Group 1: Overview of Credit Bonds - As of May 12, 2025, the total outstanding credit bond market, including financial bonds, is 431396 billion, with local government bonds (城投债) at 186206 billion, industrial bonds at 103498 billion, and financial bonds at 141692 billion [8][12] - Among local government bonds, 63480 billion are valued above 2.2%, accounting for 34.1% of the total [10][12] - The report categorizes local government bonds into four tiers based on their valuation and distribution across provinces [16] Group 2: Distribution of Local Government Bonds - In Jiangsu, Zhejiang, Anhui, and Fujian, the proportion of bonds valued above 2.2% is below 30%, but the total scale is relatively large, with Jiangsu having a rich supply of 1-3 year AA(2) bonds [10][17] - In Sichuan, Hunan, Hubei, and Jiangxi, the valuation is in the mid-range, with 30-40% of bonds valued above 2.2%, and Sichuan alone has over 4800 billion in such bonds [10][21] - In Henan, Shandong, and Shaanxi, the overall valuation is higher, with bonds valued between 2.29% and 2.40%, and the proportion of bonds above 2.2% ranges from 47% to 63% [10][12] Group 3: Industrial Bonds - As of May 12, 2025, the total outstanding industrial bonds amount to 103498 billion, with 21368 billion valued above 2.2%, representing 20.6% of the total [3][12] - The real estate sector has over 5300 billion in bonds valued above 2.2%, while sectors like construction, non-bank financials, coal, steel, and retail also show significant amounts [3][12] Group 4: Financial Bonds - The total outstanding financial bonds is 141692 billion, with 9093 billion valued above 2.2%, which is 6.4% of the total [4][12] - Among bank subordinated bonds, over 3400 billion are valued above 2.2%, primarily concentrated in bonds with a maturity of over three years [4][12] - Insurance bonds valued above 2.2% exceed 1500 billion, with major issuers including Ping An Life, Taikang Life, and Sunshine Life [4][12]

1. Report's Investment Rating for the Industry No investment rating for the industry is provided in the report. 2. Core Viewpoints of the Report - From August 2017 to January 2020, the credit bond market evolved in four stages under the intertwined influence of Sino - US trade frictions and policy hedging, presenting a pattern of "strengthened safe - haven properties of interest - rate bonds and re - structured risk pricing of credit bonds" [3][21]. - The market logic gradually returned to fundamental verification in the later stage, with external shocks having a diminishing marginal impact. Policy hedging effectiveness, credit repair rhythm, and cross - border capital flows became key variables affecting the market trend [12]. - After the implementation of the 54% tariff policy on April 2, 2025, the core logic of the credit bond market shifted to "safe - haven trading + policy hedging". Short - term high - grade varieties are favored, and in the short - term, safe - haven sentiment will dominate the market. In the medium - term, attention should be paid to economic data and the possible impact of the valuation repair of Chinese dollar - denominated bonds [100][105]. 3. Summary by Directory First Stage: Anticipation Disturbance Period (August 2017 - June 2018) - **Interest Rate Curve Differentiation and Credit Risk Pricing Re - structuring**: The bond market was in a "loose money, tight credit" policy combination. The short - end of the interest - rate bond market benefited from the targeted RRR cut in April 2018, while the long - end was suppressed by factors such as rising international oil prices, Fed rate hikes, and regulatory tightening. Private enterprise default amounts increased, and investors' behaviors diverged. The inability to transform "loose money" into "loose credit" intensified the structural contradictions in the credit bond market [22][24][25]. - **Credit Bond Financing Fluctuations due to Trade Friction Evolution**: Credit bond financing fluctuated. It declined initially due to trade friction concerns and financial risk prevention policies, then rebounded briefly in early 2018 due to liquidity release policies, and finally decreased again after the addition of tariffs and the implementation of the asset management new rules [29][30]. - **Overall Rise in Credit Bond Yields and Widening of Credit Spreads**: Credit bond yields rose overall, and credit spreads widened. Market concerns about credit risks spread from local industries to the whole market, especially in export - oriented industries. Although the targeted RRR cut in April 2018 curbed risk spread, private enterprise default events increased, and the pricing logic of the credit bond market became more complex [36][37]. - **Initial Appearance of Credit Bond Default Pressure with Wide Industry Distribution**: Credit bond defaults and extensions increased slightly. Defaults were no longer concentrated in traditional over - capacity industries but spread to more sectors. Policy uncertainties affected corporate financing efficiency and solvency [42][43]. Second Stage: Policy Hedging Period (July - November 2018) - **Differentiated Efficiency of Interest - Credit Transmission under Policy Hedging**: As Sino - US trade frictions escalated, domestic policies shifted. The central bank's RRR cut pushed short - term interest rates down, but long - term interest rates rebounded due to factors such as local government bond issuance and CPI increase. The "bull - steep" market of interest - rate bonds and the financing repair of credit bonds diverged [48]. - **Industry Financing Differentiation between Trade Pressure and Domestic Demand Hedging**: Different industries' credit bond financing showed a differentiated trend. Export - oriented industries such as commercial trade and light manufacturing saw a decline in net financing, while the public utility industry benefited from domestic demand support and had an increase in net financing [51]. - **Overall Decline in Credit Bond Yields and Narrow - range Fluctuation of Credit Spreads**: After the formal implementation of tariffs, the market's pricing of trade frictions became less sensitive. Credit bond yields declined, and credit spreads fluctuated within a narrow range. Although trade frictions escalated again in September 2018, the bond market reacted calmly. Low - grade industrial bond credit spreads widened, and the impact of domestic policies on the bond market gradually exceeded external shocks [55]. - **Relative Advantage of Non - standard Bonds of Urban Investment Entities after Trade Friction Upgrade**: Credit bond defaults increased, mainly among private enterprises. Non - standard bonds of non - urban investment entities had a significant increase in default cases, while those of urban investment entities were relatively stable, reflecting the positive role of local policy coordination [61][62]. Third Stage: Wide - Credit Verification Period (December 2018 - April 2019) - **"Time Difference" Game between Liquidity Drive and Credit Repair**: The bond market was driven by both the easing of trade frictions and domestic policy loosening. Although the G20 Summit in December 2018 and the central bank's full - scale RRR cut in January 2019 boosted market sentiment, private enterprise credit spreads remained high. The bond market turned bearish in April 2019 as economic fundamentals improved [69]. - **Differentiated Financing between State - owned and Private Enterprises under Tariff Easing and Policy Loosening**: State - owned enterprises benefited from policy loosening and had an increase in net financing, while private enterprises were still affected by the lagged impact of previous tariffs. Their net financing showed a fluctuating trend [72]. - **Credit Bond Yields Oscillated and Industrial Bond Spreads of Different Industries Differentiated**: As trade frictions eased, credit bond yields oscillated, and credit spreads differentiated. The market logic shifted to fundamental verification. Industries such as electrical equipment and chemical industry, which were affected by tariffs, had a slower credit spread repair than the overall market [74][78]. - **Credit Bond Default Situation Remained Flat Year - on - Year with Insufficient Improvement for Private Enterprises**: During the negotiation easing period, the number of credit bond extensions and defaults remained basically the same as the previous stage. Financial institutions preferred high - credit entities, and private enterprises still faced challenges in financing [81]. Fourth Stage: Resonance Period of Liquidity Stratification and Cross - border Capital Pricing (May 2019 - January 2020) - **Dual Pricing Logic of Credit Risk Events and Foreign Capital Safe - haven**: The takeover of Baoshang Bank in May 2019 led to concerns about liquidity stratification. Foreign capital increased its allocation of interest - rate bonds, and the bond market showed a pattern of safe - haven interest - rate bonds and differentiated credit bonds. The bond market was driven by both "safe - haven sentiment" and "foreign capital allocation" [85]. - **Increased Financing of Urban Investment Bonds with Swinging Trade Friction Expectations**: During the liquidity stratification stage, urban investment bond net financing continued to grow. Regulatory policies relaxed the "borrowing new to repay old" restrictions, and the central bank's policies provided a low - cost replacement space for urban investment platforms [88]. - **Overall Decline in Credit Bond Yields with Intensified Structural Differentiation**: Credit bond yields declined overall, but the market showed intensified structural differentiation. Yields of some industries such as electronics and automobiles increased, while those of infrastructure - related industries remained stable. High - grade state - owned enterprise industrial bond credit spreads narrowed, while those of AA + private enterprise industrial bonds widened [90][93]. - **Credit Bond Defaults under the Prolonged Trade Friction**: Under the continuous impact of trade frictions, credit bond defaults increased, mainly due to factors such as the slowdown of the macro - economic environment, the adjustment of corporate profit growth, and the impact on export - oriented enterprises. Non - standard bonds of urban investment platforms had relatively stable repayment performance [96]. Outlook on Credit Bond Trends in the Current Trade Friction - After the implementation of the 54% tariff policy on April 2, 2025, the credit bond market's core logic shifted. Interest - rate bonds reacted first, and the steep downward movement of the interest - rate curve opened up the valuation space for credit bonds. High - grade varieties are favored, and in the short - term, safe - haven sentiment will dominate. In the medium - term, attention should be paid to economic data and the possible impact of the valuation repair of Chinese dollar - denominated bonds. It is recommended to adopt a strategy of "moderately extending duration" + "moderately lowering credit quality" [100][105].

帮助企业找市场，深圳再出实招。日前深圳市商务局发布"2025年深圳服务企业拓展国内市场支持政策 要点十条"，提出支持企业参加消费品以旧换新补贴活动、投保国内贸易信用保险、数字化升级、组团 参展、打造国货潮品新品牌等10条具体措施，真金白银助力企业稳订单开拓国内市场。 深圳积极落实国家2025年以旧换新政策，支持符合条件的企业参与消费品以旧换新补贴活动，拓展国内 市场。对个人消费者购买家电、数码产品按照销售价格给予最高2000元补贴。 深圳对投保国内贸易信用保险且符合条件的企业，按其实际缴纳保险费用给予25%的资金补助，单家企 业每年累计资助金额最高50万元，以此降低企业交易风险；支持商圈、行业协会、企业举办系列促消费 活动，对纳入"深圳购物季"市级重点活动项目，按实际投入的50%给予最高200万元资助；支持商贸企 业数字化改造，对商贸企业消费场景数字化和信息化投入，按实际投入的20%给予最高300万元资助。 支持企业拓展国内市场、降低交易风险，对投保国内贸易信用保险且符合条件的企业按其实际缴纳保险 费用给予25%的资金补助，单家企业每年累计资助金额最高50万元。 支持商圈、行业协会、企业举办系列促消费活动，筹 ...

Core Insights - The report highlights the ongoing recovery of the domestic economy, supported by government policies aimed at bolstering the real economy, which is expected to provide a solid fundamental backing for the market [11] - The global economic landscape remains complex, with geopolitical risks and trade frictions potentially causing short-term market disturbances [11] Macroeconomic Summary - In January-February 2025, the total profit of industrial enterprises above designated size in China reached 910.99 billion yuan, a year-on-year decrease of 0.3% [4] - The Ministry of Commerce plans to release a "Health Consumption Special Action Plan" during the Consumer Expo, in collaboration with the National Health Commission [4] - The Ministry of Finance and the State Administration of Taxation announced the continuation of offshore trade stamp duty preferential policies from April 1, 2025, to December 31, 2027 [4] Industry Dynamics - The Ministry of Commerce is actively promoting the launch economy, supporting domestic and international quality brands to open their first stores and hold debut events. Related stocks include Miao Exhibition (300795) and Fengshang Culture (300860) [6] - Due to fluctuations in raw material prices and improved downstream demand, prices of several chemical products have risen. Related stocks include Juhua Co., Ltd. (600160) and Wanhua Chemical (600309) [6] - In 2024, approximately 31% of innovative drug candidates introduced by large multinational pharmaceutical companies came from China, with several companies turning losses into profits, driven by revenue from licensing transactions. Related stocks include Heng Rui Medicine (600276) and Xinlitai (002294) [6] Company Focus - Sanhua Intelligent Control (002050) reported a net profit of 3.099 billion yuan in 2024, a year-on-year increase of 6.1%, and plans to distribute a dividend of 2.5 yuan per share [8] - Donghua Testing (300354) successfully applied its self-developed torque sensor and control system in domestic humanoid robot leading enterprises [8] - Small Commodity City (600415) achieved revenue exceeding 15 billion yuan in 2024, with 4.8 million registered purchasers on Chinagoods [8] - Tuojing Technology (688072) launched three new product series to support innovations in semiconductor manufacturing, aligning with its "technology-led" strategic goals [8] Market Review and Outlook - On March 27, 2025, the A-share market saw major indices open low and rise, with the Shanghai Composite Index up 0.15%, the Shenzhen Component Index up 0.23%, and the ChiNext Index up 0.24%. Overall, more than 3,300 stocks declined [10] - The chemical sector has shown significant growth, with sulfuric acid prices increasing nearly 300% over six months, leading to over 20 stocks, including Hualitai, hitting the daily limit [10] - The semiconductor industry chain has rebounded, with the photolithography machine concept leading the gains, benefiting from accelerated domestic substitution expectations [10] - Despite the rebound in certain sectors, the overall market sentiment remains cautious, with a notable decline in stocks related to robotics and deep-sea technology [10]

- The report discusses the application of stock pair trading ideas in factor strategies, specifically focusing on reversal factors which have historically shown strong performance[1] - Traditional reversal factors include "N-month price reversal," "highest price length," and "volume ratio," which capture the trend that stocks with low past returns tend to perform better in the future and vice versa[1][2] - The report introduces a pair reversal factor that captures reversal opportunities between individual stocks within the same industry, differing from traditional pair trading by using periodic closing instead of stop-loss conditions[2][3] - The pair reversal factor is tested using a hedging strategy with a monthly rebalancing frequency, using the CSI 800 index constituents as the stock pool, and achieving an annualized excess return of 8% from 2007 to 2016[3][4] - The pair reversal factor is also applied to enhance multi-factor portfolios with weekly rebalancing, showing improved returns even after considering transaction costs, with a benchmark multi-factor portfolio return of 424.40% and a pair rebalancing portfolio return of 501.59% during the sample period from 2007 to 2016[4][5] Quantitative Models and Construction Methods 1. **Model Name**: Pair Reversal Factor - **Construction Idea**: Capture reversal opportunities between individual stocks within the same industry, similar to pair trading but with periodic closing instead of stop-loss conditions[2][3] - **Construction Process**: 1. Perform cointegration regression on the log prices of two assets to check for cointegration relationship[43][44] 2. Calculate the spread and standard deviation of the spread during the learning period[45][46] 3. Use the spread and standard deviation to determine the opening threshold and execute trades accordingly[46][49] 4. Rebalance the portfolio monthly by closing all positions and reopening new ones based on the updated spread and standard deviation[51][53] - **Evaluation**: The pair reversal factor effectively captures stock price reversals and mean reversion of price spreads, providing significant excess returns at the individual stock level[69] Model Backtest Results 1. **Pair Reversal Factor**: - **Annualized Return**: 31.17% (2007), 50.85% (2008), 51.19% (2009), 21.39% (2010), 14.26% (2011), 14.75% (2012), 25.75% (2013), 9.10% (2014), 59.01% (2015), 17.05% (2016), 1246.06% (full sample)[63] - **Maximum Drawdown**: 4.44% (2007), 4.62% (2008), 4.61% (2009), 2.97% (2010), 2.64% (2011), 2.23% (2012), 2.57% (2013), 4.99% (2014), 5.48% (2015), 4.07% (2016), 5.48% (full sample)[63] - **Win Rate**: 58.38% (2007), 60.57% (2008), 59.02% (2009), 58.26% (2010), 58.20% (2011), 59.66% (2012), 59.66% (2013), 51.02% (2014), 59.84% (2015), 59.43% (2016), 58.27% (full sample)[63] Quantitative Factors and Construction Methods 1. **Factor Name**: N-month Price Reversal - **Construction Idea**: Measure the price change over a fixed time window to capture the reversal effect[30][33] - **Construction Process**: 1. Calculate the price change over the past N months: $(\text{Current Price} - \text{Price N months ago}) / \text{Price N months ago}$[33] - **Evaluation**: Reversal factors have shown strong performance in historical studies, with high IC values and good performance in various metrics such as LS return, LS win rate, LS IR, IC IR, and IC P[33][35] Factor Backtest Results 1. **N-month Price Reversal**: - **IC**: -5.72% (1-month), -4.75% (3-month), -4.10% (6-month), -3.55% (12-month)[35] - **LS Return**: 21.84% (1-month), 20.33% (3-month), 18.13% (6-month), 17.66% (12-month)[35] - **LS Win Rate**: 64.41% (1-month), 59.32% (3-month), 56.78% (6-month), 61.02% (12-month)[35] - **LS IR**: 0.99 (1-month), 0.81 (3-month), 0.77 (6-month), 0.83 (12-month)[35] - **IC IR**: 0.72 (1-month), 0.92 (3-month), 0.78 (6-month), 0.83 (12-month)[35] - **IC P**: 0.0% (1-month), 0.2% (3-month), 0.5% (6-month), 1.1% (12-month)[35]