1. Report Industry Investment Rating - Not provided in the given content 2. Core Viewpoints of the Report - Among the bonds with discounted transactions, "25 Grid MTN024" had a relatively large deviation in bond valuation price. Among the bonds with rising net prices, "25 Qingdao Chengyang MTN002" led in terms of valuation price deviation. Among the Tier 2 and perpetual bonds with rising net prices, "22 Nanjing Bank Perpetual Bond 01" had a relatively large deviation in valuation price; among the commercial financial bonds with rising net prices, "23 Agricultural Bank of China Three - Rural Bond" led in terms of valuation price deviation. Among the bonds with a transaction yield higher than 5%, real - estate bonds ranked high. The changes in credit bond valuation yields were mainly distributed in the (0,5] interval. The transaction terms of non - financial credit bonds were mainly distributed between 2 and 3 years, with the 0.5 - 1 - year variety having the highest proportion of discounted transactions; the transaction terms of Tier 2 and perpetual bonds were mainly distributed between 4 and 5 years, and bonds of various terms were generally traded at a discount. By industry, the bonds in the electronics industry had the largest average deviation in valuation price [2] 3. Summary According to Relevant Catalogs 3.1 Discounted Transaction Tracking - Bonds such as "25 Grid MTN024", "24产融05", and "25邛崃建投PPN001A" had discounted transactions, with different remaining terms, valuation price deviations, and transaction scales. For example, "25 Grid MTN024" had a remaining term of 14.48 years, a valuation price deviation of - 0.30%, and a transaction scale of 95400000 yuan [4] 3.2 Tracking of Bonds with Rising Net Prices - Bonds like "25 Qingdao Chengyang MTN002", "24 Huaibei 03", and "25 Huai 'an Investment 03" had rising net prices, with varying remaining terms, valuation price deviations, and transaction scales. For instance, "25 Qingdao Chengyang MTN002" had a remaining term of 2.99 years, a valuation price deviation of 0.27%, and a transaction scale of 40040000 yuan [5] 3.3 Tracking of Tier 2 and Perpetual Bond Transactions - Bonds including "22 Nanjing Bank Perpetual Bond 01", "22 Ningbo Bank Tier 2 Capital Bond 01", and "22 Huaxia Bank Tier 2 Capital Bond 01" were involved in transactions, with different remaining terms, valuation price deviations, and transaction scales. For example, "22 Nanjing Bank Perpetual Bond 01" had a remaining term of 1.82 years, a valuation price deviation of - 0.01%, and a transaction scale of 81970000 yuan [6] 3.4 Tracking of Commercial Financial Bond Transactions - Bonds such as "23 Agricultural Bank of China Three - Rural Bond", "24 Bank of China (Hong Kong) Bond 01BC", and "23 Jiangnan Rural Commercial Bank Three - Rural Bond" were traded, with different remaining terms, valuation price deviations, and transaction scales. For instance, "23 Agricultural Bank of China Three - Rural Bond" had a remaining term of 0.44 years, a valuation price deviation of 0.01%, and a transaction scale of 50220000 yuan [7] 3.5 Tracking of Bonds with a Transaction Yield Higher than 5% - Bonds including "21 Gemdale 04", "20 Zunhe 01", and "24 Liaoning Fangda MTN001" had a transaction yield higher than 5%, with different remaining terms, valuation price deviations, and transaction scales. For example, "21 Gemdale 04" had a remaining term of 0.27 years, a valuation price deviation of 0.03%, and a transaction scale of 10760000 yuan [8] 3.6 Distribution of Credit Bond Transaction Valuation Deviations on the Day - The changes in credit bond valuation yields were mainly distributed in the [- 10, - 5), [- 5,0), (0,5], and (5,10] intervals, with corresponding bond numbers and transaction scales [10] 3.7 Distribution of Non - financial Credit Bond Transaction Terms on the Day - The transaction terms of non - financial credit bonds were mainly distributed between 0.5 years and 5 years, with different transaction scales and proportions of discounted transactions in each interval [12] 3.8 Distribution of Tier 2 and Perpetual Bond Transaction Terms on the Day - The transaction terms of Tier 2 and perpetual bonds were mainly distributed between 1 year and 5 years, with different transaction scales and proportions of discounted transactions in each interval [15] 3.9 Discounted Transaction Proportion and Transaction Scale of Non - financial Credit Bonds in Each Industry - Different industries had different average valuation price deviations and transaction scales for non - financial credit bonds. The electronics industry had the largest average valuation price deviation [18]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - Major players in the AI sector are increasing their investments and focusing on commercial monetization strategies, with Nvidia's strategic integration of Groq and ByteDance's significant capital expenditure plans being key highlights [6][8][15] Industry Overview AI Infrastructure - Nvidia has strategically integrated Groq to enhance its capabilities in high-efficiency inference, paying approximately $20 billion for the technology and talent [14] - ByteDance plans to invest 160 billion RMB (approximately $23 billion) in capital expenditures in 2026, with a focus on AI infrastructure [15] AI Model Development - OpenAI has improved its profitability margin to 70% as of October 2023, up from 35% in early 2024, indicating a strong balance between infrastructure investment and revenue generation [16] - ByteDance has launched a new model, Seed Prover 1.5, which has achieved significant performance metrics in formal mathematical reasoning [17] AI Applications - The competitive landscape for leading AI applications remains stable, with ByteDance's app "Doubao" achieving over 100 million daily active users (DAU) [21] - Meta's AI glasses, Ray-Ban, have seen a significant sales increase, with approximately 2.39 million units sold in Q3 2025, marking a 393% year-on-year growth [29] Capital Trends Price Increases in Supply Chain - Major memory suppliers like Samsung and SK Hynix have raised HBM3E prices by nearly 20%, attributed to increased orders from AI accelerator companies [24] - The report indicates a sustained high demand in the semiconductor sector driven by AI applications, leading to price adjustments across various components [27] Corporate Strategies - Nvidia has restructured its cloud services team to focus on internal needs rather than external sales, indicating a shift in strategy to optimize its core competencies [25] - Alibaba has launched new versions of its AI models, enhancing their capabilities and moving towards a more integrated development ecosystem [28]

1. Report Industry Investment Rating No relevant content provided. 2. Core View of the Report Last week (12/22 - 12/26), bond - type ETFs had a net capital inflow of 54.515 billion yuan. The net unit value of bond ETFs showed marginal recovery. There was no new issuance of bond ETFs. The trading volume and turnover rate of various bond ETFs showed different changes, and the performance of different types of bond ETFs also varied [2][12]. 3. Summary According to Relevant Catalogs 3.1 Issuance Progress Tracking - No new bond ETFs were issued last week [3][16]. 3.2 Stock Product Tracking - As of December 26, 2025, the circulating market values of interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs were 152.6 billion yuan, 426.4 billion yuan, and 60.9 billion yuan respectively, with credit - bond ETFs accounting for 66.6% of the total scale. The circulating market values of Haifutong CSI Short - term Financing ETF and Boshi Convertible - bond ETF ranked top two, at 65.1 billion yuan and 52.3 billion yuan respectively [18]. - Compared with the previous week, the circulating market values of interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs increased by 1.586 billion yuan, 31.621 billion yuan, and decreased by 2.768 billion yuan respectively. Products with significant scale growth last week included Yinhuakongchuangzhai ETF, Harvest CSI AAA Science and Technology Innovation Corporate Bond ETF, and Huatianfu CSI AAA Kechuang Bond ETF, with a year - on - year scale growth of over 6 billion yuan [20]. - Among credit - bond ETFs, the circulating market values of benchmark - market - making credit - bond ETFs and science - innovation bond ETFs were 124.8 billion yuan and 340.5 billion yuan respectively, increasing by 7.262 billion yuan and 56.694 billion yuan compared with the previous week [22]. 3.3 ETF Performance Tracking - Last week, the cumulative net unit values of interest - rate bond ETFs and credit - bond ETFs closed at 1.18 and 1.03 respectively [23]. - As of December 26, based on February 7 as the base date, the average cumulative return of benchmark - market - making credit - bond ETFs rose to 0.89%; based on July 17 as the base date, the cumulative return of science - innovation bond ETFs marginally recovered to 0.22%, returning to the positive range [29]. 3.4 Premium/Discount Rate Tracking - Last week, the average premium/discount rates of credit - bond ETFs, interest - rate bond ETFs, and convertible - bond ETFs were - 0.11%, - 0.06%, and - 0.10% respectively. The average trading price of credit - bond ETFs was lower than the fund's net unit value, indicating low allocation sentiment. Specifically, the weekly average premium/discount rates of benchmark - market - making credit - bond ETFs and science - innovation bond ETFs were - 0.25% and - 0.07% respectively [36]. 3.5 Turnover Rate Tracking - Last week, the turnover rate was in the order of interest - rate bond ETFs > credit - bond ETFs > convertible - bond ETFs. The weekly turnover rates of the three types of products all increased marginally, reaching 136%, 102%, and 84% respectively. Specifically, products such as Huaxia Shanghai Stock Exchange Benchmark - Market - Making Treasury Bond ETF, Southern CSI AAA Science and Technology Innovation Corporate Bond ETF, and Yongying Science - Innovation Bond ETF had relatively high turnover rates [41].

Group 1: Industry Developments - Nvidia strategically integrates Groq, paying approximately $20 billion for technology licensing and talent acquisition to enhance its AI inference chip capabilities[14] - ByteDance plans to invest 160 billion RMB (approximately $23 billion) in capital expenditures for 2026, up from 150 billion RMB in 2025, focusing on AI infrastructure[15] - OpenAI's computing profit margin has surged to 70%, doubling from 35% at the beginning of 2024, indicating improved cost management and revenue generation strategies[16] Group 2: Market Trends - The price of HBM3E memory chips is set to increase by nearly 20% due to rising demand from AI accelerator companies like Nvidia and Google[24] - Meta's Ray-Ban AI glasses sales reached approximately 2.39 million units in Q3 2025, a year-on-year increase of 393%, capturing nearly 80% of the AI glasses market[29] - Daily active users (DAU) of ByteDance's Doubao app have surpassed 100 million, marking a significant milestone for the company[21] Group 3: Competitive Landscape - Major AI applications remain stable, with ByteDance actively exploring AI software and hardware applications[6] - Alibaba has launched a new version of its large model, Qwen Code v0.5.0, marking a significant step towards developing a comprehensive AI ecosystem[28] - Domestic AI firms like MiniMax and Zhiyuan AI are making strides in model development, with MiniMax's M2.1 model achieving state-of-the-art results in multilingual programming benchmarks[17][19]

- The report introduces two quantitative factors: the "Leading Factor" and the "Right-Skewed Peak Factor," both constructed based on price-volume data to identify top-performing stocks within the "Commercial Space" concept stock pool[2][8] - The "Leading Factor" is designed to capture stocks with strong upward momentum, while the "Right-Skewed Peak Factor" focuses on stocks with a sharp and asymmetric price distribution, indicating potential for significant gains[8] - The enhanced portfolio derived from these factors includes five stocks: Sunway Communication, Sray Materials, China Satellite, Aerospace Development, and Aerospace Electronics[8]

Macro Liquidity - The US dollar index has declined, and the degree of inversion in the China-US interest rate spread has narrowed. The nominal and real yields of 10-year US Treasuries have both decreased, indicating a drop in inflation expectations [2][14] - Offshore dollar liquidity has marginally eased, while the domestic interbank funding environment remains balanced. The yield spread between 10-year and 1-year government bonds continues to widen [2][19] Market Trading Activity - Overall market trading activity has increased, with many indices experiencing a rise in volatility. Sectors such as retail, military, consumer services, light industry, and textiles are seeing trading activity above the 80th percentile [3][25] - Most indices have shown increased volatility, with sectors like communication, electronics, electric new energy, and chemicals remaining above the 80th historical percentile [3][32] - Market liquidity indicators have declined, with liquidity metrics across sectors remaining below the 70th historical percentile [3][37] Sector Research Activity - Research activity is high in sectors such as electronics, pharmaceuticals, electric new energy, machinery, and non-ferrous metals. The research interest in automotive, computing, communication, and chemicals is also on the rise [4][43] Analyst Profit Forecasts - Analysts have raised profit forecasts for the entire A-share market for 2025 and 2026. The proportion of stocks with upward revisions in profit forecasts has increased across the board [4][51] - Specific sectors such as real estate, construction, coal, consumer services, and home appliances have also seen upward adjustments in profit forecasts for 2025 and 2026 [4][51] - The profit forecasts for the CSI 300 and SSE 50 indices for 2025 and 2026 have been revised upwards, while the profit forecasts for the CSI 500 have been adjusted downwards [4][51] Northbound Trading Activity - Northbound trading activity has decreased, continuing a net sell-off of A-shares. The ratio of buy-sell amounts in sectors like communication, non-ferrous metals, and consumer services has increased, while it has decreased in electronics, computing, and banking [5][29] - For stocks with holdings below 30 million shares, net buying has primarily occurred in computing, non-bank financials, and coal sectors, while net selling has been observed in communication, non-ferrous metals, and automotive sectors [5][31] Margin Financing Activity - Margin financing activity has rapidly increased, reaching the highest point since November 2025. The net buying has been concentrated in sectors like electronics, electric new energy, and communication, while net selling has occurred in non-bank financials, oil and petrochemicals, and retail sectors [6][35] - The proportion of financing purchases has increased in sectors such as consumer services, banking, and electric new energy [6][38] Fund Activity - The positions of actively managed equity funds have continued to rise, with significant net subscriptions in ETFs, particularly those related to institutional investors. Active equity funds have mainly increased their positions in non-ferrous metals, media, and consumer services, while reducing positions in communication, home appliances, and retail sectors [7][45] - The newly established equity fund scale has increased, with active funds seeing a rise while passive funds have decreased. ETFs related to the CSI A500 index have been primarily net purchased, while sectors like military, electronics, and agriculture have seen net selling [7][52]

Quantitative Models and Construction Methods 1. Model Name: Rotation Model - **Model Construction Idea**: The model is based on the relative performance of micro-cap stocks and "Mao Index" (a large-cap index), using rolling slopes and relative net values to determine rotation signals[19][24] - **Model Construction Process**: 1. Calculate the relative net value of micro-cap stocks to the Mao Index. If the relative net value is above its 243-day moving average, the model prefers micro-cap stocks; otherwise, it prefers the Mao Index[19][24] 2. Compute the 20-day closing price slopes for both micro-cap stocks and the Mao Index. If the slopes diverge and one is positive, the model selects the index with the positive slope to adapt to potential style shifts[19][24] 3. Timing indicators include the 10-year government bond yield (threshold: 0.3) and micro-cap stock volatility crowding (threshold: 0.55). If either indicator hits the threshold, a closing signal is triggered[19][24] - **Model Evaluation**: The model effectively captures style rotation signals and provides a systematic approach to manage risk and optimize returns[19][24] 2. Model Name: Macro Timing Model - **Model Construction Idea**: This model integrates macroeconomic growth and monetary liquidity signals to determine equity allocation levels[44][45] - **Model Construction Process**: 1. Assign signal strengths to economic growth and monetary liquidity dimensions. For December, the signal strengths were 50% and 60%, respectively[45] 2. Combine these signals to recommend an equity allocation level. For December, the recommended equity allocation was 55%[45] 3. The model's performance is tracked, with a year-to-date return of 13.57% compared to a 25.65% return for the Wind All-A Index[44] - **Model Evaluation**: The model provides a balanced approach to equity allocation, leveraging macroeconomic indicators to guide investment decisions[44][45] --- Model Backtesting Results 1. Rotation Model - **Relative Net Value**: Micro-cap stocks to Mao Index relative net value was 2.06, above the 243-day moving average of 1.80[19] - **20-Day Slope**: Micro-cap stocks' 20-day slope was -0.15%, while the Mao Index's slope was 0.00%[19] - **Risk Indicators**: Volatility crowding was -17.17%, below the 55% risk threshold; 10-year government bond yield was 7.32%, below the 30% risk threshold[19] 2. Macro Timing Model - **Economic Growth Signal**: 50%[45] - **Monetary Liquidity Signal**: 60%[45] - **Equity Allocation**: 55%[45] - **Year-to-Date Return**: 13.57% (compared to Wind All-A Index's 25.65%)[44] --- Quantitative Factors and Construction Methods 1. Factor Name: Growth Factor - **Factor Construction Idea**: Measures the growth potential of companies based on financial metrics like net income and operating income growth[58][59] - **Factor Construction Process**: 1. Use single-quarter net income year-over-year growth (NetIncome_SQ_Chg1Y) and single-quarter operating income year-over-year growth (OperatingIncome_SQ_Chg1Y) as key metrics[59] 2. Combine these metrics to rank stocks and construct the factor[59] - **Factor Evaluation**: Demonstrated strong performance with an IC mean of 10.62% across all A-shares[48] 2. Factor Name: Consensus Expectation Factor - **Factor Construction Idea**: Captures market sentiment and expectations based on analysts' forecasts[58][59] - **Factor Construction Process**: 1. Use metrics like expected ROE changes over the past three months (ROE_FTTM_Chg3M) and target return over 180 days (TargetReturn_180D)[59] 2. Rank stocks based on these metrics to construct the factor[59] - **Factor Evaluation**: Performed well with an IC mean of 9.57% across all A-shares[48] 3. Factor Name: Volatility Factor - **Factor Construction Idea**: Measures stock price stability and risk using historical price and volume data[58][59] - **Factor Construction Process**: 1. Use metrics like 60-day return volatility (Volatility_60D) and CAPM residual volatility (IV_CAPM)[59] 2. Rank stocks inversely based on these metrics to construct the factor[59] - **Factor Evaluation**: Underperformed with an IC mean of -20.21% across all A-shares[48] --- Factor Backtesting Results 1. Growth Factor - **IC Mean**: 10.62% (all A-shares)[48] - **Multi-Long-Short Portfolio Return**: 20.54% (all A-shares, year-to-date)[49] 2. Consensus Expectation Factor - **IC Mean**: 9.57% (all A-shares)[48] - **Multi-Long-Short Portfolio Return**: 15.95% (all A-shares, year-to-date)[49] 3. Volatility Factor - **IC Mean**: -20.21% (all A-shares)[48] - **Multi-Long-Short Portfolio Return**: -2.96% (all A-shares, year-to-date)[49]

Investment Rating - The report indicates a positive investment outlook for the steel industry, with a rating reflecting an expected increase in the industry performance compared to the broader market [85]. Core Insights - The CITIC Steel Industry Index increased by 3.4%, outperforming the Shanghai Composite Index by 1.5%. The average profit margin for steel mills is reported at -31.2 yuan per ton, with a profitability rate of 37.2% for steel companies, indicating a stable bottom in the industry fundamentals [10][11]. - The hot-rolled coil prices showed mixed trends, with the average price for 3.0mm hot-rolled coil at 3342 yuan per ton, down 8 yuan from the previous week. The market sentiment is weak, primarily driven by low-price promotions for transactions [11]. - The coking coal market is experiencing weak performance, with the main coking coal price at 1393 yuan per ton, stable compared to last week. The demand for coking coal remains subdued due to reduced consumption by downstream coke enterprises [12]. - Iron ore prices have slightly increased, with the average price for 61.5% Fe powder at 795 yuan per ton, up 1.7% year-on-year. However, the overall supply remains constrained due to high port inventories [13]. Summary by Sections 1.1 Steel Industry Overview & Index Performance - The CITIC Steel Industry Index rose by 3.4%, outperforming the Shanghai Composite Index by 1.5%. The average profit margin for long and short process steel production is reported at -9.7 yuan and -7.4 yuan per ton, respectively [10]. 1.2 Sub-industry Fundamentals Overview - The hot-rolled coil prices are fluctuating, with a noted decrease in prices across major markets. The market is characterized by weak demand and low speculative sentiment, with a focus on essential inventory replenishment [11]. 2.1 Profitability - The profitability rate for steel companies stands at 37.2%, indicating a stable bottom in the industry fundamentals despite negative profit margins [10]. 2.2 Operating Rates - The operating rates for steel mills are reported at 78.32%, with daily average pig iron production remaining stable, reflecting a cautious approach to raw material replenishment [13]. 3.1 Steel Prices - The average price for hot-rolled coils is reported at 3342 yuan per ton, with a slight decrease noted in the previous week [11]. 3.2 Raw Material Prices - Coking coal prices remain stable at 1393 yuan per ton, while iron ore prices have seen a slight increase, indicating mixed trends in raw material pricing [12][13]. 4.1 Steel Supply and Demand Data - The report highlights the supply and demand dynamics within the steel industry, with a focus on the impact of environmental regulations and production adjustments by steel mills [11].

Investment Rating - The report highlights a positive outlook for the Q fabric market, suggesting it has the potential to become a mainstream material with increased supply capacity and market confidence [2][12]. Core Insights - The Q fabric market is gaining attention, with companies like Lite-On Optoelectronics investing in the sector to create an integrated advantage in sand mining, rod production, weaving, and more. The M9+Q fabric is identified as the most proactive material solution, while M9+ second-generation and 2.5 generation fabrics represent a more conservative approach. The Q fabric's low expansion and dielectric properties are emphasized as key advantages [2][12]. - The report suggests that the bottleneck for Q fabric lies in downstream processing difficulties, particularly in PCB upgrades, while upstream issues in silk and fabric production are being addressed by companies like Fihua and China National Materials [2][12]. - The report encourages a rational view of new entrants in the Q fabric market, emphasizing the need for upstream and downstream participants to enhance industry connectivity and penetration rates [2][12]. Summary by Sections Weekly Discussion - The Q fabric market is highlighted as a focal point, with significant investments and technological advancements expected to drive its adoption [2][12]. - The report discusses the importance of evaluating companies based on raw material advantages, technological capabilities, customer resources, and equipment strengths [2][12]. Market Performance - The report notes that the cement market is experiencing a decline in average prices, with a national average of 354 RMB/t, down 67 RMB/t year-on-year and 1 RMB/t month-on-month. The average shipment rate is 41.4%, reflecting a slight decrease [4][14]. - The glass market shows a slight decline in prices, with the average price for float glass at 1140.08 RMB/ton, down 11.32 RMB/ton, and an increase in inventory days [4][14]. - The concrete mixing station's capacity utilization is reported at 7.33%, indicating a decrease [4][14]. Price Changes - The report details that the national cement price remains stable, with fluctuations observed in specific regions. The average shipment rate has decreased by approximately 1 percentage point [24][25]. - The float glass market is experiencing a slight downward trend in prices, with increased inventory levels noted [34][35]. - The report indicates that the electronic fabric market is stable, with prices for 2400tex non-alkali yarn remaining steady [56][58].

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints - The reading of the micro - trading thermometer in the bond market continued to rebound to 45% [14] - The proportion of indicators in the over - heated range increased to 25%, with 5 indicators in the over - heated range, 6 in the neutral range, and 9 in the cold range [19] - The average value of the interest rate differential percentile increased by 5 percentage points [4] 3. Summary by Directory 3.1. Micro - trading Thermometer Reading - The "Guojin Securities Fixed Income - Bond Market Micro - trading Thermometer" rose slightly by 1 percentage point to 45%. The buying volume of funds - rural commercial banks, TL/T long - short ratio, and policy interest rate differential percentile increased by 20, 19, and 10 percentage points respectively, while the configuration disk strength, 1/10Y Treasury bond turnover rate, and money tightness expectation percentile decreased by 20, 8, and 8 percentage points respectively. High - congestion indicators include the 30/10Y Treasury bond turnover rate and institutional leverage [14] 3.2. Proportion of Indicators in the Over - heated Range - Among 20 micro - indicators, the number of indicators in the over - heated range increased to 5 (25%), in the neutral range decreased to 6 (30%), and in the cold range remained at 9 (45%). The TL/T long - short ratio moved from the neutral to the over - heated range, the buying volume of funds - rural commercial banks from the cold to the neutral range, and the configuration disk strength from the neutral to the cold range [19] - The average value of the interest rate differential percentile increased by 5 percentage points. In trading heat, the TL/T long - short ratio and the whole - market turnover rate percentile increased by 19 and 2 percentage points, while the 30/10Y and 1/10Y Treasury bond turnover rate percentiles decreased by 1 and 8 percentage points respectively. In institutional behavior, the money tightness expectation and configuration disk strength percentiles decreased by 8 and 20 percentage points, and the buying volume of funds - rural commercial banks percentile increased by 20 percentage points. The market interest rate differential percentile remained flat, the policy interest rate differential percentile increased by 10 percentage points, and the stock - bond price - ratio percentile increased by 4 percentage points, while the commodity price - ratio percentile decreased by 2 percentage points [19] 3.3. 30/10Y Treasury Bond Turnover Rate - In trading heat indicators, the proportion of indicators in the over - heated range increased to 67%, in the neutral range decreased to 17%, and in the cold range remained at 17%. The TL/T long - short ratio and the whole - market turnover rate percentile increased by 19 and 2 percentage points, and the 1/10Y and 30/10Y Treasury bond turnover rate percentiles decreased by 8 and 1 percentage points respectively. The 30/10Y Treasury bond relative turnover rate reached a historical high [22] - The 30/10Y Treasury bond turnover rate increased to 5.85, reaching a historical high, and the percentile in the past year remained at 100% [22] 3.4. Configuration Disk Strength - In institutional behavior indicators, the proportion of indicators in the over - heated range remained at 0%, in the neutral range at 50%, and in the cold range at 50%. The configuration disk strength, money tightness expectation, and listed company wealth management buying volume percentiles decreased by 20, 8, and 3 percentage points respectively, and the configuration disk strength moved from the neutral to the cold range; the buying volume of funds - rural commercial banks, fund divergence, and fund duration percentiles increased by 20, 6, and 2 percentage points respectively, and the buying volume of funds - rural commercial banks moved from the cold to the neutral range [26] 3.5. Policy Interest Rate Differential Percentile - The loose money supply supported the continued decline of short - term Treasury bond yields. The policy interest rate differential narrowed by 3bp to - 4bp, and the corresponding percentile increased significantly by 10 percentage points to 82%. The credit spread, IRS - SHIBOR 3M spread widened by 2bp and 1bp to 59bp and - 3bp respectively, the agricultural development - state development spread narrowed slightly by 1bp to 0bp. The average spread of the three remained at 18bp, and its percentile remained at 51% in the neutral range [30] 3.6. Stock - Bond Price - Ratio Percentile - All price - ratio indicators were in the cold range. The stock - bond price - ratio percentile increased by 4 percentage points, the commodity price - ratio percentile decreased by 2 percentage points, and the real - estate price - ratio percentile remained unchanged [33] - The stock - bond price - ratio increased by 6 percentage points to - 10.9%, and the percentile in the past year increased by 4 percentage points to 8%. The commodity price - ratio decreased by 5 percentage points to - 27.3%, and the corresponding percentile in the past year decreased by 2 percentage points to 34%. The real - estate price - ratio decreased by 2 percentage points to - 77.5%, and the percentile in the past year remained at 16%. The consumer goods price - ratio remained at - 81.8% and the percentile in the past year remained at 0% [34][36]