【广发金工】基于多因子加权的ETF轮动策略

Core Viewpoint - The report aims to construct a multi-factor weighted ETF rotation strategy and optimize the stock factor mapping framework to test for marginal improvement in performance [1][5]. Group 1: Background and Market Overview - The concept of index-based investment has gained recognition among investors, with ETFs becoming a significant tool for asset allocation due to their transparency, low fees, and ease of trading. As of April 2025, the number of ETFs listed on domestic exchanges reached 1,141, with a total market value of 4.04 trillion yuan, an increase from 3.73 trillion yuan at the end of 2024 [4]. - Previous reports have constructed product rotation strategies based on various factors such as Level 2 data, redemption data, and neural networks, while also mapping individual stock factors to indices [4]. Group 2: Single Factor Stock Selection and ETF Rotation Comparison - The report compares the performance of single-factor stock selection and ETF rotation, highlighting the differences in factor characteristics. The factors include low-frequency price-volume, fundamentals, ETF redemption fund flows, Level 2 data, and neural network-related factors [6][7]. - Backtesting results indicate that the long position in stocks achieved significant excess returns compared to broad indices, while the RANK_IC and annualized returns of the ETF rotation factors showed marginal declines [11][15]. Group 3: ETF Rotation Backtesting Framework Adjustments - The report attempts to retain only the top-weighted components by setting an upper limit on weight thresholds and applying equal-weight "non-linear mapping." The results show that adjusting to equal-weight mapping covering all components led to a marginal decline in the performance of the single-factor long position [2]. - The ETF selection process identifies ETFs with similar holdings but different names, filtering out products with lower factor values when the overlap of constituent stocks exceeds a threshold. When an 80% overlap threshold is set, the performance of the single-factor long position improves [2]. Group 4: Multi-Factor Weighted ETF Rotation Empirical Testing - The empirical testing of the multi-factor weighted ETF rotation strategy, starting from January 2021, shows that the RANK_IC and ICIR of the portfolio improved marginally with monthly rebalancing. The equal-weighted top 5 long position achieved an annualized return of 18.6%, while the IC-weighted and ICIR-weighted portfolio achieved approximately 20% annualized returns, indicating more stable performance compared to the equal-weighted portfolio [2][15]. Group 5: ETF Rotation Backtesting Empirical Results - The backtesting results for ETF rotation indicate that the RANK_IC and annualized returns of the factors showed a noticeable marginal decline, with the ICIR significantly lower than that of the stock selection backtesting results [15].