Fixed Income Research - The net financing amount is at a historically high level, indicating that the logic of asset scarcity has dissipated. The overall change in the issuance guidance rates published by the trading association has mostly decreased by 5 to 1 basis points. In January, the issuance scale of credit bonds increased month-on-month, with only medium-term notes seeing a decrease in issuance amount, while other varieties saw increases. The net financing amount for credit bonds increased month-on-month, with medium-term notes showing a decrease, while other varieties saw increases. Corporate bonds, directional tools had negative net financing, while corporate bonds, medium-term notes, and short-term financing bonds had positive net financing [2][3]. - In the secondary market, the transaction scale of credit bonds decreased month-on-month, with transaction amounts for all varieties declining. The yield on credit bonds remained low and fluctuated, with most varieties showing a month-on-month decline in average yield. The credit spread for most varieties narrowed month-on-month, with the varieties that widened mainly concentrated in the 7-year term. Most varieties' spreads are at historical lows. From an absolute return perspective, insufficient supply and relatively strong allocation demand will continue to drive the recovery of credit bonds. Although fluctuations are inevitable due to various factors, the conditions for a comprehensive bear market in credit bonds remain insufficient. In the long run, future yields are still in a downward channel, and the strategy of increasing allocation during adjustments remains feasible [3]. Fund Research - In January, the market for actively managed equity funds saw a significant increase in issuance, with a total of 88 new funds issued, amounting to 91.48 billion yuan. The issuance of actively managed equity funds and passive equity funds was 41.70 billion units and 19.62 billion units, respectively, with a significant increase in the issuance of actively managed equity funds. Overall, the issuance market for equity funds has warmed up significantly, especially for actively managed equity funds [6][7]. - The performance of equity markets was outstanding in January, with all types of funds showing varying degrees of increase. The average increase for commodity funds was the largest at 17.92%. The growth style outperformed the value style, and the mid-cap balanced style had the largest increase at 8.99%, while the large-cap value style had the smallest increase at approximately 4.22% [8]. Industry Research - The valuation repair of the real estate chain can continue, with positive signals from the government regarding real estate policies. The market is transitioning from a large-scale expansion phase to a focus on quality improvement. The goal is to actively construct a new development model for real estate, emphasizing both short-term and long-term strategies. The sales recovery process will significantly impact bond valuations, and investors with a higher risk appetite may consider early positioning, especially in companies showing strong performance in new financing and sales recovery [4][10]. - In the paper industry, several leading companies have announced price increases for white cardboard and corrugated paper, with expected price hikes of 200 yuan/ton for white cardboard and 30-50 yuan/ton for corrugated paper. The upcoming annual maintenance period for paper companies will disrupt supply, while the approaching Spring Festival will boost packaging demand from e-commerce, food, and beverage sectors, supporting price increases [12]. - In the metals industry, the steel sector is expected to continue a weak performance due to the Spring Festival holiday, with production and demand both shrinking. The copper market is also anticipated to see inventory accumulation due to reduced production activities during the holiday, with a focus on post-holiday demand verification [13][15].

1. Report Industry Investment Rating - Not mentioned in the report 2. Core Viewpoints of the Report - In January, all major indices in the Shanghai and Shenzhen markets rose. The CSI 500 and ChiNext 50 led the gains, both rising by over 12%, while the SSE 50 had the smallest increase of 1.17%. Among the 31 Shenwan primary industries, 26 industries rose, with the top 5 gainers being non - ferrous metals, media, petroleum and petrochemicals, building materials, and chemicals. The declining industries were banking, household appliances, non - bank finance, transportation, and agriculture, forestry, animal husbandry, and fishery [1][14]. - In December 2025, the total number of new individual investor accounts in the market reached 2.5861 million, and the number of new institutional investor accounts was 11,100. The private securities investment fund market continued to heat up, with the newly registered scale in December increasing month - on - month to 54.174 billion yuan, and the existing scale reaching 22.15 trillion yuan [2][21][23]. - In January, 88 new funds were issued, with a total issuance scale of 91.481 billion yuan. The issuance shares of active equity funds and passive equity funds were 41.704 billion and 19.62 billion respectively, and the issuance shares of active equity funds increased significantly month - on - month. All types of funds rose to varying degrees, with commodity - type funds having the largest average increase of 17.92% [3][38]. - In January, the active equity funds increased their positions in the petroleum and petrochemical, non - ferrous metals, and basic chemical industries, and reduced their positions in the national defense and military industry, pharmaceutical biology, and computer industries. The overall position of active equity funds on January 30, 2025, was 73.88%, an increase of 0.78 percentage points from the previous month [4][54][58]. - In January, the ETF market had a net capital outflow of 841.187 billion yuan. Many ETFs related to the CSI 300 and SSE 50 indices in the broad - based index suffered significant capital outflows. Among the actively traded individual securities, gold stock ETFs, China - South Korea semiconductor ETFs, mining ETFs, industrial non - ferrous metal ETFs, and ChiNext chip design ETFs led the gains, rising by 22.5% - 39.6%, while bank ETFs, E Fund's Hong Kong Stock Connect medical ETF, automobile ETFs, Peng Hua's general aviation ETF, and Huaxia's financial real - estate ETF led the losses, falling by 4.8% - 6.4% [5][61][62]. - In January, the risk - parity model rose by 2.07%, and the risk - budget model rose by 2.39% [6][73]. 3. Summary According to Relevant Catalogs 3.1 Domestic Market Situation - In January, all major stock indices in the Shanghai and Shenzhen markets rose. The CSI 500 and ChiNext 50 led the gains, both rising by over 12%, and the SSE 50 had the smallest increase of 1.17%. Among the 31 Shenwan primary industries, 26 industries rose, and 5 industries fell. In the bond market, the ChinaBond Composite Total Return Index rose by 0.22%, and the ChinaBond Treasury, financial bond, and credit bond total return indices fluctuated between a decline of 0.09% and an increase of 0.24%. The CSI Convertible Bond Index rose by 5.82%. In the commodity market, the Nanhua Commodity Index rose by 8.61% [1][14]. - In December 2025, the total number of new individual investor accounts in the market reached 2.5861 million, and the number of new institutional investor accounts was 11,100. The private securities investment fund market continued to heat up, with the newly registered scale in December increasing month - on - month to 54.174 billion yuan, and the existing scale reaching 22.15 trillion yuan [2][21][23]. 3.2 European, American, and Asia - Pacific Market Situation - In January, most major indices in the European, American, and Asia - Pacific markets rose. In the US stock market, the S&P 500 rose by 0.29%, the Dow Jones Industrial Average rose by 1.76%, and the Nasdaq Composite rose by 0.95%. In the European market, the French CAC40 fell by 0.28%, and the German DAX rose by 0.20%. In the Asia - Pacific market, the Hang Seng Index rose by 6.85%, and the Nikkei 225 rose by 5.93% [26]. 3.3 Market Valuation Situation - In January, the valuations of all major market indices rose. In terms of the historical quantile of price - to - earnings ratio, the CSI All - Share Index led the increase, rising by 8.5 percentage points. In terms of the historical quantile of price - to - book ratio, the CSI 1000 led the increase, rising by 15.3 percentage points. Among the industries, the top five industries with the highest historical quantile of price - to - earnings ratio of the Shenwan primary index last month were real estate, electronics, chemicals, commercial trade, and comprehensive. The price - to - earnings ratio quantile of the real estate industry was at a high level, and that of the electronics industry reached 96.2%. The bottom five industries with the lowest historical quantile of price - to - earnings ratio were non - bank finance, agriculture, forestry, animal husbandry, and fishery, food and beverages, beauty care, and pharmaceutical biology. The valuation of the non - bank finance industry was close to its historical low since 2013 [31]. 3.4 Overall Situation of Public - Offering Funds 3.4.1 Fund Issuance Situation - In January, 88 new funds were issued, with a total issuance scale of 91.481 billion yuan. Among them, 36 stock - type funds were issued with a scale of 19.669 billion yuan; 36 hybrid funds were issued with a scale of 46.646 billion yuan; 4 bond - type funds were issued with a scale of 49.46 billion yuan; 10 FOF funds were issued with a scale of 18.713 billion yuan; and 2 QDII funds were issued with a scale of 15.07 billion yuan. The issuance shares of active equity funds and passive equity funds were 41.704 billion and 19.62 billion respectively, and the issuance shares of active equity funds increased significantly month - on - month [38]. 3.4.2 Fund Market Return Situation - In January, the equity market performed outstandingly, and all types of funds rose to varying degrees. Commodity - type funds had the largest average increase of 17.92%. From the perspective of fund style indices, in January, the market showed a general upward trend, and the market performance of different - style funds was differentiated. The growth style outperformed the value style, and the small - and medium - cap style outperformed the large - cap style. Overall, the mid - cap balanced style had the largest increase of 8.99%, and the large - cap value style had the smallest increase of about 4.22%. Among different - scale equity - biased public - offering funds, the mini - funds with a scale of 50 million - 100 million had the largest average increase of 7.27% and a positive return ratio of 95.25%, while the super - large funds with a scale of over 10 billion had the smallest average increase of 4.79% and a positive return ratio of 87.10% [3][42][51]. 3.4.3 Active Equity Fund Position Situation - In January, active equity funds increased their positions in the petroleum and petrochemical, non - ferrous metals, and basic chemical industries, and reduced their positions in the national defense and military industry, pharmaceutical biology, and computer industries. The overall position of active equity funds on January 30, 2025, was 73.88%, an increase of 0.78 percentage points from the previous month [4][54][58]. 3.5 ETF Fund Situation - In January, the ETF market had a net capital outflow of 841.187 billion yuan. Among them, stock - type ETFs had a net outflow of 793.799 billion yuan, cross - border ETFs had a net inflow of 30.917 billion yuan, and bond - type ETFs had a net outflow of 106.172 billion yuan. In terms of liquidity, the average daily trading volume of the overall ETF market in this period reached 604.575 billion yuan, the average daily trading volume reached 226.327 billion shares, and the average daily turnover rate was 9.17%, an increase of 2.02 percentage points from December of the previous year. Many ETFs related to the CSI 300 and SSE 50 indices in the broad - based index suffered significant capital outflows. Among the actively traded individual securities, gold stock ETFs, China - South Korea semiconductor ETFs, mining ETFs, industrial non - ferrous metal ETFs, and ChiNext chip design ETFs led the gains, rising by 22.5% - 39.6%, while bank ETFs, E Fund's Hong Kong Stock Connect medical ETF, automobile ETFs, Peng Hua's general aviation ETF, and Huaxia's financial real - estate ETF led the losses, falling by 4.8% - 6.4%. The ETFs with the largest net capital inflows were non - ferrous metal ETFs, gold ETFs, chemical ETFs, power grid equipment ETFs, and semiconductor equipment ETFs, while the ETFs with the largest net capital outflows were Huatai - Peregrine CSI 300 ETF, E Fund CSI 300 ETF, Huaxia CSI 300 ETF, SSE 50 ETF, and Harvest CSI 300 ETF [5][61][62]. 3.6 Model Operation Situation - Four types of large - scale asset allocation models were constructed using stocks, bonds, commodities, and QDII. In January, the risk - parity model rose by 2.07%, and the risk - budget model rose by 2.39%. Since 2015, the annualized return of the risk - parity model has been 4.94%, with a maximum drawdown of 2.31%, and the annualized return of the risk - budget model has been 5.13%, with a maximum drawdown of 9.80%. Next month, the asset allocation weights of the models remain unchanged. For the risk - parity model, stocks: bonds: commodities: QDII = 6%: 69%: 12%: 13%; for the risk - budget model, stocks: bonds: commodities: QDII = 14%: 49%: 8%: 29% [68][73][74].

Core Insights - The concept of "reflation" is central to understanding future investment opportunities, focusing on economic vitality and market confidence restoration [1][3] - Achieving reflation requires a collaborative effort between monetary and fiscal policies to revitalize economic activity and ensure long-term market health [3][19] Reflation Mechanisms - Monetary policy plays a crucial role in creating a favorable liquidity environment, which supports investment and consumption, thereby driving nominal output recovery [5][22] - Fiscal policy is essential for repairing balance sheets and "debt resolution," with rising asset prices being key to this process. Innovative approaches like "equity finance" can guide funds to support quality listed companies, enhancing their value [6][23] Investment Priorities in A-shares - Priority One: Focus on "hard assets" that can benefit from price revaluation, as asset price recovery is a core feature in a reflation environment [8][19] - Priority Two: Utilize "quantitative thinking" for calm value discovery, employing multi-factor stock selection models to systematically assess companies and avoid emotional trading [9][24] - Priority Three: Build a "multi-strategy" portfolio to adapt to market evolution, as a single strategy may not suffice across all market phases [11][25][26] Long-term Investment Perspective - Investment should be viewed as a warm companion through economic cycles, requiring insight and patience to navigate the complexities of market fluctuations [13][27]

Quantitative Models and Construction CPPI Model - **Model Name**: CPPI (Constant Proportion Portfolio Insurance) [35] - **Construction Idea**: Dynamically adjust the allocation between risk assets and risk-free assets based on the gap between current portfolio value and the preset protection target [35] - **Construction Process**: - Calculate the protection target at time t: $ F_{t}=G\times e^{-r(T-t)} $ where $ G $ is the protection amount at the end of the protection period, $ r $ is the risk-free rate, and $ T-t $ is the remaining time [35] - Determine the amount of funds available for risk assets: $ C_{t}=V_{t}-F_{t} $ where $ V_{t} $ is the portfolio value at time t [36] - Adjust risk asset allocation using a risk multiplier $ m $ and an upper limit $ b $: $ \mathrm{E}_{t}=m i n\{m C_{t},b V_{t}\} $ $ \mathrm{E}_{t}=m a x\{m i n\{m C_{t},b V_{t}\},0\} $ $ B_{t}=V_{t}-E_{t} $ where $ E_{t} $ is the amount allocated to risk assets, and $ B_{t} $ is the amount allocated to risk-free assets [37][38] - Monthly rebalancing based on the last trading day’s closing price [39] - **Evaluation**: CPPI effectively reduces asset volatility and drawdowns but may slightly lower annualized returns due to increased allocation to risk-free assets [45] - **Parameters**: - Protection ratio $ \lambda $: [60%, 70%, 80%] - Risk multiplier $ m $: [2, 3] - Risk asset upper limit $ b $: [70%, 80%, 90%] - Risk-free asset annualized return: based on the previous year’s actual return of money market funds [52][43] Risk Budgeting Model - **Model Name**: Risk Budgeting Model [68] - **Construction Idea**: Allocate risk budgets to assets based on their risk characteristics (volatility, upside volatility, or momentum) [70] - **Construction Process**: - Optimize the risk budget allocation using the SLSQP algorithm: $ O b j e c t i v e\,f u n c t i o n=\sum_{i=1}^{n}(R C_{i}-R B_{i})^{2} $ where $ R C_{i} $ is the actual risk contribution of asset $ i $, and $ R B_{i} $ is the risk budget proportion [68] - Three allocation methods: - Volatility ranking: Higher volatility assets receive higher risk budgets - Upside volatility ranking: Higher upside volatility assets receive higher risk budgets - Momentum ranking: Higher past returns receive higher risk budgets [70] - **Evaluation**: Volatility and upside volatility rankings provide higher elasticity but larger drawdowns, while momentum ranking offers more stable returns [77] Daily Net Value Monitoring Mechanism - **Model Name**: Daily Net Value Monitoring Mechanism [79] - **Construction Idea**: Monitor daily portfolio net value to mitigate short-term market shocks [79] - **Construction Process**: - Trigger pre-warning when rolling N-day maximum drawdown exceeds threshold $ \theta $ and net value falls below M-day moving average [80] - Exit pre-warning when net value crosses above M-day moving average [81] - Adjust portfolio to 95% bonds + 5% money market during pre-warning, and revert to risk budgeting weights after stabilization [79][80] - **Evaluation**: Effectively reduces drawdowns and improves risk-return ratios without significantly impacting returns [88] --- Model Backtesting Results CPPI Model - **Annualized Return**: 4.4% to 14.6% depending on asset type [46] - **Volatility**: Reduced by 7.7% to 11.4% compared to original assets [46] - **Maximum Drawdown**: Improved by 7.5% to 19.3% [46] Risk Budgeting Model - **Maximum Drawdown Constraint (3%)**: - Best combination: Annualized return 6.82%, maximum drawdown -2.91%, Sharpe ratio 2.207, Calmar ratio 2.344 [95][96] - **Maximum Drawdown Constraint (5%)**: - Best combination: Annualized return 7.66%, maximum drawdown -4.97%, Sharpe ratio 2.010, Calmar ratio 1.541 [106][108] - **No Maximum Drawdown Constraint**: - Best combination: Annualized return 8.15%, maximum drawdown -6.36%, Sharpe ratio 1.622, Calmar ratio 1.281 [120][121] Daily Net Value Monitoring Mechanism - **Impact on Risk Budgeting Models**: - Improves Calmar ratio by up to 1.101 for 3% drawdown constraint [88] - Reduces pre-warning frequency to less than 6 times/year [94] --- Supplementary Testing Sensitivity Analysis - **3% Drawdown Constraint**: Parameter adjustments have minimal impact on annualized returns; all combinations maintain Calmar > 1 and Sharpe > 1.5 [133][134] - **5% Drawdown Constraint**: Parameter adjustments have minimal impact on annualized returns; all combinations maintain Calmar > 0.8 and Sharpe > 1.5 [135][136] - **No Drawdown Constraint**: Most combinations maintain Calmar > 1 and Sharpe > 1.4, indicating low risk of overfitting [137][138] Validation of CPPI + Daily Monitoring - **Comparison with Original Assets**: - Original assets fail to meet 3% drawdown constraint - CPPI + Daily Monitoring significantly improves Calmar ratio compared to original risk budgeting models [140]

Core Viewpoint - The article discusses the structural changes in the commodity market and the performance of CTA (Commodity Trading Advisor) strategies, highlighting the challenges and opportunities presented by recent market dynamics [4][8]. Group 1: Commodity Market Dynamics - The commodity market is undergoing significant structural changes, with extreme differentiation in performance among various sectors [4][6]. - The South China Gold Index surged by 18.21%, while the energy index fell by 14.57%, and the black sector dropped by 13.18%, indicating a divergence of over 30 percentage points between sectors [6]. - The volatility in commodities has shown a "pulse-like" characteristic, with a 200% spike in 20-day volatility due to tariff impacts, followed by a rapid decline to historical low levels [7]. Group 2: CTA Strategy Performance - Overall performance of CTA strategies has been lackluster this year, particularly before April, where they ranked at the bottom among various strategies [8][11]. - Following increased volatility in commodities, CTA strategies began to recover, climbing to the third position among strategies by July, although still lagging behind quantitative and subjective strategies [11]. - Recent improvements in the CTA environment have been noted, with strong performance observed in October amidst poor performance from other strategies [11]. Group 3: Macro and Multi-Asset Strategies - CTA strategies have evolved to incorporate macroeconomic data, allowing for a more comprehensive approach to market fluctuations [14]. - The macro strategy integrates five sub-strategies, including economic cycle strategies and risk warnings, to manage assets across different time horizons [14][15]. - A multi-asset strategy has been developed that diversifies across various asset classes, focusing on achieving higher Sharpe ratios through a combination of trend-following, term arbitrage, and cross-sectional strategies [20][22]. Group 4: Risk Management and Performance - The risk management framework for these strategies includes maintaining a margin usage of 10%-15% and controlling overall volatility to remain within 8% [18][17]. - The performance of the multi-asset strategy has shown positive contributions from all asset classes, with a distribution of 60% in equity indices, 30% in commodities, and 10% in government bonds [25].

Group 1: Fund Market Overview - In September, the market saw a total of 126 new funds issued, with a total issuance scale of 1,096.71 billion yuan, including 27 active equity funds with an issuance scale of 168.61 billion yuan and 76 index funds with an issuance scale of 807.51 billion yuan [3][4] - The performance of funds in September was generally positive, with all major fund types rising except for pure bond funds, which fell by 0.10%. Commodity funds had the highest increase, rising by 9.40% [3][4] - The average increase for large funds (over 10 billion yuan) was 7.43%, while small funds (1-10 billion yuan) had an average increase of 4.98% [4] Group 2: Financing and Margin Trading - As of September 30, the margin trading balance in the A-share market was 23,867.40 billion yuan, an increase of 1,327.62 billion yuan from the previous month [8] - The financing balance was 23,709.72 billion yuan, up by 1,328.72 billion yuan, while the securities lending balance decreased slightly to 157.68 billion yuan [8] - The electronic, power equipment, and communication sectors saw significant net buying in financing, while the defense, agriculture, and oil sectors had lower net buying [9] Group 3: Industry Insights - The price of packaging paper has been rising, with average prices for various types of paper increasing by 30 to 140 yuan per ton compared to late September [11] - The light manufacturing industry outperformed the CSI 300 index by 1.23 percentage points, while the textile and apparel industry outperformed by 2.12 percentage points during the period from October 9 to October 10 [11] - The report indicates that the recent increase in U.S. tariffs poses short-term risks, but the long-term competitiveness of Chinese manufacturing remains strong [12]

Report Industry Investment Rating No relevant content provided. Core Viewpoints - In June, all major market indices' valuations were adjusted upwards. In terms of price - to - earnings ratio and price - to - book ratio, the historical percentile increases of CSI 300 and CSI All - Share were among the top, while the ChiNext Index remained at a historical low. Among the 31 Shenwan primary industries, 23 industries rose, with the top 5 gainers being communication, national defense and military industry, non - ferrous metals, electronics, and media; the top 5 losers were food and beverage, beauty care, household appliances, coal, and transportation [1]. - In June, 70 new funds were issued, with a total issuance scale of 62.728 billion yuan. The issuance of active equity funds was booming, while the issuance of passive equity funds decreased slightly. Only commodity - type funds declined, with a 1.66% drop, and the largest gain was in equity - biased funds, up 2.68% [2]. - From the perspective of fund style indices, the growth style outperformed the value style, and the large - cap style underperformed the small - cap style. Overall, the mid - cap growth style performed outstandingly, rising 5.83%, while the large - cap value style had the smallest increase, about 2.52% [2]. - In the ETF market, last month, there was a net inflow of 59.605 billion yuan. Bond - type ETFs had a net inflow of over 90 billion yuan, and stock - type ETFs had a net outflow of 31.54 billion yuan [3]. - In June, the risk - parity model rose 1.59%, and the risk - budget model rose 2.34% [5]. Summary by Directory 1. Last Month's Market Review 1.1 Domestic Market Situation - In June, all major indices in the Shanghai and Shenzhen markets rose. The ChiNext Index rose by over 8%, and the Shenzhen Component Index and CSI 500 rose by over 4%. Among the 31 Shenwan primary industries, 23 industries rose. The top 5 gainers were communication, national defense and military industry, non - ferrous metals, electronics, and media; the top 5 losers were food and beverage, beauty care, household appliances, coal, and transportation. In the bond market, the ChinaBond Composite Full - Price Index rose 0.31%, and the total full - price indices of ChinaBond Treasury bonds, financial bonds, and credit bonds rose between 0.13% and 0.40%. The CSI Convertible Bond Index rose 3.34%. In the commodity market, the Nanhua Commodity Index rose 4.03% [13]. 1.2 European, American, and Asia - Pacific Market Situation - In June, most European, American, and Asia - Pacific markets rose. In the US stock market, the S&P 500 rose 4.89%, the Dow Jones Industrial Average rose 4.21%, and the Nasdaq rose 6.57%. In the European market, the French CAC 40 fell 1.11%, and the German DAX fell 0.37%. In the Asia - Pacific market, the Hang Seng Index rose 3.36%, and the Nikkei 225 rose 6.64% [21]. 1.3 Market Valuation Situation - In June, all major market indices' valuations were adjusted upwards. In terms of price - to - earnings ratio and price - to - book ratio, the historical percentile increases of CSI 300 and CSI All - Share were among the top, while the ChiNext Index remained at a historical low. Among industries, the top five industries with the highest historical percentiles of price - to - earnings ratio in the Shenwan primary index last month were real estate, banking, automotive, chemical, and electronics. The real estate industry's price - to - earnings ratio percentile reached 96.6%. The five industries with lower historical percentiles of price - to - earnings ratio were agriculture, forestry, animal husbandry and fishery, non - bank finance, food and beverage, light manufacturing, and household appliances, all with percentiles less than 25% [24]. 2. Overall Situation of Public Offering Funds 2.1 Fund Issuance Situation - In June, 70 new funds were issued, with a total issuance scale of 62.728 billion yuan. Among them, 33 stock - type funds were issued with a scale of 11.646 billion yuan; 14 hybrid funds were issued with a scale of 6.317 billion yuan; 14 bond - type funds were issued with a scale of 35.293 billion yuan; 4 FOF funds were issued with a scale of 7.5 billion yuan; 3 REITs funds were issued with a scale of 1.3 billion yuan; and 2 QDII funds were issued with a scale of 0.67 billion yuan. A total of 17 active equity funds were issued with a scale of 6.738 billion yuan, and 36 index funds were issued with a scale of 28.472 billion yuan. The issuance of active equity funds increased significantly, while that of passive equity funds decreased slightly [32]. 2.2 Fund Market Return Situation - In June, only commodity - type funds declined, with a 1.66% drop, and the largest gain was in equity - biased funds, up 2.68%, with a positive return ratio of 97.63%. From the perspective of fund style indices, the growth style outperformed the value style, and the large - cap style underperformed the small - cap style. The mid - cap growth style performed outstandingly, rising 5.83%, while the large - cap value style had the smallest increase, about 2.52%. Generally, smaller - scale funds in the equity market performed better. The large - scale funds with a scale of 4 - 10 billion had the largest average increase of 2.80%, with a positive return ratio of 97.52%, while the super - large - scale funds over 10 billion had the smallest increase of 2.16%, with a positive return ratio of 88.46% [2][40][43]. 2.3 Active Equity Fund Position Situation - Using Lasso regression to measure the positions of active equity funds, the position on June 30, 2025, was 75.44%, a decrease of 3.76 percentage points from the previous month [47]. 3. ETF Fund Situation - In the ETF market, last month, there was a net inflow of 59.605 billion yuan. Bond - type ETFs had a net inflow of over 90 billion yuan, and stock - type ETFs had a net outflow of 31.54 billion yuan, with funds flowing from broad - based indices such as CSI 300 to bond funds. In terms of liquidity, the average daily trading volume of the overall ETF market this period reached 265.76 billion yuan, the average daily trading volume reached 126.808 billion shares, and the average daily turnover rate reached 8.59%. At the individual bond level, most broad - based index targets had net outflows except for the CSI A500 Index. Huatai - PineBridge CSI 300 ETF had a net outflow of 5.45 billion yuan, while Huatai - PineBridge CSI A500 ETF had a net inflow of 13.54 billion yuan. Among the most actively traded targets, Financial Technology ETF, Hong Kong Securities ETF, Communication Equipment ETF, ChiNext Artificial Intelligence ETF Huabao, and 5G ETF had the highest monthly gains, rising between 15.7% and 18.8%. Food and Beverage ETF, Consumption 30 ETF, Wine ETF, Leading Home Appliance ETF, and Southeast Asia Technology ETF had the highest monthly losses, falling between 1.6% and 4.4%. In terms of fund flow, the top funds with net inflows also included Hong Kong Stock Connect Innovation Pharmaceutical ETF, Bank ETF, A500ETF Harvest, and Hong Kong Non - Bank ETF; the top funds with net outflows also included CSI 300ETF E Fund, ChiNext ETF, Harvest CSI 300ETF, and CSI A500ETF Fullgoal [3][51][52]. 4. Model Operation Situation - Four types of large - asset allocation models were constructed using stocks, bonds, commodities, and QDII. In June, the risk - parity model rose 1.59%, and the risk - budget model rose 2.34%. Since 2015, the annualized return of the risk - parity model has been 4.64%, with a maximum drawdown of 2.31%; the annualized return of the risk - budget model has been 4.45%, with a maximum drawdown of 9.80%. Next month, the asset allocation weights of the models remain unchanged. For the risk - parity model, the ratio of stocks: bonds: commodities: QDII is 6%: 70%: 12%: 11%; for the risk - budget model, it is 13%: 52%: 9%: 25% [62][63][65].

Group 1 - The report indicates that in the last month, equities, commodities, and bonds all experienced increases, with equities and commodities rising by 2.50% and 4.03% respectively, while gold decreased by 0.57% [2][10] - The performance of ETFs used in the allocation strategy showed that the CSI 300 ETF, non-ferrous ETF, and energy chemical ETF increased by 2.85%, 3.08%, and 4.37% respectively, while the gold ETF saw a significant decline of 0.75% [2][13] Group 2 - The backtested strategy from January 1, 2014, to the end of last month achieved an annualized return of 7.71%, with an annualized volatility of 3.53% and a maximum drawdown of 3.17%. The Sharpe ratio and Calmar ratio were 2.19 and 2.44 respectively, outperforming risk parity and equal-weighted strategies [3][25] - The strategy without currency assets yielded a return of 0.48% last month, which was lower than both the risk parity strategy and the equal-weighted strategy [3][28] Group 3 - The latest allocation recommendations suggest increasing exposure to equities and commodities, while maintaining a neutral position on bonds and gold. The final weights for equities, government bonds, commodities, and gold are set at 7.01%, 75.01%, 10.90%, and 7.08% respectively [4][32]

Group 1 - The strategy for low to medium risk asset allocation includes risk parity and risk budgeting models, where risk parity allocates equal risk weights across different assets, while risk budgeting allows investors to set asset risk weights based on their risk preferences [1] - The correlation between major asset classes such as equities (A-shares, Hong Kong stocks, US stocks), bonds, and commodities (precious metals, energy, chemicals) is relatively low, making it suitable to construct portfolios using corresponding ETFs [1] - The long-term correlation between bonds and equities or commodities ranges from 0 to -30%, indicating a "stock-bond seesaw" effect due to the counter-cyclical nature of interest rates affecting bond yields, while equities and commodities reflect the health or expectations of the real economy [1] Group 2 - A simple construction method for the model involves selecting broad-based indices for equities such as CSI 300 ETF, CSI 500 ETF, ChiNext ETF, and National 2000 ETF, while the bond portion can include government bond ETFs, policy financial bond ETFs, and local government bond ETFs [2] - For the commodity portion, gold ETFs and commodity futures ETFs can be included, with advanced construction methods allowing for a core-satellite approach or sector rotation strategy for equities [2]

- The report includes a global asset allocation model based on artificial intelligence, which uses machine learning to score and rank various assets for monthly equal-weighted allocation strategy[30][31] - The global asset allocation model suggests weights for May: government bond index (66.09%), Nasdaq index (17.59%), German DAX index (13.83%), and Nikkei 225 (2.49%)[30] - Historical performance of the global asset allocation model from January 2021 to April 2025 shows an annualized return of 13.76%, Sharpe ratio of 0.75, maximum drawdown of 16.53%, and excess annualized return of 9.02%[30][36] - The dynamic macro event factor-based stock-bond rotation strategy includes three different risk preference models: conservative, balanced, and aggressive[37] - The stock-bond allocation models for April show stock weights of 45% for aggressive, 13.82% for balanced, and 0% for conservative[37][39] - Historical performance of the stock-bond allocation models from January 2005 to April 2025 shows annualized returns of 19.93% for aggressive, 11.00% for balanced, and 6.06% for conservative[37][44] - The dividend timing model uses economic growth and monetary liquidity indicators to construct a timing strategy for the dividend index, showing an annualized return of 15.84%, maximum drawdown of -21.70%, and Sharpe ratio of 0.89[45][49] - The dividend timing model's recommended position for April is 0%, with most economic growth indicators showing bearish signals and cautious monetary liquidity signals[45] Model Performance Metrics - Global asset allocation model: annualized return 13.76%, Sharpe ratio 0.75, maximum drawdown 16.53%[30][36] - Stock-bond allocation models: annualized returns 19.93% (aggressive), 11.00% (balanced), 6.06% (conservative)[37][44] - Dividend timing model: annualized return 15.84%, Sharpe ratio 0.89, maximum drawdown -21.70%[45][49]