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股指分红点位监控周报:IH及IF主力合约升水,IC及IM主力合约贴水-20250918
Guoxin Securities·2025-09-18 01:44

Quantitative Models and Construction Methods - Model Name: Index Dividend Points Estimation Model Model Construction Idea: This model aims to estimate the dividend points of stock indices to account for the impact of constituent stock dividends on index futures' premium/discount levels. It is essential for accurately calculating the basis and premium/discount levels of index futures contracts[11][44][47] Model Construction Process: 1. Formula: Dividend Points = $ \sum_{n=1}^{N} \frac{\text{Dividend Amount of Constituent Stock}}{\text{Total Market Value of Constituent Stock}} \times \text{Constituent Stock Weight} \times \text{Index Closing Price} $ - N N : Number of constituent stocks - Dividend amounts are considered only if the ex-dividend date falls between the current date (t t ) and the contract expiration date (T T )[44] 2. Steps: - Obtain constituent stock weights and index closing prices - For stocks with announced dividend amounts and ex-dividend dates, use the provided data - For stocks without announced data, estimate dividend amounts based on historical net profit and payout ratios, and predict ex-dividend dates using historical patterns[45][47] Model Evaluation: The model demonstrates high accuracy for indices like the SSE 50 and CSI 300, with prediction errors around 5 points. However, the accuracy for the CSI 500 index is slightly lower, with errors around 10 points[64] - Model Name: Dynamic Prediction of Net Profit Model Construction Idea: This model predicts annual net profit for constituent stocks based on historical profit distribution patterns, enabling the estimation of dividend amounts for stocks without disclosed data[50][53] Model Construction Process: 1. Classify companies into two categories: stable and unstable profit distribution 2. For stable companies, predict based on historical profit distribution patterns 3. For unstable companies, use the previous year's corresponding period profit as the prediction value[53][55] Model Evaluation: The model effectively captures profit trends for stable companies but may face challenges with companies exhibiting irregular profit patterns[53] - Model Name: Historical Dividend Payout Ratio Estimation Model Construction Idea: This model estimates the dividend payout ratio for constituent stocks based on historical averages, assuming stability in payout ratios for companies with consistent operations[54] Model Construction Process: 1. If the company paid dividends last year, use the previous year's payout ratio 2. If no dividends were paid last year, use the average payout ratio of the past three years 3. If the company has never paid dividends, assume no dividends for the current year 4. Cap the payout ratio at 100% to avoid unrealistic estimates[56] Model Evaluation: The model is suitable for companies with stable operations but may not be accurate for firms with volatile financial policies[54] - Model Name: Ex-Dividend Date Prediction Model Model Construction Idea: This model predicts the ex-dividend dates of constituent stocks based on historical intervals between announcement and ex-dividend dates[54][59] Model Construction Process: 1. If the ex-dividend date is announced, use the provided date 2. If not, estimate based on historical intervals between announcement and ex-dividend dates 3. Default dates are used for companies with no historical data or when historical dates are deemed unreliable[59] Model Evaluation: The model effectively predicts ex-dividend dates for most companies, with approximately 90% of firms completing dividends by the end of July[59] Model Backtesting Results - Index Dividend Points Estimation Model: - SSE 50 Index: Prediction error ~5 points[64] - CSI 300 Index: Prediction error ~5 points[64] - CSI 500 Index: Prediction error ~10 points[64] Quantitative Factors and Construction Methods - Factor Name: Constituent Stock Weight Adjustment Factor Factor Construction Idea: Adjust constituent stock weights dynamically to reflect daily changes in stock prices and corporate actions[48][49] Factor Construction Process: 1. Formula: $ W_{n,t} = \frac{w_{n0} \times (1 + r_{n})}{\sum_{i=1}^{N} w_{i0} \times (1 + r_{i})} $ - wn0 w_{n0} : Weight of stock n n at the last disclosed date - rn r_{n} : Non-adjusted return of stock n n since the last disclosed date 2. Use daily disclosed weights from the China Securities Index Company to ensure accuracy[48][49] Factor Evaluation: This factor improves the precision of weight adjustments, especially during periods of corporate actions like stock splits or rights issues[49] Factor Backtesting Results - Constituent Stock Weight Adjustment Factor: - Daily weight adjustments align closely with disclosed weights, ensuring high accuracy in index calculations[49]