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]

Market Overview - The market is currently in a bull phase, but may enter a wide fluctuation stage in the short term. Compared to previous bull markets, there is still significant room for index growth, but the duration of the bull market may be more important than the magnitude of the increase due to government guidance on a "slow bull" policy. In the short term, the market may lack strong catalysts, and investors may adopt a more cautious approach as the year-end approaches, leading to a focus on consolidation and accumulation [4]. Short-term Opportunities - The market has shifted from previous range-bound fluctuations to a continuous decline influenced by overseas trading sentiment. The artificial intelligence sector continues to adjust, while sectors like chemicals, non-ferrous metals, and electric equipment have seen significant corrections. There may be short-term rebound opportunities in oversold sectors, but the overall market is expected to continue wide fluctuations. The main strategy during this phase should focus on dividend allocation [5]. Fixed Income Market - The convertible bond market and equity market both experienced declines this week. Since the beginning of 2025, both markets have been on an upward trend. Currently, the remaining duration of existing convertible bonds is shortening, and the number of quality individual bonds is decreasing. High-priced and overvalued convertible bonds may face adjustment pressure, making trading more challenging. It is recommended to assess bonds based on their terms and underlying stock conditions, and to pay attention to new bond opportunities in high-demand industries [6]. Oil and Gas Sector - The international oil price is under pressure due to supply-demand imbalances, but OPEC+ has paused production increases, which may alleviate the global oversupply situation. The resilience of the "three major oil companies" during the oil price downturn highlights their ability to navigate through cycles. With expectations of a cold winter, there is potential for significant growth in natural gas demand, making the natural gas business of the "three major oil companies" particularly valuable [8]. Chemical Industry - The organic silicon industry is expected to improve due to the implementation of decisions made at industry conferences, which may enhance the competitive landscape. Recent trends in organic silicon prices and profitability indicate this improvement. In the medium to long term, steady growth in apparent consumption will support demand, while a slowdown in new capacity additions will ease supply pressures, leading to a more favorable industry outlook [8]. AI Healthcare Sector - Medical technology company achieved a revenue of 4.05 billion RMB for FY26H1, representing a year-on-year increase of 33%. The gross profit was 1.44 billion RMB, with a gross margin of 35.7%, down 7.4 percentage points year-on-year. Adjusted EBITA was 550 million RMB, showing a 14% increase compared to the previous year after excluding one-time gains. The net profit attributable to shareholders was 520 million RMB, up 54% year-on-year [9]. Lenovo Group - Lenovo reported a revenue of 20.452 billion USD for FY26Q2, a 15% year-on-year increase. The adjusted net profit attributable to shareholders was 512 million USD, up 25% year-on-year. All business segments achieved double-digit growth, with AI-related business revenue accounting for 30% of total revenue, an increase of 13 percentage points year-on-year [9].

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]

Group 1 - The A-share market continues to exhibit a wide fluctuation pattern, with major indices experiencing weekly declines and market volume shrinking again. Financing increased slightly compared to the previous week, and stock ETFs saw net inflows, with TMT-themed ETFs being the main contributors [4] - In the fund market, pharmaceutical-themed funds performed well, while TMT-themed funds experienced significant pullbacks. Various ETFs across different investment scopes saw inflows, particularly TMT and sci-tech themed ETFs, with notable inflows into the Hang Seng Internet ETF and gold ETFs [5] - The convertible bond market saw a slight increase, with both the convertible bond and equity markets rising since the beginning of 2025, although the convertible bond market's performance was weaker. The demand for high-quality convertible bonds remains strong, and prices are at relatively high levels, necessitating careful selection [6] Group 2 - The energy storage sector is thriving, leading to price increases in lithium-ion battery materials. Short-term supply and demand are tight, with significant price hikes in lithium hexafluorophosphate. The profitability of iron phosphate is expected to recover, and prices for lithium iron phosphate materials are entering an upward channel [8] - The pharmaceutical sector saw a rise in flu-like cases, prompting attention to flu vaccines, respiratory virus testing, and cold medicine sectors. The pharmaceutical index rose by 3.29%, outperforming the CSI 300 index by 4.37 percentage points [8] - The company U (688220.SH) reported a revenue of 2.88 billion yuan for the first three quarters of 2025, a year-on-year increase of 13.42%. The net profit attributable to shareholders was a loss of 327 million yuan, a reduction in loss by 85 million yuan, with a gross margin of 25.38%, up by 1.92 percentage points year-on-year [8]

- The report highlights the launch of multiple active quantitative strategies since July 2023, including Dividend Selection Strategy and High Winning Rate Industry Strategy, aimed at tracking market trends and selecting industry-specific stocks [6][14][15] - Active quantitative strategies follow a top-down stock selection logic, leveraging industry and thematic insights to refine factor selection from a large fundamental factor pool, enabling precise identification of potential stocks within specific sectors [14] - The Dividend Series includes two products: "Central SOE High Dividend 30 Portfolio" and "Balanced Growth Dividend 50 Portfolio," focusing on stable and growth-oriented dividend strategies [15] - The Electronics Series includes two products: "Electronics Balanced Allocation Enhanced Portfolio" and "Electronics Sector Preferred Enhanced Portfolio," targeting mature sub-sector leaders within the electronics industry [15] - Weekly performance tracking shows that the Electronics Balanced Allocation Enhanced Portfolio and Electronics Sector Preferred Enhanced Portfolio achieved positive excess returns of approximately 1.86% and 1.62%, respectively, outperforming the benchmark [7][25][32] - The Dividend Series underperformed the benchmark this week, with the Central SOE High Dividend 30 Portfolio and Balanced Growth Dividend 50 Portfolio failing to exceed the returns of the CSI Dividend Total Return Index [7][16][22] - The CSI Dividend Index achieved a weekly return of 0.25%, while sub-indices like CSI Dividend Growth and CSI Dividend Low Volatility outperformed with average weekly returns of approximately 1.31% and 1.08%, respectively [7][16][19] - The Electronics Series portfolios ranked in the top 26%-28% among active technology-themed funds based on weekly returns [32] - The report emphasizes the differentiation of active quantitative strategies from traditional ones, highlighting their ability to integrate thematic and industry logic for enhanced stock selection and strategy validation [14]

- The report focuses on the performance of Smart Beta portfolios, including Value, Growth, and Small-cap styles, constructed based on high beta elasticity and long-term stable excess returns objectives[6][7][8] - Value Smart Beta portfolios include "Value 50 Portfolio" and "Value Balanced 50 Portfolio", with weekly returns of 2.58% and 2.40%, respectively, and annual returns of 19.22% and 26.57%[4][7][8] - Growth Smart Beta portfolios include "Growth 50 Portfolio" and "Growth Balanced 50 Portfolio", with weekly returns of -0.09% and -0.37%, respectively, and annual returns of 26.63% and 30.58%[4][7][17] - Small-cap Smart Beta portfolios include "Small-cap 50 Portfolio" and "Small-cap Balanced 50 Portfolio", with weekly returns of 2.55% and -0.17%, respectively, and annual returns of 48.97% and 41.26%[4][7][20] - The report highlights the excess returns of these portfolios relative to their benchmarks, such as "Value 50 Portfolio" outperforming the benchmark by 0.41% weekly and 8.89% annually[7][8][13] - Growth portfolios showed mixed results, with "Growth Balanced 50 Portfolio" achieving positive annual excess returns of 1.07%, while "Growth 50 Portfolio" underperformed by -2.88% annually[7][17][19] - Small-cap portfolios demonstrated strong performance, with "Small-cap 50 Portfolio" achieving weekly excess returns of 2.02% and annual excess returns of 19.09%[7][20][25] - The report provides detailed performance metrics, including absolute returns, excess returns, and maximum relative drawdowns for each portfolio[7][8][20]