Quantitative Models and Construction Methods 1. Model Name: Mean-Variance Model - **Model Construction Idea**: The model determines the optimal portfolio by balancing expected returns and risks, based on the mean and variance of asset returns[8] - **Model Construction Process**: 1. Define the portfolio return as a random variable 2. Use the expected return ($E[R]$) and variance ($Var[R]$) to measure the portfolio's performance 3. Solve the optimization problem to maximize expected return for a given level of risk or minimize risk for a given level of return - Formula: $ \text{Minimize: } \sigma_p^2 = \sum_{i=1}^n \sum_{j=1}^n w_i w_j \sigma_{ij} $ $ \text{Subject to: } \sum_{i=1}^n w_i = 1 $ Where $w_i$ is the weight of asset $i$, $\sigma_{ij}$ is the covariance between assets $i$ and $j$[8] - **Model Evaluation**: Flexible in adjusting portfolios based on expected returns and risks, but struggles to incorporate new market dynamics and subjective views[8] 2. Model Name: Black-Litterman Model - **Model Construction Idea**: Combines the Bayesian framework with the mean-variance model to incorporate subjective views into the portfolio optimization process[8] - **Model Construction Process**: 1. Start with a prior distribution of expected returns based on market equilibrium 2. Incorporate subjective views as additional constraints 3. Use the Bayesian approach to update the prior distribution with subjective views to form a posterior distribution - Formula: $ \Pi = \tau \Sigma w_{mkt} $ $ E[R] = \left( \tau \Sigma^{-1} + P^T \Omega^{-1} P \right)^{-1} \left( \tau \Sigma^{-1} \Pi + P^T \Omega^{-1} Q \right) $ Where $\Pi$ is the implied equilibrium return, $\tau$ is a scaling factor, $\Sigma$ is the covariance matrix, $w_{mkt}$ is the market portfolio weights, $P$ is the view matrix, $\Omega$ is the uncertainty matrix, and $Q$ is the view vector[8] - **Model Evaluation**: Flexible and allows integration of subjective views, but requires strong assumptions about return distributions and is computationally complex[8] 3. Model Name: Risk Parity Model - **Model Construction Idea**: Focuses on balancing the risk contribution of each asset in the portfolio rather than their weights[7] - **Model Construction Process**: 1. Calculate the risk contribution of each asset: $RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p}$ 2. Adjust weights to equalize the risk contributions across all assets - Formula: $ RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p} $ Where $RC_i$ is the risk contribution of asset $i$, $w_i$ is the weight of asset $i$, $\sigma_i$ is the standard deviation of asset $i$, and $\rho_{i,p}$ is the correlation between asset $i$ and the portfolio[7] - **Model Evaluation**: Enhances risk control and can incorporate multiple risk dimensions, but lacks a mechanism to optimize returns and may struggle with unrecognized risks[7] 4. Model Name: All-Weather Model (Bridgewater) - **Model Construction Idea**: Aims to achieve stable performance across all economic environments by focusing on risk parity under growth and inflation sensitivity[11] - **Model Construction Process**: 1. Classify assets based on their sensitivity to growth and inflation 2. Allocate weights to achieve risk parity across these dimensions - Formula: Not explicitly provided, but the model emphasizes balancing risk rather than returns[11] - **Model Evaluation**: Stable allocation structure with a focus on low-risk assets, but may underperform in specific market conditions due to its heavy reliance on bonds and cash[15] --- Model Backtesting Results 1. Mean-Variance Model - **Maximum Drawdown**: Exceeded 4% in some periods (e.g., 2018-2019), but quickly recovered[57] - **Sharpe Ratio**: Higher than benchmarks in optimistic scenarios, demonstrating strong risk-adjusted returns[57] 2. Black-Litterman Model - **Maximum Drawdown**: Similar to the mean-variance model, with better adaptability to subjective views[57] - **Sharpe Ratio**: Improved compared to the mean-variance model due to the integration of subjective views[57] 3. Risk Parity Model - **Maximum Drawdown**: Generally lower than the mean-variance model, reflecting its focus on risk control[57] - **Sharpe Ratio**: Moderate, as the model does not explicitly optimize returns[57] 4. All-Weather Model - **Maximum Drawdown**: Comparable to fixed-ratio models, with a focus on stability[15] - **Sharpe Ratio**: Similar to benchmarks, reflecting its conservative allocation[15] --- Quantitative Factors and Construction Methods 1. Factor Name: Monthly Frequency Slicing - **Factor Construction Idea**: Use historical slices of monthly data to reflect maximum drawdown and market sentiment[41] - **Factor Construction Process**: 1. Extract rolling 20-day returns for each year 2. Use the bottom 20% quantile to estimate pessimistic scenarios and maximum drawdown - Formula: $ \text{Max Drawdown} = \text{Min} \left( \frac{P_t - P_{peak}}{P_{peak}} \right) $ Where $P_t$ is the price at time $t$, and $P_{peak}$ is the peak price[41] - **Factor Evaluation**: Effective in capturing extreme market conditions, but limited in predicting long-term trends[41] 2. Factor Name: BootStrap State Space - **Factor Construction Idea**: Use BootStrap sampling to create a state space of asset returns under different scenarios[45] - **Factor Construction Process**: 1. Sample historical data with replacement to create new sequences 2. Calculate return distributions for pessimistic, neutral, and optimistic scenarios - Formula: $ F = B - \alpha \cdot C $ Where $F$ is the objective function, $B$ is the expected return under risk constraints, $C$ is the penalty for exceeding risk constraints, and $\alpha$ is the penalty parameter[50] - **Factor Evaluation**: Provides a robust framework for scenario analysis, but computationally intensive[45] --- Factor Backtesting Results 1. Monthly Frequency Slicing - **Maximum Drawdown**: Successfully captured extreme drawdowns in historical data, with 90% coverage for A-shares and Hong Kong stocks[40] - **Sharpe Ratio**: Not explicitly provided, but the factor is more focused on risk control[40] 2. BootStrap State Space - **Maximum Drawdown**: Achieved a 4% maximum drawdown target in most scenarios, with only minor deviations in extreme conditions[57] - **Sharpe Ratio**: Optimized under different scenarios, with higher ratios in optimistic environments[57]

Group 1 - The core viewpoint of the article emphasizes the need to maintain an overweight position in gold and Chinese technology stocks while reducing exposure to commodities and dollar assets as the market trends evolve in 2026 [2][8] - The article identifies four key factors that could potentially alter the bullish trends of stocks and gold in 2026: economic growth turning, tightening policies, high valuations, and geopolitical shocks [4][42] - Historical analysis shows that the U.S. stock market has a long bullish phase, while Chinese stocks experience more frequent bull-bear switches, making the timing of market tops more critical for Chinese stocks [3][10] Group 2 - The article outlines the importance of accurately interpreting economic and policy signals to predict market tops, noting that signals from economic and policy dimensions are generally more reliable than those from liquidity, earnings, and valuation [14][28] - For gold, the article highlights that the key determinant for its market top is the Federal Reserve's policy, with historical data showing that four out of five gold bull markets peaked when the Fed began tightening [31][32] - The current economic environment is characterized by a weak recovery in China and a potential stagflation scenario in the U.S., which could support the continuation of the stock bull market while posing risks to the gold bull market [44]

Asset Allocation - The investment research framework has evolved from the traditional Merrill Lynch clock to a Chinese-style monetary credit model, reflecting significant changes in China's economic development model and the diminishing role of investment in driving economic growth [12] - In the new era, liquidity is identified as a core factor influencing asset prices, with the monetary cycle remaining highly relevant. Additionally, international factors, exemplified by US-China relations, significantly impact export engines and cross-border capital flows, becoming crucial for capturing asset price changes [12] Historical Review of Stock and Bond Performance - The report reviews stock and bond performance since 2018, highlighting that in 2018, macroeconomic fundamentals were weak, leading to significant stock market declines while bonds provided good coupon returns. In 2019, equity markets experienced volatility, and bonds continued to offer protection [18] - The analysis indicates that from 2020 to 2025, equity markets have shown resilience driven by technology stocks and structural bull markets, while bonds have entered a bull market phase characterized by declining yields [18] Long-term Bond Market Trends - Historical data shows that each bond bull market corresponds with a downward trend in 10-year government bond yields, driven by the interplay of "debt bulls" and "asset scarcity" [20] - The current bond bull market has seen 10-year government bond yields reach new lows, indicating a significant shift in the bond market landscape [21] Equity Market Trends - The equity market is believed to be in a long-term upward trend, with the current phase identified as the third wave of a five-wave cycle. This phase is expected to last longer than previous cycles, indicating a gradual upward movement [25] - The report draws parallels with Japan's experience, noting that after the economic bubble burst in the 1990s, the Japanese stock market entered a long-term upward channel, supported by structural reforms and monetary easing [29] Core Investment Themes - The report emphasizes a bullish outlook on A-shares and Hong Kong stocks, driven by stable US-China relations and a supportive global monetary environment. It suggests that technology stocks will lead the market in the next 5-10 years [36] - The bond market is expected to maintain a volatile environment, with a focus on coupon strategies as interest rates are projected to fluctuate between 1.7% and 2.0% [36]

Core Viewpoint - The podcast episode titled "When the Big Cycle Fails, Where is the New Macro Coordinate?" hosted by fund manager Tang Jun from Zhongtai Asset Management has gained significant attention, with over 60,000 listeners in a week, indicating a strong interest among investors in learning about macroeconomic frameworks and investment strategies [2][5]. Group 1: Framework Construction - The background and reasons for the effectiveness and ineffectiveness of the Merrill Clock are discussed [5]. - The "Credit-Money" framework is introduced, explaining how to describe the current macroeconomic state based on this framework [5]. - The current macroeconomic state leads to specific asset allocation conclusions [5]. Group 2: Allocation and Portfolio Construction - The distinction between active and passive allocation is made, highlighting the problems that active allocation can solve [9]. - Preparations required for engaging in active allocation are outlined [9]. - The role of FOF (Fund of Funds) in addressing specific issues is examined [9]. - The execution of strategic and tactical layers in investment is discussed [9]. Group 3: Reflections on Human Nature - The importance of understanding human behavior in the context of investment allocation is emphasized, inviting listeners to engage in a professional and rigorous intellectual exchange [6]. Group 4: Risk and Return Concepts - The concept of risk budgeting and how to construct a portfolio within a given risk budget is explained [9]. - The notion of return streams and which assets can represent different return streams is analyzed, drawing lessons from Bridgewater's practices [9]. - The significance of having a framework in investment decision-making is highlighted [9]. - The meaning of logic in investment and the application of probabilistic thinking in market timing are discussed [9].

Core Viewpoint - Asset allocation is a complex and multi-dimensional task, often referred to as "physical labor" due to the extensive research required to achieve effective configurations [1][2][27]. Group 1: Passive vs. Active Allocation - Passive allocation, which relies on diversification to reduce volatility, faces challenges in practice, particularly for domestic investors due to limited asset classes and the poor performance of key assets like A-shares [5][9][27]. - Active allocation aims to enhance returns beyond passive strategies by making informed predictions about expected returns, addressing the shortcomings of passive allocation [2][27]. Group 2: Issues with Passive Allocation - Determining expected returns using historical data can lead to "chasing performance," where investors favor assets that have recently performed well, skewing allocation models [5][9]. - The correlation between assets is not stable; for instance, the historical negative correlation between U.S. stocks and bonds has weakened since the 2008 financial crisis, impacting the effectiveness of diversification [6][9]. - The performance of passive allocation is heavily dependent on the underlying assets' returns and their correlations, which can be problematic in markets with limited asset classes [9][27]. Group 3: The Complexity of Active Allocation - Active allocation involves timing decisions, which many investors find challenging, leading to skepticism about its feasibility [17][19]. - While achieving a high accuracy rate in timing is difficult, even a modest success rate can significantly enhance investment returns when combined with sound risk management [18][19]. - The macroeconomic drivers influencing asset performance can change, necessitating continuous adjustments to research frameworks and strategies [21][27]. Group 4: Multi-Dimensional Decision Making - Effective asset allocation requires multiple low-correlation return streams to improve the probability of successful outcomes, as relying on a single asset is often insufficient [22][23]. - A structured decision-making framework that incorporates both strategic and tactical allocations can enhance the robustness of investment strategies [23][24]. - Strict risk budgeting is essential to ensure that asset allocations align with the overall risk tolerance of the portfolio, preventing forced liquidations during market fluctuations [24][25].

Core Viewpoint - The recent interplay of growth, dividend, and gold reflects a macroeconomic transition between old and new driving forces, with structural changes taking precedence over overall economic shifts [1] Group 1: Macroeconomic Environment - The coexistence of overall price decline and the robust development of AI indicates a complex macroeconomic landscape [1] - The framework of the Merrill Lynch clock is deemed less applicable to the current macro environment, suggesting analysis through the lens of "credit expansion" driven by growth and inflation [1] - Credit expansion is categorized into government credit expansion (fiscal deficit pulse) and endogenous credit expansion (private sector social financing pulse) [1] Group 2: Credit Cycle and Bond Market - Due to the high base effect from last year's fourth quarter and ineffective recovery of private credit, the credit cycle in China may trend towards volatility or weakness [1] - If the fourth quarter shows weak credit conditions, bonds may outperform other asset classes [1] - The recent performance of the ten-year government bond ETF (511260) and the overall bond market is viewed more optimistically compared to the third quarter, with a recommendation for investors to pay attention [3][11] Group 3: Bond Market Analysis - The fundamental analysis remains a core dimension for bond evaluation, emphasizing the importance of avoiding significant timing errors in a strong trend environment [5] - Historical trends indicate that significant increases in ten-year government bond yields are closely linked to fundamental and policy influences [6] - The current liquidity easing policy from the central bank is clear, with recent increases in easing measures [9] Group 4: Central Bank Actions and Market Expectations - There is caution regarding the potential for the central bank to restart government bond purchases, as this is seen as unpredictable policy behavior [10] - The logic that increased short-term bond purchases by major banks directly implies central bank intervention is considered flawed [10] - The increase in short-term government bond allocations by major banks may be driven by their own duration management needs rather than a direct correlation with central bank actions [10]

Report Industry Investment Rating No investment rating information is provided in the report. Core Viewpoints - The currency-credit model is significant for asset allocation in a specific historical period, but with the internal transformation of the economic development model, a new way of thinking and investment framework is needed to view the new market trends of the bond and equity markets [1][3][31]. - The Merrill Lynch Investment Clock has limitations in practical application, and the Chinese version - the currency-credit model - has emerged, but it also faces the problem of weakened applicability due to economic changes [1][2][28]. Summary by Directory 1. Reexamine the Merrill Lynch Investment Clock and the Currency-Credit Model - **Merrill Lynch Investment Clock**: It is a typical framework for asset allocation, dividing the macro - economy into four quadrants based on growth and inflation. However, it has limitations such as low data frequency, time lag, and difficulty in accurately grasping cycle inflection points in real - world trading [1][11][12]. - **Growth and Inflation Cyclical Weakening in China**: Since 2012, the cyclical nature of China's economic growth (GDP) and inflation (CPI) has significantly weakened, causing the classic Merrill Lynch Investment Clock to be "ill - adapted" to the Chinese market [13]. - **Currency - Credit Model**: It is a Chinese - version of the Merrill Lynch Investment Clock, dividing the macro - economy from the currency and credit dimensions. It corresponds to the four stages of the Merrill Lynch Investment Clock and presents different asset performance in different stages. It innovatively incorporates liquidity factors into asset pricing [2][15][22]. - **Differences in Asset Pricing Logic**: The Merrill Lynch Investment Clock follows a top - down macro logic, while the currency - credit model uses the credit cycle to reflect the macro - economy and incorporates the currency cycle for a more comprehensive asset pricing [22]. - **Applicability Differences**: The Merrill Lynch Investment Clock is more suitable for the relatively mature capital markets in Europe and the United States, while the currency - credit model is more adaptable to the domestic investment environment. For example, the currency - credit model can better explain the 2015 equity market bull market [23]. - **Limitations of the Currency - Credit Model**: Due to the transformation of China's economic growth model, the currency - credit model may face weakened applicability. After 2008, investment became a key driver, and credit cycles were important. After 2020, consumption gradually replaced the credit cycle as an important indicator of economic prosperity [28][29].

Core Viewpoint - The article discusses the unusual performance of various asset classes in 2023, where traditionally opposing assets such as gold, dividends, and growth stocks have shown simultaneous gains, challenging the conventional inflation and deflation asset pricing framework [2][6][7]. Group 1: Asset Performance Analysis - In the first quarter of 2023, gold and growth stocks rose together, followed by a period in the second quarter where dividends and growth stocks also increased, and again in the third quarter where gold and growth stocks performed well together [2][4]. - The traditional asset pricing theory suggests that gold benefits from inflation, dividends are more attractive in deflationary environments, and growth stocks thrive in moderate inflation and risk-on conditions [6][7]. - The article posits that the current market dynamics cannot be solely explained by inflation or deflation, indicating that other factors, such as geopolitical risks and central bank gold purchases, are influencing asset prices [7][11]. Group 2: Macro Environment and Credit Cycles - The macroeconomic environment in China is characterized by a decline in overall prices, particularly in PPI, while excess liquidity is causing "scarce" assets to appreciate, reflecting a localized inflation phenomenon [11][15]. - The article emphasizes that the credit cycle framework is a more effective tool for understanding asset rotation in China, as it considers the underlying causes of inflation and deflation rather than just the outcomes [16][17]. - The credit cycle can be influenced by three main factors: new industry trends (e.g., AI), government-led fiscal stimulus, and traditional demand from the private sector [19][20]. Group 3: Future Outlook - The article forecasts that the credit cycle in China is likely to shift towards a "fiscal strong + credit weak" or "fiscal weak + credit weak" phase, influenced by high base effects and slowing fiscal stimulus [28][36]. - Key indicators such as the broad fiscal deficit pulse and private sector credit pulse are expected to show downward trends, indicating a potential tightening of credit conditions [30][32]. - The article concludes that without significant policy intervention, the market is likely to continue along its current trajectory, focusing on sectors that remain resilient amid a weakening credit cycle [36][37].

Core Viewpoint - The article discusses the changing landscape of wealth management, emphasizing the shift from a high-growth investment mindset to a more defensive and strategic approach in response to economic cycles and uncertainties [3][12][18]. Investment Trends - High-net-worth individuals are increasingly favoring gold as an investment, with a reported 15.7% preference, surpassing A-shares (12%) and funds (11.3%) [5][10]. - The price of international spot gold has exceeded $4,200 per ounce, marking a year-to-date increase of over 50% [8]. - There is a notable rise in investment in insurance among high-net-worth individuals, increasing by 2.9% to 10.8%, ranking fourth in investment preferences [9]. Economic Cycle Awareness - The article highlights the importance of understanding economic cycles, noting that no asset consistently performs well across all periods [16][22]. - It emphasizes the need for investors to adapt their strategies based on the economic environment, suggesting a mix of offensive and defensive asset allocations [20][26]. Wealth Management Philosophy - The concept of wealth management is evolving from merely seeking high returns to a more nuanced approach that balances risk and growth opportunities [18][35]. - The article advocates for a dual strategy of offense (investing in equities and growth assets) and defense (utilizing bonds, insurance, and trust products) to safeguard core assets [26][27]. Wealth Transfer Considerations - Effective wealth transfer involves more than just passing on money; it requires a comprehensive strategy to protect wealth from risks such as marriage and debt [29][30]. - The article stresses the importance of establishing a legal framework to ensure wealth is preserved and responsibly managed across generations [29]. Educational Initiatives - The "2025 Wu Xiaobo Lecture" series aims to equip participants with insights into wealth growth and future planning, featuring experienced instructors who will cover macro trends and asset allocation strategies [31][32][38].

Core Viewpoint - The article emphasizes the need for a paradigm shift in asset pricing and investment management, moving from traditional models to a more dynamic and adaptive approach that considers the non-linear relationships between assets and their roles within a portfolio [4][11][18]. Group 1: Asset Pricing Theory - Traditional asset pricing theories, such as the Capital Asset Pricing Model (CAPM), are based on strict assumptions of market efficiency and rational investors, which fail to explain market anomalies like momentum and value effects [4][12]. - The article argues that asset prices are influenced not only by their expected returns and risks but also by their roles in the overall investment portfolio and the dynamic relationships with other assets [4][11]. Group 2: Investment Strategy - The new investment philosophy focuses on systematically and proactively enhancing the risk-adjusted returns of investment portfolios rather than merely seeking absolute returns [4][11]. - The investment framework proposed is not about finding the "true value" of assets but about creating an adaptive system that can achieve stable growth across different market environments [7][16]. Group 3: Multi-Asset Allocation - The article discusses the importance of low correlation among assets in a multi-asset allocation strategy, which can significantly reduce the probability of negative monthly returns [22][23]. - A two-stage strategy combining CPPI (Constant Proportion Portfolio Insurance) and risk budgeting is suggested to enhance traditional methodologies and improve risk-adjusted returns [17][23]. Group 4: Market Dynamics - The article highlights that the correlation between assets is dynamic and can change with market conditions, which poses risks to traditional asset allocation frameworks that rely on historical data [12][15]. - The concept of "free lunch" in asset allocation, derived from low correlation, may diminish as market environments evolve, necessitating a deeper understanding of the underlying factors driving asset correlations [15][18]. Group 5: Future of Asset Pricing - The future of asset pricing is seen as a transition from a focus on historical data to an understanding of technological trends, industry changes, and collective human behavior [34]. - The new asset pricing framework is described as a three-dimensional investment model centered around technological advancement, new productive forces, and consensus-driven narratives [18][28].