction Process**: - GRU models are trained on historical data to identify patterns in stock returns - The models are applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) to evaluate their performance - Specific GRU models include `barra1d`, `barra5d`, `open1d`, and `close1d`, which differ in their time horizons and data inputs[4][6][8] - **Model Evaluation**: GRU models show mixed performance, with some models like `barra5d` and `close1d` performing strongly, while others like `barra1d` exhibit significant drawdowns in certain periods[4][6][8] --- Model Backtesting Results 1. Barra Style Factors - **Momentum**: Weekly return 3.49%, monthly return -6.50%, YTD return -14.88%[17] - **Beta**: Weekly return 2.21%, monthly return -7.75%, YTD return 28.44%[17] - **Volatility**: Weekly return 1.90%, monthly return -3.76%, YTD return 6.09%[17] - **Liquidity**: Weekly return 1.67%, monthly return 46.39%, YTD return 8.77%[17] - **Size**: Weekly return 0.45%, monthly return -6.89%, YTD return -39.47%[17] - **Non-linear Size**: Weekly return 0.28%, monthly return -6.47%, YTD return -34.37%[17] - **Growth**: Weekly return 0.22%, monthly return 2.03%, YTD return 0.89%[17] - **Profitability**: Weekly return 1.43%, monthly return 3.55%, YTD return 14.39%[17] - **Leverage**: Weekly return 2.13%, monthly return 4.08%, YTD return 16.59%[17] - **Valuation**: Weekly return 3.52%, monthly return 6.78%, YTD return 4.37%[17] 2. GRU Models - **barra1d**: Weekly return -0.34%, monthly return -0.65%, YTD return 4.71%[33][34] - **barra5d**: Weekly return 1.44%, monthly return 5.42%, YTD return 7.34%[33][34] - **open1d**: Weekly return 0.32%, monthly return 1.81%, YTD return 6.02%[33][34] - **close1d**: Weekly return 1.41%, monthly return 4.17%, YTD return 4.33%[33][34] - **Multi-factor Combination**: Weekly return 0.57%, monthly return 2.54%, YTD return 0.89%[33][34] --- Quantitative Factors and Construction 1. Factor Name: Fundamental Factors - **Factor Construction Idea**: Fundamental factors are derived from financial metrics to capture the underlying financial health and performance of companies[4][6][7] - **Factor Construction Process**: - Metrics such as return on assets (ROA), return on equity (ROE), and revenue growth are calculated using trailing twelve-month (TTM) data - Factors are industry-neutralized before testing[19] - **Factor Evaluation**: Fundamental factors show mixed performance, with some factors like "growth" and "profitability" performing well, while others like "static financial factors" exhibit negative returns in certain periods[4][6][7] 2. Factor Name: Technical Factors - **Factor Construction Idea**: Technical factors are based on price and volume data to capture market trends and investor behavior[4][6][7] - **Factor Construction Process**: - Metrics such as momentum, volatility, and turnover are calculated over different time horizons (e.g., 20-day, 60-day, 120-day) - Factors are industry-neutralized before testing[19] - **Factor Evaluation**: Technical factors generally show positive returns for momentum-based factors, while volatility-based factors often exhibit negative returns[4][6][7] --- Factor Backtesting Results 1. Fundamental Factors (CSI 300) - **ROA Growth**: Weekly return 0.38%, monthly return 2.38%, YTD return 26.31%[23] - **Net Profit Surprise Growth**: Weekly return 1.10%, monthly return 2.62%, YTD return 42.59%[23] - **ROC Surprise Growth**: Weekly return 2.23%, monthly return 2.23%, YTD return 35.35%[23] 2. Technical Factors (CSI 500) - **20-day Momentum**: Weekly return 5.99%, monthly return 1.74%, YTD return 3.65%[26] - **120-day Momentum**: Weekly return 1.76%, monthly return 4.01%, YTD return 3.55%[26] - **20-day Volatility**: Weekly return -1.15%, monthly return -4.31%, YTD return 25.86%[26]

- The report tracks the performance of style factors, including liquidity, volatility, and nonlinear market capitalization, which showed strong long positions, while valuation, profitability, and leverage factors exhibited strong short positions [2][16] - Barra style factors are constructed using various financial and technical metrics, such as historical beta, logarithm of total market capitalization, historical excess return momentum, and volatility calculated as a weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility [14][15] - Liquidity factor is calculated as a weighted combination of monthly turnover rate (35%), quarterly turnover rate (35%), and annual turnover rate (30%) [15] - Profitability factor is constructed using a weighted combination of analyst forecast earnings-to-price ratio (68%), inverse cash flow ratio (21%), inverse PE ratio (11%), forecast long-term earnings growth rate (18%), and forecast short-term earnings growth rate (11%) [15] - Growth factor is calculated using a weighted combination of earnings growth rate (24%) and revenue growth rate (47%) [15] - Leverage factor is constructed using market leverage ratio (38%), book leverage (35%), and asset-liability ratio (27%) [15] - GRU models, including open1d, close1d, barra1d, and barra5d, are tracked for their multi-factor performance across different stock pools, showing varied results in terms of long-short returns [3][4][5][6] - GRU models demonstrated strong performance in certain configurations, such as close1d and barra5d, while open1d and barra1d showed weaker returns in specific periods [31][33] - Multi-factor portfolios underperformed this week, with relative excess returns against the CSI 1000 index showing a decline of 0.95% [33][34] - Barra5d model exhibited strong year-to-date performance, achieving an excess return of 5.81% against the CSI 1000 index [33][34] - Technical factors, including short-term and long-term momentum and volatility metrics, showed mixed results across different stock pools, with short-term metrics generally outperforming [19][21][24][26] - Basic financial factors, such as static financial metrics and growth-related metrics, generally showed negative long-short returns, with low-growth stocks outperforming [19][21][24][26] - GRU models' long-short returns varied across stock pools, with close1d and barra5d models showing strong positive returns, while open1d and barra1d models experienced slight pullbacks [31][33] - The liquidity factor achieved a weekly return of 1.39%, while the volatility factor returned 0.92% over the same period [17] - Profitability factor showed a weekly return of -1.31%, and valuation factor returned -1.53% [17] - Growth factor achieved a weekly return of 0.21%, while leverage factor returned -0.83% [17] - GRU models' weekly returns included -0.82% for open1d, 2.88% for close1d, -0.45% for barra1d, and 1.23% for barra5d [31] - Multi-factor portfolio weekly return was -0.95% relative to the CSI 1000 index [34]

- **Barra style factors**: The report tracks several style factors including Beta, Market Cap, Momentum, Volatility, Non-linear Market Cap, Valuation, Liquidity, Profitability, Growth, and Leverage. These factors are constructed using historical data and financial metrics such as turnover rates, earnings growth rates, and market leverage ratios. For example, the Beta factor represents historical beta, while the Valuation factor is calculated as the inverse of the price-to-book ratio. The formulas for constructing these factors include weighted combinations of metrics like turnover rates and earnings ratios [14][15][16] - **Factor performance tracking**: The report evaluates the recent performance of style factors across the market. Beta, Liquidity, and Momentum factors showed strong long positions, while Market Cap, Non-linear Market Cap, and Valuation factors performed better in short positions. The tracking methodology involves selecting stocks from the Wind All A pool, excluding ST stocks, suspended stocks, and newly listed stocks under 120 days. Long positions are taken in the top 10% of stocks with the highest factor values, and short positions in the bottom 10%, with equal weight allocation [16][19][20] - **Factor backtesting results**: The report provides detailed backtesting results for style factors. For example, Beta achieved a weekly return of 4.58%, while Market Cap showed a negative weekly return of -3.55%. Other factors like Momentum and Liquidity also demonstrated varied performance across different time horizons, such as one week, one month, and year-to-date. The report highlights the annualized returns for three-year and five-year periods for each factor [17][18][19] - **GRU factor performance**: GRU factors showed weaker performance overall, with only the barra1d model achieving positive returns. Other GRU models experienced drawdowns in their long-short portfolios. This indicates potential challenges in the effectiveness of GRU factors under current market conditions [20][25][29] - **Technical factors**: Technical factors such as 20-day Momentum, 60-day Momentum, and various volatility measures (e.g., 120-day Volatility) were tracked. These factors generally showed positive returns in long positions, particularly in high-volatility and high-momentum stocks. For example, 120-day Volatility achieved a weekly return of 5.92% in the CSI 300 stock pool [24][27][31] - **Fundamental factors**: Fundamental factors like ROA growth, ROC growth, and Net Profit growth were analyzed. In the CSI 300 stock pool, Net Profit growth achieved a weekly return of 2.51%, while ROA growth showed a return of 1.19%. These factors generally favored stocks with stable and strong growth metrics [23][25][30] - **Multi-factor portfolio performance**: The report evaluates the performance of multi-factor portfolios. The barra5d model outperformed the CSI 1000 index by 0.27% this week and achieved a year-to-date excess return of 5.91%. Other models showed mixed results, with some experiencing slight drawdowns. The multi-factor portfolio achieved a weekly excess return of 0.04% relative to the CSI 1000 index [8][33][34]

Core Insights - The article provides a comprehensive analysis of market factors and their recent performance, highlighting the positive returns from liquidity and leverage factors, while noting negative returns from beta and growth factors [4][5]. Factor Performance - In the last two weeks, the liquidity factor and leverage factor yielded positive returns of 0.36% and 0.34% respectively, while the profitability factor achieved a positive return of 0.27%. Other factors like valuation and market capitalization also showed positive returns, albeit lower [4]. - For the CSI 300 stock pool, the best-performing factors included quarterly operating profit growth rate (2.54%) and quarterly net profit growth rate (2.36%), while total asset growth rate showed a negative return of -1.94% [5]. - In the CSI 500 stock pool, the top factors were the inverse of price-to-sales ratio (1.90%) and net profit gap (1.55%), with the worst performers being quarterly total asset gross margin (-2.12%) [5]. - The liquidity 1500 stock pool saw strong performance from the price-to-earnings ratio (2.19%) and inverse price-to-earnings ratio (2.09%), while total asset gross margin factors performed poorly [5]. Industry Factor Performance - Recent weeks showed a divergence in fundamental factors across industries, with net asset growth rate and net profit growth rate performing well in textiles, non-bank financials, and leisure services [6][7]. - Valuation factors, particularly the BP factor, achieved positive returns across multiple industries, while liquidity factors showed significant positive returns in the beauty and personal care sector [7]. Combination Tracking - The PB-ROE-50 combination achieved positive excess returns in the CSI 800 and overall market stock pools, with a notable excess return of 1.45% in the CSI 800 pool [8]. - Public and private fund research strategies yielded positive excess returns, with public research strategies outperforming the CSI 800 by 1.03% and private strategies by 1.89% [9]. Block Trade and Directed Issuance Tracking - The block trade combination underperformed relative to the CSI All Index, with an excess return of -0.57% [10]. - Similarly, the directed issuance combination also showed negative excess returns of -1.13% compared to the CSI All Index [11].

- The report mentions the construction of the **A-share prosperity index**, which is based on the Nowcasting target of the year-on-year growth rate of the net profit attributable to the parent company of the Shanghai Composite Index. The index is designed to observe the high-frequency prosperity of A-shares. The current prosperity index is 21.28, which has increased by 15.85 compared to the end of 2023, indicating an upward cycle[29][33][34] - The **A-share sentiment index** is constructed using market volatility and transaction volume changes, divided into four quadrants. Among these quadrants, only the "volatility up - transaction down" quadrant shows significant negative returns, while the others show significant positive returns. The sentiment index includes bottoming and peaking warning signals. Currently, the bottoming signal indicates bearishness, and the peaking signal also points to bearishness, leading to an overall bearish outlook for the market[36][39][40] - The **theme mining algorithm** is used to identify investment opportunities in thematic stocks. This algorithm processes news and research report texts, extracts theme keywords, explores relationships between themes and individual stocks, constructs theme active cycles, and builds theme influence factors. Recently, the algorithm has identified semiconductor concept stocks as having high concept heat anomalies, driven by the event of the China Semiconductor Industry Association's announcement regarding chip origin designation[46][47][48] - The **index enhancement portfolios** for CSI 500 and CSI 300 are mentioned. The CSI 500 enhancement portfolio achieved a return of 1.99% but underperformed the benchmark by 0.38%. Since 2020, the portfolio has generated an excess return of 51.20% relative to the CSI 500 index, with a maximum drawdown of -5.73%. The CSI 300 enhancement portfolio achieved a return of 2.15%, outperforming the benchmark by 0.16%. Since 2020, the portfolio has generated an excess return of 38.68% relative to the CSI 300 index, with a maximum drawdown of -5.86%[46][53][54] - The report utilizes the **BARRA factor model** to construct ten major style factors for the A-share market, including size (SIZE), beta (BETA), momentum (MOM), residual volatility (RESVOL), non-linear size (NLSIZE), valuation (BTOP), liquidity (LIQUIDITY), earnings yield (EARNINGS_YIELD), growth (GROWTH), and leverage (LVRG). Recent market style analysis shows that liquidity factors are positively correlated with beta, momentum, and residual volatility, while value factors are negatively correlated with beta, residual volatility, and liquidity. From pure factor returns, size factors have high excess returns, while residual volatility shows significant negative excess returns. High beta and high growth stocks performed well recently, while residual volatility and value factors performed poorly[58][59][60] - The report applies **factor models for performance attribution analysis** of major indices. It highlights that indices like the Shanghai Composite Index, SSE 50, and CSI 300 have significant exposure to size factors due to the market's preference for large-cap stocks, resulting in good performance in style factors. In contrast, indices like CSI 500 and Wind All A have lower exposure to size factors and performed poorly in style factors during the week[66][67][69]

Core Viewpoint - The article emphasizes the importance of setting a reasonable and scientific performance benchmark for public funds, particularly in the context of the growing scale of the CSI 300 index. It discusses how active equity funds can consistently outperform benchmarks by managing style and industry deviations effectively [1][17]. Group 1: Benchmark and Performance - The CSI 300 index serves as the primary benchmark, composed of various style factors. Active fund managers primarily focus on quality, prosperity, and momentum factors, while dividend and low valuation factors can lead to underperformance when they are strong [1][17]. - The difficulty of beating benchmarks is a common challenge for asset management institutions globally, with only about 50% of active equity funds in A-shares outperforming their benchmarks over the past 20 years [17][18]. Group 2: Style and Industry Deviation - Controlling style deviation is more critical than controlling industry deviation for fund managers aiming to outperform benchmarks. Excessive deviation can significantly impact performance negatively [3][22]. - Successful fund managers tend to exhibit smaller deviations in style and industry, maintaining a balanced approach regardless of market conditions [5][24]. Group 3: Stock Selection and Market Timing - Stock selection is more impactful on performance than industry selection, with a focus on identifying high-potential stocks rather than frequently rotating industries [26]. - Market timing is debated among fund managers, with evidence suggesting that while many lack timing ability, strategic timing can enhance returns during volatile periods [12][34]. Group 4: Risk Management and Strategy - A U-shaped risk convexity strategy is proposed to enhance the risk-return profile of portfolios, emphasizing the importance of managing volatility in equity assets [27][28]. - The relationship between volatility and returns is highlighted, with low volatility stocks often yielding better returns in the A-share market, contrary to the general belief that higher volatility equates to higher returns [9][29]. Group 5: Future Considerations - The article suggests that in the absence of clear industry trends, public funds must balance their strategies to achieve stable excess returns by leveraging combination management approaches [20][21].

Group 1 - The macro growth factor continues to rise, while inflation indicators show a weakening rebound, with domestic CPI turning negative at -0.4% and PPI's decline narrowing to -2.9%, indicating persistent internal demand issues [4] - The Federal Reserve's interest rate cut expectations are driving upward global liquidity expectations, benefiting Asian equity markets, with the Korean Composite Index rising by 5.94% and the Hang Seng Tech Index by 5.31% [4][9] - The A-share market shows a preference for growth styles, with the Sci-Tech 50 Index increasing by 5.48%, while small-cap indices outperform large-cap blue chips [4] Group 2 - Recommendations for asset allocation include favoring high-grade credit bonds in the bond market, adjusting duration flexibly, and focusing on bank and insurance sector movements [5] - In the overseas equity market, the report suggests monitoring interest rate-sensitive sectors due to limited short-term rebound potential for the dollar and significantly raised interest rate cut expectations [5] - For gold, it is recommended to increase allocations to gold and silver as they are core assets during the interest rate cut cycle, with expectations for Shanghai gold to break previous highs [5] Group 3 - The report indicates that the overall liquidity environment remains supportive for market valuation recovery and structural trends, with a significant decrease in average daily trading volume in the A-share market [56] - The A-share valuation levels have increased, with the price-to-earnings ratio rising to 50.38 times and the price-to-book ratio reaching 5.60 times, suggesting that market expectations for future corporate earnings may be overly optimistic [60] - The report highlights that the earnings expectations for A-shares are weaker than historical averages, with a projected rolling one-year earnings growth rate of 10.3% and revenue growth rate of 5.9% [61]