Report Industry Investment Rating - No information provided Core Viewpoints - This week, the three strategies jointly held 22 convertible bonds, including Pufa Convertible Bond, Muyuan Convertible Bond, 23 Hope E1, etc. The option strategy maintained low turnover with few new targets, while the sub - low - price strategy had increased turnover recently, possibly due to the high enthusiasm in the equity market [1]. - The model recommends the technology sector at the industry level, driven mainly by the momentum factor, consistent with previous reports. The recommended industries include communication, electronics, computer, power equipment, and household appliances, with a marginal increase in the computer factor score and a decline in the household appliance factor score but still within the recommended range [1]. Summary by Related Catalogs Strategy Performance - The sub - low - price strategy rose 2.72% last week, with an excess return of 0.07% compared to the Wind Convertible Bond Low - Price Index. It has risen 18.32% this year, with an excess return of 1.01% compared to the benchmark [10]. - The option strategy rose 3.21% last week, with an excess return of 0.55% compared to the Wind Convertible Bond Low - Price Index. It has risen 20.66% this year, with an excess return of 2.93% compared to the benchmark [10]. - The double - low enhancement strategy rose 3.08% last week, with an excess return of 1.16% compared to the Wind Convertible Bond Double - Low Index. It has risen 24.14% this year, with an excess return of 9.67% compared to the benchmark [10]. - The industry rotation strategy rose 3.21% last week, with an excess return of 1.30% compared to the Wind Convertible Bond Double - Low Index. It has risen 18.37% this year [10]. Risk - Return Characteristics (Last Year) - The sub - low - price strategy has an annualized return of 33.58%, a Calmar ratio of 5.58, and a maximum drawdown of 6.02% [12]. - The option strategy has an annualized return of 33.20%, a Calmar ratio of 7.09, and a maximum drawdown of 4.68% [12]. - The double - low enhancement strategy has an annualized return of 45.33%, a Calmar ratio of 5.92, and a maximum drawdown of 7.65%, with an annualized excess return of 13.60% [12]. - The industry rotation strategy has an annualized return of 43.77%, a Calmar ratio of 6.61, and a maximum drawdown of 6.62%, with an annualized excess return of 12.22% [12]. Factor Back - test Results - For the sub - low - price strategy, the priceavg factor has a weight of 100%, an IC mean of - 3.44%, an IC standard deviation of 17.53%, an ICIR of - 19.63%, an IC>0 frequency of 35.25%, and a p - Value of 0.01% [20]. - For the option strategy, the amplitude_mean_6m factor has a weight of 100%, an IC mean of - 4.41%, an IC standard deviation of 19.36%, an ICIR of - 22.80%, an IC>0 frequency of 31.42%, and a p - Value of 0.00% [20]. - For the double - low enhancement strategy, multiple factors are used with different weights. For example, the impliedvol diff1 3m, MaxPricePremium, pricechangediff di ff1 1w, priceavg priceavg, stkratio diff1 1w, Amihud diff1 3m, and MaxPricePremium factors each have a 20% or 25% weight. The pricechangediff di ff1 1w factor has an IC mean of - 3.02%, an IC standard deviation of 9.17%, an ICIR of - 32.92%, an IC>0 frequency of 24.90%, and a p - Value of 0.00% [20]. - For the industry rotation strategy, factors like diff1 1m, pricechangediff_mean 2w, and stkratio diff1 1m are used with 25% weights. The diff1 1m factor has an IC mean of - 3.29%, an IC standard deviation of 23.44%, an ICIR of - 14.04%, an IC>0 frequency of 42.31%, and a p - Value of 0.45% [20].

Group 1 - The report constructs a valuation scoring system to assess the price-performance ratio of convertible bond underlying stocks, focusing on quickly and accurately evaluating individual stock valuation levels while controlling for drawdown risks [1][9] - The valuation framework employs both vertical (relative to historical levels) and horizontal (relative to peers) analyses to position stocks in a two-dimensional space, allowing for a comprehensive assessment of their valuation [1][9] - The horizontal analysis identifies which underlying stocks have better valuation cost-effectiveness compared to others, using a set of primary and secondary indicators to filter out unsuitable metrics [1][10][11] Group 2 - The horizontal valuation framework aims to determine which convertible bonds (or underlying stocks) are relatively inexpensive at a given moment, addressing the challenge of cross-industry valuation comparisons [10][11] - The report identifies suitable primary indicators for various industries, categorizing them based on the adequacy of data points, stability across periods, and the dispersion of individual stock valuations [11][12][16] - The report highlights that the PE (3-year non-negative average) and PB indicators are widely applicable across industries, with the introduction of PE (3-year non-negative average) providing a more reliable alternative to traditional PE metrics [17][18] Group 3 - The vertical analysis framework assesses which underlying stocks have improved valuation cost-effectiveness compared to their historical levels, using data from June 2021 to the present [28][29] - The report finds that stocks with low vertical valuations tend to exhibit greater upward elasticity in bullish markets, with those above the 80% valuation threshold showing significantly lower average price increases [29][30] - The report identifies low-valued convertible bond targets worth attention, particularly in industries such as defense, basic chemicals, and construction decoration, which have shown high elasticity in the current market environment [34][36]

Report Industry Investment Rating No relevant information provided. Core Views of the Report In H1 2025, the convertible bond market showed characteristics of "prominent BETA robustness, intensified scarcity of high - quality targets, stratified investor structure, and upgraded strategy differentiation", providing a clear direction for subsequent layout. Looking ahead, four main lines should be focused on: continuing the BETA logic of "bond nature as a shield and equity nature as a spear" to capture market elasticity and downside protection; targeting scarce "high - quality underlying stocks, favorable terms, and strong bond floor" targets to dig for ALPHA returns; adapting to structural changes and upgrading arbitrage, long, and neutral strategies to "composite tools"; and managing credit, valuation, and liquidity risks through portfolio diversification, dynamic position adjustment, and derivative hedging to achieve the optimal balance of return and risk [1]. Summary by Relevant Catalogs 1. 2025 H1 Convertible Bond Market Trend Review 1.1 Data - Revealed BETA Robustness In 2025, the A - share market fluctuated more, and the small - cap style was volatile. With the decline in bond yields, robust assets were scarce. The CSI Convertible Bond Index, with its dual attributes of "bond nature as a shield and equity nature as a spear", could provide volatility buffering through its bond nature and capture upside potential when the stock market recovered. In the past year, it had a maximum drawdown of - 8.87%, a lower proportion of down - days (46.47%), and a gain of 9.72%. Its trading volume and turnover showed an upward trend, attracting funds and laying a foundation for subsequent market continuation [4][6][9]. 1.2 Convertible Bond Market: Dynamic Analysis of Volatility and Price Distribution 1.2.1 Implied Volatility Distribution Time - Series Analysis From Jun 2023 to Jun 2025, the implied volatility of the convertible bond market first decreased and then increased. In the volatility decline stage, the proportion of low - volatility bonds increased, and in the rise stage, the proportion of high - volatility bonds rose [11]. 1.2.2 Analysis of the Proportion of Convertible Bonds in Different Price Ranges From Jan to Aug 2024, the proportion of convertible bonds in the 80 - 100 yuan range was relatively high. After Aug 2024, the proportion of bonds above 100 yuan increased, especially in the 110 - 120 yuan and 120 - 130 yuan ranges, while the proportion of bonds below 80 yuan decreased. There was a correlation between changes in implied volatility and the proportion of convertible bonds in different price ranges [16][18]. 2. Private Convertible Bond Strategy Review 2.1 Latest Changes in the Number and Scale of Convertible Bond Private Fund Managers Convertible bond trading strategy private fund managers showed a significant scale distribution. Managers with a scale of 0 - 5 billion dominated, with 23 managers and 159 products. From 2020 to 2025, the number of managers increased steadily, reaching 30 in 2025. The market concentration was low, and small - scale managers had more opportunities. The convertible bond strategy type also changed, with some managers shifting from convertible bond arbitrage to long strategies and some long - only managers using stock index futures for hedging [20][22][24]. 2.2 Performance of Convertible Bond Strategies Quantitative convertible bond strategies showed significant advantages in terms of return, risk control, and risk - adjusted return. As of May 30, 2025, the annualized return of quantitative convertible bond strategies was 8.74%, significantly higher than the 5.22% of subjective convertible bond strategies, with a maximum drawdown of 1.25% and a Sharpe ratio of 2.31. However, subjective convertible bond strategies had greater return elasticity in trend - following markets [26][28][31]. 3. Conclusions and Investment Outlook 3.1 Valuation Increase after Concentrated Risk Release In 2024, the convertible bond market's credit risk was released concentratedly, leading to a large number of mispriced opportunities. As the underlying stock market recovered, the mispricing was quickly corrected. The dual - low strategy was strong from Sep 2024 to Mar 2025. The pricing logics of different types of convertible bonds (equity - biased, balanced, and bond - biased) were significantly different [32][33]. 3.2 Shrinking Stock and Stratified Investor Structure The convertible bond market's balance decreased from 799.818 billion yuan in Jun 2024 to 676.546 billion yuan in Jun 2025, mainly due to conversion and delisting, and insufficient new issuance. The investor structure showed a pattern of "partial exit and partial increase", with traditional heavy - position investors reducing their holdings of poor - quality targets, while QFIIs and private funds increased their holdings of high - quality ones, further magnifying the scarcity of high - quality convertible bonds [40][42][47]. 3.3 Convertible Bond Strategy Outlook: Anchoring Scarce Value and Embracing Diverse Opportunities in Structural Differentiation The convertible bond strategy should focus on three main lines: continuing BETA robustness by strengthening the "offense - defense balance" logic; digging for ALPHA by targeting "three - high - quality" targets; and upgrading strategies from "single - tool" to "composite strategies". Quantitative and subjective strategies can be combined in a "core + satellite" manner to adapt to different market stages [48][50][51].

Quantitative Models and Construction Methods 1. Model Name: CCBA Pricing Model - **Model Construction Idea**: The CCBA pricing model is used to calculate the pricing deviation of convertible bonds, defined as the difference between the market price and the model price, adjusted for redemption probability[6][24] - **Model Construction Process**: - The pricing deviation is calculated as: $ Pricing\ Deviation = \frac{Convertible\ Bond\ Price}{CCBA\ Model\ Price} - 1 $ - Here, the "Convertible Bond Price" represents the market price of the bond, and the "CCBA Model Price" is derived from the CCBA pricing model[6][24] - The model incorporates historical volatility as a central parameter to determine the deviation level[7] - **Model Evaluation**: The model effectively identifies valuation ranges for convertible bonds, providing insights into their relative attractiveness[6] 2. Model Name: CCB_out Pricing Model - **Model Construction Idea**: This model builds upon the CCBA model by incorporating delisting risk to refine the pricing deviation calculation[24] - **Model Construction Process**: - The pricing deviation is calculated as: $ Pricing\ Deviation = \frac{Convertible\ Bond\ Price}{CCB\_out\ Model\ Price} - 1 $ - The "CCB_out Model Price" adjusts the CCBA model price by accounting for delisting probabilities[24] - Convertible bonds are categorized into three domains: debt-biased, balanced, and equity-biased, with the lowest deviation bonds selected for each domain[24] - **Model Evaluation**: The model demonstrates strong stability and adaptability, achieving consistent returns even in volatile market conditions[24] 3. Model Name: Return Decomposition Model - **Model Construction Idea**: This model decomposes the returns of convertible bonds into three components: bond floor returns, equity-driven returns, and valuation-driven returns[17] - **Model Construction Process**: - The model uses historical data to separate the total return of convertible bonds into: - Bond floor returns, representing the fixed-income component - Equity-driven returns, reflecting the impact of the underlying stock's performance - Valuation-driven returns, capturing changes in the bond's relative pricing[17][21] - **Model Evaluation**: The model provides a detailed understanding of the drivers of convertible bond performance, aiding in strategy optimization[17] --- Quantitative Factors and Construction Methods 1. Factor Name: Pricing Deviation Factor (CCB_out) - **Factor Construction Idea**: Measures the relative valuation of convertible bonds by comparing market prices to model-derived prices[24] - **Factor Construction Process**: - The factor is calculated as: $ Pricing\ Deviation = \frac{Convertible\ Bond\ Price}{CCB\_out\ Model\ Price} - 1 $ - Bonds with the lowest deviation are selected for further analysis[24] - **Factor Evaluation**: The factor effectively identifies undervalued bonds, contributing to the success of valuation-based strategies[24] 2. Factor Name: Momentum Factor - **Factor Construction Idea**: Captures the price momentum of the underlying stock over different time horizons[29] - **Factor Construction Process**: - Momentum scores are calculated based on the stock's returns over the past 1, 3, and 6 months, with equal weighting applied to each period[29] - **Factor Evaluation**: The factor enhances the responsiveness of valuation-based strategies, improving their adaptability to market trends[29] 3. Factor Name: Turnover Factor - **Factor Construction Idea**: Measures the trading activity of convertible bonds to identify liquidity and investor interest[33] - **Factor Construction Process**: - The factor is derived from: - Bond turnover rates over 5-day and 21-day periods - The ratio of bond turnover to stock turnover over the same periods[33] - **Factor Evaluation**: The factor effectively identifies actively traded bonds, improving the liquidity profile of selected portfolios[33] --- Backtesting Results of Models 1. CCBA Pricing Model - **Annualized Return**: 8.6% - **Annualized Volatility**: 11.6% - **Maximum Drawdown**: 19.9% - **Information Ratio (IR)**: Not explicitly provided[6] 2. CCB_out Pricing Model - **Annualized Return**: 21.8% - **Annualized Volatility**: 13.6% - **Maximum Drawdown**: 15.6% - **Information Ratio (IR)**: 2.10[27] 3. Return Decomposition Model - **Annualized Return**: Not explicitly provided - **Annualized Volatility**: Not explicitly provided - **Maximum Drawdown**: Not explicitly provided - **Information Ratio (IR)**: Not explicitly provided[17] --- Backtesting Results of Factors 1. Pricing Deviation Factor (CCB_out) - **Annualized Return**: 21.8% - **Annualized Volatility**: 13.6% - **Maximum Drawdown**: 15.6% - **Information Ratio (IR)**: 2.10[27] 2. Momentum Factor - **Annualized Return**: 24.5% - **Annualized Volatility**: 14.3% - **Maximum Drawdown**: 11.9% - **Information Ratio (IR)**: 2.39[31] 3. Turnover Factor - **Annualized Return**: 23.4% - **Annualized Volatility**: 15.4% - **Maximum Drawdown**: 15.9% - **Information Ratio (IR)**: 2.15[35]