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]

2025 年 6 月 22 日 可转债策略半年度回顾以及展望 ---2025 年可转债策略半年度行情展望 瞿新荣 投资咨询从业资格号：Z0018524 quxinrong027631@gtjas.com 刘宇佩（联系人）从业资格号：F03126011 liuyupei027932@gtjas.com 报告导读： 目录 2025 年上半年可转债市场呈现 "BETA 稳健性凸显、优质标的稀缺性加剧、投资者结构分层、策略分化升级" 特征，为后续 布局锚定方向。 展望阶段需聚焦四大主线： 一是延续 "债性为盾、股性为矛" 的 BETA 逻辑，攻守平衡捕捉市场弹性与抗跌性； 二是锚定 "正股优、条款优、债底优" 稀缺标的，深挖供需错配下的 ALPHA 收益； 三是顺应结构变迁，推动套利、多头、中性策略向 "复合工具化" 迭代，量化与主观互补适配周期； 四是通过组合分散、动态调仓、衍生品对冲，管控信用、估值、流动性风险。在波动分化中锚定稀缺价值，实现收益风 险最优平衡。 请务必阅读正文之后的免责条款部分 请务必阅读正文之后的免责条款部分 请务必阅读正文之后的免责条款部分 1 二 〇 二 三 年 度 | 1. 2025 | ...

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]