The weighted average factor of analyst expected return is designed to capture the market's comprehensive expectations for the future returns of stocks. Its core logic is that not all analysts' forecasts are of equal value. Therefore, this factor uses subsequent market conditions to verify the accuracy of analysts' target price forecasts and assigns different weights to different forecasts. Specifically: nn- Weight adjustment mechanism: If the analyst's target price forecast can be verified by subsequent market trends (for example, the actual increase in the stock price is close to or exceeds the analyst's expected increase), the analyst's forecast result is given a larger weight; conversely, if the analyst's forecast is contrary to the actual trend, a smaller weight is given, and even a punitive weight can be set. nn- Factor meaning: The higher the value of this factor, the higher the overall market expectation for the future returns of the stock, and vice versa. Through weighted averaging, this factor can effectively reduce the interference of individual erroneous or biased forecasts on overall expectations. nn- **Calculation method of weight $wi$ (example, can be adjusted according to actual conditions):**n - A method similar to "backtesting" can be used to calculate the historical accuracy of each analyst's forecasts. For example: n - $Accuracy_i = frac{NumOfCorrectPredictions_i}{TotalPredictions_i}$ n - Where $NumOfCorrectPredictions_i$ represents the number of correct predictions made by analyst i in the past, and $TotalPredictions_i$ represents the total number of predictions made by analyst i in the past. n - The weight can be proportional to the accuracy of the prediction, for example: n - $w_i = frac{Accuracy_i}{sum_{j=1}^{N} Accuracy_j}$ or $w_i = Accuracy_i^k$, where k is an adjustment parameter, and the sensitivity of the weight can be adjusted according to needs. n - In addition, time decay can also be considered, and lower weights are given to predictions that are farther away in time. n - Other factors can also be considered, such as the reputation of the analyst, the rating of the institution, etc. nn- Important Tips: In actual applications, the calculation method of weights should be fully backtested and optimized to obtain the best factor effect. At the same time, it is necessary to consider changes in the market environment and dynamically adjust the weight calculation method.