Quantitative Models and Construction Methods - **Model Name**: A-Share Industry Rotation Model **Model Construction Idea**: The model uses six factors—capital, valuation, sentiment, momentum, overbought/oversold, and profitability—to build a scoring system for industry evaluation[17] **Model Construction Process**: - **Capital Factor**: Based on industry net inflow rate of major funds - **Valuation Factor**: Uses the valuation percentile of the industry over the past year - **Sentiment Factor**: Derived from the proportion of rising constituent stocks - **Momentum Factor**: Based on MACD indicator - **Overbought/Oversold Factor**: Uses RSI indicator - **Profitability Factor**: Based on the consensus forecast EPS percentile of the industry over the past year[17] **Model Evaluation**: The model provides a comprehensive scoring system to assess industry rotation trends[17] - **Model Name**: Consensus Stock Selection Model **Model Construction Idea**: The model identifies high-growth industries and selects stocks with high similarity between high-frequency capital flow trends and stock price trends[20] **Model Construction Process**: - Filters high-growth industries at the Shenwan secondary industry level based on the past 30-day performance - Calculates momentum, valuation, and frequency of price increases for stocks within these industries - Uses high-frequency minute-level capital flow data to compute changes in inflow/outflow for each stock - Selects stocks with the highest similarity between capital flow trends and price trends within the top-performing secondary industries[20] **Model Evaluation**: The model effectively identifies stocks with strong capital flow and price trend alignment[20] --- Model Backtesting Results - **A-Share Industry Rotation Model**: - **Top Scoring Industries**: Comprehensive (+10), Non-ferrous Metals (+10), Electronics (+7)[18][19] - **Low Scoring Industries**: Banking (-15), Petrochemicals (-9), Transportation (-8)[19] - **Consensus Stock Selection Model**: - **Selected Industries**: Communication Equipment, Ground Armament II, Components[21] - **Selected Stocks**: - Communication Equipment: New Yisheng, Move Communication, Feiling Kesi, Hengtong Optoelectronics, Meixin Technology - Ground Armament II: Great Wall Military Industry, Optical Shares, Inner Mongolia First Machine, Sweet Qin Equipment, Ganfa Technology - Components: Jingwang Electronics, Deep South Circuit, Fangbang Shares, Zhongjing Electronics, Shenghong Technology[21] --- Quantitative Factors and Construction Methods - **Factor Name**: Capital Factor **Construction Idea**: Measures industry net inflow rate of major funds[17] **Construction Process**: Aggregates daily net inflow data for transactions exceeding 10,000 shares or 200,000 yuan[12] **Evaluation**: Reflects the strength of capital flow within industries[17] - **Factor Name**: Valuation Factor **Construction Idea**: Uses industry valuation percentile over the past year[17] **Construction Process**: Calculates the relative valuation position of the industry within a one-year window[17] **Evaluation**: Indicates whether an industry is undervalued or overvalued[17] - **Factor Name**: Sentiment Factor **Construction Idea**: Based on the proportion of rising constituent stocks[17] **Construction Process**: Computes the percentage of stocks within the industry that have increased in price[17] **Evaluation**: Captures market sentiment towards the industry[17] - **Factor Name**: Momentum Factor **Construction Idea**: Uses MACD indicator to measure price trends[17] **Construction Process**: Applies MACD calculations to industry-level data[17] **Evaluation**: Identifies industries with strong upward or downward trends[17] - **Factor Name**: Overbought/Oversold Factor **Construction Idea**: Uses RSI indicator to assess market conditions[17] **Construction Process**: Calculates RSI values for industries to determine overbought or oversold conditions[17] **Evaluation**: Helps identify potential reversals in industry trends[17] - **Factor Name**: Profitability Factor **Construction Idea**: Based on consensus forecast EPS percentile over the past year[17] **Construction Process**: Aggregates EPS forecasts and calculates relative percentile rankings[17] **Evaluation**: Reflects the earnings potential of industries[17] --- Factor Backtesting Results - **Capital Factor**: Comprehensive (++), Non-ferrous Metals (++), Electronics (++), Banking (---), Petrochemicals (---), Transportation (---)[19] - **Valuation Factor**: Comprehensive (+++), Non-ferrous Metals (++), Electronics (+), Banking (-), Petrochemicals (---), Transportation (---)[19] - **Sentiment Factor**: Comprehensive (-), Non-ferrous Metals (+++), Electronics (+++), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Momentum Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Overbought/Oversold Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Profitability Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+++), Banking (---), Petrochemicals (---), Transportation (---)[19]

Group 1: Report Investment Rating - The operation rating for CITIC Five-Style - Growth is ★☆☆ [3][4] Group 2: Core Viewpoints - In the public fund market, the enhanced index strategy led the gains in the past week, while the ordinary stock strategy index in the equity strategy was relatively weak. The net value of non-ferrous metal ETFs rebounded, and the performance of precious metal ETFs was divergent. The style timing signal currently favors the growth style, and the style timing strategy had an excess return compared to the benchmark [4] Group 3: Summary by Related Catalogs Fund Market Review - As of the week ending on June 27, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond Index, and Nanhua Commodity Index were 3.35%, -0.10%, and -2.00% respectively [4] - In the public fund market, the enhanced index strategy had a weekly return of 3.18%. Among equity strategies, the ordinary stock strategy index was relatively weak, and neutral strategy products had more losses than gains. In the bond market, medium - and long - term pure bonds had a small pullback, and convertible bonds outperformed pure bonds. In the commodity market, the returns of energy - chemical and soybean meal ETFs pulled back, the net value of non - ferrous metal ETFs rebounded, and the performance of precious metal ETFs was divergent, with silver ETFs rising slightly and gold ETFs continuing to weaken [4] Equity Market Style - In the CITIC Five - Style, all style indices closed up last Friday, with the growth and financial styles leading. In terms of relative strength, consumption and stability were at a relatively low level, and in terms of indicator momentum, all five styles strengthened compared to the previous week, with consumption and stability having a large increase [4] - In the public fund pool, the average returns of cycle and consumption style funds outperformed the index in the past week, with excess returns of 0.60% and 0.06% respectively. Some growth - style funds shifted towards cycle and consumption styles [4] - In terms of crowding, consumption fell from a high - crowding range to a neutral range, the cycle style increased significantly, and the growth style was in a historically low - crowding range [4] Barra Factors - In the past week, the growth, liquidity, and momentum factors had better returns, the excess return of the profitability factor was compressed, the return of the volatility factor continued to decline, the dividend factor continued to weaken in terms of winning rate, and the momentum and residual volatility factors rebounded [4] - The cross - sectional rotation speed of factors decreased compared to the previous week and was currently in a historically low - quantile range [4] Style Timing - According to the latest scoring results of the style timing model, the financial style weakened slightly this week, while consumption and growth recovered, and the current signal favored the growth style [4] - The return of the style timing strategy last week was 3.41%, with an excess return of 0.63% compared to the benchmark balanced allocation [4]

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].

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]

2025 年 05 月 18 日 或仅是整理，蓄势以待机 本期要点：或仅是整理，蓄势以待机 上期提到，全天候模型发出了技术面上的风险提示信号，从这个角度 看未来一段时间市场或将进入技术上的震荡整理期。事后来看，走势 基本符合预期。 对于大盘，从走势形态看，本轮反弹已经涨了两波，第二波是从五一 之后开始的。从周期分析模型对不同级别趋势的监控分析结果来看， 当前潜在的调整仅是针对于五一之后上涨过程的调整，而四月初以来 的上行趋势或并没有已经结束的充分证据。从这个角度看，本轮调整 或最多回到 4 月底 5 月初的震荡区间，随后有望重新开始一波上行 走势，只有在那波上行趋势再次衰竭的时候才能判断本轮反弹是否真 正结束了。 在当前潜在的蓄势震荡过程中，四轮驱动模型建议关注军工、家电、 农林牧渔、汽车、电子、计算机、非银等板块。整体风格上可以适当 偏小盘，结构上可以适当偏均衡一些。 风险提示：根据历史数据构建的模型在市场变化时可能失效。 金融工程定期报告 证券研究报告 杨勇 分析师 SAC 执业证书编号：S1450518010002 yangyong1@essence.com.cn 相关报告 结构重于仓位，继续关注先 进 ...

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].