金融工程 | 动态跟踪 分红对期指的影响 20250509 研究结论 | | 收盘价 | 分红点数 | 实际价差 | 含分红价差 | | --- | --- | --- | --- | --- | | IH2505 | 2683.60 | 0.27 | -0.41 | -0.14 | | IH2506 | 2666.20 | 17.96 | -17.81 | 0.16 | | IH2509 | 2632.00 | 59.91 | -52.01 | 7.91 | | IH2512 | 2627.00 | 59.91 | -57.01 | 2.91 | 沪深 300 股指期货: | | 收盘价 | 分红点数 | 实际价差 | 含分红价差 | | --- | --- | --- | --- | --- | | IF2505 | 3840.20 | 0.74 | -5.96 | -5.21 | | IF2506 | 3808.60 | 24.07 | -37.56 | -13.49 | | IF2509 | 3743.60 | 69.49 | -102.56 | -33.06 | | IF2512 | 3706.80 ...

证券研究报告·金融工程·金工定期报告 金工定期报告 20250507 估值异常因子绩效月报 20250430 2025 年 05 月 07 日 [Table_Tag] [Table_Summary] 报告要点 ◼ 估值偏离 EPD 因子多空对冲绩效（全市场）：2010 年 2 月至 2025 年 4 月，估值偏离 EPD 因子在全体 A 股（剔除北交所股票）中，5 分组多 空对冲的年化收益为 17.65%，年化波动为 10.02%，信息比率为 1.76， 月度胜率为 71.04%，月度最大回撤为 8.93%。 ◼ 缓慢偏离 EPDS 因子多空对冲绩效（全市场）：2010 年 2 月至 2025 年 4 月，缓慢偏离 EPDS 因子在全体 A 股（剔除北交所股票）中，5 分组 多空对冲的年化收益为 16.31%，年化波动为 5.73%，信息比率为 2.85， 月度胜率为 79.23%，月度最大回撤为 3.10%。 ◼ 估值异常 EPA 因子多空对冲绩效（全市场）：2010 年 2 月至 2025 年 4 月，估值异常 EPA 因子在全体 A 股（剔除北交所股票）中，5 分组多空 对冲的年化收益为 17.30%， ...

盈利预期期限结构因子历史表现良好，走势稳定性高。 将盈利预期期限结构"动量 ff"因子与传统分析师预期 调升因子合成后，得到的"合成动量 ff"因子走势进一步改 善，兼具高收益与稳定性。 ► 选股组合表现 在沪深 300、中证 500、中证 800、中证 1000 内分别选择 "合成动量 ff"因子值排名前 50、50、100、100 名的股票， 构成选股组合，组合历史表现良好。 证券研究报告|金融工程研究报告 [Table_Date] 2025 年 5 月 2 日 [Table_Title] 前四个月全部组合跑赢基准——盈利预期期限结构选股月报 202505 [Table_Summary] ► 盈利预期期限结构因子 分析师在某一时点会对上市公司未来多年的盈利做出预 测，我们将预期盈利随未来年度变化的趋势称为盈利预期期 限结构。 2025 年 4 月，沪深 300 选股组合、中证 800 选股组合跑 输基准，超额收益分别为-0.57%、-0.02%；中证 500 选股组 合、中证 1000 选股组合跑赢基准，超额收益分别为 0.73%、 1.13%。 2025 年前 4 个月，沪深 300、中证 500、中证 ...

Group 1: Davis Double-Click Strategy - The Davis Double-Click strategy involves buying stocks with growth potential at a lower price-to-earnings (PE) ratio, waiting for growth to manifest, and then selling for a multiplier effect, achieving a dual benefit from earnings per share (EPS) and PE [1][7] - The strategy has achieved an annualized return of 26.45% during the backtest period from 2010 to 2017, exceeding the benchmark by 21.08% [9] - Year-to-date, the strategy has a cumulative absolute return of 6.18%, outperforming the CSI 500 index by 7.90%, with a weekly excess return of 2.27% [10] Group 2: Net Profit Discontinuity Strategy - The Net Profit Discontinuity strategy focuses on selecting stocks based on fundamental and technical resonance, where "net profit" refers to earnings surprises, and "discontinuity" indicates a significant upward price gap on the first trading day after earnings announcements [2][12] - This strategy has achieved an annualized return of 28.17% since 2010, with an annualized excess return of 26.67% over the benchmark [15] - The current year's cumulative absolute return for this strategy is 8.31%, exceeding the benchmark by 10.03%, with a weekly excess return of 4.31% [15] Group 3: Enhanced CSI 300 Portfolio - The Enhanced CSI 300 portfolio is constructed based on investor preferences, including GARP (Growth at a Reasonable Price), growth, and value investing styles, utilizing PBROE and PEG factors to identify undervalued stocks with strong profitability and growth potential [3][17] - The portfolio has shown stable excess returns in historical backtesting, with a year-to-date excess return of 7.41% relative to the CSI 300 index [17] - The weekly excess return for this portfolio is 1.33%, and the monthly excess return is 4.08% [17]

Quantitative Models and Factor Analysis Dividend Impact Prediction Model - **Model Name**: Dividend Impact Prediction Model - **Model Construction Idea**: The model aims to predict the impact of dividends on stock index futures contracts by estimating the dividend points and their influence on futures pricing. It incorporates historical dividend patterns, company financial reports, and market assumptions to calculate the theoretical impact of dividends on futures contracts[9][22][25] - **Model Construction Process**: 1. **Estimate Component Stocks' Net Profit**: Use annual reports, financial forecasts, and analysts' predictions to estimate the net profit of index component stocks[25][26] 2. **Calculate Pre-Tax Dividend Total**: Based on the assumption that the dividend payout ratio remains unchanged, calculate the total dividend amount using the formula: $$ \text{Estimated Dividend Total} = \text{Net Profit} \times \text{Dividend Payout Ratio} $$ If no dividends were issued in the previous year, assume no dividends for the current year[29] 3. **Calculate Dividend Impact on Index**: - **Dividend Yield**: $$ \text{Dividend Yield} = \frac{\text{Tax-Adjusted Dividend Total}}{\text{Latest Market Value}} $$ - **Dividend Points**: $$ \text{Dividend Points Impact} = \text{Stock Weight} \times \text{Dividend Yield} $$ Stock weights are adjusted using the formula: $$ \mathrm{w_{it}={\frac{w_{i0}\times\ (\ 1+R\ )}{\sum_{1}^{n}w_{i0}\times\ (\ 1+R\ )}}} $$ where \( R \) is the stock's price change over time[27] 4. **Predict Impact on Futures Contracts**: Aggregate the dividend points for all component stocks before the contract's settlement date to estimate the total dividend impact on the futures contract[31] - **Model Evaluation**: The model is robust in incorporating historical data and market assumptions, but its accuracy depends on the reliability of financial forecasts and the stability of dividend payout ratios[9][25][31] --- Backtesting Results of the Model Dividend Impact on Futures Contracts (May 2025 Contracts) - **Shanghai 50 Index Futures (IH)**: - Dividend Points: 0.43 - Remaining Impact: 0.02% - Annualized Hedging Cost (365 days): 2.22% - Annualized Hedging Cost (243 days): 2.58%[10][14] - **CSI 300 Index Futures (IF)**: - Dividend Points: 5.76 - Remaining Impact: 0.15% - Annualized Hedging Cost (365 days): 2.95% - Annualized Hedging Cost (243 days): 3.44%[10][11] - **CSI 500 Index Futures (IC)**: - Dividend Points: 3.49 - Remaining Impact: 0.06% - Annualized Hedging Cost (365 days): 12.82% - Annualized Hedging Cost (243 days): 14.93%[10][12] - **CSI 1000 Index Futures (IM)**: - Dividend Points: 5.93 - Remaining Impact: 0.10% - Annualized Hedging Cost (365 days): 14.22% - Annualized Hedging Cost (243 days): 16.56%[10][13] --- Theoretical Pricing Model for Futures Discrete Dividend Distribution Model - **Model Construction Process**: - Assumes discrete dividend payments at specific time points during the contract period - Formula: $$ \mathbf{D}=\sum_{\mathrm{i=1}}^{\mathrm{m}}\mathbf{D}_{\mathrm{i}}\,/(1+\phi) $$ where \( \phi \) is the risk-free rate between two dividend payment dates - Futures Pricing Formula: $$ F_t = (S_t - D)(1 + r) $$ where \( S_t \) is the spot price, \( D \) is the present value of dividends, and \( r \) is the risk-free rate[34] Continuous Dividend Distribution Model - **Model Construction Process**: - Assumes dividends are distributed continuously over the contract period - Formula: $$ F_t = S_t e^{(r-d)(T-t)} $$ where \( d \) is the annualized dividend yield, \( r \) is the risk-free rate, and \( T-t \) is the time to maturity[35]

Quantitative Models and Construction Methods - **Model Name**: Theoretical Pricing Model for Stock Index Futures **Model Construction Idea**: This model calculates the theoretical price of stock index futures by considering the impact of dividends, risk-free interest rates, and the time to maturity. It assumes no arbitrage conditions in the market[34][35] **Model Construction Process**: 1. **Discrete Dividend Distribution**: - Assume the futures price at time \( t \) is \( F_t \), the spot price is \( S_t \), the maturity date is \( T \), and the present value of dividends during \( T-t \) is \( D \). The risk-free rate over \( T-t \) is \( r \). - If there are \( m \) dividend payments at times \( t_1, t_2, ..., t_m \), with amounts \( D_1, D_2, ..., D_m \), the present value of dividends is: $$ \mathbf{D} = \sum_{\mathrm{i=1}}^{\mathrm{m}} \mathbf{D}_{\mathrm{i}} / (1 + \phi) $$ Here, \( \phi \) represents the risk-free rate between two dividend payments. - The theoretical futures price is: $$ F_t = (S_t - D)(1 + r) $$ 2. **Continuous Dividend Distribution**: - When dividends are distributed continuously, the theoretical futures price is: $$ F_t = S_t e^{(r-d)(T-t)} $$ Here, \( d \) is the annualized dividend yield, and \( r \) is the annualized risk-free rate[34][35] Quantitative Factors and Construction Methods - **Factor Name**: Dividend Impact Factor **Factor Construction Idea**: This factor estimates the impact of dividends on stock index futures by predicting the dividend payout of index constituents and their contribution to the index[22][25] **Factor Construction Process**: 1. **Estimate Net Profit**: Use annual reports, earnings forecasts, and other financial data to estimate the net profit of index constituents[25][26] 2. **Calculate Total Dividend**: Assume the dividend payout ratio remains constant. For companies without prior dividends, assume no dividends. For companies with negative profits, set the dividend ratio to zero[29] 3. **Calculate Dividend Impact on Index**: - Dividend Yield = Total Dividend / Latest Market Value - Dividend Impact on Index = Stock Weight × Dividend Yield - Adjust stock weights based on price changes using the formula: $$ \mathrm{w_{it} = \frac{w_{i0} \times (1+R)}{\sum_{1}^{n} w_{i0} \times (1+R)}} $$ Here, \( w_{i0} \) is the initial weight, and \( R \) is the price change[27] 4. **Predict Impact on Futures Contracts**: Aggregate the dividend impact for all constituents before the contract's expiration date[31] Backtesting Results of Models - **Theoretical Pricing Model**: - For the May contracts of major indices, the annualized hedging costs (excluding dividends) are: - CSI 300: 9.63% - CSI 500: 17.67% - CSI 1000: 19.08%[10][11][12] Backtesting Results of Factors - **Dividend Impact Factor**: - Remaining dividend impact on May contracts: - SSE 50: 0.00% - CSI 300: 0.20% - CSI 500: 0.07% - CSI 1000: 0.14%[13]

Group 1: Solution to the Declining Performance of Value Strategies - The report highlights that traditional price multiple-based valuation methods have struggled to accurately predict stock returns, leading to a decline in value strategy performance [2][8] - A new industry valuation model based on residual income is proposed, which calculates intrinsic value (IV) as the sum of equity book value and the present value of future economic profits [2][8] - The intrinsic value to market value ratio (IVM) has been shown to effectively predict stock returns, with a long/short portfolio based on IVM yielding significant CAPM alpha returns from 1999 to 2023, even after accounting for transaction costs [2][8] Group 2: Alternative Asset Allocation to Bonds - The report identifies a need for new investment options as the traditional 60/40 stock-bond allocation fails to provide adequate risk protection during stock market downturns [3][9] - The analysis of U.S. company data from 1975 to 2021 reveals low-risk "safe equities" that exhibit relatively low future earnings risk, which can provide protection during market declines while also having potential upside during market increases [3][9] - Safe equities are positively correlated with the stock market and negatively correlated with bonds, offering higher long-term returns compared to bonds, making them a suitable alternative for investors seeking to minimize losses during downturns [3][9] Group 3: Measuring Long-Term Investor Returns - The report discusses the inadequacies of existing methods for measuring long-term investment returns, particularly in relation to transaction strategies, cash flow reinvestment, and consumption [4][11] - An analysis of over 71,000 global stocks suggests that arithmetic mean returns are unsuitable for long-term investors, while geometric mean returns apply only to specific buy-and-hold strategies [4][11] - The report introduces the concept of sustainable returns, defined as net holding returns divided by total holding returns, which reflects the proportion of fixed consumption that can be supported by the investment, providing a new perspective on evaluating long-term investment performance [4][11]

Group 1: Industry Insights - The coal industry is experiencing a continued decline in prosperity, while the cement and steel industries are expected to see positive profit growth year-on-year. Conversely, the coal and glass industries are projected to have negative profit growth [1] - The automotive electronics sector is poised for a turning point, with the rise of intelligent driving and the expansion of affordable smart technology, particularly with companies like BYD leading the charge [2] - The petrochemical industry is highlighted for its strategic importance in energy and food security, with state-owned enterprises expected to play a crucial role in ensuring supply amidst geopolitical tensions [4] Group 2: Company-Specific Analysis - Yuexiu Property is projected to achieve a revenue of 86.4 billion yuan in 2024, reflecting a year-on-year growth of 7.7%, despite a significant decline in net profit due to reduced gross margins [8] - Zhongxin Fluorine Materials is facing pressure on its performance due to declining prices of its pharmaceutical and agricultural intermediate products, alongside high depreciation costs from new capacity [9] - China Petroleum's major shareholder plans to increase its stake in the company, with expected net profits of 173 billion yuan, 178.4 billion yuan, and 182.9 billion yuan for the years 2025 to 2027 [10]

Group 1 - The report introduces a quantitative model solution for addressing the value-growth style switching issue based on odds and win rates [1][8] - The overall market growth style portfolio had a return of -0.55%, while the value style portfolio had a return of 0.18% in the last week [8] Group 2 - The estimated odds for the growth style is 1.01, while for the value style it is 1.03, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 31.12%, and for the value style, it is 68.88%, based on seven win rate indicators [3][17] Group 3 - The latest investment expectations calculated show a growth style expectation of -0.38 and a value style expectation of 0.40, leading to a recommendation for the value style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 26.73%, with a Sharpe ratio of 0.98 [4][19]

Quantitative Models and Construction Methods 1. Model Name: Interest Rate Price-Volume Multi-Cycle Timing Strategy - **Model Construction Idea**: This model uses kernel regression algorithms to identify the trend patterns of interest rates, capturing support and resistance levels. It integrates signals from long, medium, and short investment cycles to form a composite timing strategy[11][23] - **Model Construction Process**: 1. **Signal Generation**: - Use kernel regression to identify support and resistance levels for interest rate data across different cycles (long, medium, short)[11] - Signals are generated based on whether the interest rate breaks through these levels in an upward or downward direction[11] 2. **Cycle Frequency**: - Long cycle: Monthly signal switching - Medium cycle: Bi-weekly signal switching - Short cycle: Weekly signal switching[11] 3. **Composite Signal Scoring**: - If at least two out of three cycles show a downward breakthrough, the signal is "bullish" - If at least two out of three cycles show an upward breakthrough, the signal is "bearish"[11][23] 4. **Portfolio Construction**: - Full allocation to long-duration bonds when at least two cycles show a downward breakthrough and the trend is not upward - 50% allocation to medium-duration bonds and 50% to long-duration bonds when at least two cycles show a downward breakthrough but the trend is upward - Full allocation to short-duration bonds when at least two cycles show an upward breakthrough and the trend is not downward - 50% allocation to medium-duration bonds and 50% to short-duration bonds when at least two cycles show an upward breakthrough but the trend is downward - Equal allocation across short, medium, and long durations in other cases[23] 5. **Stop-Loss Mechanism**: - Adjust holdings to equal allocation when the daily excess return of the portfolio falls below -0.5%[23] 6. **Benchmark**: - Equal-duration strategy: 1/3 allocation to short, medium, and long durations[23] 2. Model Name: Public Bond Fund Duration and Divergence Tracking - **Model Construction Idea**: This model uses an improved regression model to dynamically track the weekly changes in the duration and divergence of public bond funds[13] - **Model Construction Process**: 1. **Duration Calculation**: - Median, 4-week moving average, and mean values of the duration (including leverage) of medium- and long-term pure bond funds are calculated[13][20] 2. **Divergence Measurement**: - Cross-sectional standard deviation of fund durations is used to measure divergence[14] 3. **Yield-to-Maturity (YTM) Analysis**: - Median, 4-week moving average, and mean values of YTM (including leverage) are calculated for the funds[20] --- Model Backtesting Results 1. Interest Rate Price-Volume Multi-Cycle Timing Strategy - **Long-Term Performance (2007.12.31 to Latest Report Date)**: - Annualized Return: 6.3% - Maximum Drawdown: 1.55% - Return-to-Drawdown Ratio: 2 - Excess Return: 1.78% - Excess Return-to-Drawdown Ratio: 0.92[23][24] - **Short-Term Performance (Since 2023 Year-End)**: - Annualized Return: 8.05% - Maximum Drawdown: 1.62% - Return-to-Drawdown Ratio: 6.91 - Excess Return: 2.78% - Excess Return-to-Drawdown Ratio: 2.85[4][23][24] - **Historical Success Rates (18 Years)**: - Absolute Return > 0: 100% - Excess Return > 0: 100%[24] 2. Public Bond Fund Duration and Divergence Tracking - **Duration Metrics**: - Median Duration: 3.13 years - 4-Week Moving Average: 3.19 years - Mean Duration: 3.4 years - Historical 5-Year Percentile: 91.51%[13][14] - **Divergence Metrics**: - Cross-Sectional Standard Deviation: 2.03 years - Historical 5-Year Percentile: 98.46%[14] - **YTM Metrics**: - Median YTM: 1.99% - 4-Week Moving Average: 2.12% - Mean YTM: 2.1%[20]