Quantitative Models and Construction Methods 1. Model Name: Macro Timing Strategy - **Model Construction Idea**: The model is designed to provide equity allocation recommendations based on macroeconomic indicators, including economic growth and monetary liquidity[5][42] - **Model Construction Process**: - The model evaluates the strength of signals from two dimensions: economic growth and monetary liquidity - For each dimension, a percentage signal strength is assigned (e.g., 0% for economic growth and 50% for monetary liquidity in October)[42][43] - The model aggregates these signals to determine the recommended equity allocation percentage (e.g., 25% for November)[42][43] - **Model Evaluation**: The model has achieved a year-to-date return of 13.55%, underperforming the Wind All A Index, which returned 25.61% during the same period[42][45] --- Model Backtesting Results 1. Macro Timing Strategy - **Equity Allocation Recommendation**: 25% for November[42][43] - **Year-to-Date Return**: 13.55%[42][45] - **Benchmark (Wind All A Index) Return**: 25.61%[42][45] --- Quantitative Factors and Construction Methods 1. Factor Name: Value Factor - **Factor Construction Idea**: Measures the relative valuation of stocks to identify undervalued opportunities[48][60] - **Factor Construction Process**: - Includes metrics such as book-to-price ratio (BP_LR), earnings-to-price ratio (EP_FTTM), and sales-to-enterprise value ratio (Sales2EV)[60] - **Factor Evaluation**: The value factor performed strongly in the past week, with an IC mean of 12.38% in the CSI 300 stock pool and 31.97% in the CSI 500 stock pool[48][49] 2. Factor Name: Volatility Factor - **Factor Construction Idea**: Captures the risk and price fluctuation characteristics of stocks[48][61] - **Factor Construction Process**: - Includes metrics such as 60-day return volatility (Volatility_60D) and CAPM residual volatility (IV_CAPM)[61] - **Factor Evaluation**: The volatility factor showed strong performance, with an IC mean of 19.89% in the All A-share stock pool and 22.41% in the CSI 1000 stock pool[48][49] 3. Factor Name: Technical Factor - **Factor Construction Idea**: Utilizes historical price and volume data to identify trading opportunities[48][61] - **Factor Construction Process**: - Includes metrics such as 20-day turnover mean (Turnover_Mean_20D) and 240-day return skewness (Skewness_240D)[61] - **Factor Evaluation**: The technical factor achieved an IC mean of 13.68% in the All A-share stock pool and 8.17% in the CSI 500 stock pool[48][49] 4. Factor Name: Growth Factor - **Factor Construction Idea**: Focuses on identifying stocks with high growth potential based on financial metrics[48][60] - **Factor Construction Process**: - Includes metrics such as single-quarter net income growth (NetIncome_SQ_Chg1Y) and single-quarter operating income growth (OperatingIncome_SQ_Chg1Y)[60] - **Factor Evaluation**: The growth factor underperformed, with an IC mean of -6.34% in the All A-share stock pool and -10.06% in the CSI 500 stock pool[48][49] 5. Factor Name: Quality Factor - **Factor Construction Idea**: Identifies stocks with strong financial health and operational efficiency[48][61] - **Factor Construction Process**: - Includes metrics such as operating cash flow to current debt ratio (OCF2CurrentDebt) and gross margin (GrossMargin_TTM)[61] - **Factor Evaluation**: The quality factor underperformed, with an IC mean of -14.36% in the All A-share stock pool and -14.07% in the CSI 500 stock pool[48][49] --- Factor Backtesting Results 1. Value Factor - **IC Mean**: 12.38% (CSI 300), 31.97% (CSI 500), 22.41% (CSI 1000)[48][49] - **Multi-Long-Short Portfolio Return**: Positive across all stock pools[48][49] 2. Volatility Factor - **IC Mean**: 19.89% (All A-shares), 22.41% (CSI 1000)[48][49] - **Multi-Long-Short Portfolio Return**: Positive across all stock pools[48][49] 3. Technical Factor - **IC Mean**: 13.68% (All A-shares), 8.17% (CSI 500)[48][49] - **Multi-Long-Short Portfolio Return**: Positive across all stock pools[48][49] 4. Growth Factor - **IC Mean**: -6.34% (All A-shares), -10.06% (CSI 500)[48][49] - **Multi-Long-Short Portfolio Return**: Negative across all stock pools[48][49] 5. Quality Factor - **IC Mean**: -14.36% (All A-shares), -14.07% (CSI 500)[48][49] - **Multi-Long-Short Portfolio Return**: Negative across all stock pools[48][49]

Group 1: Macro Timing Strategy - The macro timing strategy has an annualized return of 7.67% as of October 31, 2025, with a Sharpe ratio of 1.45 and a Calmar ratio of 1.67, indicating a maximum drawdown of -4.60% [2][4][5] - The latest portfolio allocation includes 7.01% in CSI 300 ETF, 7.99% in CSI 500 ETF, 55.94% in government bond ETF, 11.63% in soybean meal ETF, 5.02% in non-ferrous ETF, 7.40% in gold ETF, and 5.00% in currency ETF, with no allocation to S&P 500 ETF and corporate bond ETF [7][8] Group 2: Momentum Strategy - The momentum strategy has an annualized return of 18.25% since January 2020, with a Sharpe ratio of 0.88 and a Calmar ratio of 0.64, experiencing a maximum drawdown of -28.72% [9][10] - The latest portfolio allocation includes 27.01% in Huatai-PB CSI Telecom Theme ETF, 24.92% in Fuguo CSI Tourism Theme ETF, 21.52% in Xinhua CSI Cloud Computing 50 ETF, 16.38% in Huatai-PB CSI Smart Car ETF, and 8.17% in Huaxia CSI Artificial Intelligence ETF [13][14] Group 3: Sector Rotation Strategy - The sector rotation strategy has an annualized return of 10.00% since 2020, with an excess return of 7.27% relative to CSI 300, and a maximum drawdown of -42.98% [15] - The latest portfolio includes home appliance ETF, green power ETF, steel ETF, new energy vehicle ETF, financial ETF, and agricultural ETF, while excluding non-ferrous metals ETF and transportation ETF [18][19] Group 4: Copula-Based Second-Order Stochastic Dominance Strategy - The Copula-based second-order stochastic dominance strategy has an annualized return of 14.41% since January 2020, with a Sharpe ratio of 0.68 and a maximum drawdown of -42.62% [20][24] - The latest portfolio allocation includes 5.00% in Huaxia CSI Petrochemical Industry ETF, 85.00% in Fuguo CSI 800 Bank ETF, 5.00% in Fuguo CSI All-Index Securities Company ETF, and 5.00% in Bosera CSI Oil and Gas Resources ETF [23][25] Group 5: Quantile Random Forest Technology ETF Allocation Strategy - The quantile random forest technology ETF allocation strategy has an annualized return of 13.54% since 2020, with a Sharpe ratio of 0.76 and a maximum drawdown of -29.89% [26] - The latest portfolio allocation consists of 95.63% in technology ETFs, including 4.78% in Jiahua National Communication ETF, 4.78% in Tianhong CSI Photovoltaic Industry ETF, 4.78% in Huabao CSI Military Industry ETF, 76.51% in Ping An CSI Consumer Electronics Theme ETF, and 4.78% in Fuguo CSI Technology 50 Strategy ETF [29][30]

Group 1 - The macro timing strategy has an annualized return of 7.08% and a Sharpe ratio of 1.34 as of August 29, 2025, with the latest portfolio including various ETFs such as the CSI 500 ETF (8.35%) and government bond ETFs (38.21%) [2][4][8] - The momentum strategy has an annualized return of 20.22% since 2020, with a recent portfolio allocation including the CSI Digital Economy Theme ETF (19.51%) and the Shanghai Stock Exchange Sci-Tech Innovation Board Chip ETF (20.37%) [10][14] - The industry rotation strategy has achieved an annualized return of 9.34% since 2020, with the latest holdings including non-ferrous metals ETFs and green power ETFs [19][16] Group 2 - The Copula-based second-order stochastic dominance strategy has an annualized return of 15.52% since 2020, with the latest portfolio including the Huaxia CSI Agricultural Theme ETF (6.71%) and the Guangfa CSI Major Consumption ETF (69.79%) [21][24] - The technology ETF allocation strategy based on quantile random forests has an annualized return of 12.33% since 2020, with a significant portion allocated to the Guangfa CSI All-Index Information Technology ETF (4.78%) and the Huatai-PineBridge CSI Photovoltaic Industry ETF (76.51%) [27][31]

- The macro timing strategy model suggests a recommended equity position of 45% for June, with signal strengths of 50% for economic growth and 40% for monetary liquidity[3][26][27] - The micro-cap stock rotation and timing signals remain strong, with the micro-cap/Chow index relative net value rising to 1.93 times, above its 243-day moving average of 1.41 times[4][29] - The micro-cap stock's 20-day price slope is 0.00257, indicating stronger upward momentum compared to the Chow index's -0.00019[4][29] - The risk warning has been lifted, with volatility congestion at -0.415%, well below the warning threshold of 0.55%, and the 10-year government bond yield at -0.27%, below the risk control line of 0.30%[4][29] - The market's recent rise has favored small-cap growth styles, leading to strong performance in market cap, consensus expectations, and growth factors, while technical and low-volatility factors have underperformed[4][40] - The market cap factor had the highest IC in the CSI 300 pool at 0.2241, while the growth factor had a weak signal in the CSI 500 pool with an IC of -0.0305[39] - The market cap factor also performed well in the entire A-share pool with an IC of 0.2347[39] - The weekly performance of multi-factor strategies showed the market cap factor leading with a gain of approximately +2.21% in the CSI 300 pool, while the growth factor rose by about +0.17%[39] - The consensus expectations factor and growth factor are expected to continue performing well, while technical and low-volatility factors may see a rebound as market sentiment slows down[40] - The convertible bond selection factors showed the stock growth factor leading with a gain of about 0.72%, followed by the stock consensus expectations factor with a return of about 0.63%[45] - The stock quality factor fell by about 0.26%, the stock value factor retreated by about 0.66%, and the convertible bond valuation factor had the largest decline of about 1.70%[45] Model Backtest Results - Macro timing strategy model, equity position: 45%[3][26][27] - Micro-cap stock/Chow index relative net value: 1.93 times[4][29] - Micro-cap stock 20-day price slope: 0.00257[4][29] - Volatility congestion: -0.415%[4][29] - 10-year government bond yield: -0.27%[4][29] - Market cap factor IC in CSI 300 pool: 0.2241[39] - Growth factor IC in CSI 500 pool: -0.0305[39] - Market cap factor IC in entire A-share pool: 0.2347[39] - Market cap factor weekly gain in CSI 300 pool: +2.21%[39] - Growth factor weekly gain in CSI 300 pool: +0.17%[39] - Stock growth factor weekly gain: 0.72%[45] - Stock consensus expectations factor weekly return: 0.63%[45] - Stock quality factor weekly decline: -0.26%[45] - Stock value factor weekly decline: -0.66%[45] - Convertible bond valuation factor weekly decline: -1.70%[45]

Quantitative Models and Construction Methods 1. Model Name: Macro Timing Strategy - **Model Construction Idea**: The model aims to provide signals for equity allocation based on macroeconomic growth and monetary liquidity indicators[26] - **Model Construction Process**: The model uses dynamic macro event factors to construct a stock-bond rotation strategy. The signal strength for economic growth and monetary liquidity is calculated monthly. For April, the signal strength for economic growth is 0%, and for monetary liquidity is 50%[26][27] - **Model Evaluation**: The model has shown a return of 1.06% from the beginning of 2025 to the present, compared to a 1.90% return for the Wind All A index during the same period[26] 2. Model Name: Micro Cap Timing Model - **Model Construction Idea**: The model focuses on timing and rotation signals for micro-cap stocks based on volatility and interest rate indicators[30] - **Model Construction Process**: The model uses two mid-term risk warning indicators: 1) Ten-year government bond yield YoY indicator and 2) Volatility congestion YoY indicator. On October 15, 2024, the volatility congestion indicator fell below the threshold, lifting the risk warning signal. The interest rate YoY indicator was -20.45%, not triggering the risk control threshold of 0.3[30] - **Model Evaluation**: The model has not triggered risk control, suggesting investors continue holding micro-cap stocks[30] Model Backtest Results 1. Macro Timing Strategy - **Economic Growth Signal Strength**: 0%[27] - **Monetary Liquidity Signal Strength**: 50%[27] - **Equity Allocation Recommendation**: 25%[27] - **Return from 2025 to Present**: 1.06%[26] 2. Micro Cap Timing Model - **Ten-year Government Bond Yield YoY**: -28.69%[31] - **Volatility Congestion YoY**: -50.09%[31] Quantitative Factors and Construction Methods 1. Factor Name: Value Factor - **Factor Construction Idea**: The factor aims to capture the value characteristics of stocks based on fundamental metrics[37] - **Factor Construction Process**: The value factor includes metrics such as the latest annual report book value to market value (BP_LR), future 12-month consensus expected net profit to market value (EP_FTTM), and past 12-month operating income to market value (SP_TTM)[47] - **Factor Evaluation**: The value factor performed best in the CSI 300 stock pool last week[37] 2. Factor Name: Size Factor - **Factor Construction Idea**: The factor aims to capture the size characteristics of stocks based on market capitalization[37] - **Factor Construction Process**: The size factor includes metrics such as the logarithm of circulating market capitalization (LN_MktCap)[47] - **Factor Evaluation**: The size factor showed strong positive returns in the CSI 1000 stock pool last week[37] Factor Backtest Results 1. Value Factor - **IC Mean (CSI 300)**: 25.88%[38] - **IC Mean (CSI 500)**: 10.56%[38] - **IC Mean (CSI 1000)**: 6.32%[38] - **Multi-Long Return (CSI 300)**: 10.84%[38] - **Multi-Long Return (CSI 500)**: 10.56%[38] - **Multi-Long Return (CSI 1000)**: 6.32%[38] 2. Size Factor - **IC Mean (CSI 300)**: 3.33%[38] - **IC Mean (CSI 500)**: -3.23%[38] - **IC Mean (CSI 1000)**: -1.84%[38] - **Multi-Long Return (CSI 300)**: 3.33%[38] - **Multi-Long Return (CSI 500)**: -3.23%[38] - **Multi-Long Return (CSI 1000)**: -1.84%[38]

Quantitative Models and Factor Analysis Quantitative Models and Construction - **Model Name**: Macro Timing Strategy **Construction Idea**: This model evaluates macroeconomic signals to determine equity allocation recommendations. It incorporates economic growth and monetary liquidity signals to generate timing signals for equity investments[4][27]. **Construction Process**: 1. The model assigns weights to two dimensions: economic growth and monetary liquidity. 2. Signal strength is calculated for each dimension. For April, the economic growth signal strength was 0%, while the monetary liquidity signal strength was 50%[27]. 3. Based on these signals, the recommended equity allocation for April was 25%[27]. **Evaluation**: The model provides a systematic approach to macro timing, but its performance is subject to changes in macroeconomic conditions[27]. - **Model Name**: Micro-Cap Timing and Rotation Model **Construction Idea**: This model uses indicators related to market sentiment and fundamentals to monitor micro-cap stock performance and rotation opportunities[31]. **Construction Process**: 1. **Rotation Signal**: The model tracks the relative net value of the Micro-Cap Index and the "Mao Index" (a benchmark index). A signal was triggered on October 14, 2024, when the Micro-Cap Index crossed above its annual moving average[31]. 2. **Risk Warning Indicators**: - **Volatility Congestion**: This indicator reflects market sentiment. On October 15, 2024, the indicator fell below its threshold, deactivating the risk warning[31]. - **10-Year Treasury Yield YoY**: This fundamental indicator remained at -20.45%, below the risk control threshold of 0.3[31]. **Evaluation**: The model effectively combines sentiment and fundamental indicators to guide micro-cap stock investments[31]. Model Backtesting Results - **Macro Timing Strategy**: - Year-to-date return: 1.06% - Benchmark (Wind All A Index) return: 1.90%[27] - **Micro-Cap Timing and Rotation Model**: - Volatility Congestion YoY: -50.09% - 10-Year Treasury Yield YoY: -28.69%[31][32] --- Quantitative Factors and Construction - **Factor Name**: Volume-Price Factors **Construction Idea**: These factors capture market dynamics by analyzing trading volume and price volatility[5]. **Construction Process**: - **Low Trading Volume**: Measures stocks with lower trading activity. - **Low Volatility**: Identifies stocks with stable price movements[5]. **Evaluation**: These factors performed well in a low-risk appetite environment, benefiting from market stability[5]. - **Factor Name**: Consensus Expectation Factor **Construction Idea**: This factor reflects market expectations for stocks with strong earnings forecasts[5]. **Construction Process**: - Derived from analysts' earnings forecasts and target prices. - Tracks changes in consensus expectations over time[5]. **Evaluation**: The factor performed well due to investors' preference for certainty in volatile markets[5]. - **Factor Name**: Convertible Bond Selection Factors **Construction Idea**: These factors predict convertible bond performance based on their relationship with underlying stocks and valuation metrics[46]. **Construction Process**: - **Equity Factors**: Derived from the underlying stock's consensus expectations, growth, financial quality, and valuation. - **Valuation Factor**: Based on the premium rate between the convertible bond's parity and floor price[46]. **Evaluation**: The factors achieved positive returns, indicating their effectiveness in identifying outperforming convertible bonds[46]. Factor Backtesting Results - **Volume-Price Factors**: - Low Trading Volume: Positive performance in a low-risk appetite environment[5]. - Low Volatility: Positive performance in stable market conditions[5]. - **Consensus Expectation Factor**: - Positive performance due to strong earnings forecast alignment[5]. - **Convertible Bond Selection Factors**: - Positive multi-long-short returns for equity consensus expectation, equity valuation, and convertible bond valuation factors[46].