Core Viewpoint - The article highlights the strong performance of micro-cap stocks, particularly in the context of the Shanghai Composite Index's fluctuations around the 4000-point mark, indicating a growing market interest in this segment [2]. Group 1: Performance Comparison - Since 2010, the micro-cap stock index has outperformed major indices like the Shanghai 50, CSI 300, CSI 500, CSI 1000, and National 2000 in most years, except for 2017 and 2020 [2]. - The absolute performance data shows that in 2015, the micro-cap index surged by 229%, while the CSI 300 only increased by 6% [3]. - In 2023, the micro-cap index recorded a 50% increase, significantly outperforming other indices [3]. Group 2: Excess Returns Analysis - The excess returns of the micro-cap index are attributed to PB (Price-to-Book) recovery and the switching between high and low valuations [8]. - The report indicates that the contribution of trading frequency to excess returns is limited, while the profitability of micro-cap stocks does not significantly influence their overall returns [8]. - The strategy behind micro-cap stocks is characterized by a "reverse selection" feature, where stocks that have risen significantly are removed from the index, allowing for a systematic "buy low, sell high" approach [6]. Group 3: Trading Strategy Insights - The micro-cap index employs a mechanism that automatically executes a rebalancing strategy, enhancing its ability to capture structural reversal opportunities during market volatility [6]. - The trading environment for micro-cap stocks is influenced by both short-term trading and momentum strategies, which can amplify volatility during periods of liquidity tightening or systemic risk [8].

Core Viewpoint - The article discusses the impact of "narrative trading" on asset pricing, emphasizing that asset pricing is influenced not only by fundamentals but also by popular narratives such as the restructuring of the dollar credit system and the new technological revolution [1][12]. Group 1: Narrative Economics - The influence of narratives on economic phenomena consists of a series of elements: a popular, easily spread story, public behavior, and an epidemiological model for macro-level dissemination [2][16]. - The concept of "herding behavior" is used to illustrate how narratives affect micro-level decision-making, with varying strengths across different phases of narrative development [2][18]. Group 2: Herding Effect in Asset Allocation - Traditional studies of herding behavior focus on individual stocks and short-term market sentiment, but the current narrative-driven environment poses challenges for asset allocation due to the breakdown of continuity in global fiscal, monetary, and trade environments [3][20]. - The article suggests that the herding effect can be quantified and applied to investment portfolio optimization and asset timing strategies [3][20]. Group 3: Measurement of Herding Effect - Four common indicators of herding behavior are identified: Cross-Sectional Absolute Deviation (CSAD), the quadratic coefficient of return dispersion, standard deviation of beta coefficients, and cross-correlation [4][23]. - The CSAD index, which measures the deviation of asset returns from the average, indicates the presence of herding behavior when returns cluster around a certain average level [4][23]. Group 4: Current State of Herding Effect - The CSAD index for major asset classes shows a right-skewed distribution, indicating a tendency for extreme herding behavior, with a mean-reverting characteristic suggesting that extreme trends are difficult to maintain [5][28]. - Since May 2025, the CSAD has decreased significantly, indicating a rapid herding effect, but has started to rebound slightly, suggesting a potential shift towards more balanced asset performance [5][28]. Group 5: Strategy Integration - The article proposes integrating the herding factor into a macro risk parity framework, which has shown superior annualized returns compared to traditional models [6][34]. - The new framework suggests increasing allocations to equities and commodities while reducing bond exposure, indicating a shift in investment strategy based on herding behavior [6][34]. Group 6: Domestic Equity Market Analysis - The herding effect in the domestic equity market, as measured by the CSAD, has shown a decline in right-skewness, indicating lower dispersion compared to historical levels [7][40]. - The herding effect has gone through phases of fermentation, intensification, and now a slight loosening, suggesting a gradual return to individual rationality among investors [7][40].

Summary of Conference Call Notes Industry or Company Involved - The discussion primarily revolves around the Chinese stock market, specifically focusing on major indices such as the Shanghai Composite Index, ChiNext Index, and the STAR Market (科创50). Core Points and Arguments 1. **Market Status and Key Levels** The Shanghai Composite Index has broken a critical trend line but has not stabilized above 3,950 points, which remains a significant threshold. The ability to hold above 3,936 points will determine if the upward five-wave structure continues [1][4][11]. 2. **ChiNext Index Performance** The ChiNext Index reached a new high but showed signs of fatigue. If it fails to hold above 3,171 points, it may face downward pressure and potential MACD divergence [1][3][12]. 3. **Brokerage Sector Influence** The brokerage sector acts as a market sentiment amplifier, significantly impacting overall market risk appetite. However, the sector's performance this week has been indecisive, necessitating close monitoring [1][5][15]. 4. **Sector Performance Disparity** There is a notable divergence in sector performance, with cyclical industries leading while the technology sector shows significant internal differences. The consumer sector appears weak, reflecting uncertainty in policy interpretations following the Fourth Plenary Session [1][10][19]. 5. **Investment Strategy Recommendations** It is advised not to make significant reductions or increases in positions but to consider portfolio rebalancing, favoring brokerages and cyclical sectors while watching for rebound opportunities in the consumer sector [1][17][19]. 6. **Impact of New Public Fund Regulations** New regulations for public funds will limit positive feedback mechanisms, leading to a more balanced market style and a return to fundamental stock selection. This shift will require institutional investors to adjust their strategies [1][19][21]. 7. **Market Volatility and Strategy Adjustments** The failure of momentum strategies in the current market environment suggests adopting equal-weighted index strategies or quantitative approaches to enhance performance against benchmarks [1][25][26]. 8. **Future Market Outlook** There is confidence in the overall index direction, with expectations of reaching the 4,130 to 4,200 range before the Lunar New Year. However, the current market structure allows for some rebalancing actions [1][18]. Other Important but Possibly Overlooked Content 1. **Market Reaction to Recent Events** The market's reaction to recent events has been complex, with the Shanghai Composite Index showing a jump but subsequently retreating, indicating a lack of strong momentum [1][6][10]. 2. **Investment Sentiment and Performance Metrics** Despite the ChiNext Index's recent highs, the overall market sentiment remains weak, with over 70% of stocks not showing significant gains, highlighting poor profitability across the board [1][16][22]. 3. **Sector-Specific Observations** The performance of specific sectors such as banking, real estate, and consumer goods has been lackluster, which may be tied to recent policy interpretations and market conditions [1][10][17]. 4. **Long-term Strategy for Brokerages** Brokerages are seen as having a strong potential for recovery, with a reasonable risk-reward profile, suggesting a strategic focus on this sector moving forward [1][15]. 5. **Market Dynamics and Feedback Mechanisms** The current market dynamics are influenced by feedback mechanisms that could lead to extreme trends, which the new regulations aim to mitigate, thus changing the investment landscape [1][21][22].

Group 1 - The article discusses the classification of market states into "trend" and "range" based on historical data, emphasizing the importance of recognizing these states for investment strategies [1][4] - In a trending market, momentum strategies like "buy high, sell higher" yield greater returns, while in a ranging market, mean-reversion strategies perform better [1][4] - A two-phase algorithm is developed to label historical trends and ranges in the Shanghai Composite Index, enhancing the accuracy of market state identification [2][3] Group 2 - The backtesting period is set from January 2020 to August 2025, reflecting a shift in market behavior post-2020, with increased frequency of state changes [7] - A feature variable system is constructed to identify market states, focusing on price, volume, and volatility, rather than traditional indicators [8][15] - The model training shows that all six feature indicators have an accuracy above 50%, with the volume feature achieving the highest accuracy of 63.48% [22][23] Group 3 - The decision tree model outperforms other models in predicting market states, achieving an accuracy of 80.10% in the test set [36][39] - The strategy based on the decision tree model yields a total return of 77.20%, significantly outperforming the benchmark [63] - The research highlights the potential of combining strategic signals for long-term market state identification with tactical signals for short-term changes to enhance strategy performance [64]

Report Industry Investment Rating No information provided in the report regarding industry investment rating. Core Viewpoints of the Report - The US ETF market has seen the issuance of multiple asset - allocation products. The inflow of gold ETFs is significant, and precious - metal stock ETFs have performed significantly better than precious - metal ETFs. - In the US ordinary public - offering fund market, the outflow of domestic stock funds remains around $20 billion, while the inflow of bond products is stable, slightly exceeding $10 billion [3]. Summary by Relevant Catalog 1. US ETF Innovation Products: Multiple Asset - Allocation Products Issued - Last week, 22 new products were issued in the US, including various types such as downside protection, leverage, theme, allocation, and rotation products [6]. - There were 7 new downside protection products, including Calamos' laddered downside protection products linked to Bitcoin. Arrow Funds also issued a Bitcoin strategy product [6]. - 4 single - stock leverage products were issued, linked to Figma, Futu, JD.com, and Lemonade [7]. - GMO issued a dynamic asset - allocation ETF, with 40 - 80% invested in stocks and the rest in fixed - income and liquid alternative assets, based on GMO's 7 - year asset return forecast [7]. - AlphaDroid issued two strategy products, a momentum strategy and an industry rotation product [8]. - American Century issued 2 fundamental active ETFs, for small - cap value and small - cap growth [8]. - Pictet issued 3 stock ETFs, entering the US ETF market, with one using an AI strategy and two being theme products [8]. 2. US ETF Dynamics 2.1 US ETF Funds: Significant Inflow into Gold ETFs - In the past week, US ETFs maintained a high - speed inflow of nearly $50 billion, with domestic stocks inflowing over $25 billion and commodity ETFs mainly composed of gold also having a large inflow [9]. - The inflow of US broad - based stock products was stable last week, and the gold ETF GLD ranked second in the inflow of all products. Among bond products, comprehensive products had relatively more inflows, while high - yield bonds and alternative bond products had outflows [9]. 2.2 US ETF Performance: Precious - Metal Stock ETFs Significantly Outperform Precious - Metal ETFs - Due to frequent global situation changes this year, precious - metal ETFs led by gold have continuously risen significantly, and precious - metal - related stock ETFs such as gold - mining stocks have had significantly higher increases, with many products rising around 150% [3]. 3. Recent Capital Flows of US Ordinary Public - Offering Funds - In August 2025, the total amount of non - money public - offering funds in the US was $22.98 trillion, an increase of $0.41 trillion compared to July 2025. The S&P 500 rose 1.91% in August, and the scale of domestic stock products increased by 1.62%, with the redemption pressure easing [14]. - According to weekly ICI statistics, the outflow of US domestic stock funds last week remained around $20 billion, while the inflow of bond products was stable, slightly exceeding $10 billion [14].

Group 1 - The article discusses the impact of grand narratives on asset pricing, emphasizing that economic behavior is influenced not only by rational analysis but also by prevailing narratives, as proposed by economist Robert Shiller [1] - It identifies five leading asset classes in 2025: precious metals, non-ferrous metals, emerging market stocks, technology assets, and alternative assets, all influenced by narratives such as the reconstruction of the dollar credit system and the reshaping of global supply chains [1] - The interconnectedness of these narratives creates a "narrative constellation," which is more influential than individual narratives [1] Group 2 - The rise of narratives is linked to changes in global macro variables, where traditional economic assumptions of continuity are challenged by significant non-continuous changes in fiscal and monetary conditions, trade environments, and geopolitical factors [2] - The influence of narratives poses challenges to traditional investment research methodologies, as the long timelines of grand narratives can bypass short-term validations and disrupt mean reversion assumptions [2] Group 3 - To adapt to the influence of narratives, the article suggests differentiating narrative levels for better risk-return matching, utilizing thematic asset categories that align with narratives, and increasing the use of momentum strategies during narrative-driven phases [3] - It also recommends establishing objective indicators for narrative validation and recognizing the potential for narrative bubbles, advocating for a diversified approach to narrative investments [4] Group 4 - The article notes a divergence in asset narratives during the third week of October, with U.S. stock markets rebounding amid the end of the Fed's balance sheet reduction, while Japanese stocks experienced a pullback [5] - Precious metals narratives strengthened, with gold and silver prices reaching new highs, while copper prices showed signs of retreat [6] Group 5 - The article highlights the performance of global stock markets, noting a rebound in U.S. stocks, while European stocks remained subdued due to fiscal expectations and export concerns [5] - It also discusses the dynamics of commodity prices, with gold and silver showing strong performance, while oil prices declined due to geopolitical factors and OPEC+ production increases [7] Group 6 - The article emphasizes the importance of monitoring the U.S. government's ongoing shutdown, which could impact market confidence and policy risks if it extends into November [11] - It also mentions the potential for the Fed to end its balance sheet reduction in the coming months, shifting focus towards employment risks and liquidity stability [13] Group 7 - The article discusses the recent credit fraud incidents in U.S. regional banks, highlighting vulnerabilities in the credit system under high-interest rate conditions [15] - It suggests that these incidents may not pose systemic risks but indicate weaknesses in the credit structure that could lead to further risk reassessment in the market [16] Group 8 - The article outlines the current state of China's asset pricing, noting a rise in the pricing power of Chinese assets amid global market uncertainties [9] - It highlights the performance of various sectors within the Chinese market, with a shift towards value styles and a pullback in high-growth narratives [10] Group 9 - The article reports on the recent developments in China's fiscal and monetary policies, including the expansion of the central bank's balance sheet and the need for effective credit support for the real economy [21] - It emphasizes the importance of infrastructure investment and the government's commitment to enhancing domestic demand and stabilizing the economy [29] Group 10 - The article discusses the ambitious goals set by China's government for electric vehicle charging infrastructure, aiming to significantly increase the number of charging facilities by 2027 [25][26] - It highlights the expected compound annual growth rate of 29.8% for charging facilities from 2025 to 2027, reflecting the government's commitment to supporting the electric vehicle industry [26]

Group 1: Report Information - Report Date: October 10, 2025 [1] - Report Title: Shenwan Hongyuan Gold ETF Portfolio 202510 [1] - Analysts: Shen Siyi, Deng Hu [3] - Research Support: Bai Haotian [3] - Contact: Shen Enyi [3] Group 2: Investment Ratings - No industry investment ratings are provided in the report. Group 3: Core Views - The report constructs four ETF portfolios, including the macro industry portfolio, macro + momentum industry portfolio, core - satellite portfolio, and trinity style rotation ETF portfolio, based on macro - sensitivity and momentum analysis, aiming to capture investment opportunities in different market environments [5][8]. - The current economic leading indicators are rising, liquidity indicators are slightly tight, and credit indicators remain positive. The portfolios are shifting towards a more balanced allocation, with an increased proportion of consumer sectors [5]. - The trinity style rotation model combines macro - liquidity, fundamental, and market sentiment factors to construct a medium - to long - term style rotation model, providing insights into market style preferences [5][9]. Group 4: ETF Portfolio Construction Methods 4.1 Based on Macro - Method - Calculate macro - sensitivity for broad - based, industry - theme, and Smart Beta ETFs based on economic, liquidity, and credit variables. Traditional cyclical industries are sensitive to the economy, TMT to liquidity, and consumption to credit [8]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) using macro - sensitivity and momentum, and rebalance monthly [8]. 4.2 Trinity Style Rotation ETF Portfolio - Build a medium - to long - term style rotation model centered on macro - liquidity, comparing with the CSI 300 index. Screen macro, fundamental, and market sentiment factors to construct three types of models (growth/value, market - cap, and quality) [9]. Group 5: Portfolio Details 5.1 Macro Industry Portfolio - Select the top 6 industry - theme indices based on macro - sensitivity scores, and equally weight the largest - scale corresponding ETFs. Currently, the portfolio is more balanced with an increased consumer proportion [5][10]. - October 2025 holdings include ETFs related to tourism, home appliances, chemicals, etc. [14]. - In 2025, the portfolio had varying monthly excess returns, with positive excess returns in September [15]. 5.2 Macro + Momentum Industry Portfolio - Combine macro and momentum methods. The pharmaceutical sector's weight is further reduced, and rare earth and battery sectors are selected on the momentum side [5][16]. - October 2025 holdings include multiple industry - themed ETFs [18]. - The portfolio performed well in 2025, with positive excess returns in September after a drawdown in August [19]. 5.3 Core - Satellite Portfolio - Use the CSI 300 as the core and combine broad - based, industry, and Smart Beta portfolios. Weight them at 50%, 30%, and 20% respectively [20][21]. - October 2025 holdings include a mix of broad - based and industry - themed ETFs [24][25]. - The portfolio performed steadily in 2025, outperforming the index almost every month [25]. 5.4 Trinity Style Rotation ETF Portfolio - The model currently favors small - cap growth and high - quality styles. The portfolio's factor exposure and historical performance are presented [26][27]. - October 2025 holdings include ETFs related to small - cap indices and high - growth sectors [31]. - The portfolio has shown certain performance since 2021, with positive excess returns in September 2025 [30].

Core Insights - The Bitcoin market experienced a sudden crash on September 22, 2025, with its price dropping to around $114,000, leading to a significant loss in market capitalization and the liquidation of $1.7 billion in long positions [1] - Analyst Paolo Schiavone from Goldman Sachs identified this crash as a pivotal moment, indicating a shift in market dynamics and warning that falling below the 200-day moving average would increase risks [1][2] - Following the crash, Bitcoin's price stabilized around $109,000, with market participants showing mixed emotions and a lack of decisive trading activity [3][5] Market Dynamics - The market showed signs of indecision with a horizontal consolidation phase following the initial crash, and trading volumes decreased significantly [2] - There was a split in market sentiment, with half of the participants concerned about inflation and the other half worried about growth, leading to fragmented trading behavior [2] - The futures market indicated a cooling of bullish sentiment, as the perpetual funding rate shifted from positive to near zero, suggesting a decrease in long positions [3] Technical Analysis - The 200-day moving average is viewed as a psychological threshold for traders, with its breach potentially leading to risk aversion among market participants [6] - The market's reaction to technical indicators is influenced by the distribution of holdings, with long-term holders remaining stable while short-term leverage is decreasing [5][6] - The behavior of the AI-related stocks in the U.S. market, particularly Nvidia, showed signs of fatigue, which could impact broader market sentiment [3] External Influences - The U.S. Treasury yields experienced fluctuations, with discussions around fiscal discipline resurfacing, impacting market expectations [2] - The potential for a "soft landing" in the U.S. economy remains, with GDP growth projected at 2% and core PCE around 3%, indicating that the economy has not yet reached a critical downturn [5] - The interconnectedness of global markets was evident, as the decline in Bitcoin prices also affected technology indices in the Chinese market [5] Future Outlook - The upcoming employment data in early October could significantly influence market sentiment and the Federal Reserve's interest rate decisions, with a possibility of a 50 basis point cut if job data continues to weaken [5][7] - The market is expected to experience increased volatility as it navigates through the end of September and the beginning of October, with traders advised to remain cautious [7] - The potential for a rebound exists, but it may be short-lived and fragmented due to the current market conditions and sentiment [7]

Group 1: Core Insights - The current industry allocation of the Bank of China multi-strategy system includes Electronics (11.6%), Comprehensive (11.6%), Non-Bank Financials (9.4%), and others, indicating a diversified investment approach [1] - The average weekly return for the CITIC primary industries is 1.8%, with the best-performing sectors being Communication (17.3%), Electronics (12.2%), and Computer (7.0%) [3][10] - The cumulative return of the industry rotation composite strategy this year is 25.5%, outperforming the CITIC primary industry equal-weight benchmark return of 21.6% by 3.9% [3] Group 2: Industry Performance Review - The worst-performing sectors this week include Coal (-2.7%), Textile and Apparel (-2.4%), and Banking (-1.7%) [3][10] - The current PB valuation for the Retail Trade, Defense Industry, Media, and Computer sectors exceeds the 95% percentile of the past six years, triggering a high valuation warning [12][13] Group 3: Strategy Performance - The highest excess return strategy this year is the S2 "Unfalsified Sentiment Tracking Strategy," with an excess return of 14.7% [3] - The top three industries based on the S1 "High Prosperity Industry Rotation Strategy" are Non-Bank Financials, Agriculture, and Non-Ferrous Metals [15][16] - The current macro indicators favor the following six industries: Comprehensive Finance, Computer, Media, Defense Industry, Comprehensive, and Non-Bank Financials [24]

Quantitative Models and Construction - **Model Name**: Active and Passive Rebalancing Metrics **Construction Idea**: Decompose quarterly portfolio weight changes into active discretionary decisions and passive return-driven changes to analyze fund manager behavior [38][40][42] **Construction Process**: - Formula: $W_{i,j,t}-W_{i,j,t-1}=\underbrace{W_{i,j,t}-\widehat{W}_{i,j,t}}_{\text{Active}_{i,j,t}}+\underbrace{\widehat{W}_{i,j,t}-W_{i,j,t-1}}_{\text{Passive}_{i,j,t}}$ $\widehat{w}_{i,j,t}=\frac{\left(1+r_{i,t}\right)\,w_{i,j,t-1}}{\sum\left(1+r_{i,t}\right)\,w_{i,j,t-1}}$ - **Active**: Residual weight changes after removing mechanical effects, capturing discretionary rebalancing [40][42] - **Passive**: Weight changes driven by market returns assuming no trading activity [40][41] **Evaluation**: Captures fund managers' preferences for managing portfolio concentration and distinguishes between minor adjustments and large-scale asset rotation [42][43] - **Model Name**: Threshold Demand **Construction Idea**: Focus on concentrated positions exceeding 2% of fund AUM to measure diversification-driven demand [82][83] **Construction Process**: Formula: $Threshold Demand_{i,t}=\frac{\sum_{j}\left(\widehat{w}_{i,j,t}-w_{i,j,t-1}\right)\cdot I(w_{i,j,t-1}>2\%)\cdot\text{Shares}_{i,j,t-1}}{\sum_{j}\text{Shares}_{i,j,t-1}}$ - Uses only concentrated positions (10% of fund holdings) where portfolio size and concentration matter [82][83] **Evaluation**: Effectively isolates positions where diversification constraints are most impactful [83] - **Model Name**: Fitted Demand **Construction Idea**: Use spline coefficients from weight ranges to construct demand metrics based on rebalancing intensity [83][84] **Construction Process**: Formula: $Fitted Demand_{i,t}=\frac{\sum_{j}\left(\widehat{W}_{ij,t}-W_{ij,t-1}\right)\cdot\beta_{weight}\cdot\text{Shares}_{i,j,t-1}}{\sum_{j}\text{Shares}_{i,j,t-1}}$ - $\beta_{weight}$ represents rebalancing intensity coefficients for different weight ranges [83][84] **Evaluation**: Focuses on positions within 2%-6.5% of fund AUM, capturing nuanced rebalancing behavior [83][84] Model Backtesting Results - **Active and Passive Metrics**: - Contemporaneous Active adjustment for 1% Passive weight change: -0.234% [44][49] - Next-quarter Active adjustment for 1% Passive weight change: -0.171% [44][49] - **Threshold Demand**: - Standard deviation: 0.15% - Predicts equity fund sell probability increase by 1.28%-2.20% [85][86] - **Fitted Demand**: - Standard deviation: 0.03% - Predicts equity fund sell probability increase by 0.5%-0.67% [85][86] Quantitative Factors and Construction - **Factor Name**: Rebalancing Demand **Construction Idea**: Aggregate passive-driven portfolio changes to measure demand for large-cap stocks [81][82] **Construction Process**: Formula: $Rebalancing Demand_{i,t}=\frac{\sum_{j}\left(\widehat{w}_{i,j,t}-w_{i,t-1}\right)\cdot\text{Shares}_{i,j,t-1}}{\sum_{j}\text{Shares}_{i,j,t-1}}$ - Aggregates passive-driven changes across all observed mutual funds [81][82] **Evaluation**: Predicts short-term price pressure and subsequent reversals for large-cap stocks [82][88] Factor Backtesting Results - **Rebalancing Demand**: - Predicts short-term returns: -0.44% (t=-3.21) for first 35 trading days [88][89] - Predicts subsequent reversals: +0.27% (t=2.60) for remaining quarter [88][89] - **Threshold Demand**: - Predicts short-term returns: -0.348% (t=-3.719) for first 35 trading days [88][89] - Predicts subsequent reversals: +0.178% (t=2.508) for remaining quarter [88][89] - **Fitted Demand**: - Predicts short-term returns: -0.460% (t=-3.598) for first 35 trading days [88][89] - Predicts subsequent reversals: +0.253% (t=2.616) for remaining quarter [88][89] Additional Observations - **Impact on Momentum Portfolios**: - Adjusting for rebalancing demand improves momentum portfolio returns by 230% for large-cap stocks [114] - Suggests diversification-driven demand weakens traditional momentum strategies [114] - **Price Pressure and Reversals**: - Large-cap stocks experience V-shaped return patterns due to rebalancing demand [93][94] - Short-term price pressure followed by reversals aligns with non-fundamental demand effects [93][94]