Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: MFE (Maximized Factor Exposure) Portfolio - **Model Construction Idea**: The MFE portfolio is designed to maximize single-factor exposure while controlling for constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate[61][62][65] - **Model Construction Process**: The optimization model is as follows: ``` $\begin{array}{ll} max & f^{T}w \\ s.t. & s_{l}\leq X(w-w_{b})\leq s_{h} \\ & h_{l}\leq H(w-w_{b})\leq h_{h} \\ & w_{l}\leq w-w_{b}\leq w_{h} \\ & b_{l}\leq B_{b}w\leq b_{h} \\ & 0\leq w\leq l \\ & 1^{T}w=1 \\ & \Sigma|w-w_{0}|\leq to_{h} \end{array}$ ``` - **Objective Function**: Maximize single-factor exposure, where $f$ represents factor values, and $w$ is the weight vector of stocks in the portfolio - **Constraints**: 1. Style exposure limits: $X$ is the factor exposure matrix, $w_b$ is the benchmark weight vector, $s_l$ and $s_h$ are the lower and upper bounds for style exposure[64] 2. Industry exposure limits: $H$ is the industry exposure matrix, $h_l$ and $h_h$ are the lower and upper bounds for industry exposure[64] 3. Stock weight deviation limits: $w_l$ and $w_h$ are the lower and upper bounds for stock weight deviation[64] 4. Component stock weight limits: $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark, $b_l$ and $b_h$ are the lower and upper bounds for component stock weights[64] 5. No short selling and individual stock weight limits[64] 6. Full investment constraint: The sum of weights equals 1[64] 7. Turnover rate constraint: $w_0$ is the previous period's weight, and $to_h$ is the turnover rate upper limit[64] - **Model Evaluation**: The MFE portfolio effectively isolates single-factor performance under realistic constraints, making it a robust tool for factor effectiveness testing[61][65] Model Backtesting Results - **MFE Portfolio Backtesting**: - The MFE portfolio is constructed monthly, and its historical returns are calculated after deducting a 0.3% transaction fee. The results are used to evaluate the effectiveness of factors in specific benchmark indices[65] --- Quantitative Factors and Construction Methods - **Factor Name**: DELTAROE - **Factor Construction Idea**: Measures the change in return on equity (ROE) over a specific period to capture profitability trends[14][19] - **Factor Construction Process**: - Formula: $\text{DELTAROE} = \text{ROE}_{\text{current}} - \text{ROE}_{\text{previous}}$ - Where ROE is calculated as net income divided by average equity[19] - **Factor Evaluation**: DELTAROE is a strong indicator of profitability improvement and has shown consistent positive performance across multiple indices[6][45][49] - **Factor Name**: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Captures the deviation of actual earnings from analyst expectations, standardized by the standard deviation of forecast errors[19] - **Factor Construction Process**: - Formula: $\text{SUE} = \frac{\text{Actual Earnings} - \text{Expected Earnings}}{\text{Standard Deviation of Forecast Errors}}$[19] - **Factor Evaluation**: SUE effectively identifies stocks with unexpected earnings surprises, often leading to significant price movements[6][25][45] - **Factor Name**: Trend - **Factor Construction Idea**: Measures price momentum using exponentially weighted moving averages (EWMA) with different half-lives[14] - **Factor Construction Process**: - Formula: $\text{Trend}_{120} = \frac{\text{EWMA (half-life=20)}}{\text{EWMA (half-life=120)}}$ - Formula: $\text{Trend}_{240} = \frac{\text{EWMA (half-life=20)}}{\text{EWMA (half-life=240)}}$[14] - **Factor Evaluation**: Trend factors capture momentum effects and have shown strong performance in recent weeks[9][11] --- Factor Backtesting Results - **DELTAROE**: - **Performance**: - CSI 300: Weekly return 0.41%, monthly return 1.59%[6][22] - CSI 500: Weekly return 0.95%, monthly return 1.19%[6][26] - CSI 800: Weekly return 1.08%, monthly return 1.62%[6][30] - CSI 1000: Weekly return 1.79%, monthly return 1.54%[6][34] - CSI All Share: Weekly return 1.84%, monthly return 2.41%[6][46] - **SUE**: - **Performance**: - CSI 300: Weekly return 0.30%, monthly return 1.53%[6][22] - CSI 500: Weekly return 1.20%, monthly return 1.59%[6][26] - CSI 800: Weekly return 0.34%, monthly return 0.98%[6][30] - CSI 1000: Weekly return 1.46%, monthly return 2.39%[6][34] - CSI All Share: Weekly return 0.96%, monthly return 1.14%[6][46] - **Trend**: - **Performance**: - Weekly return: 1.15%[9][11] - Monthly return: 4.58%[11] - Annualized return (1 year): 19.73%[11] - Annualized return (10 years): 13.98%[11]

Summary of Conference Call Records Industry or Company Involved - Public Fund Industry Core Points and Arguments - The expansion of public fund scale is significantly correlated with excess returns, especially in favorable market years. However, current challenges in share and scale growth are evident, with a redemption rate of approximately 10% in Q4 and 2-3% in Q1 of the current year [1][2][3] - Adjusting to narrow-based indices (such as consumption, manufacturing, TMT) can improve the probability of outperforming benchmarks, but the win rate remains below 50%. Over the past three years, more than half of narrow-based index products failed to outperform benchmarks, indicating limited effectiveness of this strategy for excess returns [1][3] - In bond funds, a lower bond content and higher stock content correlate with increased difficulty in outperforming benchmarks. The significantly lower allocation to financial and cyclical sectors compared to index weights is a key factor, with potential for future weight increases in these sectors [1][3] - Achieving relative returns under new regulations requires attention to the stability of style and industry exposure, enhancing style and industry allocations to improve portfolio performance while controlling volatility. Industry-led investments have high potential for excess returns, but the risk-reward ratio is declining, making it a suboptimal strategy [1][3][4] - For absolute returns, multi-asset allocation can simplify investment processes. Relative returns require clarity on the stability of various benchmarks' style and industry exposures, along with methods to enhance these while controlling volatility [4] Other Important but Possibly Overlooked Content - Public funds should allocate at least half of their positions to benchmark indices and use the other half to seek excess returns, regardless of whether through style or industry allocation. Financial sectors, particularly non-bank and bank stocks, are notably underweighted in public funds compared to their index allocations [10][14] - The risk control benchmark can be compared to index-enhanced products, with the median risk of these products showing a relative drawdown of about 10%. If actively managed equity products can achieve a 10% relative drawdown with higher annualized excess returns, they will outperform traditional ETFs and broad index products [5][6] - The probability of selecting industries that yield over 1% excess returns annually is low, typically around 10%, with a special case in 2021 where 30% of industries met this standard. Achieving 10% excess returns is considered a high target for public funds [9][14] - The distinction between using style versus industry for excess returns is significant; style offers stability but lower ceilings, while industry can achieve higher ceilings but with less stability in win rates [11][14]