Report Industry Investment Rating No relevant content provided. Core Viewpoints - Last month, most of the market's major index valuations were adjusted downward. In terms of price - to - earnings ratio, the historical percentile of the ChiNext Index and CSI 300 dropped to 4.5% and 44.4% respectively. In terms of price - to - book ratio, only the Sci - Tech Innovation 50's valuation percentile increased to 36.1%. Among the 31 Shenwan primary industries, only 4 industries rose [1]. - In April, 76 new funds were issued with a scale of 583.80 billion yuan. The issuance of active equity funds was cold, while the issuance share of passive equity funds increased slightly. Only commodity funds and pure - bond funds rose, with growth rates of 4.53% and 0.52% respectively. Growth style underperformed value style, and large - cap style was inferior to small - cap style. The position of active equity funds on April 30, 2025 was 81.17%, a decrease of 0.69pct from the previous month [2]. - In the ETF market, equity ETFs had the highest net inflow, reaching 18.2928 billion yuan. Most broad - based indexes had large net inflows, and some ETFs had significant gains or losses [3]. - In April, the risk - parity model dropped 0.09%, and the risk - budget model dropped 0.61% [4]. Summary by Directory 1. Last Month's Market Review 1.1 Domestic Market Situation - In April, the major indexes of the Shanghai and Shenzhen markets fluctuated and retreated. The ChiNext Index fell by more than 7%, and the Sci - Tech Innovation 50 had the smallest decline of 1.01%. Among the 31 Shenwan primary industries, only 4 industries rose, while the top 5 decliners were electrical equipment, communication, household appliances, computer, and electronics. In the bond market, the ChinaBond Composite Full - Price Index rose 0.95%, and the CSI Convertible Bond Index fell 1.31%. In the commodity market, the Nanhua Commodity Index fell 5.01% [12]. 1.2欧美及亚太市场情况 - In April, the European, American, and Asia - Pacific markets showed mixed performance. The S&P 500 fell 0.37%, the Dow Jones Industrial Average fell 3.22%, and the Nasdaq rose 0.85%. In the European market, the French CAC 40 fell 2.53%, and the German DAX rose 1.50%. In the Asia - Pacific market, the Hang Seng Index fell 4.33%, and the Nikkei 225 rose 1.20% [17]. 1.3 Market Valuation Situation - Last month, most of the market's major index valuations were adjusted downward. The historical percentile of the ChiNext Index and CSI 300's price - to - earnings ratio dropped by 11.5pct and 8.0pct respectively. Only the Sci - Tech Innovation 50's price - to - book ratio percentile increased by 0.2pct to 36.1%. The industries with the highest historical percentile of price - to - earnings ratio were real estate, steel, building materials, automobiles, and commercial trade, while those with the lowest were non - bank finance, agriculture, forestry, animal husbandry, and fishery, non - ferrous metals, light industry manufacturing, and electrical equipment [20]. 2. Overall Situation of Public Funds 2.1 Fund Issuance Situation - In April, 76 new funds were issued with a scale of 583.80 billion yuan. Among them, 6 active equity funds were issued with a scale of 14.76 billion yuan, and 53 index funds were issued with a scale of 282.19 billion yuan. The issuance of active equity funds was cold, while the issuance share of passive equity funds increased slightly [2][28]. 2.2 Fund Market Return Situation - In April, only commodity funds and pure - bond funds rose, with growth rates of 4.53% and 0.52% respectively. The pure - bond funds had the highest positive - return ratio of 98.30%. Growth style underperformed value style, and large - cap style was inferior to small - cap style. Generally, small - cap growth style was relatively resistant to decline, while large - cap growth style had the largest decline. Larger - scale funds in the equity market generally performed better [2][35]. 2.3 Active Equity Fund Position Situation - The position of active equity funds on April 30, 2025 was 81.17%, a decrease of 0.69pct from the previous month [40]. 3. ETF Fund Situation - Equity ETFs had the highest net inflow, reaching 18.2928 billion yuan. The average daily trading volume of the overall ETF market was 258.712 billion yuan, the average daily trading volume was 163.29 billion shares, and the average daily turnover rate was 9.72%. Most broad - based indexes had large net inflows. Some ETFs had significant gains or losses, and there were differences in capital inflows and outflows among different ETFs [3][44]. 4. Model Operation Situation - In April, the risk - parity model dropped 0.09%, and the risk - budget model dropped 0.61%. Since 2015, the risk - parity model has had an annualized return of 4.46% and a maximum drawdown of 2.31%; the risk - budget model has had an annualized return of 3.99% and a maximum drawdown of 9.80%. Next month, the asset - allocation weights of the models remain unchanged [4][55].

Group 1 - The report emphasizes the implementation of a multi-asset multi-strategy allocation using ETFs, which includes allocations to Chinese equities, QDII (overseas assets), Chinese bonds, and commodities, aiming for an "all-weather" strategy through risk parity methods [4][5][6] - The performance of the all-weather strategy from 2021 to the present shows an annualized return of 9.44%, significantly outperforming the Shanghai and Shenzhen 300 Index, which recorded a return of -7.89% during the same period [6][57] - The report highlights the effectiveness of three sub-strategies: style rotation, sector rotation, and size rotation, all of which consistently outperform the Shanghai and Shenzhen 300 Index [6][60] Group 2 - The report details the systematic quantitative strategies and data systems employed, which include macroeconomic analysis and the use of various market indicators to enhance decision-making and risk management [12][25] - The report outlines the performance metrics of various strategies, including annualized returns, volatility, and maximum drawdown, showcasing the effectiveness of the strategies in different market conditions [19][21][50] - The report discusses the importance of liquidity in ETFs, noting that high liquidity is essential for efficient tracking of underlying indices, with examples of significant growth in specific ETFs [33][34] Group 3 - The report provides an in-depth analysis of the sub-strategies, including a systematic quantitative timing model for gold, which focuses on demand factors and investment behaviors that influence gold prices [38][39] - The report also covers a systematic quantitative timing model for Hong Kong stocks, which has achieved an annualized return of 8.28%, outperforming the Hang Seng Index [42][44] - The report discusses the performance of the dividend and growth rotation model, which has shown a high success rate in its trading signals and an annualized return of 12.27% since 2024 [47][48]

2020 年春，李迅雷给唐军派发了一个艰巨的课题：预测疫情拐点。 作为非流行病学专业人员，时任中泰证券金工首席的唐军有些困惑，这与投资有什么直接联系？但又很巧合， 大学期间的 唐军参加数学建模比赛 前 曾拿 2003 年非典练手，传染病传播模型 还有印象 。 唐军根据 封锁前 武汉迁出 到各省的 人数和 之后 各省上报 的确诊 人数，反推 当时 武汉感染人群数量，再根据疫情起始时间反推传播系数， 这一数据处理 上产生的效果远超模型精度本身。 最终经过测算，唐军预测对了感染高峰到来的时间。 有了经验后，美国疫情开始扩散，李迅雷又派发了一个艰巨的课题：美国能不能控住疫情？ 这一次，唐军意识到自己研究的课题，并不是与投资风马牛不相及。唐军知道人员流动性降到平时 30% 才能稳住疫情不扩散，「美国根本降不到，一定会 严重挤兑，即使医疗资源翻一倍也会挤兑。」 随后，唐军提示了做空原油，那年美油期货价格首次跌到负数。 这两次研究成果收录在李迅雷公众号，至今还能搜索到。三年后，唐军担任中泰资管组合投资部首席投资经理 ， 并开始管理公募 FOF 产品 ，李迅雷的指 导和这些与投资并不直接关联的研究，拓宽了他宏观的视野，潜移默 ...

Quantitative Models and Construction Methods 1. Model Name: Basic Risk Parity Model - **Model Construction Idea**: The model ensures that each asset in the portfolio contributes equally to the overall portfolio risk[20][23] - **Model Construction Process**: - Let the return vector of assets at time T be **r** and the weight vector be **w** - Covariance between assets is denoted as **Σ**, and the portfolio's return and volatility are: $$ \sigma(w) = \sqrt{w^T \Sigma w} $$ - Marginal Risk Contribution (MRC) and Risk Contribution (RC) for asset i are: $$ MRC_i = \frac{\partial \sigma(w)}{\partial w_i} = \frac{(\Sigma w)_i}{\sqrt{w^T \Sigma w}} $$ $$ RC_i = w_i \cdot MRC_i = w_i \cdot \frac{(\Sigma w)_i}{\sqrt{w^T \Sigma w}} $$ - Total Risk Contribution (TRC) is: $$ TRC = \sum RC_i = \sqrt{w^T \Sigma w} $$ - Risk parity requires: $$ RC_i = RC_j \; \text{for all} \; i, j $$[23][24][25] - **Model Evaluation**: The model is effective in balancing risk contributions but may lead to conservative portfolios when asset volatilities differ significantly[5][20] 2. Model Name: Risk Budgeting Risk Parity - **Model Construction Idea**: Adjusts the risk budget to allocate higher weights to riskier assets, making the model more flexible for different risk preferences[5][33] - **Model Construction Process**: - Adjust the relative marginal contribution of assets to the benchmark: $$ RC_i : RC = k_i \; \text{for all} \; i $$ - When assets are uncorrelated, the allocation becomes: $$ RC_i = \frac{k_i w_i^2 \sigma_i^2}{\sum k_i} $$ - Risk budget and actual weights are related quadratically: $$ \text{If actual weight is } n \times \text{basic weight, then risk budget is } n^2 $$ - Static and dynamic risk budgeting rules: - Static: Fixed risk budgets for equities, commodities, and gold - Dynamic: Adjust risk budgets based on Sharpe ratios over the past 6 months[37][39][41] - **Model Evaluation**: Provides higher returns but increases risk. Dynamic budgeting improves returns further but has mixed effects on risk metrics[41] 3. Model Name: Macro Risk Parity Model - **Model Construction Idea**: Allocates risk based on shared macroeconomic factors rather than individual asset risks, addressing overlapping risk contributions among assets[10][64] - **Model Construction Process**: - General asset pricing model: $$ r = 1^T \times I \times f_{base} + B \times I \times F + \varepsilon $$ - **I**: Dummy variable matrix indicating asset categories - **f_base**: Benchmark returns for major asset classes - **F**: Factor returns explaining intra-class differences - **B**: Factor exposures (sensitivity of assets to factors) - **ε**: Residual returns not explained by factors[64][66][68] - Systematic and idiosyncratic risk contributions: $$ RCF_i = w_{new,i} \cdot \frac{(\Sigma_f w_{new})_i}{\sqrt{w^T \Sigma w}} $$ $$ RCE_i = w_{new,i} \cdot \frac{(E w_{new})_i}{\sqrt{w^T \Sigma w}} = \frac{w_{new,i}^2}{\sqrt{w^T \Sigma w}} $$[74][75] - **Model Evaluation**: Effective in reducing leverage and addressing overlapping risks but requires precise macro risk modeling[12][115] --- Model Backtest Results 1. Basic Risk Parity Model - **Annualized Return**: 5.03% - **Maximum Drawdown**: -5.10% - **Volatility**: 2.58% - **Sharpe Ratio**: 1.90 - **Monthly Win Rate**: 71.11% - **Monthly Profit-Loss Ratio**: 3.44[28] 2. Risk Budgeting Risk Parity - **Static Risk Budgeting**: - **Annualized Return**: 5.80% - **Maximum Drawdown**: -9.30% - **Volatility**: 5.80% - **Sharpe Ratio**: 0.97 - **Monthly Win Rate**: 58.89% - **Monthly Profit-Loss Ratio**: 2.05 - **Dynamic Risk Budgeting**: - **Annualized Return**: 6.98% - **Maximum Drawdown**: -12.38% - **Volatility**: 6.29% - **Sharpe Ratio**: 1.07 - **Monthly Win Rate**: 63.33% - **Monthly Profit-Loss Ratio**: 2.30[46] 3. Macro Risk Parity Model - **Basic Asset Classes**: - **Annualized Return**: 5.03% - **Maximum Drawdown**: -5.10% - **Volatility**: 2.58% - **Sharpe Ratio**: 1.90 - **Monthly Win Rate**: 71.11% - **Monthly Profit-Loss Ratio**: 3.44 - **Expanded Sub-Asset Classes**: - **Annualized Return**: 7.35% - **Maximum Drawdown**: -11.49% - **Volatility**: 6.63% - **Sharpe Ratio**: 1.07 - **Monthly Win Rate**: 63.33% - **Monthly Profit-Loss Ratio**: 2.29[89] 4. Refined Asset Pool - **Asset Risk Parity**: - **Annualized Return**: 6.63% - **Maximum Drawdown**: -2.84% - **Volatility**: 2.83% - **Sharpe Ratio**: 2.27 - **Monthly Win Rate**: 75.51% - **Monthly Profit-Loss Ratio**: 5.99 - **Macro Risk Parity**: - **Annualized Return**: 8.03% - **Maximum Drawdown**: -3.59% - **Volatility**: 3.79% - **Sharpe Ratio**: 2.04 - **Monthly Win Rate**: 72.45% - **Monthly Profit-Loss Ratio**: 4.32[110]

Core Viewpoint - The report from CITIC Securities indicates a weak performance in A-shares in March, with a divergence in Hong Kong stocks, a decline in US stocks, a rise in gold, and a pullback in the bond market. It suggests that the current economic environment is characterized by a Kondratiev wave downturn, impacting various asset classes [1][2]. Global Macro Outlook - The report predicts that the peak year-on-year GDP for the US will be in Q1 2025, for Japan in Q2 2025, and for the Eurozone also in Q2 2025. It anticipates a temporary improvement in the yen's performance against the dollar and a strengthening of the euro against the dollar in the future. Additionally, it forecasts that gold priced in dollars will continue to strengthen [1][3]. Asset Price and Fundamental Outlook - According to analyst expectations, the forecasted ROE for the entire A-share market and non-financial A-shares in Q1 2025 is 7.38% and 6.42%, respectively, with slight adjustments from the previous month. The intrinsic value estimate for the CSI All A Index in Q2 2025 is projected to be 5,343 points. The report also notes that the ten-year Chinese government bond yield is deviating from historical cyclical patterns [3]. Industry and Style Rotation - The report identifies high economic sentiment in industries such as agriculture, non-ferrous metals, telecommunications, transportation, and non-bank financials. Currently, institutional focus is on non-bank financials and transportation, while interest in light manufacturing, automotive, consumer services, and comprehensive industries has decreased. Recent increases in institutional attention have been noted in the "petroleum and petrochemicals," "non-ferrous metals," "steel," "consumer services," and "real estate" sectors. The machinery sector is approaching a crowded indicator threshold, while the machinery, automotive, and food and beverage sectors are in a sustained crowded state [4].

Core Insights - The report emphasizes the integration of AI in optimizing risk parity strategies, enhancing both annualized returns and Sharpe ratios across various asset classes [5][6][10] - DeepSeek's approach involves adjusting risk contributions, dynamically modifying lookback periods, and optimizing ETF selections to improve portfolio management efficiency and risk control [5][6][10] Group 1: AI Empowered Risk Parity - DeepSeek combines macroeconomic data, capital market indicators, and analyst opinions to optimize asset risk contributions, enhancing the potential returns of portfolios [5] - The annualized return of domestic stock-bond-commodity portfolios improved from 3.85% to 4.2% with a Sharpe ratio increase to 1.137 through risk contribution adjustments [6] - For overseas portfolios, the annualized return rose from 8.11% to 14.15%, with the Sharpe ratio increasing from 0.590 to 1.018 [6] Group 2: Dynamic Lookback Period Adjustments - DeepSeek dynamically adjusts the lookback period based on market cycles, optimizing the risk parity strategy's time window using AI to learn from historical data [12][14] - The report highlights that traditional fixed lookback periods may not adapt well to market changes, while AI can provide a more responsive approach [12][14] - The adjustments led to a significant increase in the annualized return from 3.85% to 4.46% and improved the Sharpe ratio from 1.059 to 1.137 [73] Group 3: ETF Selection Optimization - DeepSeek utilizes traditional indicators and forward-looking market judgments to optimize ETF selections, resulting in an annualized return of 7.18% compared to 6.75% for non-AI selected ETFs [6][10] - The AI-driven selection process considers tracking errors, premium rates, and market volatility to enhance investment outcomes [93][94] - The report outlines a systematic approach to selecting ETFs that minimizes risks associated with market speculation and currency fluctuations [16][93] Group 4: Global Risk Parity Strategy - The report discusses a three-dimensional structure for global risk parity, balancing domestic and overseas assets to achieve risk contribution equilibrium [8][9] - It emphasizes the importance of optimizing asset weights based on volatility and covariance calculations to ensure balanced risk contributions across different asset classes [8][9] - The strategy aims to achieve a stable performance across various market conditions by ensuring that different assets contribute equally to overall risk [8][9] Group 5: Future Outlook and Conclusion - The report concludes that AI's integration into risk parity strategies represents a significant advancement, allowing for more precise adjustments and improved performance metrics [38][41] - It suggests that the ongoing evolution of AI applications in finance will continue to enhance investment strategies and risk management practices [38][41] - The findings indicate a strong potential for AI-driven models to outperform traditional risk parity approaches, highlighting the need for continuous adaptation to market dynamics [38][41]

Core Viewpoint - The article emphasizes the importance of a diversified asset allocation strategy, highlighting the performance of various asset classes, particularly gold and equities, in the current market environment [2][3][4]. Group 1: Asset Performance - COMEX gold ranked second in 2024 with a return of 27.65%, closely trailing the Nasdaq index [2]. - The asset allocation strategy of fund manager Tang Jun has evolved, with gold being a significant holding, peaking at nearly 18% mid-2024, but later decreasing to 13.71% by the end of Q4 2024 [3][4]. Group 2: Investment Philosophy - Tang Jun's investment approach is characterized by a "top-down" strategy that emphasizes low correlation among underlying assets, distinguishing it from traditional FOF managers [8][10]. - He focuses on the concept of "return streams," which involves optimizing risk and enhancing portfolio performance by reducing correlation among assets [10][65]. Group 3: Macro and Micro Analysis - The macroeconomic factors influencing asset allocation include monetary policy and credit expansion, which have become more significant than traditional economic cycles [44][46]. - Tang Jun's framework for asset allocation is structured into three levels: macroeconomic drivers, expectation differences, and low-correlation return streams [106]. Group 4: Tactical Adjustments - Tactical adjustments in asset allocation are made based on market conditions, with a focus on maintaining a disciplined approach to risk management [51][88]. - The strategy includes a dynamic adjustment of asset weights based on macroeconomic indicators and market sentiment, such as the performance of small-cap stocks and the behavior of retail investors [37][39]. Group 5: Future Outlook - The outlook for 2025 suggests that domestic asset allocation will depend on credit expansion, while international considerations will focus on U.S. fiscal policies and their impact on inflation and risk assets [101][103].