Quantitative Models and Construction Methods 1. Model Name: RNN-LIN - Model Construction Idea: Simplify the traditional GRU model by using a linear RNN structure, reducing parameter complexity while maintaining competitive performance[2][17][20] - Model Construction Process: - The model uses a linear RNN structure with only a forget gate and an output gate. The hidden state is updated without non-linear activation functions - Equations: $ h_{t} = f_{t} \otimes h_{t-1} + (1 - f_{t}) \otimes c_{t} $ $ y_{t} = o_{t} \otimes h_{t} $ $ f_{t} = Sigmoid(x_{t}W_{f}) $ $ o_{t} = Sigmoid(x_{t}W_{o}) $ $ c_{t} = SiLU(x_{t}W_{c}) $ - $f_{t}$: Forget gate - $o_{t}$: Output gate - $c_{t}$: Candidate state[20][21] - The model reduces parameters by approximately 50% compared to GRU[21] - Evaluation: The linear RNN model shows slightly weaker performance than GRU but remains competitive. Adding GLU modules improves its performance significantly[22][53] 2. Model Name: DecompGRN - Model Construction Idea: Extend the linear RNN by integrating cross-sectional information directly into the RNN gating mechanism, enabling simultaneous modeling of temporal and cross-sectional data[2][50] - Model Construction Process: - The first RNN layer outputs individual stock representations at each time step - Cross-sectional information is incorporated by grouping stocks based on market capitalization and calculating group de-meaned values - The second RNN layer combines temporal and cross-sectional information in the forget and output gates - Equations: $ h_{t} = f_{t} \otimes h_{t-1} + (1 - f_{t}) \otimes c_{t} $ $ y_{t} = o_{t} \otimes h_{t} $ $ f_{t} = Sigmoid(x_{t}W_{f}) $ $ o_{t} = Sigmoid(x_{t}W_{o}) $ $ c_{t} = SiLU(x_{t}W_{c}) $ - $f_{t}$: Forget gate - $o_{t}$: Output gate - $c_{t}$: Candidate state[50][55] - Evaluation: DecompGRN outperforms the GRU baseline in terms of RankIC and RankICIR while maintaining only 43% of the GRU's parameter count[74][53] --- Model Backtest Results 1. RNN-LIN - RankIC: - CSI All Share: 0.13 - CSI 300: 0.10 - CSI 500: 0.09 - CSI 1000: 0.12[36][37] - RankICIR: - CSI All Share: 1.08 - CSI 300: 0.62 - CSI 500: 0.71 - CSI 1000: 0.96[36][37] - IC Win Rate: - CSI All Share: 0.88 - CSI 300: 0.74 - CSI 500: 0.78 - CSI 1000: 0.86[36][37] - Annualized Return (Top Group): - CSI All Share: 42.59% - CSI 300: 28.59% - CSI 500: 23.68% - CSI 1000: 32.81%[42] 2. DecompGRN - RankIC: - CSI All Share: 0.141 - CSI 300: 0.099 - CSI 500: 0.098 - CSI 1000: 0.127[55][58] - RankICIR: - CSI All Share: 1.26 - CSI 300: 0.65 - CSI 500: 0.77 - CSI 1000: 1.08[55][58] - IC Win Rate: - CSI All Share: 0.89 - CSI 300: 0.74 - CSI 500: 0.78 - CSI 1000: 0.88[55][58] - Annualized Return (Top Group): - CSI All Share: 57.68% - CSI 300: 31.69% - CSI 500: 26.9% - CSI 1000: 40.35%[57][58] --- Index Enhancement Test Results (DecompGRN) - Annualized Excess Return: - CSI 300: 10.24% - CSI 500: 10.05% - CSI 1000: 19.58%[75][85] - Tracking Error: - CSI 300: 5.07 - CSI 500: 6.1 - CSI 1000: 6.75[75][85] - Cumulative Excess Return (as of 2025-08-27): - CSI 300: 3.93% - CSI 500: 6.72% - CSI 1000: 18.26%[75][85]
AI+HI系列：DecompGRNv1：基于线性RNN的端到端模型初探
Huachuang Securities·2025-09-05 08:12