1. Report Information - Report Type: Fixed Income Report - Release Time: July 22, 2025 - Analysts: Liang Weichao, Xie Peng [1][2] 2. Core Viewpoints - The weekly report tracks the liquidity scores of individual bonds in different bond sectors based on the bond asset liquidity scores of qb. - For urban investment bonds, short - duration and low - rated high - grade liquidity bonds have increased. Regionally, Shandong, Sichuan, Tianjin, and Chongqing remained stable, while Jiangsu decreased. In terms of maturity, high - grade liquidity bonds within 1 year, 1 - 2 years, and over 5 years increased, while those in 2 - 3 years and 3 - 5 years decreased. In terms of implied ratings, the number of high - grade liquidity bonds with an implied rating of AAA remained stable, those with AA+ and AA decreased, and those with AA(2) and AA - increased. - For industrial bonds, the number of low - rated high - grade liquidity bonds increased. By industry, high - grade liquidity bonds in the coal industry increased, while those in real estate, public utilities, steel, and transportation remained stable. In terms of maturity, high - grade liquidity bonds within 1 year, 2 - 3 years, and 3 - 5 years increased, while those in 1 - 2 years and over 5 years remained stable. In terms of implied ratings, the number of high - grade liquidity bonds with an implied rating of AAA - and AA increased, while those with AAA+, AAA, and AA+ remained stable. [3][8][18] 3. Summary by Directory 3.1 Urban Investment Bonds - **Liquidity Changes**: Short - duration and low - rated high - grade liquidity urban investment bonds increased. Regionally, Shandong, Sichuan, Tianjin, and Chongqing remained stable, Jiangsu decreased. In terms of maturity, high - grade liquidity bonds within 1 year, 1 - 2 years, and over 5 years increased, 2 - 3 years and 3 - 5 years decreased. In terms of implied ratings, AAA remained stable, AA+ and AA decreased, AA(2) and AA - increased. [8] - **Yield Changes**: Regionally, the yields of high - grade liquidity urban investment bonds in Jiangsu, Shandong, Sichuan, Tianjin, and Chongqing mainly decreased, with a decline of 2 - 5bp. By maturity and implied rating, the yields of high - grade liquidity urban investment bonds mainly decreased, with a small decline of 1 - 2bp. [9] - **Top 20 Ascending Entities in Liquidity Score**: The entity levels are mainly AA, concentrated in regions such as Zhejiang, Sichuan, Tianjin, and Beijing, and the industries mainly involve construction decoration and comprehensive industries. - **Top 20 Ascending Bonds in Liquidity Score**: The bonds are mainly from regions such as Beijing, Hunan, and Zhejiang. - **Top 20 Descending Entities in Liquidity Score**: The entity levels are mainly AA, with regional distributions mainly in Zhejiang, Jiangsu, and Shandong, and the industries are mainly construction decoration, transportation, and real estate. - **Top 20 Descending Bonds in Liquidity Score**: The bonds are mainly from regions such as Jiangsu, Zhejiang, and Shandong. [12][13][15][17] 3.2 Industrial Bonds - **Liquidity Changes**: The number of low - rated high - grade liquidity industrial bonds increased. By industry, high - grade liquidity bonds in the coal industry increased, real estate, public utilities, steel, and transportation remained stable. In terms of maturity, high - grade liquidity bonds within 1 year, 2 - 3 years, and 3 - 5 years increased, 1 - 2 years and over 5 years remained stable. In terms of implied ratings, AAA - and AA increased, AAA+, AAA, and AA+ remained stable. [18] - **Yield Changes**: By industry, the yields of high - grade liquidity bonds in real estate, public utilities, transportation, coal, and steel mainly decreased, with the decline concentrated in 1 - 4bp; the yield of real estate decreased by more than 10bp. By maturity, the yields of high - grade liquidity bonds in each maturity mainly decreased, with a decline of 3 - 5bp. By implied level, the yields of high - grade liquidity bonds in each implied level mainly decreased, with the decline concentrated in 2 - 5bp. [20] - **Top 20 Ascending Entities in Liquidity Score**: The industries are mainly construction decoration, public utilities, and commerce, and the entity levels are mainly AAA and AA+. - **Top 20 Ascending Bonds in Liquidity Score**: The industries are mainly transportation and public utilities. - **Top 20 Descending Entities in Liquidity Score**: The industries are mainly construction decoration, public utilities, and commerce and retail, and the entity levels are mainly AAA and AA. - **Top 20 Descending Bonds in Liquidity Score**: The industries are mainly public utilities and transportation. [21][24][27][29][30]

Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [1] Core Viewpoints - The real estate industry is currently in a state of overall surplus and structural shortage. According to the National Bureau of Statistics, from January to June 2025, the national commercial housing sales area decreased by 3.5% year-on-year, and sales revenue fell by 5.5%. In June alone, the sales area saw a year-on-year decline of 5.5%, with sales revenue dropping by 10.8%. The main reasons for this are high base figures and weak demand. However, the decline in construction starts and completions has narrowed, reflecting the progress of "guaranteeing delivery" and the recovery of land auctions in core cities. The number of fourth-generation residential units available for sale last year and expected to enter the market this year remains relatively low. The recent Central Urban Work Conference emphasized the shift from incremental expansion to stock quality improvement, focusing on "good houses + stock operation" for real estate companies [4][5]. Summary by Sections Industry Fundamentals Tracking - New housing transaction and inventory: In the last week, the new housing transaction area in 30 major cities was 1.2098 million square meters, with a cumulative transaction area of 50.4858 million square meters for the year, down 3.4% year-on-year. The average transaction area over the past four weeks was 1.8994 million square meters, down 16.9% year-on-year and 10.5% month-on-month. Among these, first-tier cities had an average transaction area of 550,400 square meters, down 16.9% year-on-year and 5.2% month-on-month [5][13]. - Second-hand housing transactions and listings: In the last week, the transaction area of second-hand houses in 20 cities was 201,990 square meters, with a cumulative transaction area of 63.9576 million square meters for the year, up 18% year-on-year. The average transaction area over the past four weeks was 209,680 square meters, down 8.8% year-on-year and 3.5% month-on-month [6][18]. - Land market transactions: In the last week, 100 major cities had 63 new residential land supplies and 16 transactions. The average transaction price for residential land was 7,059.75 yuan per square meter, with a premium rate of 14.19%, up 6.13 percentage points month-on-month [6][28]. Market Review - Last week, the A-share real estate index fell by 2.17%, while the CSI 300 index rose by 1.09%, indicating that the real estate index underperformed the CSI 300 by 3.26 percentage points. In the Hong Kong market, the Hang Seng Property Services and Management Index fell by 0.39%, while the Hang Seng Composite Index rose by 3.31%, showing a similar underperformance [7][32].

Quantitative Models and Construction Methods Diffusion Index Model - **Model Name**: Diffusion Index Model - **Construction Idea**: The model monitors the relative performance of stocks within the micro-cap index over different time windows to identify potential turning points in market trends [41][42] - **Construction Process**: - The horizontal axis represents the percentage change in stock prices from +10% to -10% (1.1 to 0.9) - The vertical axis represents the length of the review window, ranging from 20 days to 10 days - For example, at horizontal axis 0.95 and vertical axis 15 days, the value of 0.37 indicates that if all stocks in the micro-cap index drop by 5% after 5 days, the diffusion index value is 0.37 - Formula: Diffusion Index = $\frac{\text{Number of stocks outperforming the benchmark}}{\text{Total number of stocks}}$ [41][42] - **Evaluation**: The model effectively identifies market trends but faces challenges when bottom-performing stocks are abandoned during strong upward trends [42] - **Testing Results**: Current diffusion index value is 0.94, indicating a strong upward trend [41][42] Threshold Methods - **Model Name**: Threshold Methods (First Threshold Method and Delayed Threshold Method) - **Construction Idea**: These methods use predefined thresholds to generate trading signals based on the diffusion index [45][49] - **Construction Process**: - First Threshold Method: Triggered a sell signal on May 8, 2025, when the diffusion index reached 0.9850 [45] - Delayed Threshold Method: Triggered a sell signal on May 15, 2025, when the diffusion index reached 0.8975 [49] - **Evaluation**: These methods provide clear trading signals but may lag during rapid market changes [45][49] - **Testing Results**: First Threshold Method value: 0.9850; Delayed Threshold Method value: 0.8975 [45][49] Dual Moving Average Method - **Model Name**: Dual Moving Average Method - **Construction Idea**: This method uses adaptive moving averages to generate trading signals based on market trends [50] - **Construction Process**: - The method compares short-term and long-term moving averages to identify buy or sell signals - On July 3, 2025, the method generated a buy signal [50] - **Evaluation**: The method adapts well to changing market conditions and provides timely signals [50] - **Testing Results**: Buy signal generated on July 3, 2025 [50] --- Quantitative Factors and Construction Methods Top Performing Factors - **Factor Names**: Non-liquidity factor, Unadjusted stock price factor, Beta factor, Standardized expected earnings factor, PE_TTM reciprocal factor [4][19][36] - **Construction Idea**: These factors are derived from stock characteristics and financial metrics to predict future returns [4][19][36] - **Construction Process**: - Non-liquidity factor: Measures the illiquidity of stocks - Unadjusted stock price factor: Uses raw stock prices without adjustments - Beta factor: Captures the sensitivity of stock returns to market movements - Standardized expected earnings factor: Standardizes analysts' earnings forecasts - PE_TTM reciprocal factor: Calculates the reciprocal of the trailing twelve-month price-to-earnings ratio - **Evaluation**: These factors show strong predictive power for stock returns [4][19][36] - **Testing Results**: - Non-liquidity factor IC: 0.353 (historical average: 0.04) - Unadjusted stock price factor IC: 0.348 (historical average: -0.016) - Beta factor IC: 0.247 (historical average: 0.005) - Standardized expected earnings factor IC: 0.141 (historical average: 0.014) - PE_TTM reciprocal factor IC: 0.092 (historical average: 0.017) [4][19][36] Underperforming Factors - **Factor Names**: Turnover factor, 10-day total market capitalization turnover rate factor, Liquidity factor, 10-day free float market capitalization turnover rate factor, Leverage factor [4][19][36] - **Construction Idea**: These factors are derived from trading activity and financial leverage metrics [4][19][36] - **Construction Process**: - Turnover factor: Measures trading volume relative to market capitalization - 10-day total market capitalization turnover rate factor: Calculates turnover rate over a 10-day window - Liquidity factor: Assesses the ease of trading stocks - 10-day free float market capitalization turnover rate factor: Similar to the total turnover rate but focuses on free float shares - Leverage factor: Measures financial leverage of companies - **Evaluation**: These factors exhibit weak predictive power and negative correlations with returns [4][19][36] - **Testing Results**: - Turnover factor IC: -0.336 (historical average: -0.082) - 10-day total market capitalization turnover rate factor IC: -0.286 (historical average: -0.06) - Liquidity factor IC: -0.278 (historical average: -0.041) - 10-day free float market capitalization turnover rate factor IC: -0.276 (historical average: -0.062) - Leverage factor IC: -0.225 (historical average: -0.006) [4][19][36] --- Strategy Performance Small-Cap Low-Volatility 50 Strategy - **Strategy Name**: Small-Cap Low-Volatility 50 Strategy - **Construction Idea**: Selects 50 stocks with small market capitalization and low volatility from the micro-cap index [7][19][37] - **Construction Process**: - Stocks are selected bi-weekly based on market capitalization and volatility criteria - Benchmark: Wind Micro-Cap Index (8841431.WI) - Transaction cost: 0.3% on both sides [7][19][37] - **Evaluation**: The strategy demonstrates strong performance but occasionally underperforms the benchmark [7][19][37] - **Testing Results**: - 2024 return: 7.07% (excess return: -2.93%) - 2025 YTD return: 62.07% (weekly excess return: -2.44%) [7][19][37]

Investment Rating - Industry investment rating: Stronger than the market, maintained [1] Core Viewpoints - Focus on the progress of phasing out outdated capacity and upgrading in the petrochemical industry [2] - The petrochemical index performed relatively well this week, closing at 2272.55 points, up 1.13% from last week [5] - The best performer within the petrochemical sector was oil extraction III, which rose by 2.83% [3][5] Summary by Sections 1. Oil Market - Energy prices have shown a slight decline; as of July 18, Brent crude futures and TTF natural gas futures closed at $69.33 per barrel and €33.71 per MWh, down 1.4% and 5.3% respectively [8] - U.S. crude oil inventory increased by 9,346 thousand barrels to 1,255,837 thousand barrels, while total inventory (including strategic reserves) rose by 9,046 thousand barrels to 1,658,540 thousand barrels [12] 2. Polyester - The price of polyester filament has decreased, with POY, DTY, and FDY prices at 6,550, 7,800, and 6,800 yuan per ton respectively, showing mixed changes in price spreads [17] - The inventory days for polyester filament in Jiangsu and Zhejiang increased, with FDY, DTY, and POY inventory days at 25.6, 30.7, and 25.4 days [22] 3. Olefins - Sample prices for polyethylene (PE) and polypropylene (PP) remained stable at 7,700 and 8,200 yuan per ton, with a total petrochemical inventory of 770,000 tons, an increase of 40,000 tons from last week [26]

1. Report Industry Investment Rating No information provided on the industry investment rating in the report. 2. Core Viewpoints - The change in the bond market's "macro - narrative" is a reaction to the over - trading of the "consensus expectation", and the adjustment is a trading opportunity. The probability of long - term yield decline has not decreased substantially, and the odds have increased during the adjustment [3][10]. - The current situation is at the "first step", while the market has already traded the "second step" and "third step". The basis for the "macro - narrative" of large - category assets is not solid [3][11]. - There are significant differences in policy expectations among bonds, equities, and commodities. The bond market has weak expectations for macro - policies, the equity market has expectations, and the commodity market has the strongest expectations [3][13]. - The money market has no risk, and short - term bonds are in a reasonable valuation range. The money market will remain stable and loose overall, and the risk of money price fluctuations is small [4][15]. - Maintain the view that bond adjustment is a trading opportunity, and appropriately layout for the policy expectation game opportunity of the end - of - month meeting. After the adjustment, the bond market becomes a better layout opportunity, and there is an expectation gap in the pricing of policy expectations among large - category assets [4][17]. 3. Summary of Relevant Catalogs 3.1. Macro - Narrative and Policy Expectation Differences - The bond market's "macro - narrative" has changed from trading the lagging impact of external shocks to worrying about the impact of hedging policies on prices and demand. This change is a reaction to the over - trading of the "consensus expectation" [10]. - The real economy is still maintaining its prosperity due to previous policy support and the "strong export" at the middle of the year. The bond market has already traded the marginal weakening of the third - quarter economy and the policy expectations of "anti - involution" and demand - side stimulus [3][11]. - The bond market has not significantly priced in policy stimulus expectations, the equity market has expectations for macro - policies, and the commodity market has the strongest expectations for macro - policies. There is an expectation gap that can be exploited in the end - of - month important meeting [13]. 3.2. Money Market and Short - Term Bond Situation - Although there were fluctuations in the money market in mid - July, mainly due to the tax period, the money market will remain stable and loose overall. The risk of money price fluctuations is small [4][15]. - The pricing center of 1 - year inter - bank certificates of deposit of national joint - stock banks is around 1.6%, and 1.7% is the trading ceiling for the second half of the year. There is net financing pressure in the third quarter, but the risk of price increase is small [4][15]. - The repair of the liability side of large - scale banks continues, which is supported by the deposit growth in June [4][15]. 3.3. Bond Market Investment Strategy - The previous bond market adjustment is a reaction to the over - trading of the "consensus expectation". After the adjustment, the distribution of chips is no longer concentrated, which is a good layout opportunity [4][17]. - There is an expectation gap in the pricing of policy expectations among large - category assets. After the disturbance of risk sentiment, one can play the expectation gap before the important meeting at the end of July [4][17].

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Construction Idea**: The model is based on price momentum principles, aiming to capture upward trends in industry performance[25][37] **Construction Process**: 1. Calculate the diffusion index for each industry based on price momentum 2. Rank industries by their diffusion index values 3. Select industries with the highest diffusion index values for portfolio allocation **Formula**: Not explicitly provided in the report **Evaluation**: The model performs well during upward trends but struggles during reversals, as seen in historical performance[25][37] - **Model Name**: GRU Factor Model **Construction Idea**: The model leverages GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level volume and price data for industry rotation[38][33] **Construction Process**: 1. Input minute-level volume and price data into the GRU network 2. Train the model using historical data to identify industry rotation signals 3. Generate GRU factor scores for each industry and rank them 4. Allocate portfolio weights based on GRU factor rankings **Formula**: Not explicitly provided in the report **Evaluation**: The model performs well in short cycles but faces challenges in long cycles and extreme market conditions[38][33] Model Backtesting Results - **Diffusion Index Model**: - Monthly average return: -0.81% - Excess return over equal-weighted industry benchmark: -1.61% (July 2025)[29] - Year-to-date excess return: 1.48%[24][29] - **GRU Factor Model**: - Weekly average return: -0.46% - Excess return over equal-weighted industry benchmark: -1.27% (July 2025)[36] - Year-to-date excess return: -5.75%[33][36] Quantitative Factors and Construction Methods - **Factor Name**: Diffusion Index **Construction Idea**: Measures industry momentum based on price trends[25][26] **Construction Process**: 1. Calculate the diffusion index for each industry using price data 2. Rank industries by diffusion index values 3. Select industries with the highest diffusion index values for portfolio allocation **Formula**: Not explicitly provided in the report **Evaluation**: Effective in capturing upward trends but vulnerable to reversals[25][26] - **Factor Name**: GRU Factor **Construction Idea**: Utilizes GRU deep learning networks to analyze minute-level volume and price data for industry rotation[38][33] **Construction Process**: 1. Input minute-level volume and price data into the GRU network 2. Train the model using historical data to identify industry rotation signals 3. Generate GRU factor scores for each industry and rank them 4. Allocate portfolio weights based on GRU factor rankings **Formula**: Not explicitly provided in the report **Evaluation**: Performs well in short cycles but struggles in long cycles and extreme market conditions[38][33] Factor Backtesting Results - **Diffusion Index Factor**: - Top-ranked industries (July 18, 2025): Comprehensive Finance (1.0), Comprehensive (0.998), Non-Banking Finance (0.996), Steel (0.995), Nonferrous Metals (0.994), Communication (0.993)[26][27] - Weekly changes in rankings: Consumer Services (+0.224), Food & Beverage (+0.208), National Defense (+0.091)[28] - **GRU Factor**: - Top-ranked industries (July 18, 2025): Banking (2.68), Transportation (2.42), Nonferrous Metals (-0.87), Steel (-1.92), Construction (-2.19), Coal (-2.36)[34] - Weekly changes in rankings: Building Materials (+), Banking (+), Comprehensive Finance (+)[34]

Investment Rating - The industry investment rating is "Outperform" [2] Core Insights - As of July 20, 2025, among the 120 tracked defense industry listed companies, 37 have disclosed their H1 2025 earnings forecasts, with significant growth in the shipbuilding and defense information sectors [5][12] - The defense information sector shows high growth potential, with companies like Gaode Infrared and Chengchang Technology forecasting net profit growth rates of 846% and 335% respectively [6][12] - The shipbuilding sector also demonstrates strong performance, with companies such as China Shipbuilding and China Heavy Industry predicting net profit growth rates of 109% and 105% respectively [6][12] - The report suggests that the defense industry is expected to see an inflection point in orders, driven by new technologies and products aimed at enhancing equipment performance and reducing costs [14] Summary by Sections Industry Overview - The closing index for the defense industry is at 1669.63, with a 52-week high of 1712.48 and a low of 1113.62 [2] Performance Analysis - The defense sector index has outperformed the broader market, with a 2.58% increase in the China Securities Defense Index and a 2.26% increase in the Shenwan Defense Index [15] - The top-performing stocks in the defense sector this week include Yingliu Co. (+20.37%) and Feiliwa (+15.98%) [18] Earnings Forecasts - Among the 37 companies that disclosed earnings forecasts, 14 expect positive growth, while 12 anticipate losses [12] - Notable companies with high growth forecasts include Nairui Radar, Gaode Infrared, and China Heavy Industry, all projecting substantial increases in net profits [6][12] Investment Recommendations - The report recommends focusing on two main investment themes: aerospace and new technologies/products with greater elasticity [14] - Suggested companies for investment include Feiliwa, Gaode Infrared, and China Shipbuilding among others [14] Valuation Metrics - As of July 18, 2025, the defense sector's PE-TTM valuation stands at 117.29, with 83.01% of historical data indicating lower valuations [20][22]

Industry Investment Rating - The industry investment rating is maintained at "Outperform" [2] Core Insights - The report highlights that the recombinant Staphylococcus aureus vaccine by Olin Biotech is expected to reveal its Phase III clinical trial results by the end of 2025, potentially becoming the world's first vaccine for superbugs [5][14][15] - The pharmaceutical and biotechnology sector has seen a 4% increase this week, outperforming the CSI 300 index by 2.91 percentage points, ranking second among 31 sub-industries [6][23] - The report emphasizes the strong performance of the raw material drug sector, which increased by 7.01%, and the overall positive trend in innovative drugs driven by overseas business development expectations and supportive policy documents [29][30] Summary by Sections Weekly Insights - Olin Biotech's vaccine is anticipated to fill a significant gap in the market for Staphylococcus aureus vaccines, with a high disease burden and economic loss associated with infections [5][14][15] - The pharmaceutical sector's performance is bolstered by a 9.75% increase since July 2025, again outperforming the CSI 300 index [6][23] Subsector Performance - The report details that the raw material drug sector had the highest increase this week, followed by chemical preparations and other biological products [6][26] - The report suggests a focus on innovative drugs, particularly those with strong clinical data and overseas market potential, as well as medical devices benefiting from government procurement policies [29][30][33] Recommended and Beneficiary Stocks - Recommended stocks include Olin Biotech, Xinda Biopharmaceutical, and innovative drug companies such as Hengrui Medicine and BeiGene [7][29] - Beneficiary stocks in the medical device sector include Mindray Medical and Weigao Group, while the pharmaceutical sector includes companies like Zai Lab and Innovent Biologics [7][29][34]

Economic Overview - In July, economic prices increased while volumes decreased, indicating a search for rebalancing in supply and demand, with marginal economic growth expected to slow down[1] - The Producer Price Index (PPI) showed a month-on-month increase, with the year-on-year decline in growth narrowing, primarily driven by the "anti-involution" policy expectations[1][45] Real Estate Market - The sales sentiment in the real estate market weakened, with both month-on-month and year-on-year growth turning negative; the average daily transaction area in 30 major cities decreased by 15.85% compared to June[2][11] - It is anticipated that first-tier city housing prices may stabilize by the end of the year, while second-tier cities may see stabilization by June next year[2][48] Industrial Demand - Industrial demand showed a mild recovery, with the rebar production rate increasing to 43.06%, up 0.87 percentage points from June, while prices slightly decreased by 0.16%[15] - The average operating rate for asphalt plants rose to 32.4%, indicating a recovery in demand, with asphalt inventory decreasing by 7.31%[18] Consumer Behavior - July consumer spending is expected to remain resilient, supported by a surge in tourism during the summer, with domestic tourism projected to exceed 2.5 billion trips, recovering to over 115% of 2019 levels[26] - The average daily subway ridership in major cities increased, reflecting a rebound in travel demand during the summer[23] Risks and Challenges - Potential risks include unexpected intensification of global trade frictions, geopolitical conflicts, and policy effects falling short of expectations[3]

3000 8000 2000 策略观点 等待经济政策为市场定调 投资要点 本周 A 股延续上涨势头。本周主要股指继续全数上涨,其中创业 板指上涨 3.17%表现最佳,其余主要指数中代表权重蓝筹的上证指数 和上证 50 相对较弱,分别上涨 0.69%和 0.28%。风格方面,本周风格 层面出现分化,成长、消费和周期风格继续上涨,金融和稳定风格出 现回调。市值风格方面,本周大中小盘市值风格均有上涨,中盘和小 盘指数表现明显优于大盘指数。本周代表核心资产和成长龙头的茅指 数和宁组合亦有上涨,宁组合整周上涨 1.56%,茅指数上涨 2.29%。 个人投资者情绪下行,与指数走势背离。截至7月19日个人投 资者情绪指数7日移动平均数报 0.58%. 较 7月12日的9.40%明显下 大盘指数 2000 资料来源:聚源,中邮证券研究所 研究所 分析师:黄子签 SAC 登记编号:S1340523090002 Email : huangziyin@cnpsec. com 近期研究报告 《红利研究(1):为什么是银行?终点 又在何处》 - 2025.07.14 降。虽然近两周Ａ股指数层面持续上行,但个人投资者情绪却持续走 低,这 ...