Group 1: Financial Engineering Research - The report predicts the adjustment list for the constituent stocks of major indices in December 2025 based on the adjustment rules of the CSI and Guozheng indices, and measures liquidity shocks from a market-wide perspective [1][30] - As of the end of October 2025, the ETF sizes for major market indices such as SSE 50, STAR 50, CSI 300, CSI 500, CSI 1000, and ChiNext have reached 192.6 billion, 180.1 billion, 1,254.7 billion, 181.9 billion, 170.2 billion, and 141.0 billion respectively, indicating a 4.7 times growth compared to the end of 2021 [2][30] - The report outlines the periodic adjustment rules for core indices, noting that adjustments occur twice a year for SSE 50, CSI 300, CSI 500, CSI 1000, and ChiNext, and four times a year for STAR 50 [2][30] Group 2: New Stock Research - In the first three quarters of 2025, IPO support policies have been frequent, leading to a recovery in the issuance pace and fundraising scale, with a total of 773.02 billion raised, a 61% year-on-year increase [5][6] - The report anticipates an acceleration in IPO issuance over the next year, estimating that A-class/B-class accounts with a scale of 500 million will see additional yield increases of approximately 2.82% and 2.20% respectively [7][6] - The approval pace for existing projects is tight, with a high-quality project reserve expanding, indicating a positive outlook for future IPOs [6][7] Group 3: Company Research - Yum China - Yum China's Q3 2025 revenue reached 3.206 billion USD, a year-on-year increase of 4%, with operating profit at 400 million USD, up 8% [9][10] - Same-store sales continued to show positive growth, with KFC and Pizza Hut same-store sales increasing by 2% and 1% respectively [9][10] - The company plans to return 3 billion USD to shareholders through dividends and buybacks from 2025 to 2026, with projected EPS for 2025-2027 at 2.50, 2.88, and 3.16 USD [8][9] Group 4: Company Research - Nanwei Medical - Nanwei Medical achieved revenue of 2.381 billion CNY in the first three quarters of 2025, a year-on-year increase of 18.29%, with net profit of 509 million CNY, up 12.90% [17][18] - The company’s overseas sales maintained strong growth, with revenue reaching approximately 1.4 billion CNY, a 42% year-on-year increase [18][19] - The company is focusing on integrating its CME operations, with a new production facility in Thailand expected to be operational by the end of 2025 [19] Group 5: Company Research - Yongxing Materials - Yongxing Materials reported revenue of 5.547 billion CNY in the first three quarters of 2025, a year-on-year decrease of 10.98%, with net profit down 45.25% [21][22] - The decline in performance is attributed to falling lithium prices, with the average price of lithium carbonate showing fluctuations throughout the year [22] - The company maintains a high dividend payout, planning to distribute 528 million CNY in cash dividends in 2024, representing over 50% of its net profit [23] Group 6: Company Research - I Love My Home - I Love My Home reported a revenue of 8.165 billion CNY in the first three quarters of 2025, a year-on-year decrease of 6.81%, while net profit surged by 398.75% [24][26] - The company’s transaction volume increased significantly, with total housing transaction amounts reaching 196.2 billion CNY, a 5.2% year-on-year increase [26][27] - The company continues to focus on core cities, with a total of 2,549 operational stores as of Q3 2025 [26]

Core Viewpoint - The article discusses the periodic adjustments of major market index ETFs and the liquidity impact of these adjustments, highlighting the increasing trend of index-based investment in the market [3][4]. Market Index ETF Scale - As of the end of October 2025, the scales of major index ETFs are as follows: - SSE 50: 192.6 billion - STAR 50: 180.1 billion - CSI 300: 1,254.7 billion - CSI 500: 181.9 billion - CSI 1000: 170.2 billion - ChiNext Index: 141.0 billion - The overall scale of these index ETFs has increased by 4.7 times compared to the end of 2021, indicating a more pronounced trend towards index-based investment [3]. Index Component Stock Adjustment Predictions - Predictions for adjustments in core index components include: - SSE 50: 4 stocks expected to be added (Hua Dian New Energy, SAIC Motor, Zhongke Shuguang, Northern Rare Earth) and 4 stocks expected to be removed (Poly Development, CRRC, Guodian Nanjing, Shaanxi Coal) [4]. - STAR 50: 2 stocks expected to be added (Aojie Technology -U, Shengke Communication -U) and 2 stocks expected to be removed (Huaxi Biological, Hangcai Co.) [4]. - CSI 300: 10 stocks expected to be added (Hua Dian New Energy, Shenghong Technology, Ningbo Port) and 10 stocks expected to be removed (Flaite, TCL Zhonghuan, Nasda) [4]. - CSI 500: 50 stocks expected to be added (O-film, Supor, Yingjia Gongjiu) [4]. - CSI 1000: 100 stocks expected to be added (Wan Energy Power, Laofengxiang, Xiamen Guomao) [4]. - ChiNext Index: 8 stocks expected to be added (Yinzhijie, Robot Technology, Changshan Pharmaceutical) [4]. Market Index Adjustment Liquidity Impact - The article tracks the ETF fund scales of the CSI and National Series indices and the predicted adjustments in component stock weights to construct a liquidity impact factor for the entire market index adjustments. - The highest liquidity impact from additions includes stocks like Dongshan Precision, Shenghong Technology, and Zhongke Shuguang; while the highest liquidity impact from removals includes stocks like Tangrenshen, Beiyuan Group, and Suneng Shares [4].

Core Insights - The report suggests an overweight position in small-cap and value styles for November based on quantitative model signals and calendar effects [1][5] Size and Style Rotation Monthly Strategy - As of the end of October, the quantitative model signal was -0.17, indicating a preference for large-cap stocks; however, historical data shows that small-cap stocks tend to outperform in November [1] - The current market capitalization factor valuation spread is 0.88, which is still below the historical peak range of 1.7 to 2.6, indicating that the market is not overcrowded and small-cap stocks remain attractive in the medium to long term [1] - Year-to-date, the size rotation quantitative model has yielded a return of 27.85%, with an excess return of 2.86% relative to an equal-weight benchmark [1] - The combined strategy, incorporating subjective views, has achieved a return of 26.6% with an excess return of 1.61% [1] Value and Growth Style Rotation Monthly Strategy - The monthly quantitative model signal for October was 1, recommending an overweight position in value stocks [1] - Year-to-date, the value-growth style rotation strategy has returned 18.96%, with an excess return of 1.35% compared to an equal-weight benchmark of growth and value indices [1] Style Factor Performance Tracking - Among eight major factors, the dividend and momentum factors showed high positive returns in October, while large-cap and volatility factors exhibited high negative returns [2] - Year-to-date, the volatility and momentum factors have shown strong positive returns, while liquidity and large-cap factors have shown negative returns [2] - In October, the profitability, dividend yield, and momentum factors had high positive returns, while large-cap, profitability, and beta factors had high negative returns [2] - Year-to-date, the beta, profitability volatility, and momentum factors have shown strong positive returns, while mid-cap, liquidity, and large-cap factors have shown negative returns [2] Factor Covariance Matrix Update - The report updates the latest factor covariance matrix as of October 31, 2025, which is crucial for predicting stock portfolio risks [2]

Group 1: Small and Large Cap Style Rotation - The report suggests an overweight position in small-cap style for November based on quantitative model signals and calendar effects, as historical data indicates small caps tend to outperform in November [1][8]. - The current market capitalization factor valuation spread is 0.88, indicating that small caps still have room for growth compared to large caps, which are at historical high levels of 1.7 to 2.6 [8][16]. - Year-to-date, the small and large cap rotation quantitative model has achieved a return of 27.85%, with an excess return of 2.86% relative to the benchmark [8][9]. Group 2: Value and Growth Style Rotation - The monthly quantitative model signal for value style is 1, recommending an overweight position in value style for November [23][26]. - Year-to-date, the value-growth style rotation strategy has yielded a return of 19.95%, with an excess return of 1.35% compared to the equal-weighted benchmark [23][26]. - The current model indicates that fundamental, macroeconomic, and valuation dimensions are all pointing towards value [26][27]. Group 3: Factor Performance Tracking - In October, the dividend, momentum, and value factors achieved positive returns of 0.43%, 0.38%, and 0.15% respectively, while large-cap, volatility, growth, quality, and liquidity factors experienced negative returns [29][30]. - Year-to-date, the volatility, momentum, and growth factors have positive returns of 10.17%, 1.54%, and 1.29%, while liquidity, large-cap, dividend, quality, and value factors have negative returns [29][30].

Report Summary 1. Report Industry Investment Rating No industry investment rating is provided in the report. 2. Core Viewpoints The report presents several ETF strategy indices developed by Huabao Research, aiming to help investors convert quantitative models or subjective views into practical investment strategies. It tracks the performance and positions of these indices on a weekly basis [12]. 3. Summary by Directory 3.1 ETF Strategy Index Tracking - **Overall Performance**: The table shows the performance of various ETF strategy indices for the week ending October 31, 2025, including their returns, benchmark returns, and excess returns [13]. | Index Name | Last Week Index Return | Comparison Benchmark | Last Week Benchmark Return | Excess Return | | --- | --- | --- | --- | --- | | Huabao Research Size Rotation ETF Strategy Index | -0.41% | CSI 800 | -0.05% | -0.36% | | Huabao Research SmartBeta Enhanced ETF Strategy Index | -0.01% | CSI 800 | -0.05% | 0.04% | | Huabao Research Quantitative Fire - Wheel ETF Strategy Index | 1.15% | CSI 800 | -0.05% | 1.20% | | Huabao Research Quantitative Balance ETF Strategy Index | 0.12% | SSE 50 | -0.43% | 0.55% | | Huabao Research Hot - Spot Tracking ETF Strategy Index | 0.87% | CSI All - Share Index | 0.41% | 0.46% | | Huabao Research Bond ETF Duration Strategy Index | 0.26% | ChinaBond Aggregate Index | 0.41% | -0.15% | 3.2 Specific Index Analyses - **Huabao Research Size Rotation ETF Strategy Index**: Uses multi - dimensional technical indicators and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index. As of October 31, 2025, the excess return since 2024 was 19.72%, 0.52% in the past month, and - 0.36% in the past week. The current position is 100% in the SSE 50 ETF [14][17]. - **Huabao Research SmartBeta Enhanced ETF Strategy Index**: Utilizes price - volume indicators to time self - built Barra factors and maps timing signals to ETFs based on their exposures to 9 Barra factors. As of October 31, 2025, the excess return since 2024 was 16.75%, - 3.43% in the past month, and 0.04% in the past week. The positions include several ETFs such as the 800 Free Cash Flow ETF, 800 Dividend Low - Volatility ETF, etc. [16][17]. - **Huabao Research Quantitative Fire - Wheel ETF Strategy Index**: Adopts a multi - factor approach, considering mid - to long - term fundamentals, short - term market trends, and market participants' behaviors. It uses valuation and congestion signals to identify industry risks and potential sectors. As of October 31, 2025, the excess return since 2024 was 31.54%, 2.17% in the past month, and 1.20% in the past week. The positions cover ETFs in sectors like non - ferrous metals, communications, etc. [22][23]. - **Huabao Research Quantitative Balance ETF Strategy Index**: Employs a multi - factor system including economic fundamentals, liquidity, technicals, and investor behavior to build a quantitative timing system for equity market trend analysis. It also predicts the market's large - and small - cap styles to adjust equity positions. As of October 31, 2025, the excess return since 2024 was - 11.35%, - 0.11% in the past month, and 0.55% in the past week. The positions include various bond and equity ETFs [25][29]. - **Huabao Research Hot - Spot Tracking ETF Strategy Index**: Tracks market sentiment, industry events, investor sentiment, policies, and historical trends to identify hot - spot index products and build an ETF portfolio. As of October 31, 2025, the excess return in the past month was 2.99% and 0.46% in the past week. The positions are similar to the Quantitative Balance ETF Strategy Index [29][34]. - **Huabao Research Bond ETF Duration Strategy Index**: Uses bond market liquidity and price - volume indicators to select effective timing factors and predicts bond yields through machine learning. When the expected yield is below a certain threshold, it reduces long - duration positions. As of October 31, 2025, the excess return in the past month was - 0.07% and - 0.15% in the past week. The positions mainly consist of short - term and long - term bond ETFs [34][37].

Core Viewpoint - Gold has become one of the hottest investment assets in recent years, with significant price increases and a strong historical performance, particularly in the last decade [3][4]. Group 1: Gold's Performance - Over the past 10 years, the gold ETF has only experienced two years of decline, with the maximum annual drop being -7%. In 2025, gold prices surged by 45% [3][4]. - The annual performance of the Huazhong Gold ETF shows a consistent upward trend, with notable increases in 2024 (27.45%) and 2023 (16.34%) [4]. Group 2: Investment Logic of Gold - Various investment logics surrounding gold include its reflection of currency credit, its inverse relationship with real interest rates, its correlation with the US dollar index, its safe-haven attributes during economic downturns, and its performance during inflationary periods [6]. - The underlying anchor for gold pricing is the concept of currency credit, which has been a consistent factor over decades, even predicting historical peaks in gold prices [6][9]. Group 3: Quantitative Model and Valuation - The analysis suggests that the increase in US debt issuance should correlate with gold prices. If the US debt has increased 131 times since 1960, the fair value of gold would be approximately $4,636, while a 106 times increase since 1970 would suggest a fair value of around $3,742 [10][12]. - The two critical historical points for gold pricing are 1960 and 1971, marking the beginning of credit skepticism and the end of the Bretton Woods system, respectively [12][13]. Group 4: Future Price Predictions - Based on the quantitative model, the expected peak for gold prices in the current cycle is projected to be between $3,700 and $4,600, with current prices already surpassing the 1970 baseline of $3,742 and moving towards the 1960 baseline of $4,636 [13][14].

- The BL model is an improvement of the traditional mean-variance optimization (MVO) model, developed by Fisher Black and Robert Litterman in 1990. It integrates Bayesian theory to combine subjective views with quantitative asset allocation models, optimizing asset weights based on investor forecasts of market returns. This model addresses MVO's sensitivity to expected returns and offers higher tolerance compared to purely subjective investment approaches, providing efficient asset allocation solutions[12][13] - The BL model was implemented for both global and domestic assets. For global assets, it utilized indices such as S&P 500, Hang Seng Index, and Nanhua Commodity Index. For domestic assets, it included indices like CSI 300, CSI 1000, and SHFE Gold. Two versions of BL models were developed for each market, focusing on equities, bonds, commodities, and gold[13][14] - The Risk Parity model, introduced by Bridgewater in 2005, aims to equalize risk contributions across asset classes in a portfolio. It calculates initial asset weights based on expected volatility and correlation, then optimizes deviations between actual and expected risk contributions to determine final weights[17][18] - The Risk Parity model was constructed in three steps: selecting appropriate underlying assets, calculating risk contributions of each asset to the portfolio, and solving optimization problems to determine asset weights. It was applied to both global and domestic assets, using indices like CSI 300, CSI 1000, and COMEX Gold for domestic assets, and S&P 500, Hang Seng Index, and Nanhua Commodity Index for global assets[19][21] - The macro factor-based asset allocation model incorporates six macro risks: growth, inflation, interest rates, credit, exchange rates, and liquidity. Using Factor Mimicking Portfolio methodology, high-frequency macro factors were constructed. The strategy involves calculating asset factor exposures, determining benchmark exposures, setting subjective factor deviations based on macro forecasts, and solving for asset weights to reflect macro risk judgments[23][26] - The macro factor-based model was applied to domestic assets, including indices like CSI 300, CSI 1000, and SHFE Gold. For example, in September 2025, subjective factor deviations were set as 0 for growth, inflation, interest rates, and credit, 1 for exchange rates, and 0 for liquidity, reflecting macroeconomic conditions at the time[25][27] - Domestic BL Model 1 achieved weekly returns of 0.1%, monthly returns of 0.38%, and annual returns of 3.97%, with annualized volatility of 2.23% and maximum drawdown of 1.31%[14][17] - Domestic BL Model 2 recorded weekly returns of -0.01%, monthly returns of 0.48%, and annual returns of 3.68%, with annualized volatility of 2.02% and maximum drawdown of 1.06%[14][17] - Global BL Model 1 delivered weekly returns of 0.54%, monthly returns of 0.03%, and annual returns of 1.02%, with annualized volatility of 2.04% and maximum drawdown of 1.64%[14][17] - Global BL Model 2 achieved weekly returns of 0.37%, monthly returns of 0.35%, and annual returns of 2.43%, with annualized volatility of 1.65% and maximum drawdown of 1.28%[14][17] - Domestic Risk Parity Model recorded weekly returns of 0.14%, monthly returns of 0.34%, and annual returns of 3.47%, with annualized volatility of 1.34% and maximum drawdown of 0.76%[21][22] - Global Risk Parity Model achieved weekly returns of 0.22%, monthly returns of 0.39%, and annual returns of 2.99%, with annualized volatility of 1.46% and maximum drawdown of 1.2%[21][22] - Macro Factor-Based Model delivered weekly returns of -0.25%, monthly returns of 0.73%, and annual returns of 4.29%, with annualized volatility of 1.54% and maximum drawdown of 0.64%[27][28]

Core Insights - The three major stock indices opened higher on October 27, with the Shanghai Composite Index approaching 4000 points, indicating a positive market sentiment [1] - The CSI 2000 Enhanced ETF (159552) has seen significant inflows, accumulating 66 million over three consecutive days, suggesting a shift in investor focus towards small-cap stocks [1] - Analysts from Zheshang Securities noted that small-cap stocks are likely to outperform the broader market in November, with historical data showing strong performance in previous months [1] Performance Summary - As of October 17, the CSI 2000 Enhanced ETF (159552) achieved a one-year return of 62.67%, ranking first among 33 similar products [1][2] - In the third quarter, the CSI 2000 Index recorded a gain of 14.31%, which was relatively modest compared to other major indices [1][2] - The performance of various indices in the third quarter was as follows: - ChiNext Index: 50.40% - Sci-Tech Innovation Index: 39.61% - CSI 500: 25.31% - CSI 1000: 19.17% - CSI 300: 17.90% - Wind Micro-Cap Index: 16.24% - CSI 2000: 14.31% - Shanghai Composite Index: 12.73% [2] Fund Manager Insights - Fund manager Deng Tong highlighted a significant divergence in market styles during the third quarter, with growth styles outperforming value styles [1][2] - The performance of quantitative models was negatively impacted by the underperformance of value factors and volume-price factors, leading to a decline in excess returns in the latter half of the quarter [2] - Looking ahead, Deng Tong indicated that the main hotspots in the large-cap growth sector are concentrated in the technology sector, with external uncertainties potentially affecting market confidence [2]

Core Viewpoint - The domestic index investment has seen significant growth, with investors increasingly seeking clear risk-return characteristics. Xingzheng Global Fund is leveraging its expertise in index-enhanced investment to build a diverse range of products covering large-cap, mid-cap, and Hong Kong stocks [1]. Group 1: Development of Quantitative Investment Team - Since joining Xingzheng Global Fund over two years ago, the quantitative research team has developed over 2,000 alpha factors and established a modular quantitative management system, supported by ample GPU resources [2]. - The company fosters a collaborative environment where team members share results and strategies, enhancing the overall effectiveness of the quantitative models [2]. - The team has achieved a high level of automation in its quantitative system, from data cleaning to portfolio generation, aided by strong technical support from the IT department [3]. Group 2: Focus on Alpha Factor Exploration - The core focus of the quantitative strategy is on the exploration of alpha factors, which are crucial for generating excess returns while closely tracking index characteristics [4]. - The team employs a systematic approach to develop and optimize alpha factors, including self-research and referencing external factor libraries and academic reports [4]. - Continuous iteration and optimization of alpha factors are essential, with the team integrating the latest machine learning models and conducting in-depth research on sell-side analyst expectations [4][5]. Group 3: Expansion of Index-Enhanced Product Line - Xingzheng Global Fund has identified significant growth potential in index-enhanced funds, currently only a fraction of the size of equity ETFs [7]. - The company has successfully launched several index-enhanced products, including the CSI 500 Index Enhanced strategy, which is noted for its maturity and ability to leverage alpha factors for excess returns [7][8]. - Future plans include expanding the product line to cover various styles such as quality, value, and growth, to meet diverse investor needs [8].

- The Black-Litterman (BL) model is an improved version of the mean-variance optimization (MVO) model developed by Fisher Black and Robert Litterman in 1990. It combines Bayesian theory with quantitative asset allocation models, allowing investors to incorporate subjective views into asset return forecasts and optimize portfolio weights. This model addresses MVO's sensitivity to expected returns and provides a more robust framework for efficient asset allocation[12][13][14] - The BL model was implemented for both global and domestic assets. For global assets, it utilized indices such as the S&P 500, Hang Seng Index, and COMEX Gold. For domestic assets, it included indices like CSI 300, CSI 1000, and SHFE Gold. Two variations of the BL model were constructed for each asset category[13][14][18] - The Risk Parity model, introduced by Bridgewater in 2005, aims to equalize risk contributions across asset classes in a portfolio. It calculates initial asset weights based on expected volatility and correlation, then optimizes deviations between actual and expected risk contributions to determine final portfolio weights[17][18][20] - The Risk Parity model was applied to both global and domestic assets. Global assets included indices such as CSI 300, S&P 500, and COMEX Gold, while domestic assets incorporated CSI 300, CSI 1000, and SHFE Gold. The model followed a three-step process: selecting assets, calculating risk contributions, and solving optimization problems for portfolio weights[18][20][21] - The Macro Factor-based Asset Allocation model constructs a framework using six macroeconomic risk factors: growth, inflation, interest rates, credit, exchange rates, and liquidity. It employs Factor Mimicking Portfolio methods to calculate high-frequency macro factors and integrates subjective views on macroeconomic conditions into asset allocation decisions[22][24][25] - The Macro Factor-based model involves four steps: calculating factor exposures for assets, determining benchmark factor exposures using a Risk Parity portfolio, incorporating subjective factor deviations based on macroeconomic forecasts, and solving for asset weights that align with target factor exposures[22][24][25] Model Performance Metrics - Domestic BL Model 1: Weekly return -0.11%, September return -0.14%, 2025 YTD return 3.23%, annualized volatility 2.19%, maximum drawdown 1.31%[14][17] - Domestic BL Model 2: Weekly return -0.11%, September return -0.13%, 2025 YTD return 2.84%, annualized volatility 1.99%, maximum drawdown 1.06%[14][17] - Global BL Model 1: Weekly return 0.04%, September return 0.11%, 2025 YTD return 0.84%, annualized volatility 1.99%, maximum drawdown 1.64%[14][17] - Global BL Model 2: Weekly return 0.00%, September return 0.03%, 2025 YTD return 1.84%, annualized volatility 1.63%, maximum drawdown 1.28%[14][17] - Domestic Risk Parity Model: Weekly return -0.06%, September return 0.05%, 2025 YTD return 2.99%, annualized volatility 1.35%, maximum drawdown 0.76%[20][21] - Global Risk Parity Model: Weekly return -0.07%, September return 0.13%, 2025 YTD return 2.50%, annualized volatility 1.48%, maximum drawdown 1.20%[20][21] - Macro Factor-based Model: Weekly return 0.04%, September return 0.26%, 2025 YTD return 3.29%, annualized volatility 1.32%, maximum drawdown 0.64%[26][27]