Group 1: Investment Opportunities in A-Share Index Adjustment - The upcoming December index adjustment is expected to create significant investment opportunities, particularly for stocks with a positive impact coefficient above 2, such as Tapa Group, Jiangzhong Pharmaceutical, and Zhengbang Technology [3][4] - The report highlights the importance of focusing on stocks that are newly added to major indices, with particular attention to Guangqi Technology and Zhongtian Technology, which are expected to experience substantial liquidity changes [4] - The passive fund outflows from stocks like Zhongji Xuchuang and Xinyi Sheng are projected to be limited due to their strong liquidity, despite their weights being reduced in various indices [4][5] Group 2: Animation and Film Industry Insights - The film industry is experiencing a recovery, with total box office revenue expected to exceed 50 billion yuan, driven by high-quality imported films and a resurgence in audience engagement [6][7] - The market is shifting towards high-quality content, with a notable increase in the contribution of narrative films to box office performance, indicating a growing demand for deep content [7] - Regulatory policies are expected to support the film industry, with initiatives aimed at expanding the understanding of mainstream themes and enhancing the supply of animated films and imported content [7][8] Group 3: Public REITs Market Development - The introduction of commercial real estate REITs marks a significant shift in China's public REITs market, moving from a focus solely on infrastructure to a dual focus on infrastructure and commercial real estate [8][9] - The potential market size for commercial real estate REITs is estimated to be between 800 billion and 1.5 trillion yuan, indicating a substantial opportunity for asset securitization in the commercial property sector [9][10] - The development of commercial real estate REITs is expected to enhance the liquidity and operational efficiency of the real estate market, addressing long-standing challenges in asset management [10][11]

证券研究报告·金融工程·金工定期报告 金工定期报告 20251129 新价量相关性因子绩效月报 20251128 2025 年 11 月 29 日 [Table_Tag] [Table_Summary] 报告要点 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 庞格致 执业证书：S0600524090003 panggz@dwzq.com.cn 证券分析师 凌志杰 执业证书：S0600525040007 lingzhj@dwzq.com.cn 相关研究 《 新 价 量 相 关 性 因 子 绩 效 月 报 20251031》 2025-11-06 《"技术分析拥抱选股因子"系列研究 （十四）：RPV 聪明版——聪明换手率 是更好的配料》 2023-09-27 东吴证券研究所 1 / 7 请务必阅读正文之后的免责声明部分 ◼ 新价量相关性 RPV 因子多空对冲绩效（全市场）：2014 年 1 月至 2025 年 11 月，新价量相关性 RPV 因子在全体 A 股（剔除北交所股票）中， 10 分组多空对冲的年化收益为 14.29% ...

证券研究报告·金融工程·金工定期报告 金工定期报告 20251129 "日与夜的殊途同归"新动量因子绩效月报 20251128 [Table_Tag] [Table_Summary] 报告要点 2025 年 11 月 29 日 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 庞格致 执业证书：S0600524090003 panggz@dwzq.com.cn 相关研究 《"日与夜的殊途同归"新动量因子绩 效月报 20251031》 2025-11-06 《成交量对动量因子的修正：日与夜 之殊途同归》 2022-08-17 东吴证券研究所 1 / 6 请务必阅读正文之后的免责声明部分 ◼ "日与夜的殊途同归"新动量因子多空对冲绩效（全市场）：2014 年 2 月至 2025 年 11 月，"日与夜的殊途同归"新动量因子在全体 A 股（剔 除北交所股票）中，10 分组多空对冲的年化收益率为 18.04%，年化波 动率为 8.68%，信息比率为 2.08，月度胜率为 78.17%，月度最大回撤率 为 9.07% ◼ 11 月份"日 ...

Quantitative Models and Construction Methods 1. Model Name: Black-Litterman (BL) Model - **Model Construction Idea**: The BL model is an improvement over the traditional mean-variance optimization (MVO) model. It integrates subjective views with quantitative models using Bayesian theory to optimize asset allocation weights. This approach addresses the sensitivity of MVO to expected returns and provides a more robust asset allocation solution[12][13]. - **Model Construction Process**: - The BL model combines subjective views of investors with market equilibrium returns to derive optimized portfolio weights. - The model uses the following formula to calculate the posterior expected returns: $ \mu = [( \tau \Sigma )^{-1} + P^T \Omega^{-1} P]^{-1} [( \tau \Sigma )^{-1} \Pi + P^T \Omega^{-1} Q] $ - $\mu$: Posterior expected returns - $\tau$: Scalar representing the uncertainty in the prior estimate of returns - $\Sigma$: Covariance matrix of asset returns - $\Pi$: Equilibrium returns derived from market capitalization weights - $P$: Matrix representing the views on assets - $\Omega$: Covariance matrix of the views - $Q$: Vector of expected returns based on the views - The optimized portfolio weights are then derived using the posterior expected returns and the covariance matrix[12][13]. - **Model Evaluation**: The BL model effectively addresses the sensitivity of MVO to expected returns and provides a more robust and efficient asset allocation framework. It also allows for the incorporation of subjective views, making it more flexible and practical for real-world applications[12]. 2. Model Name: Risk Parity Model - **Model Construction Idea**: The risk parity model aims to equalize the risk contribution of each asset in a portfolio. It is an improvement over the traditional mean-variance optimization model and focuses on diversifying risk rather than capital allocation[17][18]. - **Model Construction Process**: - Step 1: Select appropriate underlying assets. - Step 2: Calculate the risk contribution of each asset to the portfolio using the formula: $ RC_i = w_i \cdot \sigma_i \cdot \rho_{i,portfolio} $ - $RC_i$: Risk contribution of asset $i$ - $w_i$: Weight of asset $i$ - $\sigma_i$: Volatility of asset $i$ - $\rho_{i,portfolio}$: Correlation of asset $i$ with the portfolio - Step 3: Solve the optimization problem to minimize the deviation between actual and target risk contributions, subject to the constraint that the sum of weights equals 1[18][19]. - **Model Evaluation**: The risk parity model provides a balanced risk allocation across assets, making it suitable for achieving stable returns across different economic cycles. It is particularly effective in reducing portfolio volatility and drawdowns[18]. 3. Model Name: Macro Factor-Based Asset Allocation Model - **Model Construction Idea**: This model constructs a macro factor system covering six key risks: growth, inflation, interest rates, credit, exchange rates, and liquidity. It bridges macroeconomic research with asset allocation by translating macroeconomic views into actionable portfolio strategies[21][22]. - **Model Construction Process**: - Step 1: Calculate the factor exposure levels of assets at the end of each month. - Step 2: Use a risk parity portfolio as the benchmark and calculate the benchmark factor exposure. - Step 3: Based on macroeconomic forecasts for the next month, assign subjective factor deviation values. For example, if inflation is expected to rise, assign a positive deviation to the inflation factor. - Step 4: Combine the benchmark factor exposure with the subjective factor deviations to derive the target factor exposure for the portfolio. - Step 5: Solve the optimization problem to determine the asset allocation weights for the next month[22][25]. - **Model Evaluation**: This model effectively incorporates macroeconomic views into asset allocation, providing a systematic framework for translating macroeconomic insights into portfolio decisions. It is particularly useful for capturing macroeconomic trends and their impact on asset performance[21]. --- Model Backtesting Results 1. Black-Litterman (BL) Model - **Domestic Asset BL Model 1**: Weekly return: -0.32%, November return: 0.05%, 2025 YTD return: 4.0%, annualized volatility: 2.18%, maximum drawdown: 1.31%[14][16][17] - **Domestic Asset BL Model 2**: Weekly return: -0.15%, November return: 0.08%, 2025 YTD return: 3.77%, annualized volatility: 1.95%, maximum drawdown: 1.06%[14][16][17] - **Global Asset BL Model 1**: Weekly return: -0.17%, November return: -0.26%, 2025 YTD return: 0.78%, annualized volatility: 2.0%, maximum drawdown: 1.64%[14][16][17] - **Global Asset BL Model 2**: Weekly return: 0.01%, November return: 0.08%, 2025 YTD return: 2.7%, annualized volatility: 1.59%, maximum drawdown: 1.28%[14][16][17] 2. Risk Parity Model - **Domestic Asset Risk Parity Model**: Weekly return: -0.27%, November return: -0.09%, 2025 YTD return: 3.6%, annualized volatility: 1.32%, maximum drawdown: 0.76%[20][28] - **Global Asset Risk Parity Model**: Weekly return: -0.2%, November return: -0.07%, 2025 YTD return: 3.04%, annualized volatility: 1.42%, maximum drawdown: 1.2%[20][28] 3. Macro Factor-Based Asset Allocation Model - **Macro Factor-Based Asset Allocation Model**: Weekly return: -0.31%, November return: -0.01%, 2025 YTD return: 4.43%, annualized volatility: 1.55%, maximum drawdown: 0.64%[27][28]

Financial Engineering - The stock market experienced fluctuations with the Shanghai Composite Index dropping by 3.90% to close at 3834.89 during the week of November 17 to November 21, 2025, while the Shenzhen Component Index fell by 5.13% to 12538.07, with trading volume decreasing compared to the previous week [2]. - The 50ETF opened at 3.182 and closed at 3.101, reflecting a decline of 2.58% with a trading volume of 10.459 billion. The Huatai-PineBridge CSI 300 ETF opened at 4.730 and closed at 4.564, down 3.73% with a trading volume of 21.119 billion. The Southern CSI 500 ETF opened at 7.334 and closed at 6.922, a decrease of 5.67% with a trading volume of 12.803 billion [3]. Options Market - From November 17 to November 21, the average daily trading volume of 50ETF options increased compared to the previous week, with total open interest rising and the PCR ratio decreasing to 0.78, down 0.19 from the previous week. The Huatai-PineBridge CSI 300 ETF options also saw an increase in average daily trading volume and total open interest, with a PCR of 0.80, down 0.24. The Southern CSI 500 ETF options experienced similar trends with a PCR of 0.93, down 0.23 [4]. - Short-term volatility remained relatively stable with a slight upward trend, and the implied volatility increased significantly during the week, rising by approximately 5 percentage points. The implied volatility is currently above historical volatility levels, indicating a stable sentiment in the market [5]. Investment Recommendations - The market has shown a downward trend from high levels, with large-cap blue-chip stocks experiencing smaller declines while small-cap growth stocks fell by over 5%. The PCR ratio has decreased to historically low levels, and there is a growing expectation for a rebound from oversold conditions. The implied volatility curve indicates a significant increase in the slope of out-of-the-money contracts, suggesting greater expectations for future volatility [6].

- The report introduces two active quantitative strategies: "Dividend Selection Strategy" and "Industry High Winning Rate Strategy," launched by the Changjiang Quantitative Team since July 2023, aiming to provide alternative perspectives and investment choices for investors by tracking market hotspots and selecting industry stocks [7][14][15] - The "Dividend Series" includes two products: "Central State-Owned Enterprises High Dividend 30 Portfolio" and "Balanced Dividend 50 Portfolio," focusing on stable and growth-oriented dividend strategies. The "Industry Enhancement Series" targets the electronics sector, featuring "Electronics Balanced Allocation Enhancement Portfolio" and "Electronics Sector Preferred Enhancement Portfolio," which emphasizes mature sub-sector leading companies [15][16][21] - The "Central State-Owned Enterprises High Dividend 30 Portfolio" outperformed the CSI Dividend Total Return Index this week, achieving a weekly excess return of approximately 0.68%. The "Balanced Dividend 50 Portfolio" showed relatively high volatility recently but has achieved a significant excess return of about 6.14% since the beginning of 2025, ranking around the 40th percentile among all dividend fund products [16][21][23] - The "Electronics Balanced Allocation Enhancement Portfolio" and "Electronics Sector Preferred Enhancement Portfolio" failed to achieve positive excess returns this week. The former slightly underperformed the electronics total return index, while the latter struggled to keep pace [8][31][32]

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Core Insights - The report highlights the performance tracking of Guosen Securities' active quantitative strategies, indicating a significant adjustment in small-cap growth stocks, with the Growth Steady Portfolio achieving a year-to-date return of 48.45% [1][2][3] Performance Overview - The Excellent Fund Performance Enhancement Portfolio recorded an absolute return of -5.06% this week and 18.71% year-to-date, ranking in the 59.18th percentile among active equity funds [1][24] - The Exceeding Expectations Selected Portfolio had an absolute return of -5.67% this week and 33.39% year-to-date, ranking in the 26.72nd percentile among active equity funds [1][32] - The Broker Golden Stock Performance Enhancement Portfolio achieved an absolute return of -4.15% this week and 27.25% year-to-date, ranking in the 38.69th percentile among active equity funds [1][39] - The Growth Steady Portfolio saw an absolute return of -7.33% this week and 43.06% year-to-date, ranking in the 13.84th percentile among active equity funds [2][44] Strategy Summaries - The Excellent Fund Performance Enhancement Portfolio is constructed by benchmarking against active equity funds rather than broad indices, utilizing quantitative methods to select superior holdings [3][19] - The Exceeding Expectations Selected Portfolio is built by screening stocks based on exceeding expectations and analyst profit upgrades, focusing on both fundamental and technical criteria [4][25] - The Broker Golden Stock Performance Enhancement Portfolio is based on a selection of stocks from the broker's golden stock pool, optimized to minimize deviation from the benchmark [5][61] - The Growth Steady Portfolio employs a two-dimensional evaluation system for growth stocks, prioritizing those closer to earnings report dates and using multi-factor scoring for selection [6][40]

Core Insights - The report focuses on the evolution and optimization of profitability evaluation indicators, particularly the Return on Equity (ROE) within the PB-ROE framework, utilizing DuPont analysis to dissect the structure and potential issues of the ROE metric [1][4] - The report introduces a comprehensive profitability factor by optimizing the indicator system based on the analysis of ROE, aiming to enhance the dual optimization of the PB-ROE strategy framework in both valuation and profitability aspects [1][4] Section Summaries 1. In-depth Exploration of ROE and Profitability Styles - The PB-ROE strategy combines valuation levels with shareholder return rates, reflecting a company's ability to generate profits from shareholder capital, which directly influences net asset growth [10][14] - The relationship between ROE and GDP indicates that listed companies, as a significant part of the economy, have shown substantial growth in revenue, with the total revenue of A-share companies exceeding 72 trillion yuan in 2024, compared to 3.37 trillion yuan in 2004 [14][15] - ROE is categorized under quality style in investment factors, reflecting a company's financial health, profitability, reliability, and long-term growth potential [22][26] 2. ROE and DuPont Analysis - ROE is tested using both quarterly and TTM (Trailing Twelve Months) metrics, with the quarterly ROE factor showing a higher average Rank IC of 4.06% compared to 2.78% for TTM [28][29] - Historical high ROE stocks tend to underperform in future price performance, while portfolios constructed based on future ROE show significant excess returns, indicating the importance of ROE stability [33][34] - DuPont analysis breaks down ROE into three components: net profit margin, total asset turnover, and equity multiplier, providing a comprehensive assessment of a company's profitability, operational efficiency, and leverage [42][46] 3. ROE De-leveraging Analysis - The report discusses the linear separation of leverage factors from ROE, highlighting the economic relationship between ROA and ROE, and the limitations of ROA as a profitability measure [3][15] - The introduction of RONOA (Return on Net Operating Assets) and FCFFIC (Free Cash Flow Return on Invested Capital) aims to provide more accurate profitability assessments by excluding non-core operating activities and mitigating earnings management risks [4][6] 4. Comprehensive Profitability Factor - The integration of stable ROE, stable ROIC, stable RONOA, and FCFFIC forms a comprehensive profitability factor, enhancing the performance of the PB-ROE strategy [4][6] - The active quantitative stock selection strategy based on the PB-ROE framework has achieved an annualized return of 20.42% since 2010, significantly outperforming benchmarks like the CSI 800 [4][6]

Macro and Strategy - The report discusses the global asset management deep research series, focusing on personalized portfolios and tax efficiency, highlighting the advantages of separately managed accounts (SMA) for high-net-worth and institutional clients [7][8] - SMA allows for customized investment strategies based on individual risk preferences and tax optimization techniques, contrasting with model portfolios that lack personalization [7][8] Industry and Company Robotics Industry - Tesla plans to expand its Texas factory to produce 10 million humanoid robots annually, with production expected to start in 2027 [9][10] - The IPO guidance for Yuzhu Technology has been completed, indicating a rapid development in the domestic humanoid robot sector [10][12] - The report emphasizes the long-term investment opportunities in humanoid robots, suggesting a focus on core suppliers and companies with strong market positions [12] AI Infrastructure - Anthropic announced a $50 billion investment in AI data centers in the U.S., reflecting strong demand for AI-driven cloud infrastructure [11][12] - The report highlights the growing investment in AI infrastructure, particularly in energy supply for data centers, recommending companies involved in energy supply and cooling solutions [12][14] Food and Beverage Industry - Luckin Coffee reported a 50.2% year-on-year revenue increase in Q3 2025, but faced profit pressure due to rising delivery costs, which surged by 211.4% [16] - Yum China also saw revenue growth driven by its delivery sales, with a 32% increase in delivery revenue, maintaining a stable operating profit margin [16][17] - The report suggests that the differences in profitability between Luckin Coffee and Yum China stem from their competitive environments and membership channel contributions [16][17] Medical Device Industry - The medical sector outperformed the overall market, with a 3.29% increase in the biopharmaceutical sector, while the medical device multinational corporations (MNCs) reported varied performance across different product categories [18][19] - The report recommends focusing on innovative and export-capable A-share medical device companies, particularly those benefiting from domestic substitution trends [19] Power Equipment and New Energy - The report outlines a positive outlook for the wind power sector, expecting a 10%-20% growth in new installations in 2026, supported by strong order backlogs and price stability [20][21] - The lithium battery industry is anticipated to recover from a downtrend, with new technologies like solid-state batteries expected to accelerate commercialization [20][21] - Recommendations include focusing on companies involved in energy supply for AI data centers and those in the lithium battery supply chain [21][22] Semiconductor Equipment - Tuojing Technology reported a significant revenue increase of 124.15% year-on-year in Q3 2025, driven by the scaling of advanced packaging and storage equipment [23][24] - The company is expected to benefit from the ongoing expansion in the storage wafer market, with a focus on advanced packaging technologies [25][26]