Group 1: Market Strategy Insights - The report indicates that small-cap stocks are likely to outperform in August, supported by a quantitative model signal of 0.5, suggesting an overweight position in small-cap stocks [1] - Year-to-date, the small-cap strategy has yielded a return of 15.74%, outperforming the equal-weight benchmark return of 11.79% by 3.95% [1] - The value-growth rotation strategy shows a quantitative model signal of -0.33, indicating a shift towards value stocks, with a year-to-date return of 11.11% and an excess return of 7.63% [2] Group 2: Factor Performance Tracking - Among eight major factors, volatility and value factors have shown positive returns this month, while liquidity and momentum factors have shown negative returns [2] - Year-to-date, volatility and quality factors have performed well, whereas liquidity and large-cap factors have underperformed [2] - The report highlights that the beta, investment quality, and momentum factors have positive returns this month, while residual volatility, mid-cap, and long-term reversal factors have negative returns [2] Group 3: Covariance Matrix Update - The report updates the factor covariance matrix as of July 31, 2025, which is crucial for predicting stock portfolio risks [3] - The covariance matrix is constructed using a multi-factor model that combines factor covariance and stock-specific risk matrices for accurate estimation [3]

Core Viewpoint - The article emphasizes the importance of optimizing position strategies in quantitative frameworks for predicting bond futures, rather than solely focusing on the prediction accuracy of price movements [1][3]. Group 1: Position Strategy Optimization - The study tests various position strategies, including a full position strategy as a benchmark, a threshold-based full position strategy, and a gradual accumulation strategy that incorporates a fuzzy interval filtering mechanism [1][3]. - Continuous trading strategies convert binary probability signals into position adjustment signals, allowing for categorization based on risk preferences, such as risk-seeking, risk-averse, and risk-neutral types [1][3]. Group 2: Model and Market Conditions - The report references a multi-factor model for bond market timing, utilizing recent data to train models for predicting the next trading day, with specific market conditions defined for 2024 and 2025 [2]. - The combination of various position strategies is crucial, particularly in volatile markets, where appropriate strategy selection can significantly enhance overall model performance [3]. Group 3: Performance Insights - Binary full position strategies effectively capture returns during clear trends but come with higher volatility and transaction costs [3]. - Gradual accumulation strategies show lower trading frequency advantages, reducing transaction costs, but may have limited return capture in sideways markets [3]. - Single continuous strategies demonstrate strong performance in volatile markets, with specific strategies like Sigmoid and Atanh showing significant advantages in volatility control, especially for risk-averse investors [3].

- Model Name: Domestic Asset BL Model 1; Model Construction Idea: The BL model is an improvement of the traditional mean-variance model, combining subjective views with quantitative models using Bayesian theory; Model Construction Process: The model optimizes asset allocation weights based on investor market analysis and asset return forecasts, effectively addressing the sensitivity of the mean-variance model to expected returns; Model Evaluation: The BL model provides a higher fault tolerance compared to purely subjective investments, offering efficient asset allocation solutions[14][15] - Model Name: Domestic Asset BL Model 2; Model Construction Idea: Similar to Domestic Asset BL Model 1; Model Construction Process: The model is built on the same principles as Domestic Asset BL Model 1 but with different asset selections; Model Evaluation: Similar to Domestic Asset BL Model 1[14][15] - Model Name: Global Asset BL Model 1; Model Construction Idea: Similar to Domestic Asset BL Model 1; Model Construction Process: The model is built on the same principles as Domestic Asset BL Model 1 but targets global assets; Model Evaluation: Similar to Domestic Asset BL Model 1[14][15] - Model Name: Global Asset BL Model 2; Model Construction Idea: Similar to Global Asset BL Model 1; Model Construction Process: The model is built on the same principles as Global Asset BL Model 1 but with different asset selections; Model Evaluation: Similar to Global Asset BL Model 1[14][15] - Model Name: Domestic Asset Risk Parity Model; Model Construction Idea: The risk parity model aims to equalize the risk contribution of each asset in the portfolio; Model Construction Process: The model calculates the risk contribution of each asset and optimizes the deviation between actual and expected risk contributions to determine final asset weights; Model Evaluation: The model provides stable returns across different economic cycles[20][21] - Model Name: Global Asset Risk Parity Model; Model Construction Idea: Similar to Domestic Asset Risk Parity Model; Model Construction Process: The model is built on the same principles as Domestic Asset Risk Parity Model but targets global assets; Model Evaluation: Similar to Domestic Asset Risk Parity Model[20][21] - Model Name: Macro Factor-Based Asset Allocation Model; Model Construction Idea: The model constructs a macro factor system covering growth, inflation, interest rates, credit, exchange rates, and liquidity; Model Construction Process: The model uses the Factor Mimicking Portfolio method to construct high-frequency macro factors and optimizes asset weights based on subjective macro views; Model Evaluation: The model bridges macro research and asset allocation, reflecting subjective macro judgments in asset allocation[23][24][27] - Domestic Asset BL Model 1, Weekly Return: 0.02%, July Return: 0.61%, 2025 YTD Return: 2.46%, Annualized Volatility: 2.16%, Maximum Drawdown: 1.31%[17][19] - Domestic Asset BL Model 2, Weekly Return: -0.06%, July Return: 0.48%, 2025 YTD Return: 2.41%, Annualized Volatility: 1.93%, Maximum Drawdown: 1.06%[17][19] - Global Asset BL Model 1, Weekly Return: -0.09%, July Return: 0.56%, 2025 YTD Return: 0.95%, Annualized Volatility: 1.95%, Maximum Drawdown: 1.64%[17][19] - Global Asset BL Model 2, Weekly Return: -0.07%, July Return: 0.51%, 2025 YTD Return: 1.59%, Annualized Volatility: 1.7%, Maximum Drawdown: 1.28%[17][19] - Domestic Asset Risk Parity Model, Weekly Return: -0.02%, July Return: 0.36%, 2025 YTD Return: 2.7%, Annualized Volatility: 1.46%, Maximum Drawdown: 0.76%[22][23] - Global Asset Risk Parity Model, Weekly Return: -0.03%, July Return: 0.3%, 2025 YTD Return: 2.16%, Annualized Volatility: 1.66%, Maximum Drawdown: 1.2%[22][23] - Macro Factor-Based Asset Allocation Model, Weekly Return: -0.03%, July Return: 0.38%, 2025 YTD Return: 2.76%, Annualized Volatility: 1.36%, Maximum Drawdown: 0.64%[28][29]

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, providing data on their excess returns over different time - frames [4][5][6]. 3. Summary by Directory 1. ETF Strategy Index Tracking - **ETF Strategy Index Last Week's Performance**: - The table shows the last week's performance of six ETF strategy indices, including their returns, benchmark returns, and excess returns. For example, the Huabao Research Size Rotation ETF Strategy Index had a last - week return of 1.78%, a benchmark (CSI 800) return of 2.10%, and an excess return of - 0.33% [13]. 1.1. Huabao Research Size Rotation ETF Strategy Index - **Strategy Principle**: It uses multi - dimensional technical indicator factors and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index. The model outputs weekly signals to determine positions and obtain excess returns [4][14]. - **Performance**: As of July 25, 2025, the excess return since 2024 was 17.92%, the recent one - month excess return was 0.16%, and the recent one - week excess return was - 0.33% [4][14]. - **Position**: As of July 25, 2025, it held 100% of the CSI 300 ETF [18]. 1.2. Huabao Research SmartBeta Enhanced ETF Strategy Index - **Strategy Principle**: It uses price - volume indicators to time self - built Barra factors and maps timing signals to ETFs based on their exposures to 9 Barra factors to obtain excess returns. The selected ETFs cover mainstream broad - based index ETFs and some style and strategy ETFs [4]. - **Performance**: As of July 25, 2025, the excess return since 2024 was 15.29%, the recent one - month excess return was - 1.20%, and the recent one - week excess return was 0.25% [4]. - **Position**: As of July 25, 2025, it held multiple ETFs such as the Value 100ETF, CSI 2000ETF, etc. [23]. 1.3. Huabao Research Quantitative Cyclone ETF Strategy Index - **Strategy Principle**: It starts from a multi - factor perspective, including the grasp of medium - and long - term fundamental dimensions, the tracking of short - term market trends, and the analysis of the behavior of various market participants. It uses valuation and crowding signals to indicate industry risks and multi - dimensionally digs out potential sectors to obtain excess returns [5][22]. - **Performance**: As of July 25, 2025, the excess return since 2024 was 8.22%, the recent one - month excess return was 4.29%, and the recent one - week excess return was 2.67% [5][22]. - **Position**: As of July 25, 2025, it held ETFs like the Building Materials ETF, Non - Ferrous Metals 60ETF, etc. [26]. 1.4. Huabao Research Quantitative Balance ETF Strategy Index - **Strategy Principle**: It uses a multi - factor system including economic fundamentals, liquidity, technical aspects, and investor behavior to build a quantitative timing system for equity market trend analysis. It also builds a prediction model for market size styles to adjust equity market position distribution and obtains excess returns through comprehensive timing and rotation [5][26]. - **Performance**: As of July 25, 2025, the excess return since 2024 was - 2.75%, the recent one - month excess return was - 2.91%, and the recent one - week excess return was - 1.03% [5][26]. - **Position**: As of July 25, 2025, it held ETFs such as the 10 - Year Treasury Bond ETF, CSI 1000ETF, etc. [30]. 1.5. Huabao Research Hot - Spot Tracking ETF Strategy Index - **Strategy Principle**: It tracks and mines hot - spot index target products in a timely manner based on strategies such as market sentiment analysis, industry event tracking, investor sentiment and professional views, policy and regulatory changes, and historical deduction. It constructs an ETF portfolio that can capture market hot - spots in time to provide investors with short - term market trend references [6][30]. - **Performance**: As of July 25, 2025, the recent one - month excess return was - 0.38%, and the recent one - week excess return was 1.48% [6][30]. - **Position**: As of July 25, 2025, it held ETFs such as the Non - Ferrous Metals 50ETF, Hong Kong Stock Consumption ETF, etc. [33]. 1.6. Huabao Research Bond ETF Duration Strategy Index - **Strategy Principle**: It uses bond market liquidity and price - volume indicators to screen effective timing factors and predicts bond yields through machine - learning methods. When the expected yield is below a certain threshold, it reduces the long - duration positions in the bond investment portfolio to improve long - term returns and drawdown control ability [6][33]. - **Performance**: As of July 25, 2025, the recent one - month excess return was 0.19%, and the recent one - week excess return was 0.12% [6][33]. - **Position**: As of July 25, 2025, it held ETFs such as the 10 - Year Treasury Bond ETF, Treasury Bond ETF 5 - 10 Years, etc. [38].

Group 1 - The article highlights the importance of risk management and market understanding in trading, emphasizing that holding positions is not merely a numbers game but requires respect for the market [1][4] - The recent increase in market volatility due to the Federal Reserve's interest rate hike has led to a significant rise in short positions among institutions, contrasting with technical analysis suggesting bullish signals [1][3] - The protagonist recalls lessons from a mentor about the significance of emotional intelligence in trading, indicating that quantitative models cannot fully capture market sentiment [1][3] Group 2 - The narrative illustrates the tension between quantitative trading strategies and the need for human judgment, as the new director dismisses concerns about reducing positions based on a quantitative model [1][3] - The protagonist applies a personal trading principle, focusing on capital flow, sentiment indicators, and maintaining a clear trading purpose, which leads to a decision to reduce exposure during market downturns [3] - The closing reflections emphasize that trading involves understanding the underlying dynamics of the market and maintaining a steadfast approach amidst fluctuations [4]

Group 1 - The report from JPMorgan undermines the optimistic projections of a $2 trillion stablecoin market, highlighting a disconnect between market expectations and reality [1] - The surge in trading volume among major stablecoin issuers coincided with the passage of the legislation, suggesting prior knowledge of the outcomes [1] Group 2 - The volatility in the tech sector is illustrated by the rapid shifts in expert recommendations, leading to significant market fluctuations [3] - The stock market is characterized as a battleground for pricing power, where those lacking it are at a disadvantage [4] Group 3 - The white liquor stock crash in 2025 serves as a cautionary tale, with early warning signs in inventory data being overlooked [5] - The rise in oil prices was foreshadowed by unusual increases in institutional inventory data prior to media coverage of Middle Eastern conflicts [7] Group 4 - JPMorgan's confidence in its quantitative models contrasts sharply with the struggles of ordinary investors to interpret market signals accurately [9] - The importance of solid data foundations for financial innovations is emphasized, suggesting that unsupported judgments can lead to poor decision-making [10]

Group 1 - The report highlights that market sentiment continues to rise, with both domestic and international equity strategy indices showing gains [1][4] - The report provides an overview of various ETF strategy indices, including their performance metrics and methodologies for generating excess returns [12][13] Group 2 - The Huabao Research Large and Small Cap Rotation ETF Strategy Index utilizes multi-dimensional technical indicators and machine learning models to predict the return differences between large and small cap indices, achieving an excess return of 17.48% since 2024 [14][17] - The Huabao Research SmartBeta Enhanced ETF Strategy Index employs price-volume indicators to time self-built Barra factors, resulting in an excess return of 16.32% since 2024 [18][19] - The Huabao Research Quantitative Fire Wheel ETF Strategy Index focuses on multi-factor analysis to identify potential sectors, achieving an excess return of 3.63% since 2024 [22][23] Group 3 - The Huabao Research Quantitative Balance ETF Strategy Index incorporates economic fundamentals, liquidity, technical indicators, and investor behavior to adjust equity market positions, with an excess return of -0.88% since 2024 [25][27] - The Huabao Research Hotspot Tracking ETF Strategy Index tracks market sentiment and significant industry events to capture market trends, reporting an excess return of -3.76% in the last month [30][32] - The Huabao Research Bond ETF Duration Strategy Index uses liquidity and price-volume indicators to optimize bond portfolio duration, achieving a near-zero excess return in the last month [34][36]

Group 1 - The central bank's recent policy shift indicates a significant change in approach, moving away from previous commitments to adjust interest rates and instead focusing on strengthening domestic circulation [1] - The A-share market is heavily reliant on potential interest rate cuts from the US Federal Reserve, which could act as a catalyst for a bull market [3] - Current economic indicators from the US suggest a precarious situation, with nominal GDP growth at 2.03% and CPI at 2.76%, leading to negative real growth when adjusted for inflation [3] Group 2 - The current A-share market is characterized as a "slow bull" phase, where retail investors risk becoming "patsies" if they follow market trends without understanding underlying dynamics [3] - Institutions are seen as manipulating market conditions, creating a challenging environment for retail investors who may lack the tools to navigate these complexities [5] - Quantitative models are highlighted as essential tools for retail investors to identify market signals and avoid being misled by institutional trading patterns [5] Group 3 - The concept of "strong return" and "strong withdrawal" is introduced, indicating significant shifts in trading power that can signal market changes [7] - Institutional trading activity is increasing, with over 3,000 stocks showing signs of institutional involvement, suggesting a potential acceleration in market movements [10] - The presence of "institutional inventory" is crucial for understanding stock performance, as active institutional participation can lead to significant price movements [9]

Group 1 - The report maintains a tactical benchmark view on A-shares, citing the elimination of policy uncertainty and a decline in risk-free interest rates as factors that enhance market performance [4][11][13] - The tactical benchmark view on government bonds is upheld, with the report noting an imbalance between financing demand and credit supply, which limits the downward movement of interest rates [4][11][13] - The tactical allocation view on gold is downgraded to benchmark, as geopolitical tensions have eased and market risk appetite has rebounded, reducing gold's appeal as a safe-haven asset [4][11][13] - A tactical underweight view on the US dollar is maintained, with concerns over fluctuating policies and persistent fiscal deficit issues impacting the dollar's credibility [4][14] Group 2 - The report highlights that the recent market sentiment is stable, with expectations for economic recovery and a favorable environment for equity assets due to declining risk-free rates and high trading volumes [11][12] - The report indicates that the geopolitical situation in the Middle East and improved China-US relations have boosted global risk appetite, suggesting structural opportunities within equity markets [11][12] - The report emphasizes that the current macroeconomic environment limits the potential for significant downward adjustments in bond yields, as the market has already priced in the prevailing interest rate levels [11][12]

Group 1: ETF Innovations and New Products - Roundhill launched a series of weekly dividend ETFs linked to stocks like Meta, Netflix, Amazon, Berkshire, and Robinhood, offering 1.2 times weekly returns [1] - Rainwater Equity introduced its first ETF focusing on companies with high customer loyalty, such as software providers and exchanges, which are expected to deliver stable earnings growth [2] - WisdomTree released an inflation-protected ETF utilizing a momentum-based long-short commodity strategy, covering 18 commodities and holding long positions in gold and silver [2] Group 2: ETF Fund Flows and Performance - U.S. stock ETFs saw significant inflows exceeding $30 billion last week, with international stock products also attracting over $10 billion [3] - Vanguard's S&P 500 ETF has seen substantial inflows, surpassing $680 billion in total assets, leading other products by nearly $80 billion [6][10] - Technology ETFs, particularly in the semiconductor and cybersecurity sectors, have rebounded significantly, with some products gaining over 20% since May [10] Group 3: Fund Flow Trends - For the week of June 4 to June 11, U.S. domestic equity funds experienced outflows of approximately $11.2 billion, while bond products continued to see inflows exceeding $8 billion [11]