Abstract This year, the equity market has shown signs of recovery, leading to an overall increase in the scale of active equity funds and significant outperformance relative to index products. According to our statistics, from January to November 2025, active funds (+29.2%) achieved an excess return of over 12 percentage points compared to index funds (+17.0%). However, as the market enters a period of consolidation, the question of whether active funds can continue to outperform indices remains a key concern for many investors. This report delves into the topic of excess returns in active equity funds, analyzing it from two dimensions: the "vertical" (timing predictions: when active funds outperform indices) and the "horizontal" (cross-sectional predictions: how to use excess return information for fund selection).

**"Vertical": Predicting Active Equity Excess Returns Over Time** We identified six indicators that can predict active equity excess returns based on three key logics: 1. **Investment Themes and Institutional Pricing Power**: - Tracking error dispersion (TE-S) and active equity concentration (HHI) show long-term predictive power. When active funds exhibit concentrated holdings or low divergence in views, their pricing power over related assets increases, leading to higher expected excess returns. 2. **Risk-Taking Reflects Confidence**: - The quarter-over-quarter change in tracking error (TE-MDQ) has short-term predictive power. An increase in tracking error suggests fund managers have strong confidence in their stock-picking abilities, signaling potential future excess returns. 3. **Market Sentiment**: - Market momentum (MMT), trading volume (AMT), and implied volatility (VIX) also play roles. Strong market performance, high trading volume, or rising volatility often attract incremental capital, creating opportunities for active managers to generate excess returns.

**Short-Term Timing Model**: Using TE-MDQ, MMT, and VIX, we built a quarterly timing model with a 69% historical accuracy rate. For Q4 2025, the model predicts active funds will outperform indices by 2.33%.

**Long-Term Timing Model**: Based on AMT and TE-S, the annual timing model also shows 69% accuracy but forecasts underperformance (-6.49%) for active funds from Q4 2025 to Q3 2026.

**"Horizontal": Selecting Funds Using Excess Return Information** We identified two effective factors for predicting future fund performance: 1. **Long-Term Information Ratio (INFO_LONG)**: Measures excess returns per unit of active risk. 2. **Tracking Error Change (TRACK_D)**: Reflects adjustments in active risk exposure.

**Fund Selection Strategy**: A quarterly rebalanced portfolio of the top 30 funds based on a composite of these factors (after adjusting for fees) delivered an annualized return of 11.1% (vs. 7.5% for the benchmark) from Q1 2016 to Q3 2025, with lower drawdowns (-35.4% vs. -45.4%) and an 80% annual win rate.

**Key Takeaways**: - Active funds regained their edge in 2025 after two years of underperformance. - New regulations on performance benchmarks emphasize disciplined active management and stable excess returns. - Combining excess return and tracking error metrics can help identify high-quality funds, though the strategy may lag during strong market rallies.

The analysis underscores the importance of timing and selection in active fund investing, offering insights for navigating evolving market conditions.