股指分红点位监控周报：分红进度过半，各股指期货主力合约均贴水-20250702

Quantitative Models and Construction Methods - Model Name: Index Dividend Points Estimation Model Model Construction Idea: This model estimates the dividend points of index constituents to account for the natural decline in index levels caused by dividend ex-dates, which is critical for accurately calculating the basis and premium/discount levels of stock index futures[12][40][43] Model Construction Process: 1. Formula: $\text{Dividend Points} = \sum_{n=1}^{N} \left( \frac{\text{Dividend Amount of Constituent Stock}}{\text{Total Market Value of Constituent Stock}} \times \text{Constituent Stock Weight} \times \text{Index Closing Price} \right)$ - $N$: Number of constituent stocks - Dividend amount, total market value, and weight are calculated for each constituent stock[40] 2. Steps: - Identify constituent stocks and their weights - For stocks with announced dividend amounts and ex-dates, use the provided data - For stocks without announced data, estimate dividend amounts using historical net profit and payout ratios, and predict ex-dates using historical patterns[41][46][50] - Adjust weights dynamically based on daily price changes using the formula: $W_{n,t} = \frac{w_{n0} \times (1 + r_{n})}{\sum_{i=1}^{N} w_{i0} \times (1 + r_{i})}$ - $w_{n0}$: Initial weight of stock $n$ - $r_{n}$: Non-adjusted return of stock $n$ since the last weight update[44][45] Model Evaluation: The model demonstrates high accuracy for indices like the SSE 50 and CSI 300, with prediction errors generally within 5 points. However, the error for the CSI 500 index is slightly larger, around 10 points[56][60] Model Backtesting Results - Index Dividend Points Estimation Model: - SSE 50 Index: Prediction error ~5 points[60] - CSI 300 Index: Prediction error ~5 points[60] - CSI 500 Index: Prediction error ~10 points[60] Quantitative Factors and Construction Methods - Factor Name: Net Profit Prediction Factor Factor Construction Idea: Predict net profit dynamically based on historical profit distribution patterns to estimate dividend amounts for constituent stocks[46][49] Factor Construction Process: 1. Classify companies into "stable" and "unstable" profit distribution categories based on historical data 2. For stable companies, use historical profit distribution patterns for prediction 3. For unstable companies, use the previous year's profit as the prediction value[49][51] Factor Evaluation: The dynamic prediction method effectively captures profit trends for most companies, providing a reliable basis for dividend estimation[49] - Factor Name: Dividend Payout Ratio Prediction Factor Factor Construction Idea: Use historical payout ratios to estimate the current year's payout ratio for companies without announced data[50] Factor Construction Process: 1. If the company paid dividends last year, use last 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% if it exceeds this threshold[52] Factor Evaluation: Historical payout ratios provide a stable and effective basis for prediction, especially for companies with consistent dividend policies[50][52] - Factor Name: Ex-Dividend Date Prediction Factor Factor Construction Idea: Predict ex-dividend dates using historical intervals between announcement and ex-dividend dates, with adjustments for outliers[50][55] Factor Construction Process: 1. If the ex-dividend date is announced, use the provided date 2. If not announced, estimate based on historical intervals between announcement and ex-dividend dates 3. Apply default dates (e.g., July 31, August 31, or September 30) if historical data is insufficient or inconsistent[55] Factor Evaluation: The method captures the majority of ex-dividend dates accurately, with over 90% of companies completing dividends by the end of July[55] Factor Backtesting Results - Net Profit Prediction Factor: High accuracy for stable companies; less reliable for companies with volatile profit distributions[49] - Dividend Payout Ratio Prediction Factor: Effective for companies with consistent payout policies; less applicable for companies with irregular dividend histories[50][52] - Ex-Dividend Date Prediction Factor: Accurately predicts dates for over 90% of companies, with most dividends completed by July[55]