Group 1 - The current stock market is experiencing a bullish trend, with the Shanghai Composite Index stabilizing above 3600 points, and the market is expected to continue this upward momentum for over four years starting from September 2024, with Hong Kong stocks being a key breakout point [1][2][3] - The market has been in a downward cycle for approximately four years since 2021, and historical data suggests that the upcoming bullish cycle will be symmetrical to the previous bearish cycle, indicating significant potential for growth [1][2] - The influx of overseas capital and a shift in China's economic structure, including changes in industry and asset allocation, are expected to drive the stock market as a new "reservoir" for funds [2][3] Group 2 - The Hong Kong stock market is anticipated to benefit from its high marketization and regulatory framework, attracting foreign investment and focusing on quality assets, which may lead to a premium for H-shares over A-shares [3][4] - Investment opportunities are being identified in sectors such as military industry, innovative pharmaceuticals, and financial technology, with a focus on utilizing ETF rotation strategies for timing and asset allocation [4][5] - The military industry is undergoing significant changes, with increased asset securitization and a shift towards performance-driven investment logic, moving away from reliance on asset injections and shell mergers [4][5] Group 3 - The innovative pharmaceutical sector is expected to replicate the rapid growth seen in the new energy vehicle market, with leading companies potentially increasing their market capitalization significantly [5][6] - The financial technology sector is also viewed positively, with many Hong Kong brokerage firms trading below a price-to-book ratio of 1, indicating potential for value reassessment as digital assets and cross-border payments gain traction [6][7] - The ETF rotation strategy employed by the fund manager emphasizes strong timing and position management, utilizing a five-dimensional timing model that incorporates macroeconomic, liquidity, sentiment, technical, and overseas indicators [6][7][8]

Core Viewpoint - The rapid development of ETFs in the A-share market has led to a significant increase in their scale and number, surpassing actively managed funds, indicating a growing preference for passive investment strategies among investors [4][5]. Group 1: ETF Growth and Market Dynamics - As of June 15, 2025, the total scale of stock ETFs (including off-market linked funds) reached 3.81 trillion yuan, with the number of ETFs totaling 2,031, exceeding the scale of actively managed funds at 2.84 trillion yuan [4][5]. - The A-share market exhibits significant industry and style differentiation, suggesting that merely holding a single ETF for the long term may not yield optimal investment experiences [4][6]. - The investment objective of ETFs is to closely track the net value performance of specific indices, making the choice of index crucial for investors seeking substantial returns [6][10]. Group 2: ETF Rotation Strategy Development - A common method for constructing ETF rotation strategies involves aggregating effective stock factors at the index level, allowing for index rotation effects [2][11]. - The use of the AGRU model based on daily K-line volume and price data has resulted in the identification of high-performing stock selection factors in the A-share market [12][16]. - Monthly rebalancing of the strategy yielded an average IC of 7.80%, with an annualized excess return of 4.92% and a maximum drawdown of -14.02% [31][39]. Group 3: Performance of Fixed Number ETF Rotation Strategies - Limiting the number of held ETFs to 5, 10, or 15 resulted in varying annualized excess returns: 12.34% for 5 ETFs, 8.75% for 10 ETFs, and 8.13% for 15 ETFs, with corresponding maximum drawdowns of -12.17%, -8.83%, and -8.66% respectively [59][65]. - The strategy consistently achieved positive excess returns annually, with a notable 8.74% excess return year-to-date [63][65]. Group 4: Factor Testing and Adjustments - The factor's performance was enhanced through the adjustment of the loss function, leading to improved multi-directional return performance [17][19]. - The AGRU factor demonstrated strong stock selection effects across various stock pools, with annualized excess returns of 21.97% for the CSI 300 pool and 11.46% for the CSI 500 pool [64][65]. Group 5: MMR Algorithm and Risk Diversification - The MMR (Maximum Marginal Relevance) algorithm was employed to reduce the correlation among selected investment targets, enhancing the stability of the strategy's performance [45][50]. - The strategy's annualized excess return improved from 7.94% to 8.43% after implementing the MMR adjustments, with a corresponding increase in the information ratio [50][52].

Group 1: ETF Rotation Strategy Tracking - The ETF rotation strategy, constructed using GBDT+NN machine learning factors, has shown excellent performance in out-of-sample testing, with an IC value of 20.91% and a long position excess return of 0.61% last week [2][12][13] - The strategy's annualized excess return is 11.91%, with a maximum drawdown of 17.31% and an information ratio of 0.68, indicating strong recent performance [2][18][16] - The strategy has recorded an excess return of 0.88% last week, 1.44% for the month, and 0.15% year-to-date, reflecting its recent success [2][18] Group 2: High-Frequency Factor Overview - Various high-frequency factors have demonstrated strong performance, with the price range factor achieving a long position excess return of 1.01% last week and 5.84% year-to-date [3][22] - The volume-price divergence factor has shown a long position excess return of 10.13% this year, while the regret avoidance factor has underperformed with a return of -0.30% [3][22] - The overall performance of high-frequency factors has been commendable, with the price range factor and volume-price divergence factor leading in returns [3][22] Group 3: High-Frequency Factor Performance Tracking - The price range factor measures the activity level of stocks within different price ranges, indicating investor expectations for future price movements, and has shown stable performance this year [4][25] - The volume-price divergence factor assesses the correlation between stock price and trading volume, with lower correlation suggesting higher future price increases, although its performance has been inconsistent in recent years [4][25] - The regret avoidance factor reflects investor behavior, showing stable excess returns, indicating that regret avoidance sentiment significantly impacts expected stock returns [4][25] Group 4: Combined Strategies Performance - The high-frequency "gold" combination strategy has an annualized excess return of 10.68% and a maximum drawdown of 6.04%, with recent excess returns of 0.14% last week and 5.98% year-to-date [5][54] - The high-frequency and fundamental resonance combination strategy has shown an annualized excess return of 14.98% and a maximum drawdown of 4.52%, with recent excess returns of 0.28% last week and 4.99% year-to-date [5][60]

Group 1: ETF Rotation Strategy Tracking - The ETF rotation strategy, constructed using GBDT+NN machine learning factors, has shown strong performance in out-of-sample testing, with an annualized excess return of 11.90% and a maximum drawdown of 17.31% [2][12][17] - Recent performance indicates a weekly excess return of 0.77% and a monthly excess return of 1.10%, while the year-to-date excess return stands at -0.19% [20][24] - The strategy's information ratio is 0.68, reflecting its effectiveness in generating excess returns relative to risk [24] Group 2: High-Frequency Factor Overview - High-frequency factors have demonstrated overall strong performance, with the price range factor yielding a year-to-date excess return of 4.79% and the price-volume divergence factor achieving 10.08% [3][20] - The regret avoidance factor has underperformed with a year-to-date excess return of -0.56%, while the slope convexity factor has shown a year-to-date excess return of -3.64% [3][20] - The high-frequency "gold" combination strategy has an annualized excess return of 10.69% and a maximum drawdown of 6.04% [5][60] Group 3: High-Frequency Factor Performance Tracking - The price range factor measures the activity level of stocks within different price ranges, showing strong predictive power and stable performance this year [4][28] - The price-volume divergence factor assesses the correlation between stock price and trading volume, with recent performance indicating a mixed stability [4][39] - The regret avoidance factor reflects investor behavior, showing stable out-of-sample excess returns, while the slope convexity factor illustrates the impact of order book elasticity on expected returns [4][51] Group 4: Combined Strategies Performance - The high-frequency and fundamental resonance combination strategy has an annualized excess return of 14.98% and a maximum drawdown of 4.52% [5][64] - Recent performance for this combined strategy includes a weekly excess return of 0.63% and a monthly excess return of 2.00%, with a year-to-date excess return of 4.69% [67]