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 ...

[Table_Title] 美容护理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 金 金融工程点评 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：-1.22% 波动率（年化）：30.79% 夏普率：-0.04 最大回撤：39.17% 指数期间总回报率：-33.91% [Table_Message]2025-05-21 太 平 洋 证 券 股 份 有 限 公 司 证 券 研 究 报 告 [Table_Summary] 资料来源：Wind，太平洋研究院 资源来源：Wind，太平洋研究院 图表 3 回撤（绝对值） 图表 4 最大回撤（绝对值） 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% 2023-03-07 2023-04-13 2 ...

Investment Rating - The industry rating is optimistic, expecting overall returns to exceed the CSI 300 index by more than 5% in the next six months [10]. Core Insights - The report highlights that on May 21, 2025, the equity market saw most sectors rise, with the Shanghai Composite Index and Shenzhen Component Index increasing by 0.21% and 0.44% respectively, while the Shenwan Real Estate Index fell by 0.53% [3]. - The report notes significant transactions in the real estate sector, including two land parcels in Tianjin that sold for a total of 2.339 billion yuan, with a floor price of approximately 5,526 yuan per square meter for one parcel [5]. - The report also mentions that the real estate sector's individual stock performance showed notable gains for companies such as Airport Holdings and Guangdong Hongyuan A, with increases of 10.04% and 10.00% respectively [4]. Market Trends - The report indicates that the real estate market is experiencing fluctuations, with a mixed performance across different regions and companies [3][4]. - It provides updates on land transactions, including a low-density residential land parcel in Zhoushan that sold for 346 million yuan, reflecting a year-on-year increase of approximately 20% in floor price [6]. - The report discusses the status of corporate bonds, noting that Xiamen Anju Group's 4 billion yuan bond issuance has been accepted for review [6]. Company Ratings - The report does not provide specific ratings for individual companies within the real estate sector, indicating a lack of ratings for real estate development and services [3].

Group 1 - The report indicates that the bond market is experiencing slight increases in short-term rates and slight decreases in long-term rates, with expectations of a small adjustment following the interest rate cut, while the long-term outlook remains bullish due to weak fundamentals driving new highs in the bond market [4][13]. - The A-share market shows a slight upward trend with insufficient trading volume, and it is expected to continue a rotational rise until the trading volume effectively breaks through 1.5 trillion [5][17]. - The U.S. stock market has recovered all losses from the trade war, with the potential for new highs due to upcoming tax cuts and significant stock buybacks by companies like Apple and Google, although concerns about unsustainable debt levels are emerging [23][24]. Group 2 - The report highlights that the onshore RMB against the USD closed at 7.2035, down 159 basis points, but is expected to appreciate towards 7.1 due to favorable trade conditions [6][26]. - The commodity market saw a 0.28% increase in the Wenhua Commodity Index, with precious metals and feed sectors leading the gains, although the overall structure remains bearish with expectations of continued fluctuations [27][30]. - Important domestic policies include the completion of the China-ASEAN Free Trade Area 3.0 negotiations, which is expected to enhance cooperation and market expansion between China and ASEAN countries [34].

Investment Rating - The report maintains a "Buy" rating for the company Zhejiang Natural (605080) with a target price based on the last closing price of 31.97 [1] Core Insights - The company's Q1 performance exceeded expectations, driven by new product launches and capacity releases, with a revenue of 3.6 billion yuan, representing a year-on-year growth of 30.4%, and a net profit of 0.96 billion yuan, up 148.3% year-on-year [4][7] - The outdoor sports industry is experiencing rapid growth, with the company positioned to benefit from the increasing penetration of TPU materials over PVC products and the emergence of new product categories [7] - The company has established a strategic focus on three core business areas: inflatable mattresses, waterproof/thermal bags, and water sports products, with expectations for stable growth and new market expansions [7] Financial Performance Summary - In 2024, the company reported a revenue of 1 billion yuan, a year-on-year increase of 21.7%, and a net profit of 190 million yuan, up 41.7% year-on-year [4][9] - The gross margin for 2024 was 33.7%, with a net profit margin of 18.5%, reflecting significant improvements in profitability due to effective cost control and operational efficiency [6][9] - The company forecasts revenues of 1.38 billion yuan in 2025, 2.01 billion yuan in 2026, and 2.57 billion yuan in 2027, with corresponding net profits of 270 million yuan, 380 million yuan, and 494 million yuan respectively [9][10] Operational Highlights - The company has shown significant improvement in its operational capabilities, with inventory turnover days reduced by 5.9 days to 112 days, and accounts receivable turnover days increased by 5.5 days to 63 days [6] - The gross profit margin for Q1 2025 improved to 38.8%, indicating enhanced profitability from operational efficiencies [6] Market Position and Growth Potential - The company is recognized for its technological strength and industry barriers, with a favorable outlook for continued growth in the outdoor sports supply chain [7] - The strategic expansion of production capacity in overseas locations such as Cambodia and Vietnam is expected to enhance profitability as orders increase [7]

Group 1 - The report highlights that among 31 industries, 17 industries have returns exceeding that of the CSI 300 index, indicating potential for superior returns through industry selection [5][12]. - The analysis of the industry indices relative to the CSI 300 shows that a simple average allocation can yield an excess return of 34% over the specified period [12]. - It is noted that holding a single industry may lead to significant drawdowns and longer recovery times, suggesting the necessity of timing in investment decisions [12]. Group 2 - The report discusses the deviation recovery strategy, emphasizing that a simple approach based on relative deviation to the CSI 300 may overlook one-sided deviation opportunities [22]. - The analysis of the top three drawdowns across industries reveals that the steel and petrochemical sectors are often in a single drawdown cycle, while industries like food and beverage, retail, and non-bank financials show significant differences in maximum drawdowns compared to other drawdowns [25][26]. - The effective deviation screening algorithm is introduced, which involves calculating the maximum drawdown and filtering based on statistical measures to identify suitable industries for investment [30][38]. Group 3 - The report indicates that the household appliances and food and beverage sectors have a higher number of drawdowns compared to other industries, suggesting increased volatility [33]. - The iterative method for filtering effective drawdowns significantly reduces the proportion of selected drawdowns, enhancing the stability of the strategy [55][56]. - The report concludes that the deviation recovery strategy is more applicable to industries with stable volatility patterns over time, while it may miss opportunities in sudden market movements [69].

- The industry crowding monitoring model was constructed to monitor the daily crowding levels of Shenwan primary industry indices. The model identifies industries with high crowding levels, such as textiles, beauty care, and light manufacturing, while industries like media and electronics show lower crowding levels. It also tracks significant daily changes in crowding levels for industries like environmental protection, food & beverage, and real estate[4] - The Z-score premium rate model was developed to screen ETF products for potential arbitrage opportunities. This model uses rolling calculations to identify ETFs with significant deviations from their intrinsic value, providing signals for potential trades while warning of possible price corrections[5] - The industry crowding monitoring model highlights that defense, non-bank finance, and environmental protection sectors saw significant inflows of main funds, while sectors like automobiles, electrical equipment, and basic chemicals experienced outflows. Over the past three days, coal, beauty care, and banking sectors were favored, while computing, electronics, and electrical equipment were reduced[4] - The Z-score premium rate model provides ETF signals, including top inflows for ETFs like Sci-Tech 50 ETF (+5.77 billion yuan) and Sci-Tech 100 Index ETF (+2.27 billion yuan), while ETFs like Shanghai 50 ETF (-4.86 billion yuan) and ChiNext ETF (-3.46 billion yuan) saw significant outflows[6][7] - The industry crowding monitoring model's evaluation indicates its effectiveness in identifying crowded sectors and tracking fund flows, aiding investors in understanding market dynamics[4] - The Z-score premium rate model is evaluated as a useful tool for identifying arbitrage opportunities in ETFs, though it requires caution due to potential risks of price corrections[5] - The industry crowding monitoring model's testing results show significant fund flow changes in various sectors, such as coal (+4.28 billion yuan over three days) and computing (-129.02 billion yuan over three days)[14][15] - The Z-score premium rate model's testing results include ETF fund flow data, such as Sci-Tech 50 ETF (+5.77 billion yuan) and Shanghai 50 ETF (-4.86 billion yuan)[6][7]

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]

Quantitative Models and Construction Methods - **Model Name**: Oil and Petrochemical Index Trend Tracking Model - **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 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, a short observation window is used to capture the local trend. 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 current price is considered to have broken out of 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 trend is considered to continue, and the trend direction remains the same as on day T-1 5. For the stock market, where volatility is higher and small wave opportunities are more frequent, N is set to 1 for tracking 6. The model considers both long and short returns for the oil and petrochemical index, combining the results for final evaluation[3] - **Model Evaluation**: The model's net value showed a consistent downward trend, with significant drawdowns during specific periods. It is deemed unsuitable for direct application to the Shenwan Level-1 Oil and Petrochemical Index[4] Model Backtesting Results - **Annualized Return**: -24.35%[3] - **Annualized Volatility**: 19.77%[3] - **Sharpe Ratio**: -1.23[3] - **Maximum Drawdown**: 44.48%[3] - **Total Return of the Index During the Period**: -6.61%[3]

Group 1: Market Overview - The bond market is experiencing a slight decline, with expectations of a small adjustment following interest rate cuts. The short-term outlook indicates a higher probability of filling the gap downwards, while the long-term view suggests that fundamental weaknesses will continue to drive the bond market to new highs [20][4]. - The A-share market is showing continued volatility, with small-cap stocks performing actively while high-position stocks are retreating. This indicates ongoing rotational increases in the market, expected to persist until trading volume effectively breaks through 1.5 trillion [24][4]. - The U.S. stock market has broken through key resistance levels, increasing the likelihood of new highs. Corporate buybacks are providing support, with the S&P 500 recovering the previously mentioned resistance level of 5700 points [29][30]. Group 2: Sector Performance - In the A-share market, sectors such as technology, consumer goods, and dividends are expected to rotate upwards. The market is currently seeing a shift towards consumption and pharmaceuticals, with new consumption and innovative pharmaceuticals attracting more capital [24][4]. - The commodity market is experiencing a decline, with the Wenhua Commodity Index down by 0.22%. The market does not perceive the recent interest rate cuts as significantly stimulating demand [36][4]. - The foreign exchange market shows the onshore RMB against the USD at 7.2194, appreciating by 50 basis points. The RMB is expected to rise to around 7.1 due to favorable impacts from U.S.-China trade relations [34][6]. Group 3: Policy and Economic Indicators - The May LPR (Loan Prime Rate) has been released, with both the 5-year and 1-year rates lowered by 10 basis points. The 5-year LPR is now at 3.5%, and the 1-year LPR is at 3% [40][41]. - In April, the total retail sales of consumer goods increased by 5.1% year-on-year, indicating a recovery in consumption supported by policy measures [41][40]. - The National Development and Reform Commission has highlighted the need to address "involution" in competition, which distorts market mechanisms and disrupts fair competition [41][40].