Quantitative Models and Construction Methods - **Model Name**: "百元转股溢价率" (Premium Rate per 100 Yuan Conversion) **Model Construction Idea**: Compare convertible bond valuation with equity valuation using historical percentile metrics to assess relative allocation value [4][15] **Model Construction Process**: Fit a cross-sectional curve of conversion premium rate and conversion value at each time point. Substitute conversion value = 100 into the fitted formula to derive "百元转股溢价率". Formula: $$ y_{i}=\alpha_{0}+\,\alpha_{1}\cdot\,{\frac{1}{x_{i}}}+\epsilon_{i} $$ Here, \( y_{i} \) represents the conversion premium rate of the \( i \)-th bond, and \( x_{i} \) represents the conversion value of the \( i \)-th bond [44] **Model Evaluation**: Provides a relative valuation perspective for convertible bonds versus equities [15] - **Model Name**: "修正 YTM – 信用债 YTM" (Adjusted YTM Minus Credit Bond YTM) **Model Construction Idea**: Adjust convertible bond yield-to-maturity (YTM) by removing the impact of conversion clauses to compare with credit bond YTM [4][15] **Model Construction Process**: $$ \text{Adjusted YTM} = \text{Convertible Bond YTM} \times (1 - \text{Conversion Probability}) + \text{Expected Conversion Annualized Return} \times \text{Conversion Probability} $$ Conversion probability is calculated using the Black-Scholes model, incorporating stock price, strike price, stock volatility, remaining term, and discount rate. The median of the differences between adjusted YTM and credit bond YTM is then computed: $$ \text{"修正 YTM – 信用债 YTM" Median} = \text{median}\{X_1, X_2, ..., X_n\} $$ Here, \( X_i \) represents the difference between adjusted YTM and credit bond YTM for the \( i \)-th bond [45][46] **Model Evaluation**: Suitable for assessing relative allocation value between debt-heavy convertible bonds and credit bonds [15] Quantitative Factors and Construction Methods - **Factor Name**: 转股溢价率偏离度 (Conversion Premium Rate Deviation) **Factor Construction Idea**: Measure deviation of conversion premium rate from fitted values to assess valuation differences [21] **Factor Construction Process**: $$ \text{Conversion Premium Rate Deviation} = \text{Conversion Premium Rate} - \text{Fitted Conversion Premium Rate} $$ Fitted values are determined by the cross-sectional curve fitting process [21] **Factor Evaluation**: Effective in comparing valuation across different convertible bonds [21] - **Factor Name**: 理论价值偏离度 (Theoretical Value Deviation) **Factor Construction Idea**: Assess price expectation differences using Monte Carlo simulation [21] **Factor Construction Process**: $$ \text{Theoretical Value Deviation} = \frac{\text{Convertible Bond Closing Price}}{\text{Theoretical Value}} - 1 $$ Monte Carlo simulation considers conversion, redemption, downward revision, and repurchase clauses, simulating 10,000 paths at each time point using the same credit term limit rate as the discount rate [21] **Factor Evaluation**: Provides a comprehensive valuation perspective by incorporating multiple convertible bond clauses [21] - **Composite Factor Name**: 转债综合估值因子 (Convertible Bond Comprehensive Valuation Factor) **Factor Construction Idea**: Combine conversion premium rate deviation and theoretical value deviation for enhanced valuation analysis [21] **Factor Construction Process**: $$ \text{Convertible Bond Comprehensive Valuation Factor} = \text{Rank(Conversion Premium Rate Deviation)} + \text{Rank(Theoretical Value Deviation)} $$ **Factor Evaluation**: Demonstrates superior performance across various convertible bond categories [21] - **Factor Name**: 转债市场情绪捕捉指标 (Convertible Bond Market Sentiment Capture Indicator) **Factor Construction Idea**: Use momentum and volatility deviation to identify market sentiment [29] **Factor Construction Process**: $$ \text{Market Sentiment Capture Indicator} = \text{Rank(20-day Momentum)} + \text{Rank(Volatility Deviation)} $$ **Factor Evaluation**: Effective in guiding convertible bond style rotation strategies [29] Model Backtesting Results - **"百元转股溢价率" Model**: Rolling three-year percentile at 47.4%, rolling five-year percentile at 50.9% [4][15][18] - **"修正 YTM – 信用债 YTM" Model**: Current median value at -0.03% [4][15][18] Factor Backtesting Results - **转股溢价率偏离度 Factor**: Enhanced excess returns in the past four weeks for偏股,平衡,偏债 convertible bonds at 1.33%, 0.27%, and 0.04%, respectively [5][23] - **理论价值偏离度 Factor**: Demonstrates superior performance in偏股 convertible bonds [20][21] - **转债综合估值因子 Factor**: - 偏股转债低估指数: IR = 1.22, annualized return = 24.91%, annualized volatility = 20.39%, max drawdown = -22.83%, Calmar ratio = 1.09, monthly win rate = 63.64% [24] - 平衡转债低估指数: IR = 1.16, annualized return = 13.77%, annualized volatility = 11.87%, max drawdown = -16.04%, Calmar ratio = 0.86, monthly win rate = 60.23% [24] - 偏债转债低估指数: IR = 1.29, annualized return = 12.21%, annualized volatility = 9.45%, max drawdown = -17.59%, Calmar ratio = 0.69, monthly win rate = 56.82% [24] Style Rotation Backtesting Results - **转债风格轮动 Model**: - IR = 1.47, annualized return = 24.23%, annualized volatility = 16.54%, max drawdown = -15.54%, Calmar ratio = 1.56, monthly win rate = 65.91% [35] - Recent four-week return = 2.24%, year-to-date return = 26.75% [31][32]

Report Industry Investment Rating - The report gives a one-star rating (★☆☆) for the CITIC Five-Style - Financial, indicating a bullish bias but with limited operability in the market [3]. Core Viewpoints - In the public fund market, the returns of equity and bond strategies showed slight differentiation in the past week. The energy and chemical ETF had a significant net value increase, while the non-ferrous metal ETF had a slight decline. The financial and cyclical styles of the CITIC Five-Style recorded positive returns, and the style timing model signals a preference for the financial style this week [3]. - Among the Barra factors, the residual volatility factor performed well in the past week, and the factor cross-sectional rotation speed increased slightly this week. The style timing strategy had a return of 0.44% last week, with an excess return of 0.66% compared to the benchmark balanced allocation [3]. Summary by Relevant Catalogs Recent Market Returns - As of the week ending June 13, 2025, the weekly returns of the Tonglian All-A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond, and Nanhua Commodity Index were -0.41%, 0.17%, and 2.14% respectively [3]. - In the public fund market, equity strategies showed mixed performance, with index enhancement strategies slightly回调 and market neutral strategies under slight pressure. Bond strategies saw better performance in medium - and long - term pure bonds, and the convertible bond index weakened slightly. Commodity strategies had significant increases in the energy and chemical ETF and the soybean meal ETF [3]. CITIC Style Index - Last Friday, the returns of the CITIC Five-Style index were differentiated, with the financial and cyclical styles recording positive returns. The style rotation chart showed a slight decline in the consumer and stable styles in terms of relative strength, and the cyclical style strengthened marginally in terms of indicator momentum [3]. - Only growth-style funds outperformed the index in the public fund pool in the past week, with an excess return of 0.15%. Some financial-style funds shifted towards consumer and cyclical styles [3]. Barra Factors - In the past week, the residual volatility factor had a weekly excess return of 0.82%. The scale factor's excess return continued to compress, and the leverage and growth factors' returns strengthened slightly. The medium - and long - term momentum and growth factors had better performance in terms of win - rate [3]. - The factor cross - sectional rotation speed increased slightly this week and is currently in the medium - to low - percentile range of history [3]. Style Timing Model - According to the latest score of the style timing model, the financial style rebounded this week, while the consumer and cyclical styles declined, and the current signal favors the financial style. The style timing strategy's return last week was 0.44%, with an excess return of 0.66% compared to the benchmark balanced allocation [3].

Quantitative Model and Construction Model Name: Relative Index Trend Tracking Model for Social Services - **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity, where prices are always in a certain trend. Reversal trends are shorter in duration compared to trend continuations. In cases of narrow-range consolidation, the model assumes the continuation of the previous trend. For large-scale trends, given a short observation window, the movement will follow the local trend within the window. When a reversal occurs, the price change at the start and end of the observation window will significantly exceed the range caused by random fluctuations, thus eliminating the impact of randomness[3] - **Model Construction Process**: 1. Calculate the difference between the closing price on day T and the closing price on day T-20, denoted as `del` 2. Calculate the volatility (`Vol`) over the period from T-20 to T (excluding T) 3. If the absolute value of `del` exceeds N times `Vol`, the current price is considered to have broken out of the original oscillation range, forming a trend. The trend direction (long/short) corresponds to the sign of `del` 4. If the absolute value of `del` is less than or equal to N times `Vol`, the current movement is considered to continue the previous trend direction (same as T-1) 5. For tracking, N is set to 1, considering the higher volatility of the stock market compared to the bond market, which provides more short-term opportunities 6. The model evaluates the combined results of long and short returns for the social services sector relative to the CSI 300 index[3] - **Model Evaluation**: The model is not suitable for direct application to the relative value of the SW First-Level Social Services Index due to its poor cumulative return performance during most of the backtesting period. However, it showed strong performance during a short period of rapid net value growth[4] --- Model Backtesting Results Relative Index Trend Tracking Model for Social Services - **Annualized Return**: -2.87%[3] - **Annualized Volatility**: 21.22%[3] - **Sharpe Ratio**: -0.14[3] - **Maximum Drawdown**: 23.32%[3] - **Total Return**: -20.18%[3]

Investment Rating - The industry is rated as "Neutral," indicating that the expected overall return in the next six months will be between -5% and 5% compared to the CSI 300 index [10]. Core Insights - The model assumes that the price movements of the underlying assets exhibit good local continuity, with trend reversals occurring less frequently than trend continuations. It also posits that during narrow consolidations, the previous trend will likely continue [3]. - The model's performance from March 7, 2023, to January 26, 2024, showed fluctuations around the original value without achieving significant cumulative returns. However, a short-term sharp increase was observed from January 26 to February 6, 2024, followed by a prolonged downtrend [4]. - The model's annualized return was -7.36%, with a volatility of 16.87%, a Sharpe ratio of -0.44, and a maximum drawdown of 27.58% [3]. Summary by Sections Model Overview - The model is designed to track the relative value of the Shenwan Level 1 Light Industry Manufacturing Index against the CSI 300 index, using a multi-directional signal approach [3]. - The tracking period for the model is set from March 7, 2023, to March 18, 2025 [3]. Performance Evaluation - The model's net value fluctuated around the original value during the initial tracking period, indicating a lack of strong cumulative returns. The model is deemed unsuitable for direct application to the Shenwan Level 1 Light Industry Manufacturing Index relative value [4]. - The model's performance metrics include a total return rate of -15.60% during the index period [3].

Quantitative Model and Construction - **Model Name**: Relative Index Trend Tracking Model for Steel Industry - **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity, with prices always in a certain trend. Reversal trends are assumed to last significantly shorter than trend continuation periods. In cases of narrow consolidation, the model assumes the continuation of the previous trend. For large-scale trends, given a short observation window, the movement is expected to follow the local trend within the window. When a reversal occurs, the price change at the start and end of the observation window will exceed the range caused by random fluctuations, thus filtering out random noise[3][4] - **Model Construction Process**: 1. Calculate the difference between the closing price on day T and day T-20, denoted as `del` 2. Calculate the volatility (`Vol`) over the period from T-20 to T (excluding T) 3. If the absolute value of `del` exceeds N times `Vol`, the price is considered to have exited the original oscillation range, forming a trend. The trend direction (long or short) corresponds to the sign of `del` 4. If the absolute value of `del` is less than or equal to N times `Vol`, the current movement is considered a continuation of the previous trend (same direction as day T-1) 5. For the steel industry, N is set to 1 to capture smaller wave opportunities due to higher market volatility compared to bonds 6. The model tracks both long and short returns, combining them for final evaluation[3] - **Model Evaluation**: The model is not suitable for direct application to the relative value of the SW First-Level Steel Index. It underperformed during the tracking period, with significant downward trends in specific sub-periods. The model's annualized return was lower than the total return of the index, and it remained in a drawdown state for most of the tracking period[4] Model Backtest Results - **Annualized Return**: -15.42%[3] - **Annualized Volatility**: 15.00%[3] - **Sharpe Ratio**: -1.03[3] - **Maximum Drawdown**: 34.62%[3] - **Total Return of Index During Period**: -9.08%[3]

金 金融工程点评 [Table_Title] 环保指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 一般证券业务登记编码：S1190123080008 模型概述 结果评估： 区间年化收益：16.82% 波动率（年化）：24.07% 夏普率：0.70 最大回撤：27.18% 指数期间总回报率：-4.63% [Table_Message]2025-05-21 太 平 洋 证 券 股 份 有 限 公 司 证 券 研 究 报 告 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 [Table_Summary] 融 工 程 点 评 ◼ 设计原理：模型假定标的价格走势具有很好的局部延续性，标的价格永远处 于某一趋势中，出现反转行情的持续时间明显小于趋势延续的时间，若出现 窄幅盘整的情况，亦假设其延续之前的趋势。当处于大级别的趋势之中时， 给定较短时间的观察窗口，走势将延续观察窗口内的局部趋势。而当趋势发 生反转时，在观察窗 ...

Quantitative Model and Construction 1. Model Name: Coal Index Trend Tracking Model - **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity, with prices always in a certain trend. Reversal periods are significantly shorter than trend continuation periods. In cases of narrow consolidation, the model assumes the continuation of the previous trend. For large-scale trends, given a short observation window, the movement will follow the local trend within the window. When a reversal occurs, the price change at the start and end of the observation window will exceed the range caused by random fluctuations, thus filtering out random noise[2][3]. - **Model Construction Process**: 1. Calculate the difference between the closing price on day T and day T-20, denoted as `del` 2. Calculate the volatility (`Vol`) over the period from T-20 to T (exclusive) 3. If the absolute value of `del` exceeds N times `Vol`, the current price is considered to have broken out of the original oscillation range, forming a trend. The trend direction (long/short) corresponds to the sign of `del`. Otherwise, the trend direction remains the same as on day T-1 4. For tracking, N is set to 1, considering the higher volatility of the stock market compared to the bond market, which provides more short-term opportunities 5. The model evaluates both long and short returns for the coal index and combines the results for final assessment[3] - **Model Evaluation**: The model is not suitable for direct application to the Shenwan Level-1 Coal Index due to poor annualized return performance and significant drawdowns during the tracking period[4] --- Model Backtesting Results 1. Coal Index Trend Tracking Model - **Annualized Return**: -8.01%[3] - **Annualized Volatility**: 22.67%[3] - **Sharpe Ratio**: -0.35[3] - **Maximum Drawdown**: 22.79%[3] - **Total Return**: -8.75%[3]

- The "All-weather model" issued a technical risk signal, indicating potential market consolidation in the near term[1][7] - The "Cycle analysis model" monitors trends at different levels, suggesting the current adjustment is limited to the post-May 1st rally, with no evidence of the broader uptrend since early April ending[1][7] - The "Four-wheel drive industry rotation model" recommends focusing on sectors such as military, home appliances, agriculture, automotive, electronics, computers, and non-bank financials during the consolidation phase[7][16]

Investment Rating - The industry is rated positively, with expectations of overall returns exceeding the CSI 300 Index by more than 5% over the next six months [13]. Core Insights - The model assumes that the price trends of the underlying assets exhibit strong local continuity, with reversal trends lasting significantly shorter than trend continuation periods. In cases of narrow consolidation, it is assumed that the previous trend will continue [4]. - The model's annualized return is reported at 24.17%, with a volatility of 26.54% and a Sharpe ratio of 0.91. The maximum drawdown recorded is 14.04%, and the total return rate during the index period is 19.46% [4]. - The model is designed for the Shenwan Level 1 Non-Bank Financial Index and is suitable for tracking this index from March 7, 2023, to March 18, 2025 [4][5]. Summary by Sections Model Overview - The model calculates the difference between the closing price on day T and the closing price 20 days prior, assessing volatility over the same period. A significant deviation from the historical volatility indicates a trend formation [4]. Performance Evaluation - The model's net value has shown a slow upward trend from March 7, 2023, to September 12, 2024, with a sharp increase influenced by market conditions and macro policies during a specific period [5]. - The model is deemed suitable for the Shenwan Level 1 Non-Bank Financial Index, demonstrating good annualized returns without prolonged significant drawdowns [5].

Investment Rating - The industry rating is "Neutral," indicating that the overall return is expected to be between -5% and 5% relative to the CSI 300 index over the next six months [12]. Core Insights - The model used for evaluating the defense and military industry assumes that price trends exhibit good local continuity, with reversals occurring less frequently than trend continuations. The model aims to identify trends based on price movements and volatility [4][5]. - The evaluation period for the model is from March 7, 2023, to March 18, 2025, with a focus on the Shenwan Level 1 Defense and Military Industry Index [4]. - The model's annualized return is -0.37%, with a volatility of 29.80% and a maximum drawdown of 29.79%. The total return during the evaluation period is -3.81% [4][5]. Summary by Sections Model Overview - The model is designed to track price movements and identify trends based on historical data, with specific algorithms to determine when a price has deviated from its previous range [4]. Results Assessment - The model experienced a decline in net value during specific periods, indicating a long-term drawdown and an inability to achieve favorable cumulative returns. The model is deemed unsuitable for direct application to the Shenwan Level 1 Defense and Military Industry Index due to negative returns [5]. Performance Metrics - The model's performance metrics include an annualized return of -0.37%, a volatility of 29.80%, a Sharpe ratio of -0.01, and a maximum drawdown of 29.79% [4].