“风起云涌”风格轮动系列研究（一）：从微观出发的风格轮动—找到风格切换的领先特征

Group 1 - The report focuses on constructing a style timing and rotation model from a micro perspective, utilizing micro data to enhance the strategy system [6][62] - The model is based on four style factors: valuation, market capitalization, volatility, and momentum, using 80 micro indicators to create a scoring system [7][62] - The backtesting period from January 1, 2014, to July 31, 2025, shows an annualized return of 20.90% with a volatility of 26.12% and a maximum drawdown of -40.57% [57][62] Group 2 - The model's out-of-sample performance has been stable since its development in March 2024, with a return of 55.36% for the entire year of 2024, outperforming the market benchmark by 35.72% [57][62] - The report highlights the construction of style labels based on specific broad indices to overcome limitations of using the entire A-share market for style timing [18][20] - The random forest model is selected for predicting the direction of style factors, enhancing the performance of the timing strategy [23][25] Group 3 - The performance metrics for the valuation factor before timing show an annualized return of 7.90% compared to the benchmark's 6.85%, with a maximum drawdown of -60.33% [30][32] - After applying the timing model, the valuation factor's annualized return improves to 15.19%, significantly outperforming the benchmark [38][40] - The momentum factor shows a pre-timing annualized return of 10.11%, which increases to 15.73% post-timing, indicating improved performance [42][47] Group 4 - The volatility factor's pre-timing performance indicates an annualized return of 10.93%, while post-timing performance shows an increase to 15.73% [48][53] - The equal-weighted composite factor, derived from the four style factors, achieves an annualized return of 20.05% with a maximum drawdown of -41.97% [52][55] - The scoring system for the style factors is based on historical prediction accuracy, further refining the composite factor's performance [56][59]