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].

金 金融工程点评 [Table_Message]2025-05-12 建筑材料指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 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：1.98% 波动率（年化）：24.36% 夏普率：0.08 最大回撤：25.11% 指数期间总回报率：-29.59% 太 平 洋 证 券 股 份 有 限 公 司 证 券 研 究 报 [Table_Title] [Table_Summary] 融 工 程 点 评 告 ◼ 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 ...

Quantitative Factors and Construction Methods Factor Name: Beta - Construction Idea: Measures the sensitivity of a stock's returns to market returns[14] - Construction Process: Calculated using the weighted regression of individual stock returns against market returns over the last 490 trading days[14] - Evaluation: Beta factor performed well in the recent week[8][10] Factor Name: Turnover Rate and Average Price Correlation (1 Month) - Construction Idea: Measures the correlation between turnover rate and average price over the past month[13] - Construction Process: Calculated as the correlation coefficient between turnover rate and average price over the past 20 trading days[13] - Evaluation: This factor showed good performance in the recent week and month[8][10] Factor Name: Turnover Rate Volatility (1 Month) - Construction Idea: Measures the volatility of turnover rate over the past month[13] - Construction Process: Calculated as the standard deviation of turnover rate over the past 20 trading days[13] - Evaluation: This factor performed well in the recent month and year[8][10] Factor Name: Reversal (1 Month) - Construction Idea: Measures the cumulative returns over the past month[13] - Construction Process: Calculated as the cumulative returns over the past 20 trading days[13] - Evaluation: This factor showed good performance in the recent week and month[8][10] Factor Name: Specificity (1 Month) - Construction Idea: Measures the specificity of stock returns relative to the Fama-French three-factor model[13] - Construction Process: Calculated as 1 minus the R-squared value from the regression of daily returns against the Fama-French three factors over the past 20 trading days[13] - Evaluation: This factor performed well in the recent year[8][10] Factor Name: Residual Volatility (Fama-French Three-Factor Model, 1 Month) - Construction Idea: Measures the residual volatility of stock returns relative to the Fama-French three-factor model[13] - Construction Process: Calculated as the standard deviation of residuals from the regression of daily returns against the Fama-French three factors over the past 20 trading days[13] - Evaluation: This factor showed good performance in the recent year[8][10] Factor Name: Excess Return Volatility (1 Month) - Construction Idea: Measures the volatility of excess returns over the past month[13] - Construction Process: Calculated as the standard deviation of excess returns over the past 20 trading days[13] - Evaluation: This factor performed well in the recent year[8][10] Factor Name: Small Market Capitalization - Construction Idea: Measures the logarithm of market capitalization[13] - Construction Process: Calculated as the logarithm of market capitalization[13] - Evaluation: This factor showed good performance in the recent week and year[8][10] Factor Backtesting Results Information Coefficient (IC) Performance - Beta: Recent week IC: 13.69%, Recent month IC: 0.85%, Recent year IC: 1.73%, Historical IC: 0.44%[9] - Turnover Rate and Average Price Correlation (1 Month): Recent week IC: 11.30%, Recent month IC: 7.07%, Recent year IC: 2.49%, Historical IC: 1.70%[9] - Turnover Rate Volatility (1 Month): Recent week IC: 6.15%, Recent month IC: 5.29%, Recent year IC: 2.99%, Historical IC: 2.51%[9] - Reversal (1 Month): Recent week IC: 11.08%, Recent month IC: 4.52%, Recent year IC: 2.87%, Historical IC: 2.15%[9] - Specificity (1 Month): Recent week IC: 11.05%, Recent month IC: 3.76%, Recent year IC: 3.63%, Historical IC: 2.41%[9] - Residual Volatility (Fama-French Three-Factor Model, 1 Month): Recent week IC: 5.42%, Recent month IC: 3.27%, Recent year IC: 3.62%, Historical IC: 2.48%[9] - Excess Return Volatility (1 Month): Recent week IC: -0.20%, Recent month IC: 1.88%, Recent year IC: 3.29%, Historical IC: 2.18%[9] - Small Market Capitalization: Recent week IC: 7.12%, Recent month IC: 2.70%, Recent year IC: 2.03%, Historical IC: 1.89%[9] Long Portfolio Performance - Beta: Recent week excess return: 1.08%, Recent month excess return: -0.75%, Recent year excess return: 6.46%, Historical cumulative excess return: -5.34%[11] - Turnover Rate and Average Price Correlation (1 Month): Recent week excess return: 1.08%, Recent month excess return: 2.92%, Recent year excess return: 2.72%, Historical cumulative excess return: 16.63%[11] - Turnover Rate Volatility (1 Month): Recent week excess return: 0.93%, Recent month excess return: 1.96%, Recent year excess return: 10.68%, Historical cumulative excess return: 32.01%[11] - Reversal (1 Month): Recent week excess return: 0.37%, Recent month excess return: 0.22%, Recent year excess return: 0.75%, Historical cumulative excess return: -1.18%[11] - Specificity (1 Month): Recent week excess return: 0.67%, Recent month excess return: 0.67%, Recent year excess return: 10.17%, Historical cumulative excess return: 16.91%[11] - Residual Volatility (Fama-French Three-Factor Model, 1 Month): Recent week excess return: 0.34%, Recent month excess return: 0.82%, Recent year excess return: 8.10%, Historical cumulative excess return: 18.57%[11] - Excess Return Volatility (1 Month): Recent week excess return: 0.02%, Recent month excess return: 0.09%, Recent year excess return: 7.20%, Historical cumulative excess return: 10.83%[11] - Small Market Capitalization: Recent week excess return: 0.95%, Recent month excess return: 0.12%, Recent year excess return: 10.84%, Historical cumulative excess return: 59.20%[11]