Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 1.06% this week and 4.21% year-to-date [1][5] - The CSI 500 index enhanced portfolio recorded an excess return of -0.05% this week and 6.45% year-to-date [1][5] - The CSI 1000 index enhanced portfolio had an excess return of 0.72% this week and 9.41% year-to-date [1][5] - The CSI A500 index enhanced portfolio reported an excess return of 0.36% this week and 6.44% year-to-date [1][5] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month volatility, one-month volatility, and standardized unexpected earnings performed well [1][6] - In the CSI 500 component stocks, factors like quarterly revenue growth year-on-year, standardized unexpected revenue, and non-liquidity shocks showed strong performance [1][6] - For the CSI 1000 component stocks, factors such as EPTTM one-year percentile, SPTTM, and BP performed well [1][6] - In the CSI A500 index component stocks, factors like BP, quarterly EP, and three-month volatility showed good performance [1][6] - Among publicly offered fund heavy stocks, factors like quarterly unexpected magnitude, standardized unexpected earnings, and standardized unexpected revenue performed well [1][6] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.37%, a minimum of -0.21%, and a median of 0.32% this week [1][19] - The CSI 500 index enhanced products had a maximum excess return of 0.92%, a minimum of -0.09%, and a median of 0.35% this week [1][20] - The CSI 1000 index enhanced products had a maximum excess return of 0.98%, a minimum of -0.21%, and a median of 0.24% this week [1][22] - The CSI A500 index enhanced products had a maximum excess return of 0.70%, a minimum of -0.19%, and a median of 0.36% this week [1][24]

Core Viewpoint - The article discusses the evolution and optimization of dividend strategies in investment, highlighting the increasing popularity of dividend-focused index funds and the emergence of various optimized dividend indices in the market [2][3]. Group 1: Overview of Optimized Dividend Indices - There are currently 10 optimized dividend indices available in the market, which are categorized alongside traditional high-dividend indices like the CSI Dividend Index and Low Volatility Dividend Index [5]. - The performance of these optimized indices has shown that many have outperformed traditional indices over various time frames, indicating their potential as investment options [7][8]. Group 2: Performance Comparison - From 2015 to the present, several optimized indices, such as the New China Trust Dividend Value Index and the Leading Dividend 50 Index, have achieved significant returns, with the Leading Dividend 50 Index showing a return of 307.35% [7]. - The analysis indicates that most optimized indices have provided excess returns compared to traditional indices, particularly in specific time frames [8][9]. Group 3: Optimization Logic - The article outlines several key optimization angles for dividend indices, including the introduction of multi-factor selection criteria that incorporate market indicators like low volatility and low beta to enhance returns [13][14]. - Timeliness in reflecting a company's operational and stock performance is emphasized, with some indices adjusting their selection criteria based on the latest dividend announcements [16][18]. - The importance of assessing the stability of a company's earnings, such as using ROE stability as a filter, is highlighted to ensure consistent performance [19]. Group 4: Identifying Value Traps - The article discusses methods to identify potential value traps in high-dividend strategies, suggesting that indices should exclude companies that have recently underperformed in the market [20]. - The approach of quarterly adjustments to remove underperforming stocks is recommended as a straightforward method to mitigate risks associated with value traps [20]. Group 5: Growth Considerations - Some optimized indices aim to identify companies with strong profitability and growth potential, reflecting a more aggressive investment strategy [21]. - The article suggests that as market risk appetite increases, these growth-oriented dividend indices may gain more attention and perform well [22]. Group 6: Conclusion - The article concludes that the various optimization strategies for dividend indices focus on enhancing the reflection of a company's fundamentals, aiming to select stocks with favorable future prospects while excluding those with deteriorating fundamentals [25].

一、本周指数增强组合表现 沪深300指数增强组合本周超额收益0.32%，本年超额收益3.16%。 中证500指数增强组合本周超额收益0.64%，本年超额收益6.49%。 中证1000指数增强组合本周超额收益0.52%，本年超额收益8.57%。 中证A500指数增强组合本周超额收益0.35%，本年超额收益6.13%。 二、本周选股因子表现跟踪 沪深300成分股中预期净利润环比、3个月盈利上下调、单季超预期幅度等因 子表现较好。 中证500成分股中非流动性冲击、单季ROE、预期PEG等因子表现较好。 中证1000成分股中三个月换手、一个月换手、非流动性冲击等因子表现较 好。 中证A500指数成分股中3个月盈利上下调、单季超预期幅度、DELTAROE等 因子表现较好。 公募基金重仓股中非流动性冲击、三个月换手、一个月换手等因子表现较 好。 三、本周公募基金指数增强产品表现跟踪 沪深300指数增强产品本周超额收益最高1.07%，最低-0.38%，中位数 0.11%。 中证500指数增强产品本周超额收益最高0.90%，最低-0.43%，中位数 0.45%。 中证1000指数增强产品本周超额收益最高1.00%，最低-0. ...

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, as well as the performance of common stock selection factors in different stock selection spaces[10][11][14] - Guosen JinGong's index enhancement portfolios are constructed using a multi-factor stock selection approach, targeting benchmarks such as the CSI 300, CSI 500, CSI 1000, and CSI A500 indices[10][11] - The construction process of these portfolios includes earnings forecasting, risk control, and portfolio optimization[11] - The report monitors the performance of factors in different stock selection spaces, including the CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices[14][15] - The factor library includes over 30 common factors from dimensions such as valuation, reversal, growth, profitability, liquidity, corporate governance, and analysts[15][16] - The report constructs single-factor MFE (Maximized Factor Exposure) portfolios for each factor in the respective stock selection spaces and tracks their performance relative to their benchmarks[14][17][19][21][23][25] - The construction of the MFE portfolios involves an optimization model with the objective function of maximizing single-factor exposure while controlling for various constraints such as style exposure, industry exposure, individual stock weight deviation, and component stock weight proportion[41][42][43] - The report provides detailed performance tracking of public fund index enhancement products, including those based on the CSI 300, CSI 500, CSI 1000, and CSI A500 indices[27][28][29][32][35][38] - The performance metrics for these products include excess returns over different periods, such as the past week, past month, past quarter, and year-to-date[31][34][37][40] Factor Performance Monitoring - In the CSI 300 index space, factors such as expected net profit QoQ, 3-month earnings revisions, and single-quarter surprise magnitude performed well recently, while 3-month reversal, single-quarter EP, and expected EPTTM performed poorly[1][17] - In the CSI 500 index space, factors such as illiquidity shock, single-quarter ROE, and expected PEG performed well recently, while 1-year momentum, idiosyncratic volatility, and single-quarter SP performed poorly[1][19] - In the CSI 1000 index space, factors such as 3-month turnover, 1-month turnover, and illiquidity shock performed well recently, while 1-year momentum, EPTTM 1-year percentile, and single-quarter operating profit growth YoY performed poorly[1][21] - In the CSI A500 index space, factors such as 3-month earnings revisions, single-quarter surprise magnitude, and DELTAROE performed well recently, while expected BP, 1-month reversal, and expected EPTTM performed poorly[1][23] - In the public fund heavy-holding index space, factors such as illiquidity shock, 3-month turnover, and 1-month turnover performed well recently, while 1-year momentum, expected EPTTM, and 1-month reversal performed poorly[1][25] Public Fund Index Enhancement Product Performance - CSI 300 index enhancement products: highest weekly excess return 1.07%, lowest -0.38%, median 0.11%; highest monthly excess return 2.89%, lowest -0.64%, median 0.49%[2][31] - CSI 500 index enhancement products: highest weekly excess return 0.90%, lowest -0.43%, median 0.45%; highest monthly excess return 2.93%, lowest -0.45%, median 1.07%[2][34] - CSI 1000 index enhancement products: highest weekly excess return 1.00%, lowest -0.41%, median 0.26%; highest monthly excess return 3.22%, lowest -0.16%, median 1.35%[2][37] - CSI A500 index enhancement products: highest weekly excess return 0.41%, lowest -0.19%, median 0.09%; highest monthly excess return 1.08%, lowest -0.46%, median 0.33%[3][40]

Group 1 - The core viewpoint of the article is to track the performance of index enhancement portfolios and the effectiveness of various stock selection factors across different indices [1][2][3] Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.37% for the week and 2.84% year-to-date [5] - The performance of the Zhongzheng 500 index enhancement portfolio showed an excess return of 1.06% for the week and 5.87% year-to-date [5] - The Zhongzheng 1000 index enhancement portfolio had an excess return of 1.73% for the week and 8.10% year-to-date [5] - The Zhongzheng A500 index enhancement portfolio reported an excess return of 0.53% for the week and 5.78% year-to-date [5] Group 3 - In the HuShen 300 component stocks, factors such as one-month reversal, expected PEG, and expected EPTTM performed well [6] - In the Zhongzheng 500 component stocks, one-month reversal, single-quarter SP, and SPTTM factors showed strong performance [6] - For Zhongzheng 1000 component stocks, factors like DELTAROA, executive compensation, and standardized expected external earnings performed well [6] - In the Zhongzheng A500 index component stocks, three-month reversal, single-quarter ROE, and one-month reversal factors were effective [6] - Among public fund heavy stocks, one-month reversal, three-month reversal, and single-quarter EP factors performed well [6] Group 4 - The HuShen 300 index enhancement products had a maximum excess return of 1.10%, a minimum of -0.76%, and a median of 0.06% for the week [19] - The Zhongzheng 500 index enhancement products had a maximum excess return of 0.99%, a minimum of -0.08%, and a median of 0.40% for the week [21] - The Zhongzheng 1000 index enhancement products had a maximum excess return of 0.81%, a minimum of -0.28%, and a median of 0.26% for the week [20] - The Zhongzheng A500 index enhancement products had a maximum excess return of 0.39%, a minimum of -0.52%, and a median of 0.23% for the week [22] Group 5 - The total number of public fund HuShen 300 index enhancement products is 67, with a total scale of 778 billion [16] - There are 70 Zhongzheng 500 index enhancement products with a total scale of 454 billion [16] - The Zhongzheng 1000 index enhancement products consist of 46 products with a total scale of 150 billion [16] - The Zhongzheng A500 index enhancement products have 35 products with a total scale of 223 billion [16]

Group 1 - The core viewpoint of the article is to track and analyze the performance of various index enhancement portfolios and the factors influencing stock selection across different indices [1][2][3]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.54% for the week and 2.44% year-to-date [5][19]. - The performance of the Zhongzheng 500 index enhancement portfolio indicated an excess return of 1.29% for the week and 4.77% year-to-date [5][21]. - The Zhongzheng 1000 index enhancement portfolio achieved an excess return of 1.67% for the week and 6.24% year-to-date [5][21]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 0.21% for the week and 5.19% year-to-date [5][25]. Group 3 - In the HuShen 300 component stocks, factors such as expected PEG, quarterly ROE, and quarterly EP performed well [6][4]. - In the Zhongzheng 500 component stocks, factors like three-month reversal, one-month reversal, and three-month turnover showed strong performance [6][8]. - For the Zhongzheng 1000 component stocks, one-month reversal, specificity, and three-month reversal were notable factors [6][10]. - In the Zhongzheng A500 index component stocks, three-month reversal, expected PEG, and expected EPTTM were effective factors [6][12]. - Among public fund heavy stocks, one-month reversal, three-month reversal, and expected PEG were the best-performing factors [6][14]. Group 4 - The public fund index enhancement products for HuShen 300 had a maximum excess return of 0.57%, a minimum of -0.34%, and a median of 0.05% for the week [19]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.06%, a minimum of -0.28%, and a median of 0.25% for the week [21]. - The Zhongzheng 1000 index enhancement products reported a maximum excess return of 0.97%, a minimum of -0.55%, and a median of 0.23% for the week [21]. - The Zhongzheng A500 index enhancement products had a maximum excess return of 0.58%, a minimum of -0.49%, and a median of 0.02% for the week [25].

Group 1 - The core viewpoint of the article is to track the performance of various index enhancement portfolios and stock selection factors across different indices, highlighting their excess returns and factor performance over the recent week and year-to-date [1][2][3]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of -1.26% for the week and 1.88% year-to-date [5]. - The performance of the ZhongZheng 500 index enhancement portfolio showed an excess return of -0.50% for the week and 3.40% year-to-date [5]. - The ZhongZheng 1000 index enhancement portfolio had an excess return of -0.78% for the week and 4.40% year-to-date [5]. - The ZhongZheng A500 index enhancement portfolio reported an excess return of -0.22% for the week and 4.88% year-to-date [5]. Group 3 - In the HuShen 300 component stocks, factors such as one-year momentum, one-month reversal, and DELTAROE performed well [6]. - In the ZhongZheng 500 component stocks, factors like one-month reversal, SPTTM, and executive compensation showed strong performance [6]. - For ZhongZheng 1000 component stocks, factors such as non-liquidity shock, three-month earnings adjustments, and expected net profit month-on-month performed well [6]. - In the ZhongZheng A500 component stocks, one-month reversal, one-year momentum, and executive compensation were among the top-performing factors [6]. Group 4 - The HuShen 300 index enhancement products had a maximum excess return of 0.44%, a minimum of -0.66%, and a median of -0.06% for the week [19]. - The ZhongZheng 500 index enhancement products had a maximum excess return of 0.48%, a minimum of -1.30%, and a median of -0.35% for the week [19]. - The ZhongZheng 1000 index enhancement products had a maximum excess return of 1.09%, a minimum of -0.82%, and a median of -0.06% for the week [19]. - The ZhongZheng A500 index enhancement products had a maximum excess return of 0.46%, a minimum of -0.38%, and a median of -0.22% for the week [19]. Group 5 - The total number of public fund index enhancement products includes 67 for HuShen 300 with a total scale of 77.8 billion, 69 for ZhongZheng 500 with 45.2 billion, 46 for ZhongZheng 1000 with 15 billion, and 35 for ZhongZheng A500 with 22.3 billion [16].

Quantitative Models and Factors Summary Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhancement Portfolio - **Model Construction Idea**: The model aims to outperform the benchmark indices (CSI 300, CSI 500, and CSI 1000) by using multi-factor stock selection, risk control, and portfolio optimization[11][12] - **Model Construction Process**: - **Return Prediction**: Predicting stock returns using multiple factors - **Risk Control**: Controlling the risk exposure of the portfolio - **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to risk constraints - **Formula**: $$ \begin{array}{ll} \text{max} & f^{T} w \\ \text{s.t.} & s_{l} \leq X(w - w_{b}) \leq s_{h} \\ & h_{l} \leq H(w - w_{b}) \leq h_{h} \\ & w_{l} \leq w - w_{b} \leq w_{h} \\ & b_{l} \leq B_{b} w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ - **Explanation**: - \( f \): Factor values - \( w \): Stock weight vector - \( X \): Factor exposure matrix - \( w_{b} \): Benchmark index component weights - \( s_{l}, s_{h} \): Lower and upper bounds for style factor exposure - \( H \): Industry exposure matrix - \( h_{l}, h_{h} \): Lower and upper bounds for industry exposure - \( w_{l}, w_{h} \): Lower and upper bounds for individual stock deviation - \( B_{b} \): 0-1 vector indicating whether a stock is a benchmark component - \( b_{l}, b_{h} \): Lower and upper bounds for component stock weight - \( l \): Upper limit for individual stock weight - \( \mathbf{1}^{T} w = 1 \): Full investment constraint[35][36][37] Model Backtest Results - **CSI 300 Index Enhancement Portfolio**: - Weekly excess return: 0.79% - Monthly excess return: 2.38%[5][14] - **CSI 500 Index Enhancement Portfolio**: - Weekly excess return: 0.58% - Monthly excess return: 2.73%[5][14] - **CSI 1000 Index Enhancement Portfolio**: - Weekly excess return: 1.17% - Monthly excess return: 4.33%[5][14] Quantitative Factors and Construction Methods Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Measures the valuation of a stock by comparing its book value to its market price[17] - **Factor Construction Process**: - **Formula**: $$ \text{BP} = \frac{\text{Net Assets}}{\text{Total Market Value}} $$ - **Explanation**: - Net Assets: The book value of the company's equity - Total Market Value: The market capitalization of the company[17] Factor Name: Expected BP - **Factor Construction Idea**: Uses consensus estimates to predict the book-to-price ratio[17] - **Factor Construction Process**: - **Formula**: $$ \text{Expected BP} = \frac{\text{Consensus Estimated Net Assets}}{\text{Total Market Value}} $$ - **Explanation**: - Consensus Estimated Net Assets: The average of analysts' estimates for the company's net assets - Total Market Value: The market capitalization of the company[17] Factor Name: Non-Liquidity Shock - **Factor Construction Idea**: Measures the impact of non-liquidity on stock returns[17] - **Factor Construction Process**: - **Formula**: $$ \text{Non-Liquidity Shock} = \frac{\sum_{i=1}^{20} |\text{Daily Return}_i|}{\text{Average Trading Volume}} $$ - **Explanation**: - Daily Return: The daily return of the stock - Average Trading Volume: The average trading volume over the past 20 trading days[17] Factor Backtest Results - **CSI 300 Index**: - **Best Performing Factors (Weekly)**: Expected BP, Expected Net Profit QoQ, BP - **Worst Performing Factors (Weekly)**: Specificity, Standardized Unexpected Revenue, Three-Month Institutional Coverage[1][18] - **CSI 500 Index**: - **Best Performing Factors (Weekly)**: Quarterly Net Profit YoY Growth, Standardized Unexpected Earnings, Quarterly Surprise Magnitude - **Worst Performing Factors (Weekly)**: Three-Month Reversal, One-Month Reversal, One-Year Momentum[1][20] - **CSI 1000 Index**: - **Best Performing Factors (Weekly)**: Expected PEG, Standardized Unexpected Revenue, BP - **Worst Performing Factors (Weekly)**: One-Month Reversal, Executive Compensation, DELTAROA[1][22] - **Public Fund Heavy Index**: - **Best Performing Factors (Weekly)**: Expected Net Profit QoQ, Standardized Unexpected Earnings, Quarterly Operating Profit YoY Growth - **Worst Performing Factors (Weekly)**: Three-Month Reversal, Executive Compensation, One-Month Reversal[2][24]

证券研究报告 | 2025年04月12日 低换手因子表现出色，中证 1000 指增组合年内超额 3.15% 核心观点 金融工程周报 国信金工指数增强组合表现跟踪 因子表现监控 以沪深 300 指数为选股空间。最近一周，非流动性冲击、三个月换手、一个 月换手等因子表现较好，而单季 EP、单季 ROE、预期 EPTTM 等因子表现 较差。最近一月，非流动性冲击、三个月换手、一个月反转等因子表现较好， 而单季 ROA、单季 ROE、单季 EP 等因子表现较差。 以中证 500 指数为选股空间。最近一周，预期净利润环比、非流动性冲击、 3 个月盈利上下调等因子表现较好，而 BP、预期 BP、单季 SP 等因子表现 较差。最近一月，一个月换手、股息率、预期净利润环比等因子表现较好， 而 BP、特异度、预期 BP 等因子表现较差。 以中证 1000 指数为选股空间。最近一周，三个月机构覆盖、三个月换手、 一个月换手等因子表现较好，而特异度、BP、预期 BP 等因子表现较差。最 近一月，一个月波动、一个月换手、三个月换手等因子表现较好，而特异度、 预期 PEG、预期净利润环比等因子表现较差。 以公募重仓指数为选股空间。最近 ...

Quantitative Models and Construction - **Model Name**: Broker Gold Stock Performance Enhanced Portfolio **Model Construction Idea**: The model aims to optimize the broker gold stock pool by leveraging multi-factor selection and portfolio optimization techniques to outperform the benchmark index, specifically the actively managed equity fund index[12][37][41] **Model Construction Process**: 1. Use the broker gold stock pool as the stock selection universe and constraint benchmark[37][41] 2. Apply multi-factor selection to identify stocks with high alpha potential[12][37] 3. Optimize the portfolio to control deviations in individual stocks and style factors relative to the broker gold stock pool[41] 4. Align industry allocation with the overall distribution of public equity funds[41] **Model Evaluation**: The model demonstrates stable performance, consistently outperforming the actively managed equity fund index annually from 2018 to 2022, ranking in the top 30% of active equity funds each year[12][42] Quantitative Factors and Construction - **Factor Name**: Single Quarter ROE **Factor Construction Idea**: Measures profitability and efficiency of equity utilization within a single quarter[3][26] **Factor Construction Process**: 1. Calculate the return on equity (ROE) for the latest quarter using the formula: $ ROE = \frac{Net\ Income}{Shareholders'\ Equity} $ 2. Rank stocks based on ROE values and group them into quintiles for analysis[26] **Factor Evaluation**: Demonstrates strong performance in the most recent month[3][26] - **Factor Name**: Single Quarter Net Profit Growth Rate **Factor Construction Idea**: Captures the growth momentum of companies by analyzing quarterly net profit changes[3][26] **Factor Construction Process**: 1. Compute the growth rate using the formula: $ Growth\ Rate = \frac{Net\ Profit_{Current\ Quarter} - Net\ Profit_{Previous\ Quarter}}{Net\ Profit_{Previous\ Quarter}} $ 2. Rank stocks based on growth rates and group them into quintiles for analysis[26] **Factor Evaluation**: Exhibits strong performance in the most recent month[3][26] - **Factor Name**: Analyst Net Upgrade Magnitude **Factor Construction Idea**: Reflects market sentiment by tracking the magnitude of analyst upgrades[3][26] **Factor Construction Process**: 1. Aggregate analyst upgrades for each stock over a defined period 2. Normalize the upgrade magnitude relative to the total number of analysts covering the stock 3. Rank stocks based on normalized upgrade magnitude and group them into quintiles for analysis[26] **Factor Evaluation**: Performs well in the most recent month[3][26] - **Factor Name**: Total Market Capitalization **Factor Construction Idea**: Represents the size of a company, often used as a proxy for stability and liquidity[3][26] **Factor Construction Process**: 1. Calculate market capitalization using the formula: $ Market\ Cap = Share\ Price \times Outstanding\ Shares $ 2. Rank stocks based on market capitalization and group them into quintiles for analysis[26] **Factor Evaluation**: Shows strong performance year-to-date[3][26] - **Factor Name**: Operating Cash Flow **Factor Construction Idea**: Measures the cash generated from core business operations, indicating financial health[3][26] **Factor Construction Process**: 1. Extract operating cash flow data from financial statements 2. Rank stocks based on operating cash flow and group them into quintiles for analysis[26] **Factor Evaluation**: Performs well year-to-date[3][26] - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Quantifies earnings surprises relative to market expectations[3][26] **Factor Construction Process**: 1. Calculate SUE using the formula: $ SUE = \frac{Actual\ Earnings - Expected\ Earnings}{Standard\ Deviation\ of\ Earnings\ Forecasts} $ 2. Rank stocks based on SUE values and group them into quintiles for analysis[26] **Factor Evaluation**: Demonstrates strong performance year-to-date[3][26] Backtesting Results Model Backtesting Results - **Broker Gold Stock Performance Enhanced Portfolio**: - Absolute return (March 2025): 3.69%[5][40] - Excess return relative to actively managed equity fund index (March 2025): 3.39%[5][40] - Absolute return (Year-to-date 2025): 7.96%[5][40] - Excess return relative to actively managed equity fund index (Year-to-date 2025): 3.31%[5][40] - Active equity fund ranking (Year-to-date 2025): 21.13% percentile (733/3469)[5][40] Factor Backtesting Results - **Single Quarter ROE**: Positive performance in the most recent month[3][26] - **Single Quarter Net Profit Growth Rate**: Positive performance in the most recent month[3][26] - **Analyst Net Upgrade Magnitude**: Positive performance in the most recent month[3][26] - **Total Market Capitalization**: Positive performance year-to-date[3][26] - **Operating Cash Flow**: Positive performance year-to-date[3][26] - **SUE**: Positive performance year-to-date[3][26]