Report Industry Investment Rating - Not provided in the content Core Viewpoints - The report analyzes the secondary market situation, ETF product profiles (including market performance, fund inflows and outflows, trading volume, margin trading, and new issuance and listing) of ETF funds from February 23 - February 27, 2026, and presents data on various indexes and ETFs [1][10][19] Summary by Directory 1 Secondary Market Overview - **Index Returns**: Among A - shares, overseas major broad - based indexes, gold index, and Nanhua Commodity Index, CSI 2000, Nikkei 225, and Nanhua Commodity Index had the highest weekly returns, which were 0.35%, 0.00%, and - 0.25% respectively. The highest - return industries among Shenwan primary industries were non - ferrous metals (2.78%), public utilities (2.50%), and basic chemicals (2.31%), while the lowest - return industries were non - bank finance (-3.98%), computer (-3.44%), and agriculture, forestry, animal husbandry and fishery (-2.94%) [1][10][15] - **Index Valuations**: The PE valuation quantile of the Hang Seng Index was the highest at 90.24%, and that of the S&P 500 was the lowest at 15.14%. Among industries, the highest - valuation quantile industries were public utilities (98.76%), coal (98.35%), and communication (97.11%), while the lowest - valuation quantile industries were non - bank finance (1.24%), food and beverage (2.07%), and beauty care (5.79%) [10][14][15] 2 ETF Product Profile 2.1 ETF Market Performance - **By ETF Type**: Bond ETFs had the best average performance with a weighted average return of 0.22%, while commodity ETFs had the worst performance with a weighted average return of - 3.81% [19] - **By Index and Listing Board**: ETFs corresponding to CSI 2000 and CSI 500 had better market performance with weighted average returns of 0.31% and - 0.14% respectively, while those related to ChiNext Innovation 50 and ChiNext had worse performance with weighted average returns of - 1.78% and - 1.65% respectively [19] - **By Industry Sector**: Biopharmaceutical sector ETFs had the best average performance with a weighted average return of 2.41%, while financial real - estate sector ETFs had the worst performance with a weighted average return of - 3.33% [22] - **By Theme**: Innovative drug and new - energy ETFs had better performance with weighted average returns of 4.40% and 0.70% respectively, while non - bank and consumer electronics ETFs had relatively poor performance with weighted average returns of - 3.70% and - 3.04% respectively [22] 2.2 ETF Fund Inflows and Outflows - **By ETF Type**: Bond ETFs had the largest net fund inflow of 211.55 billion yuan, while industry - themed ETFs had the smallest net fund inflow of - 197.31 billion yuan [2][24] - **By Index and Listing Board**: CSI 300 ETFs had the largest net fund inflow of 45.56 billion yuan, while Hong Kong stock ETFs had the smallest net fund inflow of - 49.87 billion yuan [2][24] - **By Industry Sector**: Traditional manufacturing sector ETFs had the largest net fund inflow of 32.95 billion yuan, while cyclical sector ETFs had the smallest net fund inflow of - 121.65 billion yuan [2][28] - **By Theme**: New - energy and dividend ETFs had the largest net fund inflows of 34.78 billion yuan and 20.57 billion yuan respectively, while chip semiconductor and non - bank ETFs had the smallest net fund inflows of - 28.10 billion yuan and - 12.79 billion yuan respectively [2][28] 2.3 ETF Trading Volume - **By ETF Type**: Commodity ETFs had the largest increase in the average daily trading volume change rate of 47.73%, while bond ETFs had the largest decrease of - 10.53% [34] - **By Index and Listing Board**: US stock ETFs had the largest increase in the average daily trading volume change rate of 52.99%, while ChiNext Innovation 50 had the largest decrease of - 12.87% [36] - **By Industry Sector**: Biopharmaceutical sector had the largest increase in the average daily trading volume change rate of 18.48%, while the cyclical sector had the largest decrease of - 34.76% [39] - **By Theme**: Non - bank and innovative drug ETFs had the largest average daily trading volumes in the past 5 days, which were 127.49 billion yuan and 91.83 billion yuan respectively. Low - carbon environmental protection and new - energy ETFs had the largest increase or the smallest decrease in the average daily trading volume change rate, which were 40.73% and 19.86% respectively. Military and central and state - owned enterprise ETFs had the largest decrease or the smallest increase in the average daily trading volume change rate, which were - 28.80% and - 21.80% respectively [43] 2.4 ETF Margin Trading - The net margin purchase of all stock - type ETFs was - 4.54 billion yuan, and the net margin short - sale was 4.17 billion yuan. During the sample period, Cathay CSI All - Index Securities Company ETF had the largest net margin purchase, and Southern CSI 1000 ETF had the largest net margin short - sale [2][49] 2.5 ETF New Issuance and Listing - During the sample period, 9 funds were established and 4 funds were listed [3][51]

- The report introduces the **RSI (Relative Strength Index)** as a factor, which is calculated using the formula: $ RSI = 100 - \frac{100}{1 + RS} $ where RS represents the ratio of the average gain to the average loss over a 12-day period. RSI values above 70 indicate an overbought market, while values below 30 indicate an oversold market[2] - The report also calculates **Net Purchase (NETBUY)** as a factor, using the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) \times (1 + R(T)) $ where NETBUY(T) is the net purchase amount, NAV(T-1) is the ETF's net asset value from the previous trading day, and R(T) is the return on the current day[2] - The report tracks **ETF performance metrics** such as daily price changes, RSI values, net purchases, and trading volumes for various ETFs, categorized into "broad-based" and "thematic" indices. Examples include ETFs tracking indices like CSI 300, CSI 500, and sector-specific indices such as non-bank financials and dividends[2][4] - The report provides detailed **RSI values** for various ETFs, such as: - CSI 300 ETFs: RSI values range from 40.03 to 41.72 - CSI 500 ETFs: RSI values range from 38.32 to 40.20 - CSI 1000 ETFs: RSI values range from 39.63 to 40.21 - Thematic ETFs (e.g., Semiconductor, Renewable Energy): RSI values range from 30.34 to 52.96[4][6] - The report evaluates **net purchase amounts** for ETFs, with examples including: - CSI 300 ETFs: Net purchases range from -20.87 billion to 1.15 billion - CSI 500 ETFs: Net purchases range from -4.85 billion to 1.01 billion - CSI 1000 ETFs: Net purchases range from -12.83 billion to 0.02 billion - Thematic ETFs: Net purchases range from -16.49 billion to 62.07 billion[4][6] - The report highlights **trading volumes** for ETFs, with examples including: - CSI 300 ETFs: Trading volumes range from 0.29 billion to 39.32 billion - CSI 500 ETFs: Trading volumes range from 1.03 billion to 45.84 billion - CSI 1000 ETFs: Trading volumes range from 0.35 billion to 35.07 billion - Thematic ETFs: Trading volumes range from 0.04 billion to 75.07 billion[4][6] - The report provides **qualitative evaluations** of the RSI and NETBUY factors, noting their utility in identifying overbought/oversold conditions and tracking fund flows, respectively[2]

Quantitative Models and Construction Methods 1. Model Name: RSI (Relative Strength Index) - **Model Construction Idea**: RSI is a momentum oscillator that measures the speed and change of price movements. It is used to identify overbought or oversold conditions in the market[2][4] - **Model Construction Process**: The RSI is calculated using the following formula: $ RSI = 100 - \frac{100}{1 + RS} $ Where: - $ RS = \frac{\text{Average Gain over N periods}}{\text{Average Loss over N periods}} $ - N is typically set to 12 days in this report[2][4] RSI values above 70 indicate an overbought market, while values below 30 indicate an oversold market[2][4] 2. Model Name: Net Purchase Amount (NetBuy) - **Model Construction Idea**: This model calculates the net purchase amount of ETFs to assess fund flows and investor sentiment[2] - **Model Construction Process**: The NetBuy is calculated using the following formula: $ NETBUY(T) = NAV(T) - NAV(T-1) \times (1 + R(T)) $ Where: - $ NETBUY(T) $ is the net purchase amount on day T - $ NAV(T) $ is the net asset value on day T - $ R(T) $ is the return on day T[2] --- Model Backtesting Results RSI Model - RSI values for various ETFs are provided in the report, such as: - Huatai-PineBridge CSI 300 ETF: RSI = 32.66[4] - E Fund CSI 300 ETF: RSI = 32.12[4] - Southern CSI 500 ETF: RSI = 31.27[4] - ChinaAMC CSI 500 ETF: RSI = 31.79[4] - Southern CSI 1000 ETF: RSI = 33.80[4] NetBuy Model - NetBuy values for various ETFs are provided in the report, such as: - Huatai-PineBridge CSI 300 ETF: NetBuy = 0.03 billion CNY[4] - E Fund CSI 300 ETF: NetBuy = 3.75 billion CNY[4] - Southern CSI 500 ETF: NetBuy = 4.75 billion CNY[4] - ChinaAMC CSI 500 ETF: NetBuy = 0.76 billion CNY[4] - Southern CSI 1000 ETF: NetBuy = 3.39 billion CNY[4] --- Quantitative Factors and Construction Methods 1. Factor Name: Institutional Holding Ratio - **Factor Construction Idea**: This factor measures the proportion of ETF holdings owned by institutional investors, reflecting institutional participation and confidence[3] - **Factor Construction Process**: The institutional holding ratio is derived from the latest semi-annual or annual reports of ETFs, excluding holdings by linked funds. The data is estimated and may have deviations due to reporting delays or missing data[3] --- Factor Backtesting Results Institutional Holding Ratio - Institutional holding ratios for various ETFs are provided in the report, such as: - Huatai-PineBridge CSI 300 ETF: 87.11%[4] - E Fund CSI 300 ETF: 90.08%[4] - Southern CSI 500 ETF: 83.21%[4] - ChinaAMC CSI 500 ETF: 80.57%[4] - Southern CSI 1000 ETF: 93.16%[4]

- The report introduces the RSI (Relative Strength Index) as a quantitative factor, calculated using the formula: $ RSI = 100 - 100 / (1 + RS) $, where RS represents the ratio of average gains to average losses over a 12-day period. RSI values above 70 indicate an overbought market, while values below 30 suggest an oversold market[2] - Another quantitative factor mentioned is the net subscription amount (NETBUY), calculated using the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $, where NETBUY(T) represents the net subscription amount, NAV(T-1) is the ETF's net asset value from the previous trading day, and R(T) is the return rate for the current day[2] - The report tracks daily trends in intraday trading using 5-minute interval price data, highlighting the highest and lowest prices with red dots. However, it notes potential data gaps due to missing intraday information[2] - The report categorizes ETFs into "Broad-based" and "Thematic" groups based on the indices they track, such as CSI 300, CSI 500, and industry-specific indices like non-bank financials and dividends[2] - The report provides detailed metrics for various ETFs, including RSI values, net subscription amounts, institutional holdings, and transaction volumes, offering insights into market trends and fund performance[4][6]

- The report introduces the RSI (Relative Strength Index) as a quantitative factor, calculated using the formula: $ RSI = 100 - 100 / (1 + RS) $, where RS represents the ratio of average gains to average losses over a 12-day period. RSI values above 70 indicate an overbought market, while values below 30 suggest an oversold market[2] - Another quantitative factor mentioned is the net subscription amount (NETBUY), calculated using the formula: $ NETBUY(T) = NAV(T) - NAV(T-1) * (1 + R(T)) $, where NETBUY(T) represents the net subscription amount, NAV(T) is the ETF's net asset value, and R(T) is the return on the current trading day[2] - The report tracks various ETFs based on their underlying indices, such as CSI 300, CSI 500, CSI A500, and thematic indices like non-bank financials, dividends, and China internet sectors. These indices serve as benchmarks for ETF performance analysis[2][4] - The report provides daily monitoring of ETF metrics, including RSI values, net subscription amounts, trading volumes, and institutional holdings, offering insights into market trends and investor behavior[2][4] - The RSI values for different ETFs range widely, with examples including 43.34 for Huatai-PineBridge CSI 300 ETF, 36.27 for Southern CSI 500 ETF, and 50.91 for Ping An CSI A50 ETF, reflecting varying market conditions across indices[4] - Net subscription amounts also vary significantly, such as 11.01 billion for Huatai-PineBridge CSI 300 ETF, -2.28 billion for China Asset Management CSI 500 ETF, and 1.88 billion for Guotai CSI A500 ETF, indicating diverse investor activity[4] - Institutional holdings percentages are highlighted, with examples like 87.11% for Huatai-PineBridge CSI 300 ETF, 93.09% for China Asset Management CSI 300 ETF, and 56.96% for E Fund CSI A500 ETF, showcasing the level of institutional participation in these funds[4]

Market Liquidity Overview - R007 decreased from 1.5292% to 1.4920%, a reduction of 3.72 basis points; DR007 fell from 1.4805% to 1.4149%, down 6.56 basis points. The spread between R007 and DR007 increased by 2.84 basis points [9][12] - The net outflow of funds this week was 72.445 billion yuan, with a decrease in net inflow of 65.34 billion yuan compared to last week. Fund supply was -16.047 billion yuan, while fund demand was 56.398 billion yuan. Specifically, fund supply decreased by 64.689 billion yuan, with net financing purchases down by 103.682 billion yuan [12][15] Industry Sector Liquidity Tracking - Most sectors in the CITIC first-level industry index experienced declines, with a weak overall market style and continued sector differentiation. The number of declining sectors exceeded those that rose, with the oil and petrochemical sector showing the most significant increase at 7.18%, while media and computer sectors led the declines at 6.96% and 5.48%, respectively [17][20] Style Sector Liquidity Tracking - Most style indices saw declines, with the growth style experiencing the largest drop of 3.58%, followed by the consumer style at 2.45%. The average daily trading volume for the growth style was the highest at 55.07%, indicating it was the most active sector [3][10]

Market Overview - The performance of major indices during the sample period shows that the South China Commodity Index, SGE Gold 9999, and CSI 300 had returns of 6.43%, -0.49%, and -1.07% respectively [1][10] - Among the Shenwan first-level industries, the top performers were Oil & Petrochemicals, Coal, and Utilities with returns of 8.06%, 3.79%, and 3.42% respectively, while Media, Non-ferrous Metals, and Computers lagged with returns of -6.97%, -5.47%, and -5.29% respectively [1][14] ETF Product Overview Market Performance - The weighted average return for style ETFs was the highest at 0.79%, while industry theme ETFs had the lowest average return at -3.44% during the sample period [18][20] - MSCI China A-share concept and US stock ETFs performed relatively well with weighted average returns of -0.62% and -0.82% respectively, while Japan stock and Sci-Tech Board related ETFs performed poorly with returns of -6.10% and -4.94% respectively [18][22] Fund Flow - Industry theme ETFs saw the highest net inflow of 356.80 billion, while broad-based ETFs experienced the largest net outflow of -389.41 billion [2][25] - The US stock ETFs had the highest net inflow of 12.54 billion, while the CSI 500 ETF had the lowest net outflow of -100.91 billion [2][29] - The cyclical sector ETFs had the highest net inflow of 363.76 billion, while the technology sector ETFs had the lowest net outflow of -85.15 billion [30][32] New Issuance and Listing - During the sample period, one new fund was established and seven funds were listed [3] Trading Volume - The trading volume for style ETFs increased the most, with a daily average trading volume change rate of 30.27%, while commodity ETFs saw the largest decrease at -10.07% [35][41] - US stock ETFs had the highest increase in daily average trading volume change rate at 39.40%, while the CSI 500 had the largest decrease at -19.13% [37][39]

Group 1: Manufacturing Sector Insights - February Manufacturing PMI decreased to 49.0%, down 0.3 percentage points from the previous month, primarily due to the impact of the Spring Festival holiday on production and operations[1] - Production index recorded at 49.6%, a decline of 1.0 percentage points, reflecting reduced operational rates and capacity utilization during the holiday[1] - New orders index slightly fell to 48.6%, indicating stable terminal demand without significant shrinkage[1] Group 2: Non-Manufacturing Sector Insights - February Non-Manufacturing Business Activity Index rose by 0.1 percentage points to 49.5%, showing slight improvement in overall economic sentiment[2] - Construction PMI recorded at 48.2%, down 0.6 percentage points, affected by holiday-related workforce absences and project delays[2] - Service sector PMI increased to 49.7%, up 0.2 percentage points, driven by concentrated consumer spending during the holiday period[2] Group 3: Market Expectations and Future Outlook - Business activity expectation index for manufacturing rose to 53.2%, indicating positive market recovery expectations post-holiday[1] - Construction industry confidence is recovering, with the business activity expectation index returning above the critical point at 50.9%[2] - Overall, the economic outlook for March suggests potential marginal recovery in both manufacturing and non-manufacturing sectors as operations resume nationwide[4]

Investment Rating - The investment rating for the company is "Buy" with a maintained rating [4]. Core Insights - The company reported a robust performance in Q4 2025, with revenue of 15.4 billion RMB (up 21% year-on-year) and a net profit of 4.3 billion RMB. For the full year 2025, total revenue reached 62.4 billion RMB (up 17% year-on-year) and net profit was 33.4 billion RMB, significantly boosted by 19.9 billion RMB in investment gains [1][2]. Revenue Breakdown - Accommodation booking revenue for Q4 2025 was 6.3 billion RMB (up 21% year-on-year), with annual revenue of 26.1 billion RMB (up 21%), driven by outbound travel and international hotel bookings [2]. - Transportation ticketing revenue for Q4 2025 was 5.4 billion RMB (up 12% year-on-year), with annual revenue of 22.5 billion RMB (up 11%) [2]. - Vacation packages revenue for Q4 2025 was 1.1 billion RMB (up 21%), with annual revenue of 4.7 billion RMB (up 8%) [2]. - Business travel management revenue for Q4 2025 was 808 million RMB (up 15%), with annual revenue of 2.8 billion RMB (up 13%) [2]. International Business Performance - The inbound tourism segment remains a core pillar of the company's long-term strategy, with strong demand in 2025. The Asia-Pacific region continues to be the largest source of inbound travelers, and interest from Western markets is also growing. The company served approximately 20 million inbound tourists in 2025, connecting them to around 150,000 hotels [3]. - The international OTA platform's booking volume grew by approximately 60% year-on-year, indicating strong progress in the international market, with international business contributing 40% to total revenue and bookings in 2025 [3]. Future Outlook - The company is expected to achieve revenues of 71.1 billion RMB, 80.6 billion RMB, and 91.7 billion RMB for 2026, 2027, and 2028, respectively, with year-on-year growth rates of 13.9%, 13.3%, and 13.8% [4][9]. - The forecasted net profits for 2026, 2027, and 2028 are 16.8 billion RMB, 19.0 billion RMB, and 22.0 billion RMB, with respective growth rates of -49.6%, 13.3%, and 15.5% [4][9].

Investment Rating - The industry rating is "Outperform" [1] Core Insights - Aircraft manufacturers are slowly recovering their production capacity, but the backlog of aircraft orders remains at a historically high level. In January 2026, Boeing delivered 46 aircraft, while Airbus delivered 19. IBA predicts that Airbus will deliver slightly more than 900 aircraft in 2026, and Boeing is expected to deliver around 670 aircraft for the year. The demand for aircraft orders continues to be strong, with backlog levels remaining high [2][7][8]. - The secondary aircraft trading market has been strengthening since the pandemic's impact has diminished. Narrow-body aircraft, particularly the Airbus A320/A321 series and Boeing 737NG series, dominate the market. Demand comes from existing operators, as well as from dismantling traders and spare parts suppliers. The market value and rental levels for wide-body aircraft are also on the rise, driven by high engine overhaul costs and a shortage of maintenance slots. Despite limited availability of aircraft and engines for sale, the overall aircraft trading market is expected to remain strong [2][41]. - Overall, while aircraft manufacturers' production capacity has improved, it still struggles to meet the ongoing demand for aircraft. The aircraft leasing industry is expected to benefit from the tight supply-demand dynamics. The Asia-Pacific aviation market has significant growth potential, providing broader development space for Chinese aircraft leasing companies. Compared to global leasing leader AerCap, Chinese leasing companies are currently undervalued and possess higher order elasticity, making them worthy of attention [2]. Summary by Sections 1. Aircraft Supply Continues to be Tight - Aircraft manufacturers are facing production constraints due to supply chain issues and labor shortages, leading to delivery delays. The backlog of orders remains high, with a total of 15,560 aircraft orders as of the end of January 2026 [2][8]. 2. Civil Aviation Passenger Demand Update - Global aviation passenger traffic growth has slowed, with a year-on-year increase of 5.6% in December 2025, slightly down from 5.8% in November. The load factor was 83.7%, a slight decrease from 83.9% in December of the previous year [13][17]. - International routes are showing steady growth, with international passenger RPK increasing by 7.7% year-on-year in December 2025. The Asia-Pacific region's international passenger traffic remains strong, with a year-on-year growth of 7.5% [20]. 3. Aircraft Leasing Company Dynamics - As of June 30, 2025, Bohai Leasing has the highest number of owned aircraft (628), while China Aircraft Leasing has the least (151). In terms of aircraft orders, Bohai Leasing also leads with 442 orders [39][45]. - The average remaining lease term for China Aircraft Leasing is relatively long at 7.9 years, ensuring long-term stability for the company's leases [48].