Group 1 - The report emphasizes the increasing demand for robust allocation strategies that can navigate economic cycles and hedge against extreme risks amid heightened global macro uncertainty and geopolitical conflicts [1] - The core objective of the All Weather strategy is to achieve stable returns through diversified allocation of underlying assets and macro risks, utilizing a risk parity approach to balance risk contributions across different macro scenarios [1][6] - The performance metrics indicate that the strategy has achieved a monthly absolute return win rate of 73.88% and an annual win rate of 100% over the backtesting period, with a maximum drawdown of approximately -4.82% [3][35] Group 2 - The All Weather strategy aims to fully diversify risks and traverse macro cycles, originally proposed by Bridgewater Associates, which has gained recognition during market downturns [6][7] - The strategy employs a risk parity model to ensure equal risk contribution from various macro asset combinations, enhancing the robustness of the portfolio [10][11] - The report outlines the importance of optimizing models, asset selection, and adjusting for macro cycles as key elements influencing the performance of the All Weather strategy [12][14] Group 3 - The report details the selection of low-correlation, high-representative ETFs and indices as the foundation for the All Weather strategy, ensuring effective risk diversification [15][18] - Backtesting results show that the risk parity model effectively disperses portfolio risk, achieving an annualized return of 5.84% with a volatility of 4.46% over the period from 2015 to 2026 [22][25] - The All Weather ETF-FOF strategy has demonstrated a cumulative return of approximately 6.10% with a volatility of 2.97% and a Sharpe ratio of 1.54, indicating improved performance metrics compared to the risk parity model [33][35] Group 4 - The report suggests future optimization of the strategy through refining macro scenario classifications, expanding the range of underlying assets, and deepening alpha extraction in niche segments [56]

Core Viewpoint - The article discusses the recent volatility in gold and silver markets, highlighting significant price fluctuations and the impact on various investment strategies, particularly the all-weather strategy, which has faced challenges due to extreme market conditions [2][12]. Group 1: Market Volatility - Silver experienced a short squeeze, leading exchanges to raise margin requirements and limit positions, followed by a dramatic 30% drop in silver prices and the largest single-day decline in gold since 1983 [2]. - Many subjective CTA and macro private equity funds, including notable firms referred to as "China's Bridgewater," faced substantial drawdowns, with some products experiencing declines of over 20% in early February [2]. Group 2: Performance of Investment Strategies - The all-weather strategy, which typically includes low-correlation assets, suffered significant losses during this period, indicating a potential over-allocation to gold and silver [2]. - The article notes that the past year saw the all-weather strategy, quantitative long positions, and public technology beta tools as the most popular categories in the wealth market [3]. Group 3: Bridgewater's Influence - Bridgewater has become a benchmark for all-weather strategies, attracting high-net-worth individuals seeking alternatives to traditional private equity products [4]. - The popularity of all-weather strategies aligns with the market's demand for low-volatility products, but the recent gold and silver turmoil has shattered the idealized perception of these strategies [4][12]. Group 4: Challenges in the Domestic Market - Domestic all-weather strategies face limitations due to a lack of effective inflation-hedged bonds and the impact of policy on commodity liquidity, which can lead to significant market disruptions [12][13]. - The article emphasizes that the domestic macro hedge funds do not strictly adhere to the all-weather framework, often opting for a more flexible approach that does not rely solely on risk parity models [13][17]. Group 5: Future Outlook - The article suggests that as the macro environment becomes increasingly complex, more private equity firms are venturing into multi-asset and multi-strategy approaches to address the allocation anxieties of high-net-worth individuals [17][18]. - The anticipated influx of over 50 trillion yuan in maturing deposits may create new investment opportunities amidst global market volatility [16].

Core Viewpoint - The report aims to assist institutions targeting absolute returns in asset allocation by constructing a strategy that seeks stable investment returns with a target annualized return of no less than 6%, annualized volatility not exceeding 5%, maximum drawdown not exceeding 5%, and a return-to-drawdown ratio greater than 1 [3][6]. Asset Selection and Strategic Benchmark Construction - The report selects five major asset classes: A-share equities, Hong Kong equities, bonds, commodities, and overseas equities, ensuring that each has corresponding ETF products with significant scale in the public fund market [3][9]. - Two asset allocation models are constructed: "domestic stock and bond assets" and "global major asset" models, with monthly rebalancing and a backtesting period from January 1, 2017, to February 13, 2026 [3][13]. Strategic Layer - The strategic asset weight center is determined using risk parity and ES risk parity models, which aim to balance the risk contribution of each asset class [4][16]. - The historical performance of the strategic benchmark portfolio indicates that the selected domestic stock and bond assets are suitable for constructing an absolute return target portfolio [4][33]. Tactical Layer - A macro scoring model is developed to adjust asset weights based on ten high-frequency macro factors, allowing for tactical adjustments to asset weights on a monthly basis [4][35]. - The macro scoring model aims to capture short-term changes in economic conditions and enhance returns while controlling drawdowns [4][65]. Backtesting Results - Under the macro scoring model, the core return-risk indicators of the ETF combinations meet preset targets, with the global major asset ETF strategies achieving annualized returns of 10.85% and 10.77% under high-risk weight adjustment rules [5][72]. - The ES risk parity model shows superior risk management capabilities, with optimal control of annualized volatility and maximum drawdown, achieving a significant advantage in the return-to-drawdown ratio [5][72]. Risk Parity Model Performance - The risk parity model for domestic stock and bond assets achieved an annualized return of 4.21% with a maximum drawdown of -2.64%, while the global major asset model improved annualized returns to 5.94% [22][33]. - The ES risk parity model, while slightly sacrificing returns, effectively reduced portfolio volatility and drawdown risk, achieving annualized returns of 4.50% and 5.30% for domestic and global models, respectively [31][33]. Macro Scoring Framework - The macro scoring framework identifies macroeconomic indicators that significantly impact asset performance, using a combination of domestic and overseas factors to adjust asset weights dynamically [35][57]. - The report emphasizes the importance of macroeconomic factors in influencing asset returns and employs a scoring system to guide tactical adjustments [35][63].

Group 1 - The report aims to assist institutions targeting absolute returns in asset allocation, with a core goal of designing a strategy that can continuously generate stable return expectations, aiming for an annualized return of no less than 6%, annualized volatility not exceeding 5%, maximum drawdown not greater than 5%, and a return-to-drawdown ratio greater than 1 for the ETF allocation portfolio [6][14]. - The selected asset classes include A-share equities, Hong Kong equities, bonds, commodities, and overseas equities, with corresponding ETF products that are relatively large in scale within the public fund market [6][15]. - The report constructs two asset allocation models: "domestic stock and bond assets" and "global asset classes," with monthly rebalancing and a backtesting period from January 1, 2017, to February 13, 2026 [6][14]. Group 2 - The strategic layer utilizes risk parity and ES risk parity models to set the central weights of asset classes, indicating that the selected domestic stock and bond assets have the foundational basis for constructing an absolute return target portfolio [23][34]. - The tactical layer employs a macro scoring model to adjust asset weights based on macroeconomic factors, with ten high-frequency macro factors identified to significantly impact the returns of various asset classes [49][50]. - The backtesting results of the macro scoring asset allocation ETF portfolio indicate that the core return-risk indicators of each ETF portfolio meet the preset targets, with the global asset ETF strategies achieving annualized returns of 10.85% and 10.77% under high-risk weight adjustment rules [6][13]. Group 3 - The report details the selection of various asset classes, including specific indices for A-share equities, Hong Kong equities, bonds, overseas equities, and commodities, ensuring that the selected ETFs are viable for practical investment [15][17]. - The report establishes initial parameter settings for the asset allocation model, emphasizing the need for strict weight limits to control risk levels and achieve absolute return objectives [21][24]. - The risk parity model aims to equalize the risk contribution of each asset to the overall portfolio, with a focus on achieving a balance between risk and return [25][27].

Group 1: Strategic Asset Pool Definition and Long-Term Characteristics - The report defines a global strategic asset allocation framework, covering equity, fixed income, alternative assets, and cash[1] - It analyzes long-term characteristics of equity assets in global, developed, and emerging markets, including sovereign and credit bonds, real estate, commodities, and private equity[1] - The analysis provides a solid data and theoretical foundation for subsequent return forecasts and portfolio construction[1] Group 2: Long-Term Economic Assumptions and Return Forecast Models - The report establishes long-term economic assumptions and return forecast models based on key macroeconomic variables such as economic growth, inflation, and interest rates[2] - It creates corresponding long-term return prediction models for various asset classes and estimates correlations and potential risk scenarios among different assets[2] Group 3: Strategic Portfolio Construction and Optimization - Strategic portfolio construction considers investor constraints and goal settings, including return targets, risk tolerance, liquidity needs, and regulatory/tax constraints[3] - Optimization methods include the classic mean-variance model, Black-Litterman model, Kelly-CVaR model, and risk parity model, with the mean-variance model and Kelly-CVaR showing superior long-term returns compared to single asset strategies[3] - The report emphasizes the importance of establishing a global market-weighted portfolio as a benchmark for strategic asset allocation[3] Group 4: Market Trends and Performance Metrics - The MSCI indices indicate that the U.S. market dominates with a weight of 64% in the MSCI ACWI index, followed by Japan at 4.9% and the UK at 3.3%[15] - The report highlights that the long-term volatility of MSCI EM is significantly higher than that of MSCI World, with both indices showing similar return patterns over time[23] - The Sharpe ratio for MSCI World and MSCI EM is similar, with long-term limits around -0.5 to +0.8, indicating comparable risk-adjusted returns[23]

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].

1. Report Industry Investment Rating The provided content does not include the industry investment rating. 2. Core View of the Report The report focuses on the localization dilemma of the traditional risk parity model with "low volatility and low returns" in a low - interest - rate environment. By introducing the "Relaxed Risk Parity (RRP)" framework and a dynamic return anchoring mechanism, it constructs an all - weather enhanced strategy that does not rely on macro - timing and balances "risk diversification" and "return elasticity", achieving a logical advancement from localization adaptation to global allocation [1]. 3. Summary According to Relevant Catalogs 3.1 Introduction - In the low - interest - rate era, the traditional single - asset investment framework faces challenges. Asset allocation has become a core source of returns. The mainstream asset - allocation models include the mathematical optimization system and the macro - cycle system, but they face "acclimatization" in the Chinese market. The RRP framework is introduced to solve the problems [15][16][18]. 3.2 Global - scale Main Asset - Allocation Model Introduction - **Logic Divergence and Evolution Path of Mainstream Models**: The mainstream models are divided into the mathematical optimization system starting from the Markowitz mean - variance model and the macro - cycle system represented by the Merrill Lynch Clock. The former evolves from capital allocation to risk allocation, and the latter deepens from state segmentation to timing rotation [20][21][22]. - **China - adaptation Process of Asset - Allocation Models**: The traditional Merrill Lynch Clock has "acclimatization" in the Chinese market. A currency - credit model is proposed as a local alternative. The risk - parity strategy also has a localization dilemma due to the lack of leverage tools and hedging products, and the RRP framework is a solution [26][30]. 3.3 What is Risk Parity? - **Allocation and Hedging, Risk Parity and Factor Parity**: Risk parity is an asset - allocation philosophy based on "risk budgeting". It has evolved into the asset/macro parity mode represented by Bridgewater and the factor parity mode represented by AQR and academia [32]. - **Logical Architecture and Core Calculation Rules**: Based on Euler's theorem, the risk - parity strategy decomposes the total portfolio risk into the risk contributions of each asset and makes them equal through optimization algorithms [33]. 3.4 Relaxed Improvement of Risk Parity - **Logical Reasoning and Core Modeling of RRP**: The traditional risk - parity model has defects such as over - reliance on low - volatility assets and loss of mean - variance efficiency. The RRP framework relaxes the hard constraint of equal risk contributions to a soft - penalty term and constructs a comprehensive optimization model [43][44]. - **Back - test Results**: - **From "Defensive Trap" to "Efficiency Leap"**: Compared with the standard risk - parity portfolio (V1), the relaxed risk - parity portfolio (V2) has significantly improved in terms of annualized return, Sharpe ratio, winning rate, and has more flexible bond - leverage use and dynamic portfolio structure [70]. - **From "Local Trial - and - Error" to "Global Allocation"**: The globalized relaxed risk - parity portfolio (V3) has a diversified structure and lower trading friction. It can use the dislocation of Sino - US economic cycles to achieve better performance [76][79]. - **Generalization Ability of the Model under Different Parameter Windows**: When the parameter - estimation window is extended from 1 year to 2 years, the RRP model still shows better risk - return characteristics, proving its long - term effectiveness [85]. 3.5 Subsequent Strategy Optimization - **AI - enabled**: Introduce a deep - reinforcement learning framework to construct an intelligent agent that can dynamically adjust core parameters according to the macro - environment [86]. - **Response to Extreme Environments**: Use the idea of volatility parity and graph - theory algorithms to improve the robustness of the strategy in extreme environments [88]. - **Paradigm Reconstruction**: Shift from asset - based to factor - based risk control, and construct a three - dimensional allocation system [89].

Investment Rating - The report indicates a positive investment rating for the industry, suggesting an "Overweight" position relative to the CSI 300 index, with expected returns exceeding 15% [36]. Core Insights - The report highlights that gold has once again led the gains among major asset classes, with global asset allocation models recording positive returns. The domestic asset BL models showed returns of 0.28% and 0.26%, while global models recorded returns of 0.14% and 0.12% for the week [1][2][4]. Summary by Sections 1. Major Asset Performance Tracking - For the week of January 12 to January 16, 2026, major asset performances were as follows: SHFE gold increased by 2.57%, Hang Seng Index by 2.23%, and CSI 1000 by 1.27%. Conversely, the CSI 300 and S&P 500 saw declines of 0.57% and 0.45% respectively [7][10]. 2. Major Asset Allocation Strategy Tracking - The report details the performance of various quantitative asset allocation models. The domestic asset BL model 1 achieved a weekly return of 0.26%, while model 2 achieved 0.28%. The global asset BL model 1 and 2 recorded returns of 0.12% and 0.14% respectively for the same week [10][17][21]. 2.1. BL Model Strategy Tracking - The domestic asset BL model 1 has a year-to-date return of 1.13% with an annualized volatility of 2.85%. The global asset BL model 1 has a year-to-date return of 0.69% with an annualized volatility of 2.9% [17][18]. 2.2. Risk Parity Model Strategy Tracking - The domestic risk parity model reported a weekly return of 0.20% and a year-to-date return of 0.49%, with an annualized volatility of 1.16%. The global risk parity model achieved a weekly return of 0.13% and a year-to-date return of 0.38% [21][22]. 2.3. Macro-Factor Based Asset Allocation Strategy - The macro-factor based asset allocation strategy yielded a weekly return of 0.23% and a year-to-date return of 0.61%, with an annualized volatility of 1.73% [29].

Group 1 - The core point of the article highlights the impressive performance of Bridgewater Associates, the world's largest hedge fund, which achieved record returns in 2025, breaking a trend of annual returns below 3% from 2012 to 2024 [1][18] - Bridgewater's All Weather strategy focuses on risk parity, balancing risk contributions rather than simply allocating funds evenly across different assets, which has led to strong performance in various market conditions [19][21] - Domestic alternatives to Bridgewater's strategy have emerged, with many private equity firms adopting risk parity models and achieving returns between 20% to 40% in 2025 [19][20] Group 2 - The China Europe Wealth Management's Multi-Asset All Weather strategy achieved a return of 10.78% over the past year, significantly outperforming its benchmark of 5.72%, with a maximum drawdown of only 1.94% and a Sharpe ratio of 3.77 [22] - The service offerings for high-net-worth clients include a comprehensive suite of products, featuring over 80 private equity products from more than 30 top managers, covering various mainstream strategies [25][26] - A dedicated remote advisory team of over 30 members, with an average of more than 8 years of experience, has provided personalized investment solutions and ongoing support to thousands of high-net-worth clients, achieving a customer satisfaction rate exceeding 90% [28][30] Group 3 - The investment strategy includes continuous tracking and personalized asset diagnosis reports, which help clients navigate market fluctuations and avoid irrational decisions during periods of volatility [30][33] - Recent events, such as the "Insight 2026" investment strategy conference, have facilitated direct communication between investors and fund managers, enhancing the understanding of market dynamics and investment strategies [32][33] - The overall service model aims to provide clients with a comprehensive investment experience, ensuring they are well-informed and supported throughout their investment journey [33]