BOCIC(SH:601696) Bank of China Securities·2025-06-25 13:12Quantitative Models and Construction Methods Single Strategy Models - Model Name: High Prosperity Industry Rotation Strategy Construction Idea: Tracks industry profitability expectations using multi-factor models based on analysts' consensus data to select industries with upward profitability trends [13][15][16] Construction Process: 1. Constructs three types of factors: - Type 1: Long-term profitability factors (e.g., ROE_FY2, ROE_FY1) - Type 2: Quarterly changes in profitability (e.g., EPS_F2_qoq, EPS_F3_mom) - Type 3: Monthly changes in profitability (e.g., EPS_F3_qoq_d1m) 2. Filters industries with extreme valuations using PB percentile thresholds [30] 3. Selects top 3 industries based on composite factor rankings and allocates equally [21][30] Evaluation: Demonstrates strong performance in tracking industry cycles and avoiding valuation bubbles [13][26] - Model Name: Implicit Sentiment Momentum Strategy Construction Idea: Captures "unverified sentiment" by removing the relationship between turnover rate changes and returns, aiming to identify market sentiment-driven opportunities [32][33] Construction Process: 1. Uses OLS regression to remove "expected sentiment" from daily industry returns, leaving residuals as "unverified sentiment" [34] 2. Constructs momentum factors based on cumulative "unverified sentiment" returns over various time windows (e.g., 1 month, 12 months) [35] 3. Enhances the strategy by neutralizing fundamental impacts, adjusting for volatility, and applying composite factor methods [36] Evaluation: Effectively captures sentiment-driven market dynamics ahead of fundamental data releases [32][37] - Model Name: Macro Indicator Style Rotation Strategy Construction Idea: Uses macroeconomic indicators to predict industry styles (e.g., value, momentum) and maps them to industry selection [43][44] Construction Process: 1. Constructs macro indicators (e.g., PMI, CPI, M1) using historical positioning, surprise, and marginal change metrics [48][49] 2. Builds style factors (e.g., Value, Beta, Momentum) based on industry exposures [50][51] 3. Maps style predictions to industry scores and selects top industries [61] Evaluation: Addresses limitations of traditional top-down models by incorporating style-based predictions [43][61] - Model Name: Mid-to-Long-Term Momentum Reversal Strategy Construction Idea: Explores the "momentum-reversal" structure in industry returns, combining short-term momentum and long-term reversal factors [70][71] Construction Process: 1. Constructs momentum factors based on single-month returns and reversal factors based on multi-month returns (e.g., 12-month momentum, 24-36 month reversal) [76][78] 2. Combines factors using rank-weighted methods and adjusts for turnover rates [80][85] Evaluation: Balances short-term trends and long-term recovery opportunities effectively [70][84] - Model Name: Fund Flow Industry Rotation Strategy Construction Idea: Tracks institutional and tail-end fund flows to identify industry momentum [91][92] Construction Process: 1. Constructs "institutional trend strength factors" based on net buy amounts [93][94] 2. Constructs "tail-end inflow strength factors" based on post-14:30 net inflow data [96][103] 3. Combines factors and excludes high-concentration industries [100][101] Evaluation: Enhances stability by avoiding crowded trades [91][101] - Model Name: Financial Report Failure Reversal Strategy Construction Idea: Utilizes mean-reversion characteristics of long-term effective financial factors after short-term failures [108][109] Construction Process: 1. Constructs financial factors (e.g., ROA, YOY) using profit and balance sheet data [110][114] 2. Identifies "long-term effective factors" and "recently failed factors" based on rolling windows [116][117] 3. Combines factors using zscore methods [117] Evaluation: Captures recovery opportunities in temporarily underperforming factors [108][118] - Model Name: Traditional Low-Frequency Multi-Factor Scoring Strategy Construction Idea: Combines factors from four dimensions (momentum, valuation, liquidity, quality) for quarterly industry rotation [122][123] Construction Process: 1. Selects top-performing factors from each dimension (e.g., 1-year momentum, ROE_TTM) [124][125] 2. Combines factors using rank-weighted methods [135] 3. Filters industries with low weights in the CSI 800 index [135] Evaluation: Suitable for long-term holding with robust risk control [122][129] Composite Strategy Models - Model Name: Volatility-Controlled Composite Strategy Construction Idea: Allocates funds across single strategies based on inverse negative volatility [138][139] Construction Process: 1. Calculates negative volatility for each strategy over a rolling window (e.g., 63 days) [139][140] 2. Allocates funds proportionally to inverse negative volatility [139][147] 3. Adjusts allocation frequencies to match individual strategy cycles (weekly, monthly, quarterly) [141][146] Evaluation: Balances risk and return effectively, achieving high annualized excess returns [138][144] --- Model Backtest Results Single Strategy Results - High Prosperity Strategy: Annualized excess return 16.69%, max drawdown -12.95%, IR 1.29 [26] - Implicit Sentiment Strategy: Annualized excess return 18.61%, max drawdown -17.83%, IR 1.04 [37] - Macro Style Strategy: Annualized excess return 7.01%, max drawdown -23.46%, IR 0.30 [63] - Momentum Reversal Strategy: Annualized excess return 11.42%, max drawdown -14.91%, IR 0.77 [84] - Fund Flow Strategy: Annualized excess return 11.64%, max drawdown -12.16%, IR 0.96 [101] - Financial Report Strategy: Annualized excess return 9.13%, max drawdown -10.54%, IR 0.87 [118] - Low-Frequency Multi-Factor Strategy: Annualized excess return 12.00%, max drawdown -13.25%, IR 0.91 [129] Composite Strategy Results - Volatility-Controlled Composite Strategy: Annualized excess return 12.2%, max drawdown -6.8%, IR 1.80 [144][147]