Quantitative Models and Construction Methods 1. Model Name: Industry Consensus Expectation Dispersion Indicator - **Model Construction Idea**: The model is based on the hypothesis that industries with higher dispersion in analyst consensus expectations are more likely to deliver earnings surprises, which can lead to excess returns when market expectations shift from divergence to consensus[2][11]. - **Model Construction Process**: 1. For each stock, calculate the dispersion of FY1 net profit forecasts using the formula: $ \text{Dispersion} = \frac{\text{Max(FY1 Net Profit Forecast)} - \text{Min(FY1 Net Profit Forecast)}}{|\text{Median(FY1 Net Profit Forecast)}|} $ This measures the degree of disagreement among analysts regarding a stock's net profit forecast[12]. 2. To mitigate the impact of outliers, apply a winsorization process to the dispersion values within each industry[12]. 3. Aggregate the stock-level dispersion values to the industry level using weighted averages based on stock weights[12]. 4. Adjust for inherent differences in profit volatility across industries by calculating the rolling 2-year percentile rank of the dispersion indicator for each industry. This ensures cross-industry comparability[14]. - **Model Evaluation**: The model effectively identifies industries with higher potential for earnings surprises, particularly during the April earnings season, as evidenced by its historical performance[15]. --- Model Backtesting Results 1. Industry Consensus Expectation Dispersion Indicator - **April Performance**: - Median excess return for the long portfolio (industries with the highest dispersion): 2%[15]. - Excess return win rate: 88% (2018-2025 period)[15]. - **Overall Performance**: - Industries with high dispersion consistently outperformed the equal-weighted benchmark, while industries with low dispersion underperformed[15]. - **Latest Results (as of February 2026)**: - Industries with the highest dispersion: Retail, Communication, Food & Beverage[18]. - Industries with the lowest dispersion: Basic Chemicals, Non-Ferrous Metals, Building Materials[18].