Quantitative Models and Construction Methods 1. Model Name: Recurrent Neural Network (RNN) for Trend Strategy Enhancement - **Model Construction Idea**: Use RNN to predict whether a trend-following strategy will be profitable on a given trading day based on early morning market data. If the model predicts profitability, execute the trend-following strategy; otherwise, refrain from trading[3][22][61] - **Model Construction Process**: - Input: Early morning market data (e.g., opening price, closing price, high, low, volume, etc.) - Output: Binary classification (1 for profitable trend trading, 0 for non-profitable trend trading) - Model Structure: 13 input features → 200 LSTM units → 1 output node with Sigmoid activation function - Loss Function: Cross-entropy loss - Optimization: Gradient descent with backpropagation through time (BPTT) to minimize the loss function[46][61][66] - Training Data: Historical intraday data from 2010 to 2013, labeled based on profitability of trend strategies using metrics like Hurst index or K-line body ratio (R)[61][63] - Trading Rules: - Predict profitability at 33 minutes after market open - If predicted probability (p) > 120-day moving average of p (MA120(p)), execute trend-following trades; otherwise, no trades - Positions are closed at the end of the day or upon hitting stop-loss levels[63][66] - **Model Evaluation**: The RNN effectively filters out low-profitability signals, improving overall strategy performance. It demonstrates robustness to transaction costs and parameter stability[73][74][85] 2. Factor Name: Component Stock Consistency Indicator (R) - **Factor Construction Idea**: Measure the consistency of component stock movements to estimate market trend strength. Higher consistency indicates a stronger trend, suitable for trend-following strategies[14][21] - **Factor Construction Process**: - Formula: $$R = \frac{\lambda_1}{\sum_{i=1}^{300} \lambda_i} \times 100\%$$ where \( \lambda_1, \lambda_2, ..., \lambda_{300} \) are eigenvalues of the covariance matrix of component stock returns - Interpretation: \( R \) represents the variance contribution of the first principal component. Higher \( R \) values indicate stronger consistency among component stocks[14] - **Factor Evaluation**: The indicator effectively identifies market conditions suitable for trend-following strategies, as demonstrated by its ability to differentiate between high-consistency and low-consistency markets[14][21] --- Model Backtesting Results 1. RNN Model - **Annualized Return**: 18.47% (out-of-sample)[72] - **Cumulative Return**: 80.72% (out-of-sample)[72] - **Maximum Drawdown**: -8.63% (out-of-sample)[72] - **Win Rate**: 39.52% (out-of-sample)[72] - **Profit-Loss Ratio**: 2.27 (out-of-sample)[72] - **Average Return per Trade**: 0.17% (out-of-sample)[72] 2. Component Stock Consistency Indicator (R) - **High Consistency Example**: \( R = 86.4\), suitable for trend trading[16] - **Low Consistency Example**: \( R = 45.2\), unsuitable for trend trading[19] --- Factor Backtesting Results 1. Component Stock Consistency Indicator (R) - **High \( R \) Market**: Demonstrates strong trend-following profitability[16] - **Low \( R \) Market**: Demonstrates poor trend-following profitability[19]