Quantitative Models and Construction Methods 1. Model Name: Hang Seng Index R Timing Model - **Model Construction Idea**: The model integrates macroeconomic indicators, liquidity, inflation, and market sentiment to predict the monthly rise or fall of the Hang Seng Index R using a Logit regression approach. It transforms continuous variables into binary variables for prediction purposes[3][8][91] - **Model Construction Process**: - Selected four key variables as inputs: 1. Directional variable: USDHKD expectation revision lag_2 2. Threshold variable: CPI expectation revision lag_1 3. Directional variable: Citi China Economic Surprise Index monthly average lag_3 4. Volatility variable: Hang Seng Index put-call ratio change[8][85][87] - Applied Logit regression to train the model using 80% of the data as the training set and 20% as the test set - The regression equation is as follows: $ Return+1 = α + β1 + β2−1 + β3−2 + β4−3 + β5 + β6ℎ + β7 + β8 $ - Where `Return` is the binary variable representing the rise (1) or fall (0) of the Hang Seng Index R - `Var` represents the continuous variable transformed into a binary state variable - `Duration` is the state duration variable - `Change` is the state change variable (1 for change, 0 for no change) - `Entropy` is the information entropy over the past six periods - `Quarter` encodes the quarter of the variable (0 to 3 for Q1 to Q4)[39][91] - The model generates buy (1) or hold (0) signals based on the predicted value of the dependent variable[93] - **Model Evaluation**: The model demonstrates strong predictive ability with an out-of-sample AUC of approximately 0.70. Overfitting is controlled, with a degree of overfitting around 9.15%. The USDHKD expectation revision lag_2 is identified as the most significant variable, indicating that marginal changes in liquidity expectations dominate the prediction of Hang Seng Index R movements[8][91][95] --- Model Backtesting Results 1. Hang Seng Index R Timing Model - **Annualized Excess Return**: Approximately 10.74% from 2015 to January 2026[3][8][95] - **Monthly Win Rate**: About 81.95%[3][8][95] - **Annual Win Rate**: About 81.82%[3][8][95] - **Out-of-Sample AUC**: Approximately 0.70[3][8][91] - **Performance by Year**: - 2015: Annualized return 6.46%, excess annualized return 11.34%, IR 0.64 - 2016: Annualized return 9.16%, excess annualized return 4.66%, IR 0.28 - 2017: Annualized return 36.35%, excess annualized return -3.50%, IR -0.74 - 2018: Annualized return -8.06%, excess annualized return 4.16%, IR 0.45 - 2019: Annualized return 17.23%, excess annualized return 2.33%, IR 0.13 - 2020: Annualized return 21.20%, excess annualized return 21.55%, IR 1.39 - 2021: Annualized return 10.12%, excess annualized return 24.89%, IR 1.79 - 2022: Annualized return 5.59%, excess annualized return 20.74%, IR 0.78 - 2023: Annualized return 18.39%, excess annualized return 32.21%, IR 1.70 - 2024: Annualized return 37.07%, excess annualized return 11.50%, IR 0.78 - 2025: Annualized return 21.74%, excess annualized return -8.13%, IR -0.70 - 2026 (up to January): Annualized return 6.88%, excess annualized return 0.00%, IR 1.10[95][96] --- Quantitative Factors and Construction Methods 1. Factor Name: USDHKD Expectation Revision (lag_2) - **Factor Construction Idea**: Captures the marginal changes in liquidity expectations driven by USDHKD exchange rate revisions[3][8][46] - **Factor Construction Process**: - Calculated as the difference between the current month's forecast and the previous month's forecast for the USDHKD exchange rate - Transformed into a binary variable using a directional approach: positive values are set to 1, and negative values to 0[42][46] - **Factor Evaluation**: Demonstrates strong predictive ability with an out-of-sample AUC of 0.75 and a beta coefficient of -2.41, indicating that a slowdown in USD appreciation or an acceleration in depreciation increases the probability of Hang Seng Index R rising[46][61][63] 2. Factor Name: CPI Expectation Revision (lag_1) - **Factor Construction Idea**: Reflects inflation expectations and their impact on market sentiment and economic conditions[3][8][46] - **Factor Construction Process**: - Calculated as the difference between the current month's forecast and the previous month's forecast for CPI - Transformed into a binary variable using a threshold approach: values below the 20th percentile of the past 12 months are set to 1, and others to 0[42][46] - **Factor Evaluation**: Shows good predictive ability with an out-of-sample AUC of 0.66 and a beta coefficient of -1.42, indicating that a slowdown in inflation expectations increases the probability of Hang Seng Index R rising[46][53][55] 3. Factor Name: Citi China Economic Surprise Index Monthly Average (lag_3) - **Factor Construction Idea**: Measures the extent to which economic data exceeds or falls short of expectations, indicating economic momentum[3][8][56] - **Factor Construction Process**: - Calculated as the monthly average of the Citi China Economic Surprise Index - Transformed into a binary variable using a directional approach: positive values are set to 1, and negative values to 0[42][56] - **Factor Evaluation**: Demonstrates strong predictive ability with an out-of-sample AUC of 0.69 and a beta coefficient of 1.55, indicating that better-than-expected economic data increases the probability of Hang Seng Index R rising[56][61][64] 4. Factor Name: Hang Seng Index Put-Call Ratio Change - **Factor Construction Idea**: Captures market sentiment and risk aversion through changes in the put-call ratio of Hang Seng Index options[3][8][83] - **Factor Construction Process**: - Calculated as the change in the put-call ratio over the past six periods - Transformed into a binary variable using a volatility approach: values falling below the 20th percentile of the past 12 periods are set to 1, and others to 0[42][83] - **Factor Evaluation**: Provides incremental information for risk management, particularly in avoiding rapid market downturns. However, it has limited predictive ability for medium- to long-term trends, with an out-of-sample AUC of 0.48 and a beta coefficient of -1.71[83][85] --- Factor Backtesting Results 1. USDHKD Expectation Revision (lag_2) - **Out-of-Sample AUC**: 0.75 - **Beta**: -2.41 - **P-value**: 0.0006[61][63] 2. CPI Expectation Revision (lag_1) - **Out-of-Sample AUC**: 0.66 - **Beta**: -1.42 - **P-value**: 0.0333[45][46] 3. Citi China Economic Surprise Index Monthly Average (lag_3) - **Out-of-Sample AUC**: 0.69 - **Beta**: 1.55 - **P-value**: 0.0027[61][64] 4. Hang Seng Index Put-Call Ratio Change - **Out-of-Sample AUC**: 0.48 - **Beta**: -1.71 - **P-value**: 0.0053[80][83]

Core Insights - The market can remain irrational longer than one can maintain solvency, indicating a shift in market timing recognition by the company as of 2024 [2] - The company's fund management scale doubled, and third-party platform data improved the team's roadshow effectiveness [2] - Investors are increasingly concerned about market sentiment rather than raw data, especially when entrusting funds to quantitative private equity [2] Group 1: Company Strategy and Development - The company, led by He Rongtian, has pioneered various financial strategies, including ETF arbitrage and ABS pricing, establishing itself as a leader in fixed income research [3][4] - The concept of market timing proposed by the company faced initial resistance, as many in the industry adhered to the efficient market hypothesis, believing that market prices reflect all available information [3][4] - The company emphasizes a dual-track timing system, focusing on market cost-effectiveness rather than merely predicting price movements [8] Group 2: Market Dynamics and Quantitative Strategies - The characteristics of the A-share market, such as high volatility and multiple hotspots, create a favorable environment for timing strategies [9] - The company’s macro-quantitative timing model successfully predicted market risks, allowing it to avoid significant losses during downturns [9][10] - The company has developed a systematic approach to style timing, adjusting portfolio allocations based on relative returns rather than individual stock predictions [10][12] Group 3: Industry Trends and Future Outlook - The company has witnessed a significant evolution in the quantitative investment landscape, transitioning from marginalization to mainstream acceptance over the past decade [21][22] - The integration of causal modeling and AI with quantitative strategies is seen as the next frontier for the industry [21][22] - The company aims to maintain a balance between growth and performance stability, emphasizing the importance of a steady approach to expansion [16]