1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The performance of the first batch of Sci - tech Bond ETFs has been on the market for nearly 4 months, and the second batch for nearly 2 months. There is a differentiation in the yields of different products within the same batch, with some Sci - tech Bond ETFs leading in performance [1]. - Products tracking the "Shenzhen AAA Sci - tech Bonds" have outperformed. The best - performing Sci - tech Bond ETFs in both batches are the only ones tracking the "Shenzhen AAA Sci - tech Bonds" in their respective batches [2]. - Most Sci - tech Bond ETFs continue to increase their duration, while the duration of benchmark - making credit bond ETFs is relatively stable [3]. - The trading volume ratio of Sci - tech Bond ETFs and benchmark - making ETFs to credit bonds remains low. Attention should be paid to the investment opportunities in the constituent bonds of Sci - tech Bond ETFs, especially when the spread between "non - constituent bonds - constituent bonds" is high [3]. 3. Summary by Related Catalogs 3.1 Performance Differentiation of Sci - tech Bond ETFs - In the first batch, the best - performing is Invesco Great Wall Sci - tech Bond ETF with a since - listing yield of 0.35%, followed by E Fund Sci - tech Bond ETF with a yield of 0.29%, and the yields of other ETFs are below 0.2% [1]. - In the second batch, the best - performing is Wanjia Sci - tech Bond ETF with a since - listing yield of 0.48%, and the yields of Huatai - Peregrine, Dacheng, and Tianhong Sci - tech Bond ETFs are between 0.41% - 0.46% [1]. 3.2 Reasons for Performance Differentiation - The "Shenzhen AAA Sci - tech Bonds" have performed relatively well. The index yield of the "Shenzhen AAA Sci - tech Bonds" in the past 3 months is 0.32%, while those of the "CSI AAA Sci - tech Bonds" and "Shanghai AAA Sci - tech Bonds" are 0.21% and 0.20% respectively [2]. - There are only 2 ETFs tracking the "Shenzhen AAA Sci - tech Bonds", so the trading is less crowded. Also, the "Shenzhen AAA Sci - tech Bonds" do not have requirements for the implied ratings of constituent bonds, leaving a certain spread [2]. 3.3 Scale and Component Bond Changes - On November 14, the scale of credit bond ETFs reached 493.7 billion yuan, basically unchanged from November 7. The weekly scale changes of each ETF are generally within (- 2%, 2%) [2]. - The newly issued central and state - owned enterprise bonds with a term of over 5 years are still the types of bonds being increased by Sci - tech Bond ETFs. The 2 - 3 - year term is also a major term for bond - increasing, mainly in the finance and power industries. The bonds being reduced are concentrated in the 2 - 3 - year term, mainly in the building materials industry [2]. 3.4 Duration Changes - 18 Sci - tech Bond ETFs, accounting for 75%, continue to increase their duration. Among them, China Merchants Sci - tech Bond ETF's duration increased by 0.2 years to 4.05 years last week, becoming the Sci - tech Bond ETF with the longest duration [3]. - The duration of benchmark - making credit bond ETFs is relatively stable, basically unchanged from November 7 [3]. 3.5 Individual Bond Strategy - The trading volume ratio of Sci - tech Bond ETFs and benchmark - making ETFs to credit bonds remains low. Attention should be paid to the investment opportunities in the constituent bonds of Sci - tech Bond ETFs. When the spread between "non - constituent bonds - constituent bonds" is high, there is room for compression [3]. - Last week, the spreads between the "non - constituent bonds - constituent bonds" of Sichuan Expressway Investment Group, Dongfeng Motor Group, and China Railway Co., Ltd. have narrowed from over 20bp to within 20bp. This week, attention should continue to be paid to the non - constituent bonds of Shaanxi Yanchang Petroleum, China National Energy Conservation and Environmental Protection Group, and China Merchants Highway Network Technology [3].

Quantitative Models and Construction Methods Model Name: Three-Dimensional Timing Model - **Model Construction Idea**: The model uses three dimensions: liquidity, divergence, and prosperity to judge market trends[8] - **Model Construction Process**: - The model evaluates the current liquidity trend, market divergence, and prosperity level - It uses technical indicators to assess the market status, such as the overbought condition of the CSI 300 index[8] - The model's historical performance is visualized to validate its effectiveness[17] - **Model Evaluation**: The model indicates a downward trend in a high turnover market, suggesting a low probability of short-term upward movement[8] Model Name: ETF Hot Trend Strategy - **Model Construction Idea**: The strategy selects ETFs based on their price trends and market attention[28] - **Model Construction Process**: - Identify ETFs with both highest and lowest price trends using K-line highest and lowest price shapes - Construct support and resistance factors based on the relative steepness of the regression coefficients of the highest and lowest prices over the past 20 days - Select the top 10 ETFs with the highest turnover rate in the past 5 and 20 days to form a risk parity portfolio[28] - **Model Evaluation**: The strategy includes ETFs from semiconductor, non-ferrous metals, 5G communication, battery industries, and growth styles[29] Model Name: Capital Flow Resonance Strategy - **Model Construction Idea**: The strategy monitors the resonance of margin trading and large order funds to select favored industries[32] - **Model Construction Process**: - Define the margin trading capital factor as the net buying of financing minus the net selling of securities lending, neutralized by the Barra market value factor - Define the active large order capital factor as the net inflow of the industry, neutralized by the time series of trading volume over the past year - Combine the two factors to construct the strategy, excluding extreme industries and large financial sectors to improve stability[35] - **Model Evaluation**: The strategy has shown stable positive excess returns since 2018, with an annualized excess return of 13.5% and an IR of 1.7[35] Model Backtesting Results - **Three-Dimensional Timing Model**: Historical performance shows a consistent downward trend in high turnover markets[17] - **ETF Hot Trend Strategy**: The strategy has achieved cumulative excess returns over the CSI 300 index this year[30] - **Capital Flow Resonance Strategy**: The strategy recorded a negative excess return last week, with an absolute return of -2.4% and an excess return of -2.0% relative to the industry equal weight[35] Quantitative Factors and Construction Methods Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market returns[40] - **Factor Construction Process**: - Calculate the beta coefficient of each stock based on its historical returns relative to the market index - Form portfolios of high and low beta stocks to compare their performance[40] - **Factor Evaluation**: High beta stocks significantly outperformed low beta stocks, recording a positive return of 2.19% last week[40] Factor Name: Growth Factor - **Factor Construction Idea**: Measures the growth potential of stocks based on their earnings and revenue growth[40] - **Factor Construction Process**: - Calculate the growth rate of earnings and revenue for each stock - Form portfolios of high and low growth stocks to compare their performance[40] - **Factor Evaluation**: Growth stocks continued to outperform value stocks, with the growth factor achieving a return of 1.51% last week[40] Factor Backtesting Results - **Beta Factor**: - Year-to-date: 26.61% - Last month: 2.39% - Last week: 2.19%[41] - **Growth Factor**: - Year-to-date: -0.44% - Last month: 4.74% - Last week: 1.51%[41]