Investment Rating - The investment rating for the company is "Buy" [8][12]. Core Views - The company reported a revenue of 13.57 billion yuan in the first half of 2025, a year-on-year decline of 10.8%, with a net profit attributable to shareholders of 560 million yuan, down 40.2% year-on-year [4]. - The company expects a profit turning point to emerge in Q3 2025, driven by price adjustments in response to industry competition [5]. - Retail channels have shown resilience, with overseas revenue growing by 42.16% year-on-year, indicating potential for rapid growth in international markets [6]. - Effective cost control measures have led to a decrease in accounts receivable, with a 22.5% year-on-year decline [7]. Company Overview - The latest closing price is 11.92 yuan, with a total market capitalization of 28.5 billion yuan [3]. - The company has a debt-to-asset ratio of 43.4% and a price-to-earnings ratio of 274.02 [3]. Financial Forecast - Revenue is projected to be 27.34 billion yuan in 2025, a decrease of 2.6% year-on-year, with a significant rebound in net profit expected to reach 1.36 billion yuan in 2025, reflecting a year-on-year increase of 1155% [10][11].

Group 1: Report Industry Investment Rating - There is no information provided regarding the report industry investment rating in the given content. Group 2: Core Viewpoints of the Report - The report analyzes the journey of Japanese government bonds from a 2% to 1% yield, identifying three stages of fluctuations and the factors influencing them, including policy changes, economic conditions, and institutional behaviors. It also draws lessons from Japan's experience in dealing with low - interest - rate bond market volatility for China [12][15][34]. Group 3: Summary by Directory 1. Replay: What Happened to Japanese Government Bonds from 2% to 1%? - **Stage One (1999 - 2001)**: After a period of rapid rise and recovery, the 10 - year Japanese government bond oscillated between 1.5% - 2%. Fiscal expansion and zero - interest policies rebalanced the supply - demand pattern of government bonds. Banks passively increased their government bond holdings due to low lending demand and narrow interest spreads, while bond funds' scale recovered, and the central bank started using non - traditional tools to intervene in the market [12][15][20]. - **Stage Two (2001 - 2002)**: The 10 - year Japanese government bond oscillated between 1% - 1.5%. The launch of QE and the resolution of financial institution risks were the main themes. Banks' willingness to buy government bonds weakened due to bad loan restructuring, while insurance companies increased their government bond allocation to hedge against equity risks. Public bond funds' scale shrank significantly, and capital flowed overseas [12][34][36]. - **Stage Three (2003 - 2010)**: The 10 - year Japanese government bond oscillated between 1.2% - 2%. Japan maintained low fiscal stimulus, resulting in low growth and low inflation. Fiscal and monetary policies formed a structural division, with fiscal prudence and monetary easing. During the financial crisis, the central bank's policy changes constrained interest rate fluctuations, and the bond market had large retracements in a low - interest environment [12][48][50]. 2. Experience: Bond Market Volatility and Institutional Responses in Japan's Low - Interest Environment - **Experience One (1999 - 2001)**: The Japanese banking system absorbed the supply shock of government bonds. From 1997 - 2001, the proportion of government bonds held by banks increased from 5.23% to 10.13%, digesting 28.19% of the government bond increment. In contrast, Chinese commercial banks have stronger government bond - taking capacity and greater structural adjustment space [60][62]. - **Experience Two (2001 - 2002)**: The insurance industry had greater potential for government bond allocation than banks. In 2001 - 2002, the year - on - year growth rate of insurance funds' government bond purchases increased from 28% to 57%, reaching 2.83 trillion yen in 2002. Regulatory policy relaxation also increased the industry's government bond - taking ability [67][68]. - **Experience Three (2003 - 2010)**: The fixed - income fund industry coped with the market volatility of low - interest rates and high retracements. Bond funds' passive management became popular, some funds obtained excess returns through credit screening and duration strategies, and the industry explored solutions through product innovation, such as monthly - dividend products [71][72][73].

Employment Data - July non-farm payrolls increased by only 73,000, significantly below expectations, with prior months' data revised down by a total of 258,000[12] - The unemployment rate rose slightly to 4.2%, indicating a still robust labor market, but declining labor force participation suggests a weakening supply-demand balance due to reduced immigrant labor[12] Market Reaction - The disappointing employment data led to a sharp decline in the US dollar index and US stocks, with markets betting on potential interest rate cuts by the Federal Reserve in September, October, and December[3] - Historical patterns suggest that US stocks may experience seasonal weakness in Q3, but strong earnings and guidance from tech stocks like META provide support for future gains[3] Federal Reserve Outlook - The Federal Reserve is expected to initiate rate cuts in September, with the current interest rate maintained at 4.25%-4.50%[12] - Fed officials expressed concerns about inflation and labor market conditions, with some advocating for a 25 basis point cut due to economic slowdown and stable inflation expectations[33][34] Risks - There is a risk of inflation rising above expectations, which could delay the pace of rate cuts by the Federal Reserve[4][39]

Quantitative Models and Construction 1. Model Name: Financial Report Fraud Detection Score - **Model Construction Idea**: This model identifies and quantifies the risk of financial fraud in corporate reports by detecting anomalies such as fabricated revenue, inflated profits, hidden liabilities, and other manipulative practices[20][39]. - **Model Construction Process**: - The model captures six types of anomalies: fabricated/inflated revenue, inflated profits, inflated assets, hidden liabilities, fund misappropriation, and benefit transfers[20]. - Scores are assigned based on the severity of detected anomalies, with higher scores indicating higher fraud risk[20]. - The model updates its scores in sync with financial report disclosures[20]. - **Model Evaluation**: The model demonstrates strong predictive power for ST events, with an AUC of 0.806, significantly outperforming the benchmark factor of net profit (AUC = 0.656)[5][34][38]. 2. Model Name: Corporate Behavior Profiling Score - **Model Construction Idea**: This model evaluates the operational health of companies and quantifies their default risk based on comprehensive behavioral profiling[20]. - **Model Construction Process**: - The score is derived from operational data, with higher scores indicating better operational health and lower risk[20]. - The model is updated in alignment with financial report disclosure schedules[20]. - **Model Evaluation**: This model outperforms the Financial Report Fraud Detection Score, with an AUC of 0.865, and demonstrates stronger predictive power for ST events[5][34][39]. 3. Model Name: Cox Proportional Hazards Model (Survival Analysis) - **Model Construction Idea**: This model predicts the probability of ST events by analyzing the impact of risk events (e.g., regulatory actions, shareholder actions) on the survival time of stocks[44][61]. - **Model Construction Process**: - The survival function \( S(t) \) is defined as the probability of a stock surviving beyond time \( t \), with \( S(0) = 1 \) and \( S(t) \) decreasing over time[41][44]. - The Cox model formula is: \[ h(t, X) = h_0(t) \cdot \exp(\beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_n x_n) \] where \( h_0(t) \) is the baseline hazard, \( X \) represents risk events, and \( \beta \) are coefficients[61]. - Risk ratios (HR) are calculated as \( HR = e^\beta \), with \( HR > 1 \) indicating risk factors and \( HR < 1 \) indicating protective factors[61]. - Risk events include regulatory letters (e.g., inquiry letters), shareholder actions (e.g., equity freezes), and regulatory measures (e.g., public censure)[45][61]. - **Model Evaluation**: The model achieves an AUC of 0.810 and demonstrates flexibility in incorporating new risk events, making it suitable for high-frequency monitoring[6][73][75]. --- Model Backtesting Results Financial Report Fraud Detection Score - **AUC**: 0.806[5][34] - **Recall Rate**: Average recall rate exceeds 90% for ST predictions, with a minimum recall rate above 80%[5][30]. Corporate Behavior Profiling Score - **AUC**: 0.865[5][34] - **Recall Rate**: Average recall rate exceeds 90%, with a minimum recall rate above 80%[5][31]. Cox Proportional Hazards Model - **AUC**: 0.810[6][73] - **Recall Rate**: Recall rates improved significantly after 2019, averaging around 85%[6][71]. --- Quantitative Factors and Construction 1. Factor Name: Financial Report Fraud Detection Score - **Factor Construction Idea**: Quantifies the risk of financial fraud based on anomalies in financial reports[20]. - **Factor Construction Process**: - Scores are assigned based on six types of anomalies, with higher scores indicating higher fraud risk[20]. - **Factor Evaluation**: Demonstrates strong predictive power for ST events, with an AUC of 0.806[5][34]. 2. Factor Name: Corporate Behavior Profiling Score - **Factor Construction Idea**: Measures operational health and default risk based on corporate behavior[20]. - **Factor Construction Process**: - Scores are derived from operational data, with higher scores indicating better operational health[20]. - **Factor Evaluation**: Outperforms the Financial Report Fraud Detection Score, with an AUC of 0.865[5][34]. 3. Factor Name: Risk Events (Cox Model) - **Factor Construction Idea**: Analyzes the impact of risk events on stock survival time[44][61]. - **Factor Construction Process**: - Risk events include regulatory letters, shareholder actions, and regulatory measures[45][61]. - Risk ratios are calculated to determine the significance of each event[61]. - **Factor Evaluation**: Provides high-frequency tracking and flexibility, with an AUC of 0.810[6][73]. --- Factor Backtesting Results Financial Report Fraud Detection Score - **AUC**: 0.806[5][34] - **Recall Rate**: Average recall rate exceeds 90% for ST predictions, with a minimum recall rate above 80%[5][30]. Corporate Behavior Profiling Score - **AUC**: 0.865[5][34] - **Recall Rate**: Average recall rate exceeds 90%, with a minimum recall rate above 80%[5][31]. Risk Events (Cox Model) - **AUC**: 0.810[6][73] - **Recall Rate**: Recall rates improved significantly after 2019, averaging around 85%[6][71]

- The report tracks the performance of style factors, including Beta, liquidity, leverage, profitability, and market capitalization, with Beta and liquidity showing strong long positions, while leverage, profitability, and market capitalization exhibit strong short positions [2][16] - Style factors are constructed using various metrics, such as historical Beta, logarithm of total market capitalization, historical excess return averages for momentum, and a weighted combination of volatility measures for the volatility factor. For example, the volatility factor is calculated as $ 0.74 * historical excess return volatility + 0.16 * cumulative excess return deviation + 0.1 * historical residual return volatility $ [15] - Fundamental factors, including growth-related financial metrics and static financial metrics, are tested across different stock pools (e.g., CSI 300, CSI 500, CSI 1000). Growth-related financial factors generally show mixed or negative performance, while static financial factors exhibit varied results depending on the stock pool [3][4][5][6][18][20][23][25] - Technical factors, such as momentum and volatility, generally show positive performance across stock pools, with high-volatility and high-momentum stocks being dominant. For example, the 120-day momentum factor and 20-day volatility factor are highlighted for their significant contributions [3][4][5][6][18][20][23][26] - GRU factors are tested using different models (e.g., barra1d, barra5d, close1d), with performance varying across stock pools. For instance, barra1d shows strong positive performance in CSI 500 and CSI 1000 pools, while close1d experiences significant drawdowns in CSI 1000 [3][4][5][6][18][20][23][26] - Multi-factor strategies and GRU-based long portfolios are evaluated against the CSI 1000 index. GRU long portfolios show weak performance this week, with relative drawdowns of 0.11%-0.25%, while the barra5d model demonstrates strong year-to-date performance, achieving an excess return of 8.36% [7][30][31]

Industry Investment Rating - The investment rating for the construction materials industry is "Outperform the Market" and is maintained [1] Core Insights - The report emphasizes the ongoing theme of "anti-involution" in the construction materials sector, with a focus on the marginal changes in the fundamentals. The recent Politburo meeting highlighted the importance of high-quality urban renewal and the need to regulate chaotic competition among enterprises, which is expected to influence capacity management in key industries [4] - In the cement sector, a policy document released by the Cement Association on July 1 is anticipated to enhance the enforcement of production limits, leading to a potential decrease in capacity and an increase in utilization rates. The report predicts a gradual price recovery in August as demand improves [4] - The glass industry is experiencing a downward trend in demand due to the real estate sector's impact, with supply-demand imbalances persisting. However, the report notes that most companies in the float glass sector meet environmental standards, which may prevent drastic capacity cuts but could raise costs and accelerate maintenance schedules [5] - The fiberglass segment is expected to benefit from the AI industry, with demand for low-dielectric products projected to rise significantly. The report highlights a clear upgrade in product structure, indicating a potential explosive growth in demand [5] - The consumer building materials sector has reached a profitability bottom, with no further downward price pressure. The report notes a strong push for price increases across various categories, suggesting a potential improvement in profitability [5] Summary by Sections Cement - Cement prices are currently declining due to seasonal factors, with a 2.13% decrease in the price of ordinary cement (P.O 42.5) reported this week. The monthly production in June 2025 saw a year-on-year decline of 5.3% [8] Glass - The glass market is facing challenges, with a 0.76% increase in prices this week, but overall demand remains weak. The report indicates that the industry is still grappling with supply-demand contradictions [13] Fiberglass - The fiberglass industry is experiencing a positive trend driven by AI-related demand, with expectations for both volume and price increases [5] Consumer Building Materials - The consumer building materials sector is showing signs of recovery, with companies actively raising prices after years of competitive pressure. This sector includes waterproofing materials, coatings, and gypsum boards [5] Recent Company Announcements - Oriental Yuhong reported a revenue of 13.569 billion yuan for the first half of 2025, a year-on-year decrease of 10.84%, with a net profit of 564 million yuan, down 40.16% [17] - Rabbit Baby's associated company, Hanhai Group, was listed on the Shenzhen Stock Exchange, with Rabbit Baby holding a 1.85% stake post-IPO [17]

Investment Rating - The industry investment rating is "Strongly Outperforming the Market" and is maintained [1] Core Viewpoints - This week, commodity prices have retreated, leading to a weakening in the petrochemical sector. Continuous attention is required on the progress of phasing out old facilities and upgrading within the petrochemical industry [2] - The oil and petrochemical index closed at 2262.71 points, down 2.94% from last week, indicating a weaker performance compared to other sectors [3][7] - In the upstream sector, geopolitical factors may provide a premium for oil, benefiting upstream stocks. In the refining sector, a recovery in demand and progress in eliminating outdated capacity would be favorable for midstream refining [2] Summary by Sections Oil - Energy prices have fluctuated, with Brent crude oil futures and TTF natural gas futures closing at $69.54 per barrel and €33.81 per megawatt-hour, respectively, reflecting increases of 1.1% and 4.2% from last week [10] - U.S. crude oil inventories have risen, with total crude and petroleum product inventories (excluding strategic reserves) at 1,257,771 thousand barrels, an increase of 7,087 thousand barrels [15] Polyester - The price of polyester filament remains stable, with POY, DTY, and FDY prices at 6,680, 7,930, and 6,930 yuan per ton, respectively. The price differentials have decreased by 101 yuan per ton [18] - The inventory days for polyester filament in Jiangsu and Zhejiang have increased, with operating rates for filament and downstream looms at 91.5% and 55.5%, respectively, both showing slight declines [22] Olefins - Sample prices for polyethylene (PE) and polypropylene (PP) are 7,710 and 8,050 yuan per ton, with changes of 0.13% and -1.11% from last week. The petrochemical inventory for olefins has increased by 70,000 tons, totaling 800,000 tons [26]

1. Report Industry Investment Rating There is no information provided regarding the report industry investment rating in the given content. 2. Core Viewpoints of the Report - The policy tone has been revealed, and expectations have been revised. The bond yield's阶段性 top is clear, with the 10 - year Treasury bond's mid - term top forming around 1.75% [3][10][12]. - Tax policy changes have a "one - time" impact on the nominal interest rate center. The expected tax burden spread is around 5BP, and it may affect the selection of the cheapest to deliver bond in far - month Treasury bond futures contracts [4][14]. - It is necessary to re - define the interest rate's fluctuation center. The 1.75% mid - term top of the 10 - year Treasury bond may be challenged but remains relatively reliable, and the 1.65% fluctuation center is still valid. There is a possibility of opening up downward interest rate space in the second half of the year [5][15][16]. 3. Summary According to the Directory 3.1 How to Redefine the Interest Rate Center? - **Policy Expectations and Bond Yield Top** - The prediction of policy deployment is mostly fulfilled. The demand - side pulling policy pattern remains unchanged, and there is no unexpected urban renewal policy. The "anti - involution" policy exists but with lower - than - expected progress and attention [3][10][11]. - The "anti - involution" policy has long - term impacts on price and interest rate pricing, but the results are not linearly the same as historical trends [11]. - The demand - side pulling policy maintains its pattern, and the pricing difference between commodities and bonds regarding demand - pulling policies should end with commodity pricing correction [11]. - The monetary policy's task of "lowering social comprehensive financing costs" persists. Liquidity is expected to remain stable and loose in Q3, and a new round of policy interest rate cuts and liquidity easing is in the making [11]. - From the perspective of policy expectations, the mid - term top of the 10 - year Treasury bond around 1.75% has formed [3][12][16]. - **Impact of Tax Policy Changes** - Starting from August 8, 2025, the interest income of newly issued Treasury bonds, local government bonds, and financial bonds will be subject to value - added tax. The actual tax burden for self - operated financial institutions is 6.34%, and for asset management institutions is 3.26% [4][13]. - The theoretical tax burden spread for long - duration bonds is 5 - 12BP, but it is expected to be around 5BP considering previous factors [4][13][14]. - Near - month Treasury bond futures contracts are less affected, while far - month contracts may see an impact on the selection of the cheapest to deliver bond, and tax burden differences can be considered in determining conversion factors [4][14]. - **Redefining the Interest Rate Fluctuation Center** - The interest rate increase since early July is driven by expectations of "anti - involution" and demand - side policies, with risk preference playing a role in asset re - pricing [15]. - Given the "high - first - then - low" trend of the fundamentals throughout the year, the 1.75% mid - term top of the 10 - year Treasury bond may be challenged but is still relatively reliable. The 1.65% fluctuation center is still valid. There is potential for interest rates to decline in the second half of the year [5][15][16].

Market Performance Review - The A-share market experienced a rise followed by a decline, with all major indices closing down after the Politburo meeting, where the CSI 1000 had the smallest drop of 0.54%, while the CSI A50 and CSI 300 fell by 2.48% and 1.75% respectively [3][12] - There was a noticeable divergence in market styles, with growth and consumption sectors experiencing smaller declines, while financial, stability, and cyclical styles saw significant pullbacks [3][12] - Mid-cap and small-cap indices outperformed large-cap indices during the week, with the NING and MAO indices, representing core assets and growth leaders, also declining, with the NING combination down 1.74% and the MAO index down 0.84% [3][12] Investor Sentiment and Market Outlook - The personal investor sentiment index has continued to decline, entering a negative zone, with the 7-day moving average reported at -0.42% as of August 2, down from 4.35% on July 26 [4][19] - The recent Politburo meeting did not indicate any large-scale stimulus plans, suggesting that the focus will shift back to demand recovery rather than potential supply-side reductions [4][32] - The current market dynamics indicate a potential vacuum in buying momentum, necessitating a cautious approach to investment strategies [4][32] Sector Analysis - The healthcare and communication sectors saw gains of over 2%, driven by significant partnerships and strong performance in the CPO sector, respectively, indicating a return to prosperity trading [15] - Conversely, sectors such as coal, non-ferrous metals, construction materials, and steel experienced substantial declines due to the withdrawal of "anti-involution" trading sentiment following policy announcements [15] Investment Strategy and Recommendations - The report emphasizes a return to prosperity trading, highlighting opportunities for valuation recovery in technology growth sectors, particularly in AI applications, computing power chains, and optical modules [5][33] - The fundamentals of innovative pharmaceuticals and CROs are showing signs of transformation, with continued trading logic for Chinese pharmaceuticals going overseas [5][33]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is used to monitor the critical points of trend changes in the micro-cap stock index by analyzing the distribution of stock price movements over a specific time window [5][39] - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a retrospective or forward-looking window. For example, if the horizontal axis is 0.95 and the vertical axis is 15 days, the value of 0.31 indicates that if all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.31. The model uses thresholds to signal trading actions: - **First Threshold Method (Left-Side Trading)**: Triggered a sell signal on May 8, 2025, when the index reached 0.9850 [43] - **Delayed Threshold Method (Right-Side Trading)**: Triggered a sell signal on May 15, 2025, at 0.8975 [47] - **Double Moving Average Method (Adaptive Trading)**: Triggered a buy signal on July 3, 2025 [48] - **Model Evaluation**: The model effectively identifies trend changes but may be influenced by the distribution of constituent stocks and their updates [39][40] 2. Model Name: Small-Cap Low-Volatility 50 Strategy - **Model Construction Idea**: This strategy selects 50 stocks with small market capitalization and low volatility from the micro-cap stock index, rebalancing every two weeks [7][36] - **Model Construction Process**: - Select stocks with the smallest market capitalization and lowest volatility from the micro-cap index - Rebalance the portfolio bi-weekly - Benchmark: Wind Micro-Cap Stock Index (8841431.WI) - Transaction cost: 0.3% on both sides [7][36] - **Model Evaluation**: The strategy demonstrates strong performance in 2025 but underperformed in 2024, indicating sensitivity to market conditions [7][36] --- Model Backtesting Results 1. Diffusion Index Model - **First Threshold Method**: Triggered sell signal at 0.9850 on May 8, 2025 [43] - **Delayed Threshold Method**: Triggered sell signal at 0.8975 on May 15, 2025 [47] - **Double Moving Average Method**: Triggered buy signal on July 3, 2025 [48] 2. Small-Cap Low-Volatility 50 Strategy - **2024 Return**: 7.07%, underperformed by -2.93% relative to the benchmark [7][36] - **2025 YTD Return**: 69.79%, underperformed by -1.88% relative to the benchmark [7][36] --- Quantitative Factors and Construction Methods 1. Factor Name: Unadjusted Stock Price Factor - **Factor Construction Idea**: Measures the rank IC of unadjusted stock prices within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the rank IC of unadjusted stock prices weekly - Compare with historical averages for evaluation [4][17] - **Factor Evaluation**: Demonstrated strong performance this week with a rank IC of 0.177, significantly above the historical average of -0.015 [4][17] 2. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the systematic risk of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the beta of each stock relative to the market - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Performed well this week with a rank IC of 0.15, above the historical average of 0.006 [4][17] 3. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Captures the illiquidity of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Measure illiquidity based on trading volume and price impact - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Strong performance this week with a rank IC of 0.143, above the historical average of 0.04 [4][17] 4. Factor Name: 10-Day Return Factor - **Factor Construction Idea**: Tracks the short-term momentum of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the 10-day return for each stock - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Positive performance this week with a rank IC of 0.105, above the historical average of -0.061 [4][17] 5. Factor Name: PE_TTM Reciprocal Factor - **Factor Construction Idea**: Measures valuation based on the reciprocal of the trailing twelve-month price-to-earnings ratio [4][17] - **Factor Construction Process**: - Calculate the reciprocal of PE_TTM for each stock - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Moderate performance this week with a rank IC of 0.041, above the historical average of 0.017 [4][17] --- Factor Backtesting Results Top 5 Factors by Weekly Rank IC 1. **Unadjusted Stock Price Factor**: Weekly rank IC = 0.177, Historical Average = -0.015 [4][17] 2. **Beta Factor**: Weekly rank IC = 0.15, Historical Average = 0.006 [4][17] 3. **Illiquidity Factor**: Weekly rank IC = 0.143, Historical Average = 0.04 [4][17] 4. **10-Day Return Factor**: Weekly rank IC = 0.105, Historical Average = -0.061 [4][17] 5. **PE_TTM Reciprocal Factor**: Weekly rank IC = 0.041, Historical Average = 0.017 [4][17] Bottom 5 Factors by Weekly Rank IC 1. **Turnover Factor**: Weekly rank IC = -0.189, Historical Average = -0.082 [4][17] 2. **Momentum Factor**: Weekly rank IC = -0.132, Historical Average = -0.005 [4][17] 3. **Residual Volatility Factor**: Weekly rank IC = -0.13, Historical Average = -0.04 [4][17] 4. **10-Day Free Float Turnover Factor**: Weekly rank IC = -0.12, Historical Average = -0.062 [4][17] 5. **Liquidity Factor**: Weekly rank IC = -0.118, Historical Average = -0.041 [4][17]