Quantitative Models and Construction Methods 1. Model Name: Corporate Governance Impact Model - **Model Construction Idea**: The model aims to analyze the impact of corporate governance on stock prices through multiple dimensions, including shareholder behavior, debt management, working capital management, litigation and compliance, ESG scores, and disclosure transparency[1][2][3] - **Model Construction Process**: - **Shareholder Behavior**: Analyzes the impact of major shareholders' increase/decrease in holdings, stock pledges, and the sensitivity of management compensation to profits on stock prices[16][17][18] - **Debt Management**: Evaluates the impact of debt governance on stock prices through indicators such as asset-liability ratio, interest-bearing debt ratio, current ratio, and cash flow interest coverage ratio[21][22][23] - **Working Capital Management**: Assesses the impact of working capital management on stock prices through indicators such as accounts receivable turnover, inventory turnover, working capital turnover, and cash turnover[32][33][34] - **Litigation and Compliance**: Measures the impact of corporate violations and litigation events on stock prices through the number of violations and litigation cases within a certain period[38][39][40] - **ESG Scores**: Evaluates the impact of ESG performance on stock prices through environmental management scores, social management scores, and governance management scores[41][42][43] - **Disclosure Transparency**: Assesses the impact of information disclosure on stock prices through the evaluation of information disclosure and whether the company discloses ESG reports[49][50][54] - **Model Evaluation**: The model comprehensively evaluates the impact of corporate governance on stock prices through multiple dimensions, providing a complete analysis framework[56] Model Backtesting Results - **Corporate Governance Impact Model**: - **Shareholder Behavior**: Major shareholders' increase in holdings positively impacts stock prices, while high stock pledge ratios negatively impact stock prices[16][17][18] - **Debt Management**: Reasonable asset-liability ratios and low interest-bearing debt ratios positively impact stock prices, while high ratios negatively impact stock prices[21][22][23] - **Working Capital Management**: High accounts receivable turnover and inventory turnover positively impact stock prices, while low turnover rates negatively impact stock prices[32][33][34] - **Litigation and Compliance**: Fewer violations and litigation cases positively impact stock prices, while frequent violations and litigation cases negatively impact stock prices[38][39][40] - **ESG Scores**: High ESG scores positively impact stock prices, while low scores negatively impact stock prices[41][42][43] - **Disclosure Transparency**: High-quality information disclosure and ESG report disclosure positively impact stock prices, while poor disclosure negatively impacts stock prices[49][50][54] Quantitative Factors and Construction Methods 1. Factor Name: Shareholder Behavior - **Factor Construction Idea**: Analyzes the impact of major shareholders' increase/decrease in holdings, stock pledges, and the sensitivity of management compensation to profits on stock prices[16][17][18] - **Factor Construction Process**: - **Major Shareholders' Increase/Decrease in Holdings**: Evaluates the impact of major shareholders' increase/decrease in holdings on stock prices through the signal transmission mechanism[17] - **Stock Pledges**: Assesses the impact of stock pledges on stock prices through the risk transmission mechanism[18] - **Management Compensation Sensitivity to Profits**: Measures the impact of management compensation sensitivity to profits on stock prices through the interest binding mechanism[20] - **Factor Evaluation**: The factor effectively captures the impact of shareholder behavior on stock prices through multiple mechanisms[16][17][18] 2. Factor Name: Debt Management - **Factor Construction Idea**: Evaluates the impact of debt governance on stock prices through indicators such as asset-liability ratio, interest-bearing debt ratio, current ratio, and cash flow interest coverage ratio[21][22][23] - **Factor Construction Process**: - **Asset-Liability Ratio**: Measures the impact of the overall debt burden and long-term solvency risk on stock prices[22] - **Interest-Bearing Debt Ratio**: Assesses the impact of the proportion of interest-bearing debt on stock prices[26] - **Current Ratio**: Evaluates the impact of short-term solvency on stock prices[28] - **Cash Flow Interest Coverage Ratio**: Measures the impact of operating cash flow's ability to cover interest expenses on stock prices[31] - **Factor Evaluation**: The factor comprehensively evaluates the impact of debt management on stock prices through multiple indicators[21][22][23] Factor Backtesting Results - **Shareholder Behavior**: - **Major Shareholders' Increase/Decrease in Holdings**: Positive impact on stock prices when major shareholders increase holdings, negative impact when they decrease holdings[17] - **Stock Pledges**: Negative impact on stock prices when stock pledge ratios are high[18] - **Management Compensation Sensitivity to Profits**: Positive impact on stock prices when compensation is reasonably sensitive to profits, negative impact when sensitivity is too high or too low[20] - **Debt Management**: - **Asset-Liability Ratio**: Positive impact on stock prices within a reasonable range, negative impact when too high or too low[22] - **Interest-Bearing Debt Ratio**: Positive impact on stock prices when low, negative impact when high[26] - **Current Ratio**: Positive impact on stock prices within a reasonable range, negative impact when too low or too high[28] - **Cash Flow Interest Coverage Ratio**: Positive impact on stock prices when high, negative impact when low[31]

Group 1: Federal Reserve Balance Sheet Structure - The Federal Reserve's balance sheet is crucial for understanding changes in dollar liquidity, primarily impacting the financial system through "double-entry bookkeeping" [1] - The balance sheet expansion involves asset purchases to inject liquidity into the financial market or real economy, categorized into regular open market operations, unconventional quantitative easing, and reserve management purchases [1] - The main assets include U.S. Treasury securities and mortgage-backed securities, which reflect the implementation of quantitative easing or tightening policies [12] Group 2: Historical Changes in the Balance Sheet - The Federal Reserve's balance sheet has been in a trend of absolute expansion since its inception, influenced by economic development and institutional changes [2] - The historical changes can be divided into four phases: 1) Gold standard era (1914-1940), 2) Institutional establishment (1941-2007), 3) Breakthrough of norms (2008-2019), and 4) Flexible response (2020-present) [2] - The COVID-19 pandemic accelerated the expansion of the balance sheet, with asset purchases aimed at maintaining market liquidity and supporting macroeconomic recovery [2] Group 3: Asset Allocation Implications - Statistical analysis post-2008 shows that balance sheet reduction has a more significant and certain impact on U.S. Treasury yields compared to expansion [3] - The effect of balance sheet expansion on Treasury yields is most pronounced within 30 trading days post-announcement, gradually diminishing thereafter [3] - Both expansion and reduction of the balance sheet have ambiguous effects on U.S. stock market movements, necessitating consideration of the macroeconomic fundamentals [3]

Group 1: Manufacturing Sector Insights - The manufacturing PMI rose to 50.1%, marking a return to the expansion zone after 8 months[2] - The production index increased by 1.7 percentage points to 51.7%, attributed to reduced uncertainties in the external trade environment[2] - The new orders index improved by 1.6 percentage points to 50.8%, indicating the first return to expansion in the second half of the year[2] Group 2: Trade and Pricing Dynamics - New export orders increased by 1.4 percentage points to 49.0%, with a significant slowdown in contraction[2] - The factory price index's contraction pace continued to slow, while raw material purchase prices expanded, indicating ongoing operational pressures for enterprises[2] - Inventory levels for raw materials and finished products continued to decline, reflecting a de-stocking trend[2] Group 3: Non-Manufacturing Sector Performance - The non-manufacturing business activity index rose by 0.7 percentage points to 50.2%, returning to the expansion zone[3] - The construction sector's business activity index surged by 3.2 percentage points to 52.8%, driven by favorable weather and upcoming holidays[3] - The service sector's business activity index saw a slight increase of 0.2 percentage points to 49.7%, remaining below the expansion threshold[3] Group 4: Future Outlook and Risks - The composite PMI output index rose by 1.0 percentage point to 50.7%, driven by the rebound in both manufacturing and non-manufacturing sectors[3] - The outlook for January 2026 suggests continued expansion in manufacturing, supported by a stable external trade environment and incremental policy implementations[3] - Risks include potential underperformance of policy deployments and uncertainties in the external environment due to rising global trade protectionism[3]

Macro and Strategy Research - The core support for A-share performance in 2026 is expected to come from price stability rather than volume growth, with PPI showing signs of recovery in October and November 2025, indicating a potential narrowing of year-on-year declines in 2026 [3][4] - The "anti-involution" policy is anticipated to provide significant price support in 2025, with ongoing efforts to regulate capacity in key industries, which may stabilize prices and reduce the risk of PPI turning negative [4][5] - External factors, including potential interest rate cuts by the Federal Reserve ahead of the 2026 midterm elections, could positively influence PPI recovery and global commodity prices [5] Fixed Income Research - The report discusses how bond ETFs' premiums and discounts affect the underlying securities' prices, particularly during market adjustments, where investor confidence impacts ETF net asset values [6][7] - The liquidity of underlying assets is significantly affected during deep discounts, leading to increased market pressure and potential price discovery issues [8] - The report emphasizes the importance of understanding the relationship between ETF pricing and underlying bond performance, particularly in the context of market fluctuations and liquidity constraints [9] Industry Research - In the steel sector, demand is expected to weaken seasonally, leading to increased inventory pressure, while macroeconomic conditions remain supportive for price stability [19][21] - The copper market is facing supply constraints due to incidents at major mines, which may support prices despite weak demand; the sector is expected to benefit from increased demand in electric vehicles and infrastructure [22] - The aluminum industry is projected to see stable profits due to strict production limits and potential demand growth from new energy sectors, with the "anti-involution" policy expected to improve the supply landscape [22] - Gold prices are influenced by geopolitical risks and U.S. economic data, with long-term trends favoring gold as a hedge against economic instability [22] - The rare earth sector is poised for growth due to strategic export controls and increasing demand from high-tech industries, suggesting a positive outlook for related companies [23]

Quantitative Models and Construction Methods 1. Model Name: HP Filter - **Model Construction Idea**: The HP filter is used to decompose a time series into trend and cyclical components, aiming to remove long-term trends and short-term noise from macroeconomic factors[10][9] - **Model Construction Process**: The HP filter solves the following optimization problem to balance trend smoothness and data fit: $$\operatorname*{min}\left\{\sum_{t=1}^{T}(y_{t}-g_{t})^{2}+\lambda\sum_{t=2}^{T-1}[(g_{t+1}-g_{t})-(g_{t}-g_{t-1})]^{2}\right\}$$ - \(y_t\): Original time series data - \(g_t\): Trend component - \(\lambda\): Smoothing parameter, where larger \(\lambda\) results in a smoother trend In this report, a larger \(\lambda\) is used to remove long-term trends, and a smaller \(\lambda\) is applied to filter out noise, resulting in a mid-cycle series for further analysis[10] - **Model Evaluation**: The HP filter aligns with classical macroeconomic analysis frameworks but suffers from endpoint bias and cannot identify different frequency cycles[3][42] 2. Model Name: Fourier Transform - **Model Construction Idea**: Fourier Transform decomposes a time series into a combination of sine waves with different frequencies, amplitudes, and phases, enabling the identification of dominant cycles in macroeconomic data[25][26] - **Model Construction Process**: The Fourier Transform is defined as: $$F(f)=\int_{-\infty}^{\infty}f(x)e^{-i2\pi f(x)}\,\mathrm{d}x$$ - \(f(x)\): Time series data - \(F(f)\): Frequency domain representation Since most macroeconomic data are non-stationary, the HP filter is first applied to remove long-term trends, producing a stationary series. The Fourier Transform is then used to extract the main cycles and fit the periodic series[25][26] - **Model Evaluation**: Suitable for analyzing historical data and identifying economic cycle patterns, but assumes constant periodic structures over time, which may reduce short-term fit[3][42] 3. Model Name: Hybrid Filtering - **Model Construction Idea**: Combines the strengths of HP filtering and Fourier Transform to achieve both extrapolation capability and flexibility in cycle fitting[42] - **Model Construction Process**: - Apply Fourier Transform to identify periodic patterns in macroeconomic data - Use HP filtering to observe short-term trends in macroeconomic factors - Combine the results to create a series that retains both periodicity and trend information[42] - **Model Evaluation**: Balances the advantages of both methods, providing better adaptability for macroeconomic data analysis[42] 4. Model Name: Merrill Lynch Clock Model - **Model Construction Idea**: Divides the economic cycle into four phases based on economic growth and inflation, using PMI YoY growth as a proxy for economic growth and PPI YoY growth for inflation[68][72] - **Model Construction Process**: - Recovery: PMI YoY up, PPI YoY down → 60% stocks, 40% bonds - Expansion: PMI YoY up, PPI YoY up → 60% commodities, 40% stocks - Stagflation: PMI YoY down, PPI YoY up → 60% cash, 40% commodities - Recession: PMI YoY down, PPI YoY down → 60% bonds, 40% cash[72] - **Model Evaluation**: Achieves higher returns and Sharpe ratio compared to a balanced allocation model, with a monthly win rate of 56.49%[68][70] 5. Model Name: Monetary-Credit Model - **Model Construction Idea**: Adapts the Merrill Lynch Clock for the Chinese market by focusing on monetary and credit conditions, using M2 YoY growth for monetary policy and social financing YoY growth for credit conditions[76] - **Model Construction Process**: - Loose Monetary & Loose Credit: M2 YoY up, social financing YoY up → 60% stocks, 40% commodities - Tight Monetary & Loose Credit: M2 YoY down, social financing YoY up → 60% commodities, 40% stocks - Tight Monetary & Tight Credit: M2 YoY down, social financing YoY down → 60% cash, 40% bonds - Loose Monetary & Tight Credit: M2 YoY up, social financing YoY down → 60% bonds, 40% stocks[76] - **Model Evaluation**: Slightly lower annualized returns than the Merrill Lynch Clock but demonstrates more stable excess returns since 2020[76][85] --- Model Backtesting Results 1. HP Filter - **Annualized Excess Return**: 1.43%-3.16% for stock index timing[57][58] - **Annualized Excess Return**: 4.84%-9.91% for stock-bond timing[60][61] 2. Fourier Transform - **Core Cycle**: Identified a 38-44 month cycle across all macroeconomic factors, suggesting a 3-4 year mid-cycle pattern[26][83] 3. Merrill Lynch Clock Model - **Annualized Return**: 11.71% - **Annualized Excess Return**: 5.82% - **Sharpe Ratio**: 1.037 - **Monthly Win Rate**: 56.49%[68][70] 4. Monetary-Credit Model - **Annualized Return**: 9.93% - **Annualized Excess Return**: 4.04% - **Sharpe Ratio**: 0.589 - **Monthly Win Rate**: 56.90%[76][79] --- Quantitative Factors and Construction Methods 1. Factor Name: PMI YoY Growth - **Construction Idea**: Represents economic growth trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of the Purchasing Managers' Index (PMI)[9][83] 2. Factor Name: PPI YoY Growth - **Construction Idea**: Represents inflation trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of the Producer Price Index (PPI)[9][83] 3. Factor Name: M1 YoY Growth - **Construction Idea**: Reflects changes in narrow money supply[9][83] - **Construction Process**: Derived from the year-over-year growth rate of M1[9][83] 4. Factor Name: M2 YoY Growth - **Construction Idea**: Reflects changes in broad money supply[9][83] - **Construction Process**: Derived from the year-over-year growth rate of M2[9][83] 5. Factor Name: Social Financing YoY Growth - **Construction Idea**: Represents credit supply conditions[9][83] - **Construction Process**: Derived from the year-over-year growth rate of total social financing[9][83] 6. Factor Name: 1-Year Treasury Yield YoY Difference - **Construction Idea**: Reflects interest rate trends[9][83] - **Construction Process**: Calculated as the year-over-year difference in 1-year treasury yields[9][83] 7. Factor Name: Industrial Production YoY Growth - **Construction Idea**: Represents industrial output trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of industrial production[9][83] 8. Factor Name: Corporate Profit YoY Growth - **Construction Idea**: Reflects corporate profitability trends[9][83] - **Construction Process**: Derived from the year-over-year growth rate of corporate profits[9][83] --- Factor Backtesting Results Stock Index Timing - **Annualized Excess Return**: 1.43%-3.16% for factors like M1 YoY, PPI YoY, and PMI YoY[57][58] Stock-Bond Timing - **Annualized Excess Return**: 4.84%-9.91% for factors like M1 YoY, PPI YoY, and PMI YoY[60][61]

情绪扰动叠加资金博弈，部分品种价格波动或加大 | 研 | | | | | | | | ——金属行业周报 | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 究 | 分析师： | | 张珂 | | S1150523120001 | | SAC NO： | 2025 年 12 月 30 | | 日 | | | | | | 钢铁 | | | | | | | | | | | | 投资要点： | | | 有色金属 | | | | | | | | | | | | | | | 证券分析师 | | |  | | 行业情况及产品价格走势初判 | | | | | | | | | | 张珂 | | | | 钢铁：随着淡季的逐步深入，钢材需求改善空间不大，后续随着需求季节性走 | | | | | | | | | | | zhangke@bhzq.com | | | | | | | | | | | | | | | 研究助理 | | | | | | | | | | | 铜：目前在高铜价的影 ...

Report Industry Investment Rating No information provided in the given content. Core Viewpoints - The issuance guidance rates announced by the National Association of Financial Market Institutional Investors during the period from December 22 to December 28, 2025, showed a differentiated trend, with most high - grade rates declining and most medium - and low - grade rates rising, with an overall change range of - 3 BP to 2 BP [1][15][63]. - The issuance scale of credit bonds decreased compared with the previous period. Corporate bonds remained at zero issuance, the issuance amounts of corporate bonds and private placement notes decreased, while the issuance amounts of medium - term notes and commercial paper increased. The net financing of credit bonds decreased compared with the previous period [1][13][63]. - In the secondary market, the trading volume of credit bonds increased compared with the previous period. The trading volumes of corporate bonds, corporate bonds, medium - term notes, and private placement notes increased, while the trading volume of commercial paper decreased [1][19][63]. - The yields of most credit bonds declined during this period. The credit spreads of medium - and short - term notes, corporate bonds, and urban investment bonds were differentiated, with most 1 - year and 7 - year spreads widening and most 3 - year and 5 - year spreads narrowing [1][22][63]. - From the perspective of absolute return, the shortage of supply and relatively strong allocation demand will promote the continued recovery of credit bonds. In the long run, the yields are still in a downward channel, and the idea of increasing allocation during adjustments is still feasible. From the perspective of relative return, although the compression space of credit spreads at all tenors is insufficient, the probability of unilateral callback in the short term is also small. Therefore, it is still possible to achieve the coupon strategy through credit downgrade and extending the duration [1][63]. Summary by Directory 1. Primary Market Situation 1.1 Issuance and Maturity Scale - From December 22 to December 28, 2025, a total of 211 credit bonds were issued, with an issuance amount of 254.432 billion yuan, a 2.51% decrease compared with the previous period. The net financing of credit bonds was 42.433 billion yuan, a decrease of 18.343 billion yuan compared with the previous period [13]. - Corporate bonds had zero issuance, with a net financing of - 6.252 billion yuan, an increase of 0.498 billion yuan compared with the previous period. Corporate bonds issued 74 bonds, with an issuance amount of 49.363 billion yuan, a 46.55% decrease compared with the previous period, and a net financing of 15.757 billion yuan, a decrease of 29.511 billion yuan compared with the previous period. Medium - term notes issued 66 bonds, with an issuance amount of 109.469 billion yuan, a 30.15% increase compared with the previous period, and a net financing of 78.532 billion yuan, an increase of 37.169 billion yuan compared with the previous period. Commercial paper issued 60 bonds, with an issuance amount of 90.117 billion yuan, a 23.36% increase compared with the previous period, and a net financing of - 44.152 billion yuan, a decrease of 25.187 billion yuan compared with the previous period. Private placement notes issued 11 bonds, with an issuance amount of 5.483 billion yuan, a 52.24% decrease compared with the previous period, and a net financing of - 1.452 billion yuan, a decrease of 1.312 billion yuan compared with the previous period [13]. 1.2 Issuance Interest Rates - The issuance guidance rates announced by the National Association of Financial Market Institutional Investors were differentiated, with most high - grade rates declining and most medium - and low - grade rates rising, with an overall change range of - 3 BP to 2 BP. By tenor, the 1 - year variety had an interest rate change range of - 2 BP to 0 BP, the 3 - year variety had an interest rate change range of - 3 BP to 2 BP, the 5 - year variety had an interest rate change range of - 3 BP to 2 BP, and the 7 - year variety had an interest rate change range of - 2 BP to 1 BP. By grade, the key AAA - grade and AAA - grade varieties had an interest rate change range of - 3 BP to - 1 BP, the AA + - grade variety had an interest rate change range of - 1 BP to 2 BP, the AA - grade variety had an interest rate change range of 0 BP to 2 BP, and the AA - - grade variety had an interest rate change range of 0 BP to 1 BP [15]. 2. Secondary Market Situation 2.1 Market Trading Volume - From December 22 to December 28, 2025, the total trading volume of credit bonds was 1.030617 trillion yuan, a 7.72% increase compared with the previous period. Corporate bonds, corporate bonds, medium - term notes, commercial paper, and private placement notes traded 28.754 billion yuan, 446.075 billion yuan, 347.636 billion yuan, 145.597 billion yuan, and 62.555 billion yuan respectively [19]. 2.2 Credit Spreads - In medium - and short - term notes, the credit spreads of each variety were differentiated. The 1 - year credit spreads widened; among the 3 - year notes, the credit spreads of AA - grade and AA - - grade widened, while the spreads of AAA - grade and AA + - grade narrowed; the 5 - year credit spreads narrowed; among the 7 - year notes, the credit spread of AAA - grade narrowed, while the spread of AA + - grade widened [22]. - In corporate bonds, the credit spreads of each variety were differentiated. The 1 - year AAA - grade credit spread narrowed, while the spreads of other varieties widened; among the 3 - year notes, the credit spreads of AAA - grade and AA + - grade narrowed, while the spreads of AA - grade and AA - - grade widened; the 5 - year credit spreads narrowed; among the 7 - year notes, the credit spread of AAA - grade narrowed, while the spreads of other varieties widened [27]. - In urban investment bonds, the credit spreads of each variety were differentiated. The 1 - year credit spreads widened; the 3 - year credit spreads narrowed; among the 5 - year notes, the credit spreads of AAA - grade and AA + - grade narrowed, while the spreads of AA - grade and AA - - grade widened; among the 7 - year notes, the credit spread of AAA - grade narrowed, while the spreads of other varieties widened [37]. 2.3 Term Spreads and Rating Spreads - For AA + medium - and short - term notes, the 3Y - 1Y term spread narrowed by 1.20 BP, the 5Y - 3Y term spread narrowed by 3.20 BP, and the 7Y - 3Y term spread widened by 3.22 BP. The 3Y - 1Y term spread was at a low - to - middle historical percentile (21.6%), the 5Y - 3Y term spread was at a low - to - middle historical percentile (34.5%), and the 7Y - 3Y term spread was at a historical median (41.9%). In terms of rating spreads, the (AA - )-(AAA) spread of 3 - year medium - and short - term notes widened by 3.00 BP, the (AA)-(AAA) spread widened by 3.00 BP, and the (AA + )-(AAA) spread widened by 2.00 BP [47]. - For AA + corporate bonds, the 3Y - 1Y term spread narrowed by 3.69 BP, the 5Y - 3Y term spread widened by 3.12 BP, and the 7Y - 3Y term spread widened by 8.55 BP. The 3Y - 1Y term spread was at a historical low (12.2%), the 5Y - 3Y term spread was at a low - to - middle historical percentile (36.3%), and the 7Y - 3Y term spread was at a historical median (42.9%). In terms of rating spreads, the (AA - )-(AAA) spread of 3 - year corporate bonds widened by 6.00 BP, the (AA)-(AAA) spread widened by 6.00 BP, and the (AA + )-(AAA) spread remained unchanged from the previous period [52]. - For AA + urban investment bonds, the 3Y - 1Y term spread narrowed by 0.72 BP, the 5Y - 3Y term spread narrowed by 1.67 BP, and the 7Y - 3Y term spread widened by 2.25 BP. The 3Y - 1Y term spread was at a low - to - middle historical percentile (20.3%), the 5Y - 3Y term spread was at a low - to - middle historical percentile (31.2%), and the 7Y - 3Y term spread was at a historical median (46.8%). In terms of rating spreads, the (AA - )-(AAA) spread of 3 - year urban investment bonds narrowed by 1.00 BP, the (AA)-(AAA) spread widened by 1.00 BP, and the (AA + )-(AAA) spread remained unchanged from the previous period [54]. 3. Credit Rating Adjustment and Default Bond Statistics 3.1 Credit Rating Adjustment Statistics - From December 22 to December 28, 2025, a total of 2 companies had their ratings (including outlooks) adjusted, both of which were upgrades. They were Wenzhou Transportation Development Group Co., Ltd. and Guangxi Energy Group Co., Ltd. [60]. 3.2 Default and Extension Bond Statistics - There were no credit bond defaults during the period from December 22 to December 28, 2025. One issuer, Bohai Leasing Co., Ltd., had its credit bonds extended, namely "18 Bojin 03" and "18 Bozu 05", with a total bond balance of 823 million yuan at the time of extension [62]. 4. Investment Viewpoints - The overall idea is to continue to be optimistic about the credit bond market in the long term, but pay attention to short - term fluctuations. In terms of configuration, the coupon strategy can be moderately optimistic, and the trading strategy can be kept optimistic. When selecting bonds, focus on the trend of interest - rate bonds and the coupon value of individual bonds. At the same time, it is possible to achieve the coupon strategy through credit downgrade and extending the duration according to one's own capital characteristics, but pay attention to the rhythm [1][63].

1. Report Industry Investment Rating - No relevant content provided in the given report. 2. Core Viewpoints of the Report - The report focuses on the main characteristics of bond ETF premiums and discounts and their impact on component bonds. It analyzes the influence mechanism of bond ETF premiums and discounts on component bonds, the characteristics and influencing factors of premiums and discounts, the volume - price changes of component bonds during premium and discount periods, and provides corresponding conclusions and insights [8][61]. 3. Summary According to Relevant Catalogs 3.1 Bond ETF Premium and Discount Impact Mechanism on Component Bonds - The premium - discount rate is used to measure the deviation between the bond ETF price and the net value. In the discount stage, investors redeem shares in the primary market and sell ETFs in the secondary market, leading to a decline in the ETF price and net value. Arbitrage behavior can repair the discount to some extent. In the premium stage, investors subscribe for shares in the primary market and buy ETFs in the secondary market, causing the ETF price and net value to rise, and arbitrage can repair the premium. Different redemption mechanisms (physical redemption and cash redemption) have different impacts on ETFs [9][10][12]. 3.2 Characteristics and Influencing Factors of Bond ETF Premiums and Discounts 3.2.1 When Do Premiums and Discounts Occur? - Local - government bond ETFs had continuous deep discounts from 2022 - 2023, mainly due to low trading activity. Since 2024, they have maintained a slight premium. Credit - type ETFs had discounts from September 2022 to April 2023 and in the second half of 2025, and slight premiums in 2024 and the second quarter of 2025. The physical redemption mode often has a deeper discount than the cash redemption mode [17][20][21]. 3.2.2 How Do Turnover, Share, and Net Value Change During Premium and Discount Stages? - Turnover: In the deep - discount stage, turnover is prone to peak, but the correlation has weakened since 2025 [28][29][40]. - Share: There is synchronicity between short - term deep discounts and share redemptions [33][34][36]. - Net Value: In the deep - discount stage, the ETF net value often recovers before the price [38]. - Summary: In the deep - discount stage, the underlying asset liquidity of the ETF is extremely restricted. Turnover is prone to peak, but it does not necessarily correspond to continuous large - scale redemptions. Since 2025, the correlation between the premium - discount rate and turnover, share, and net - value changes has weakened [40]. 3.3 Volume - Price Change Characteristics of ETF Component Bonds During Premium and Discount Stages 3.3.1 Volume: Trading Activity - The trading activity is measured by the ratio of the number of bonds with transactions to the number of bonds without transactions. The trading activity of component bonds in different indexes responds differently to ETF premiums and discounts. The urban investment index shows an anti - intuitive phenomenon, while the Shanghai and Shenzhen market - making indexes conform to the theoretical mechanism [43][44][46]. 3.3.2 Price: Credit Spread - The credit spread is measured by the difference between the bond's yield to maturity and the yield of the same - term China Development Bank bond. In the deep - discount stage of the urban investment index, the credit spread of non - component bonds widens more significantly. In the Shanghai and Shenzhen market - making indexes, the credit spread of component bonds widens significantly during premium and discount periods, indicating higher price - discovery efficiency [53][54][56]. 3.4 Main Conclusions and Insights - In the deep - discount stage, the underlying asset liquidity is restricted, and large - scale redemptions often occur during short - term discounts in the continuous premium stage. Different indexes have different response patterns to ETF premiums and discounts. When selecting bonds in the discount stage, it is necessary to judge the source of the discount. The lack of liquidity in the credit - bond market is a major constraint, and bond ETFs should improve market efficiency and provide protection during market adjustments [62][63].

投资策略 [Table_MainInfo] A 股业绩支撑：政策呵护逻辑下的价格水平 ——A 股市场投资策略专题报告 | 究 | 分析师： | 宋亦威 | SAC NO： | 年 | 月 | 日 | S1150514080001 | 2025 | 12 | 30 | [Table_Analysis] | [Table_Summary] | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 证券分析师 | 投资要点： | 宋亦威 | | | | | | | | | | | | 就 | 年行情的业绩支撑而言，并不来自于量的超预期增长，而来自于 | ⚫ | 2026 | 022-23861608 | 价格端的有效支撑。从今年 | 月、11 | 月的情况来看，PPI | 价格已经连续 | 10 | | | | | songyw@bhzq.com | 两月出现环比转正，这种态势如能持续，将意味着明年 | 同比降幅将 | PPI | | | | | | | | | | | [Table_Author] | 严 ...

Macro and Strategy Research - The profit growth rate of industrial enterprises in China has marginally declined by 1.8 percentage points to 0.1% year-on-year for the period from January to November 2025, with November showing a significant drop of 13.1% compared to October, which is a decrease of 7.6 percentage points [4] - The industrial added value growth rate for November was 4.8%, a slight decrease of 0.1 percentage points from October, influenced by insufficient domestic demand and a high base effect from the previous year [4] - The revenue profit margin for January to November was 5.29%, down by 2.0% year-on-year, indicating a further expansion of the decline compared to the previous months [4] - Among 41 industrial sectors, 18 sectors achieved positive profit growth during the same period, with notable growth in sectors such as black metal smelting and processing, non-ferrous metal mining, and high-tech manufacturing [5] Fund Research - The market saw a continued inflow of nearly 50 billion yuan into the CSI A500 index, with the ETF market scale reaching a new high of over 6 trillion yuan [7][11] - The average return for equity funds was 2.69%, with 87.08% of funds reporting positive returns, while bond funds and other categories also showed positive performance [10] - The ETF market experienced a net inflow of 914.98 billion yuan, with bond ETFs leading the inflow at 599.48 billion yuan [10] Company Research: WuXi AppTec - WuXi AppTec is positioned as a leading integrated CRDMO provider, offering end-to-end drug development and manufacturing services, with a focus on continuous development through both organic and inorganic growth strategies [15] - The CRO industry is thriving due to the high costs and long timelines associated with drug development, leading to increased demand for specialized services [15] - WuXi Chemistry reported a strong performance in its integrated services, with a significant number of new molecules added to its pipeline, indicating robust growth potential [15] - The company has streamlined its operations by divesting its clinical services research business, allowing it to focus on core competencies and enhance its service offerings [16] Industry Research: Light Industry Manufacturing & Textile Apparel - The Chinese government plans to continue funding support for the "old-for-new" consumption policy in 2026, which has already driven over 2.5 trillion yuan in sales for related products in 2025 [19] - Retail sales of clothing and footwear saw a year-on-year increase of 3.5% in November, reflecting a positive trend in consumer spending [19] - The light industry manufacturing sector underperformed compared to the CSI 300 index, indicating challenges in the current market environment [19]