- The report introduces a framework to study the impact of macroeconomic variables such as economic growth and inflation on asset prices, using expected indicators, actual CPI inflation rates, and GDP growth rates to construct inflation shocks and growth shocks, and estimate the sensitivity (beta coefficients) of different risk factors/assets to these macroeconomic shocks[2][3][16] - The model employs a bivariate regression model to simultaneously estimate the sensitivity of an asset or factor to inflation and economic growth shocks, considering the potential correlation between inflation and economic growth[3][16] - The regression model used is: $$ r_{t+1}=c+\beta_{\pi}\pi_{t+1}^{s}+\beta_{g}g_{t+1}^{s}+\varepsilon_{t+1} $$ where $\pi_{t+1}^{s}$ and $g_{t+1}^{s}$ represent unexpected inflation and unexpected economic growth, respectively: $$ \begin{array}{l} \pi_{t+1}^{s}=\pi_{t+1}-E_{t}\pi_{t+1} \\ g_{t+1}^{s}=g_{t+1}-E_{t}g_{t+1} \end{array} $$ [16][17] - The data used includes actual GDP data from the US Bureau of Economic Analysis and CPI inflation data from the US Bureau of Labor Statistics, along with actual GDP growth and inflation expectations from the Survey of Professional Forecasters (SPF), covering the period from June 1970 to September 2023[18][21] - The report finds that most assets have positive growth beta coefficients, with exceptions like duration assets and gold, which help diversify growth risk in a portfolio. Traditional assets like stocks and fixed income generally have negative inflation beta coefficients, indicating poor performance in high inflation environments, while commodities and real bonds (inflation-linked bonds) can hedge against inflation risk[4][27][28] - The report also discusses how investors can use this framework to construct portfolios resilient to various macroeconomic environments by incorporating macroeconomic beta coefficients into portfolio construction[4][15][52] Model Backtesting Results - **Short Rate**: Growth Beta: 0.38, Inflation Beta: 0.52, Adj. R²: 0.39[25] - **10-year Nominal Yield**: Growth Beta: 0.18, Inflation Beta: 0.32, Adj. R²: 0.31[25] - **Nominal 30y-10y Slope**: Growth Beta: -0.05, Inflation Beta: -0.05, Adj. R²: 0.19[25] - **10-year Real Yield**: Growth Beta: 0.05, Inflation Beta: 0.04, Adj. R²: 0.03[25] - **Equities**: Growth Beta: 3.75, Inflation Beta: -1.99, Adj. R²: 0.24[25] - **REITs**: Growth Beta: 3.36, Inflation Beta: -0.89, Adj. R²: 0.13[25] - **Credit Spread**: Growth Beta: -0.15, Inflation Beta: 0.03, Adj. R²: 0.19[25] - **Commodities**: Growth Beta: 2.00, Inflation Beta: 7.62, Adj. R²: 0.37[25] - **Gold**: Growth Beta: -1.78, Inflation Beta: 5.84, Adj. R²: 0.23[25] Factor Construction and Evaluation - **Growth Beta**: Constructed by regressing asset returns on unexpected economic growth, calculated as the difference between actual GDP growth and expected GDP growth[16][17] - **Inflation Beta**: Constructed by regressing asset returns on unexpected inflation, calculated as the difference between actual CPI inflation and expected CPI inflation[16][17] - **Evaluation**: The model shows that unexpected macroeconomic shocks significantly impact asset returns, with different assets exhibiting varying sensitivities to growth and inflation shocks. This highlights the importance of considering these sensitivities in portfolio construction to enhance resilience against macroeconomic fluctuations[4][27][28]

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Quantitative Models and Construction Methods - **Model Name**: JumpModel **Construction Idea**: JumpModel extends the traditional Hidden Markov Model (HMM) by introducing jump processes to better capture abrupt market state changes, addressing the limitations of smooth state transitions in HMM[13][14][15] **Construction Process**: 1. In HMM, the state transition probability is defined as: $ P(S_{t}|S_{t-1})=P_{i j},\quad S_{t},S_{t-1}\in\{1,2,...,K\} $ Here, $ P_{ij} $ represents the transition probability from state $ i $ to state $ j $[13] 2. JumpModel introduces a jump process to account for abrupt changes: $ P(S_{t}|S_{t-1},J_{t})=(1-\lambda)P_{i j}+\lambda Q_{i j} $ Where $ \lambda $ controls the probability of jump events, and $ Q_{ij} $ represents the transition probability under jump conditions[14][15] 3. Observed variables in JumpModel are modeled with higher variance to capture extreme events: $ Y_{t}|S_{t},J_{t}\sim{\mathcal{N}}{\big(}\mu_{S_{t}}+J_{t},\sigma_{S_{t}}^{2}+\sigma_{J}^{2}{\big)} $ This allows the model to better respond to market shocks and tail risks[16][17][19] **Evaluation**: JumpModel improves responsiveness to market volatility and extreme events, making it more adaptive during rapid market changes compared to HMM[19] - **Model Name**: XGBoost **Construction Idea**: XGBoost leverages ensemble learning to enhance prediction accuracy, particularly in high-dimensional and multi-feature datasets[4][31] **Construction Process**: 1. Features used for training include asset-specific return characteristics (e.g., EMA, Sortino ratio) and macroeconomic indicators (e.g., VIX index, bond yield curve)[32] 2. Preprocessing techniques such as exponential moving averages and log differences are applied to stabilize data and extract key signals[31][32] 3. Default parameters are used to avoid overfitting and ensure generalization across different market environments[34] **Evaluation**: XGBoost demonstrates robust predictive performance, balancing complexity and reliability without requiring extensive parameter tuning[34] - **Model Name**: Mean-Variance Optimization **Construction Idea**: This model dynamically adjusts portfolio weights based on predicted market states to optimize the trade-off between risk and return[5][42] **Construction Process**: 1. Objective function: $ \text{max } \mu - \text{risk} - \alpha \times |w - w_{\text{pre}}|_1 $ Where $ \mu $ represents expected returns, $ \text{risk} $ denotes systematic risk exposure, and $ \alpha |w - w_{\text{pre}}|_1 $ accounts for transaction costs[43] 2. Constraints: $ 0 \leq w \leq w_{\text{max}} $ 3. Covariance matrix is used to model risk transmission and asset interdependencies[43] 4. Rolling window approach is applied for iterative training and validation, ensuring adaptability to market changes[30][43] **Evaluation**: The model effectively balances risk and return, dynamically reallocating weights based on market predictions[45] --- Model Backtesting Results - **JumpModel**: - Annualized return: 6.37%[5] - Information ratio (IR): 0.58[5] - **XGBoost**: - Performance in Shanghai-Shenzhen 300 Index: Successfully avoided major downturns and captured upward trends during backtesting from 2018 to 2025[35][37] - Performance in CSI 500 Index: Higher trading frequency observed due to increased volatility, leading to potential higher transaction costs[39][41] - Performance in long-term bond index: Lower trading frequency due to stable bull market conditions, effectively capturing upward trends[41][44] - **Mean-Variance Optimization**: - Annualized return: 6.37%[49] - Information ratio (IR): 0.58[49] - Sharpe ratio: 1.50 (2022)[55] - Maximum drawdown: 13.9% (2021)[55] - Volatility: 12.9% (2020)[55] --- Quantitative Factors and Construction Methods - **Factor Name**: Jump Intensity Parameter ($ \lambda $) **Construction Idea**: $ \lambda $ determines the sensitivity of JumpModel to market state transitions, balancing responsiveness and stability[20] **Construction Process**: 1. High $ \lambda $ values suppress frequent state transitions, enhancing stability in low-volatility environments[20] 2. Low $ \lambda $ values increase responsiveness to abrupt market changes, suitable for trend reversal scenarios[20] 3. Rolling window cross-validation is used to optimize $ \lambda $ based on Sharpe ratio maximization[30] **Evaluation**: Proper tuning of $ \lambda $ ensures adaptability to varying market conditions, reducing false signals while capturing key transitions[30] --- Factor Backtesting Results - **Jump Intensity Parameter ($ \lambda $)**: - Performance in Shanghai-Shenzhen 300 Index: - $ \lambda = 10 $: Frequent short-term signal generation[22] - $ \lambda = 30 $: Balanced responsiveness and stability[25] - $ \lambda = 50 $: Focused on long-term trends, reduced noise sensitivity[28]

Core Viewpoint - The article emphasizes the significance of Warren Buffett's early investment experiences, highlighting how they shaped his investment philosophy and strategies during his later years at Berkshire Hathaway [4][20]. Group 1: Early Investment Performance - During the partnership period from 1957 to 1969, Buffett achieved an annualized return of 29.5%, significantly outperforming the Dow Jones index, which had a return of 7.4% [4]. - Buffett's early investments were primarily in traditional industries, focusing on undervalued "cigar butt" stocks rather than high-tech companies [4][20]. Group 2: Investment Methodology - Buffett's investment decisions were based on quantitative analysis of balance sheets rather than speculative future predictions, ensuring consistency and replicability in his investment approach [4]. - The early investment philosophy was influenced by Benjamin Graham, emphasizing the importance of understanding the evolution of Buffett's investment thought process [4]. Group 3: Case Studies - The first case study involves Greif Brothers Cooperage Corporation, where Buffett identified a significant undervaluation with a stock price of $18.25 compared to a net current asset value of $20.47 per share [8][9]. - The second case study is Union Street Railway, where Buffett recognized the company's undervaluation with a stock price of $30-35 against a net cash value of $48.13 per share, leading to a substantial return on investment [12][14]. - The third case study focuses on Philadelphia and Reading Company, where Buffett saw value in the company's hidden assets and governance improvements, resulting in a 14-year annualized return of approximately 22% [15][18]. Group 4: Investment Categories - Buffett categorized his investments into three types: undervalued common stocks, arbitrage stocks, and control stocks, each with different risk-return profiles and investment strategies [19]. - The focus remained on traditional industries and mature business models, with a cautious approach towards emerging technologies and high-growth sectors [20].