Report's Investment Rating for the Industry The provided content does not mention the industry investment rating. Core Views of the Report - The report constructs multiple ETF portfolios, including macro industry, macro + momentum industry, core - satellite, and trinity style rotation portfolios, to capture investment opportunities and manage risks in the ETF market [1][5]. - It combines macro - based and momentum - based methods to form complementary strategies, aiming to improve the performance of the portfolios [12]. - The trinity style rotation model uses macro liquidity as the core to build a long - term style rotation model and selects ETFs based on the model's results [6]. Summary by Relevant Catalog 1. ETF Portfolio Construction Methods 1.1 Based on Macro Method - Calculate the macro - sensitivity scores of economic, liquidity, and credit for industry - themed ETFs, and adjust the scores according to the latest indicators. Select the top 6 industry - themed indices and corresponding largest - scale ETFs for equal - weight allocation [1][7]. - Traditional cyclical industries are sensitive to the economy, TMT is sensitive to liquidity, and consumption is sensitive to credit. State - owned enterprises and ESG - related themes have low sensitivity to liquidity and credit [5]. 1.2 Trinity Style Rotation - Build a long - term style rotation model centered on macro liquidity, including growth/value, market capitalization, and quality models. Combine the results of the three models to get the final style preference [6]. - Screen ETFs with high exposure to the target style, control industry exposure, and set allocation limits to obtain the ETF allocation model [6]. 2. Macro Industry Portfolio - Select industry - themed ETFs that have been established for over 1 year and have a current scale of over 200 million. Calculate and adjust sensitivity scores, and remove liquidity scores if there is a significant divergence between liquidity and credit. Then select the top 6 industry - themed indices and corresponding largest - scale ETFs for equal - weight allocation [7][8]. - Currently, with economic forward - looking indicators rising and liquidity and credit indicators tightening, the portfolio is value - oriented with high proportions of banks and cyclical sectors. The December 2025 holdings include Huabao CSI Bank ETF, Cathay CSI Coal ETF, etc. [9]. - The portfolio has large fluctuations and outperformed the benchmark significantly in November 2025 [11]. 3. Macro + Momentum Industry Portfolio - Combine macro - based and momentum - based methods. Use clustering to group industry - themed indices and select the product with the highest 6 - month return from each group for equal - weight allocation [12]. - The December 2025 holdings include Huabao CSI Bank ETF, Cathay CSI Coal ETF, and others. The battery and metal industries selected by momentum have increased [15]. - The portfolio has performed well this year and outperformed the CSI 300 significantly in November 2025 [16]. 4. Core - Satellite Portfolio - Design a "core - satellite" portfolio with the CSI 300 as the core to address the high volatility and rapid industry rotation of industry - themed ETFs [18]. - Calculate macro - sensitivity scores for domestic broad - based, industry - themed, and Smart Beta ETFs, construct three stock portfolios, and weight them at 50%, 30%, and 20% respectively [18]. - The December 2025 holdings include Huatai - Peregrine CSI 300 ETF, Huaxia SSE 50 ETF, etc. The portfolio has been stable this year and outperformed the index almost every month, including in November 2025 [21][23]. 5. Trinity Style Rotation ETF Portfolio - The model currently favors small - cap growth + high - quality segments. The factor exposures and historical performance are presented in the report [24]. - The December 2025 holdings include Southern CSI 500ETF, Southern CSI 1000ETF, etc. [30].

Quantitative Factors and Construction Methods - **Factor Name**: Large Buy Order Transaction Amount Ratio **Factor Construction Idea**: This factor captures the buying behavior of large funds by analyzing the proportion of large buy orders in the total transaction amount for a given day [7] **Factor Construction Process**: 1. Utilize tick-by-tick transaction data to reconstruct buy and sell order data based on the bid and ask sequence numbers [7] 2. Filter transactions based on order size to identify large orders [7] 3. Calculate the proportion of large buy order transaction amounts to the total transaction amount for the day [7] **Formula**: $ \text{Large Buy Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Amount}}{\text{Total Transaction Amount}} $ **Factor Evaluation**: This factor effectively reflects the buying behavior of large funds [7] - **Factor Name**: Net Active Buy Transaction Amount Ratio **Factor Construction Idea**: This factor measures the active buying behavior of investors by analyzing the net active buy transaction amount as a proportion of the total transaction amount for a given day [7] **Factor Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell indicator [7] 2. Calculate the net active buy transaction amount by subtracting the active sell amount from the active buy amount [7] 3. Compute the proportion of the net active buy transaction amount to the total transaction amount for the day [7] **Formula**: $ \text{Net Active Buy Transaction Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Transaction Amount}} $ **Factor Evaluation**: This factor provides insights into the active buying tendencies of investors [7] Factor Backtesting Results - **Large Buy Order Transaction Amount Ratio**: - Top 10 stocks with the highest 5-day average values: 1. Guofa Co., Ltd. (600538.SH): 86.5%, 97.9% time-series percentile [9] 2. Jilin Expressway (601518.SH): 86.3%, 82.3% time-series percentile [9] 3. Chongqing Iron & Steel (601005.SH): 85.8%, 83.1% time-series percentile [9] 4. Zijin Bank (601860.SH): 85.6%, 62.6% time-series percentile [9] 5. Jianyuan Trust (600816.SH): 85.5%, 90.5% time-series percentile [9] - **Net Active Buy Transaction Amount Ratio**: - Top 10 stocks with the highest 5-day average values: 1. Fangda Special Steel (600507.SH): 24.8%, 100.0% time-series percentile [10] 2. Liaogang Co., Ltd. (601880.SH): 18.3%, 95.1% time-series percentile [10] 3. Overseas Chinese Town A (000069.SZ): 17.6%, 99.6% time-series percentile [10] 4. Qixia Construction (600533.SH): 14.6%, 99.2% time-series percentile [10] 5. Wanwei High-Tech (600063.SH): 14.3%, 99.6% time-series percentile [10] Additional Results for Indices, Industries, and ETFs - **Indices**: - Large Buy Order Transaction Amount Ratio (5-day average): - Shanghai Composite Index: 74.2%, 25.1% time-series percentile [12] - CSI 300: 73.1%, 10.7% time-series percentile [12] - Net Active Buy Transaction Amount Ratio (5-day average): - Shanghai Composite Index: -4.3%, 95.5% time-series percentile [12] - CSI 300: -4.5%, 100.0% time-series percentile [12] - **Industries**: - Large Buy Order Transaction Amount Ratio (5-day average): - Steel: 80.6%, 46.9% time-series percentile [13] - Construction: 79.5%, 62.6% time-series percentile [13] - Net Active Buy Transaction Amount Ratio (5-day average): - Steel: 4.0%, 40.7% time-series percentile [13] - Construction: 2.4%, 70.8% time-series percentile [13] - **ETFs**: - Large Buy Order Transaction Amount Ratio (5-day average): - ChinaAMC SSE 50 ETF (510050.SH): 89.3%, 74.1% time-series percentile [15] - GF CSI All-Index IT ETF (159939.SZ): 89.2%, 54.3% time-series percentile [15] - Net Active Buy Transaction Amount Ratio (5-day average): - Huaan SSE STAR Chip ETF (588290.SH): 17.3%, 99.6% time-series percentile [16] - Harvest CSI Battery Theme ETF (562880.SH): 13.9%, 92.2% time-series percentile [16]

Report Summary 1. Market Overview - A-share market: The Shanghai Composite Index fell 0.07% to 3359.90 points, the Shenzhen Component Index dropped 0.47% to 10005.03 points, and the ChiNext Index declined 0.83% to 2009.89 points. The trading volume of A-shares in the two markets was 1091.8 billion yuan. Leading gainers were transportation (0.88%), food and beverage (0.73%), and banking (0.69%), while leading losers were media (-1.91%), computer (-1.79%), and petroleum and petrochemical (-1.71%) [2][6]. 2. Stock ETF - Top trading volume: The top trading volume stock ETFs included嘉实中证A500ETF (down 0.21%, premium rate -0.21%),华夏中证A500ETF (down 0.21%, premium rate -0.07%), and华夏上证50ETF (up 0.59%, premium rate 0.53%) [3][7]. 3. Bond ETF - Top trading volume: The top trading volume bond ETFs were海富通中证短融ETF (up 0.01%, premium rate 0.01%),国泰上证10年期国债ETF (up 0.02%, premium rate 0.07%), and易方达上证基准做市公司债ETF (up 0.08%, premium rate 0.16%) [4][9]. 4. Gold ETF - Gold prices: Gold AU9999 had a 0.00% change, and Shanghai Gold fell 0.41%. The top trading volume gold ETFs were华安黄金ETF (down 0.39%, premium rate -0.24%),博时黄金ETF (down 0.39%, premium rate -0.25%), and易方达黄金ETF (down 0.37%, premium rate -0.22%) [12]. 5. Commodity Futures ETF - Performance:华夏饲料豆粕期货ETF fell 0.45% with a premium rate of 0.08%,建信易盛郑商所能源化工期货ETF dropped 0.43% with a premium rate of 0.49%, and大成有色金属期货ETF declined 0.36% with a premium rate of -0.65% [13][14]. 6. Cross - border ETF - Overseas markets: The US stock market was closed the previous day, the German DAX fell 1.12%, the Hang Seng Index rose 1.26%, and the Hang Seng China Enterprises Index rose 1.38%. The top trading volume cross - border ETFs were广发中证香港创新药ETF (up 0.27%, premium rate 0.41%),易方达中证香港证券投资主题ETF (up 0.31%, premium rate 0.24%), and华夏恒生科技ETF (up 0.14%, premium rate 0.51%) [15]. 7. Money ETF - Top trading volume: The top trading volume money ETFs were银华日利ETF,华宝添益ETF, and货币ETF建信添益 [17].