Core Viewpoint - The article discusses the characteristics of a typical bottom rebound cycle in the stock market, highlighting the relationship between macro indices and micro sectors, and presents a quantitative model to identify rebound signals and select outperforming sectors and stocks [1][3][5]. Group 1: Bottom Rebound Characteristics - A typical bottom rebound cycle includes a wave-like downward trend followed by a significant upward rebound, characterized by several large declines and smaller recoveries before a major rally [7]. - The model developed in the article identifies rebound signals based on historical data, with the China Securities Index triggering 118 rebound signals from 2006 to 2025, averaging about 6 signals per year [8]. Group 2: Empirical Analysis - The empirical results show that the proposed model can accurately identify the market's phase bottoms and select the most promising sectors and stocks for constructing excess return portfolios [5][8]. - The strategy based on the rebound signals from the broad market index outperformed the China Securities Index, achieving a cumulative return of 11,753.66% and an annualized return of 28.11% from 2006 to 2025, compared to the index's 565.01% cumulative return and 10.33% annualized return [5][19]. Group 3: Sector and Stock Selection - The article validates the effectiveness of applying broad market rebound signals to sector rotation, demonstrating that sectors experiencing significant declines during downturns tend to rebound more strongly after the market bottom [15][16]. - The model's application to specific industry indices accurately predicts their phase bottoms, with sector-specific stock portfolios significantly outperforming their respective industry indices [5][19]. Group 4: Performance Metrics - The strategy of holding the top-performing sectors for varying durations (20, 60, 120, and 240 days) consistently outperformed the China Securities Index, with the best performance recorded for the 20-day holding strategy, yielding a total return of 1,605.55% and an annualized return of 15.85% [21]. - The article provides detailed performance statistics for different holding periods, indicating that the strategies based on rebound signals yield superior risk-adjusted returns compared to the broad market index [21][22].

1. Report Industry Investment Rating There is no information about the report industry investment rating in the provided content. 2. Core View of the Report The report presents several ETF strategy indices constructed with the help of ETFs, and tracks the performance and holdings of these indices on a weekly basis. These indices aim to obtain excess returns relative to the market through different strategies [11]. 3. Summary by Relevant Catalog 3.1 ETF Strategy Index Tracking - **ETF Strategy Index Last Week's Performance**: - **HuaBao Research Large - Small Cap Rotation ETF Strategy Index**: Last week's index return was -2.18%, the benchmark was CSI 800 with a return of -3.24%, and the excess return was 1.06% [12]. - **HuaBao Research SmartBeta Enhanced ETF Strategy Index**: Last week's index return was -3.18%, the benchmark was CSI 800 with a return of -3.24%, and the excess return was 0.06% [12]. - **HuaBao Research Quantitative Fire - Wheel ETF Strategy Index**: Last week's index return was -5.49%, the benchmark was CSI 800 with a return of -3.24%, and the excess return was -2.25% [12]. - **HuaBao Research Quantitative Balance Art ETF Strategy Index**: Last week's index return was -1.36%, the benchmark was SSE 300 with a return of -2.19%, and the excess return was 0.83% [12]. - **HuaBao Research Hot - Spot Tracking ETF Strategy Index**: Last week's index return was -5.24%, the benchmark was CSI All - Share Index with a return of -4.10%, and the excess return was -1.15% [12]. - **HuaBao Research Bond ETF Duration Strategy Index**: Last week's index return was 0.05%, the benchmark was ChinaBond Aggregate Index with a return of 0.00%, and the excess return was 0.05% [12]. 3.2 HuaBao Research Large - Small Cap Rotation ETF Strategy Index - **Strategy Principle**: It uses multi - dimensional technical indicator factors and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index. The model outputs signals weekly to predict the strength of the index in the next week and determines the holdings accordingly to obtain excess returns relative to the market [13]. - **Performance**: As of 2026/3/20, the excess return since 2024 was 27.36%, the excess return in the past month was 0.86%, and the excess return in the past week was 1.06%. The holdings included 100% of Huatai - Peregrine SSE 300 ETF (fund code: 510300.SH) [13][17]. 3.3 HuaBao Research SmartBeta Enhanced ETF Strategy Index - **Strategy Principle**: It uses price - volume indicators to time the self - built Barra factors, and then maps the timing signals to ETFs based on the exposure of ETFs to 9 major Barra factors to obtain returns exceeding the market. The selected ETFs cover mainstream broad - based index ETFs and some style and strategy ETFs [17]. - **Performance**: As of 2026/3/20, the excess return since 2024 was 22.21%, the excess return in the past month was 3.51%, and the excess return in the past week was 0.06%. The holdings included Hongli Low - Volatility 100ETF (25.46%, fund code: 515100.SH), High - Dividend ETF (25.19%, fund code: 563180.SH), Wanjia Free Cash Flow 800ETF (24.68%, fund code: 563580.SH), and ICBC Hongli ETF (24.66%, fund code: 159905.SZ) [17][21]. 3.4 HuaBao Research Quantitative Fire - Wheel ETF Strategy Index - **Strategy Principle**: It starts from a multi - factor perspective, including the grasp of the medium - and long - term fundamental dimension, the tracking of short - term market trends, and the analysis of the behaviors of various market participants. It uses valuation and crowding signals to prompt industry risks and multi - dimensionally dig out potential sectors to obtain excess returns relative to the market [21]. - **Performance**: As of 2026/3/20, the excess return since 2024 was 48.36%, the excess return in the past month was - 0.14%, and the excess return in the past week was - 2.25%. The holdings included Penghua Petroleum ETF (22.39%, fund code: 159697.SZ), Electronic ETF (19.86%, fund code: 159997.SZ), Fuguo Tourism ETF (19.43%, fund code: 159766.SZ), Cathay Building Materials ETF (19.26%, fund code: 159745.SZ), and Chemical ETF (19.06%, fund code: 159870.SZ) [21][25]. 3.5 HuaBao Research Quantitative Balance Art ETF Strategy Index - **Strategy Principle**: It uses a multi - factor system including economic fundamentals, liquidity, technical aspects, and investor behavior factors to build a quantitative timing system for trend judgment of the equity market. It establishes a prediction model for the market large - small cap style to adjust the equity market position distribution and comprehensively obtains excess returns relative to the market through timing and rotation [25]. - **Performance**: As of 2026/3/20, the excess return since 2024 was - 8.00%, the excess return in the past month was 1.25%, and the excess return in the past week was 0.83%. The holdings included Cathay 10 - Year Treasury Bond ETF (9.19%, fund code: 511260.SH), Enhanced 500ETF (6.37%, fund code: 159610.SZ), Southern CSI 1000ETF (6.18%, fund code: 512100.SH), Cathay SSE 300 Enhanced ETF (32.52%, fund code: 561300.SH), Fuguo Government Financial Bond ETF (22.95%, fund code: 511520.SH), and Haifutong Short - Term Financing ETF (22.77%, fund code: 511360.SH) [25][28]. 3.6 HuaBao Research Hot - Spot Tracking ETF Strategy Index - **Strategy Principle**: It tracks and digs out hot - spot index target products in a timely manner according to strategies such as market sentiment analysis, industry major event tracking, investor sentiment and professional views, policy and regulatory changes, and historical deduction, and constructs an ETF portfolio that can capture market hot - spots in a timely manner to provide investors with a reference for short - term market trends and help them make more informed investment decisions [28]. - **Performance**: As of 2026/3/20, the excess return in the past month was - 3.41%, and the excess return in the past week was - 1.15%. The holdings included Huitianfu Non - Ferrous Metals ETF (39.17%, fund code: 159652.SZ), Boshi Hong Kong Stock Dividend ETF (24.78%, fund code: 513690.SH), Haifutong Short - Term Financing ETF (18.24%, fund code: 511360.SH), and E Fund Hong Kong Stock Connect Pharmaceutical ETF (17.81%, fund code: 513200.SH) [28][32]. 3.7 HuaBao Research Bond ETF Duration Strategy Index - **Strategy Principle**: It uses bond market liquidity indicators and price - volume indicators to screen effective timing factors and predicts bond yields through machine - learning methods. When the expected yield is lower than a certain threshold, it reduces the long - duration positions in the bond investment portfolio to improve the long - term return and drawdown control ability of the portfolio [32]. - **Performance**: As of 2026/3/20, the excess return in the past month was 0.24%, and the excess return in the past week was 0.05%. The holdings included Cathay 10 - Year Treasury Bond ETF (49.96%, fund code: 511260.SH), Fuguo Government Financial Bond ETF (25.09%, fund code: 511520.SH), and Ping An Treasury Bond ETF (24.95%, fund code: 511020.SH) [32][35].

Group 1 - The core viewpoint of the report indicates that domestic asset BL strategy 1 has achieved a return of 2.05% in 2026, with a monthly return of 0.5% in February 2026 [1][4]. - Domestic asset BL strategy 2 has a return of 2.0% for 2026, with a February return of 0.34% [1][4]. - The domestic risk parity strategy has a return of 1.12% for 2026, with a February return of 0.18% [1][4]. - The macro factor-based asset allocation strategy has a return of 1.63% for 2026, with a February return of 0.22% [1][4]. Group 2 - In February 2026, the leading asset was the CSI 1000, which increased by 3.71%, while the Hang Seng Index and South China Commodity Index experienced declines of 3.61% and 1.32%, respectively [7][8]. - The correlation between the CSI 300 and the total wealth index of government bonds over the past year is -33.02%, indicating a negative relationship [14]. - The domestic asset BL strategy 1 has a maximum drawdown of 1.2% and an annualized volatility of 4.21% [21][31]. - The domestic risk parity strategy has a maximum drawdown of 0.5% and an annualized volatility of 1.56% [40]. Group 3 - The macroeconomic outlook as of the end of February 2026 indicates a "weak recovery, low inflation" scenario, with the manufacturing PMI dropping to 49.0% [44]. - The monetary policy remains moderately loose, with the LPR remaining unchanged for nine consecutive months, indicating a lower probability of comprehensive interest rate cuts [44]. - The report suggests a cautious attitude towards growth and inflation, reflected in the adjustment of the macro six-factor exposure deviation values to "-1, 0, 0, -1, +1, +1" for March [47]. Group 4 - The report outlines the performance of various asset allocation strategies, with the domestic asset BL strategy 1 yielding 2.05% and the risk parity strategy yielding 1.12% for 2026 [21][40]. - The global asset BL strategy 1 has a return of 0.94% for 2026, with a February return of -0.32% [50]. - The global risk parity strategy has a return of 0.88% for 2026, with a February return of 0.17% [50].

Core Viewpoint - The article draws a parallel between successful restaurant operations and investment strategies, emphasizing that both require a solid foundation and understanding of underlying principles to achieve consistent profitability [3][4]. Group 1: Investment Strategies - Successful investment in stocks relies on understanding the underlying logic of making money, similar to running a restaurant [6]. - In the 20-year history of private equity in China, some strategies yield similar returns across different products, while others show significant disparities, highlighting the importance of selecting the right strategy [9]. - A robust investment strategy should remain effective even if key decision-makers change, ensuring stability in performance [10][13]. Group 2: Market Environment - Effective strategies are rooted in stable market principles that do not depend on fleeting trends or extreme market conditions, but rather on long-term human behaviors and market rules [16][18]. - Two classic "hardcore" strategies are identified: quantitative long positions and macro hedging [20][28]. - Quantitative long strategies leverage market emotions, particularly in environments with high retail investor participation, allowing for consistent opportunities as market sentiment fluctuates [22][24][26]. Group 3: Macro Hedging - Macro hedging can be divided into two types: rotational and allocation-based, with allocation strategies being more stable as they do not rely on precise market timing [29][32]. - Allocation strategies, such as all-weather strategies, benefit from the low correlation between different asset classes, ensuring performance across various economic conditions [34][35]. Group 4: Strategy Characteristics - True "hardcore" private equity strategies do not depend on star managers, do not require perfect market conditions, and do not bet on a single direction [37]. - The effectiveness of different strategies is subjective and should align with individual preferences and goals, as the best strategy is the one that suits the investor's needs [40][41].

Core Viewpoint - The article emphasizes the value of the "enhanced" strategy within the CSI 1000 index, highlighting its significant returns and the importance of understanding the underlying factors driving these results [2][6]. Performance Summary - The CSI 1000 Enhanced ETF (SZ159680) has achieved a return of 41.96% over the past year and 94.57% over the past two years, indicating strong performance compared to broad-based ETFs [2][3]. - The cumulative excess return since the fund's inception is 47.61%, with a recent one-month excess return of 1.21% [2]. Fund Characteristics - The enhanced strategy allows for better performance during market fluctuations, with the fund outperforming the index during gains and minimizing losses during downturns [2]. - The fund's top holdings include stocks like OFILM, Zhenyu Technology, and Quectel, which are characterized by good liquidity and volatility, providing opportunities for quantitative models to identify undervalued stocks [3]. Capital Flow - The CSI 1000 Enhanced ETF has seen a net inflow of approximately 78 million yuan over the past five days, indicating a strong demand for this fund [6]. - The overall trend for CSI 1000 ETFs has been positive, with significant capital inflows, suggesting a preference for this investment style over short-term speculation [6]. Market Environment - The trading volume in the market remains above 2 trillion yuan, but is still below previous peaks, which historically benefits small-cap stocks due to reduced capital pushing large-cap stocks [10]. - The CSI 1000 index has a historical success rate of 90% during the period from after the Spring Festival to before the Two Sessions, driven by capital replenishment, policy expectations, and a data vacuum [10]. Strategic Positioning - The CSI 1000 Enhanced ETF is positioned as a "defensive core" investment, combining the beta of the index with the alpha from the quantitative model, making it suitable for investors looking for exposure to small-cap stocks without taking on excessive risk [10]. - The recent high net asset value was achieved even with market trading volumes not returning to 3 trillion yuan, indicating that structural market conditions can still allow for excess returns from small-cap enhanced strategies [10].

Group 1 - The report indicates that in January 2026, domestic asset BL strategy 1 achieved a return of 1.55%, while strategy 2 achieved 1.65%. The risk parity strategy yielded 0.94%, and the macro factor-based strategy returned 1.4% [1][4][19]. - The performance of major asset classes in January 2026 showed that gold led the gains with an increase of 18.48%, followed by the CSI 1000 at 8.68%, and the Nanhua Commodity Index at 8.61% [7][8]. - The report highlights the correlation between various asset classes, noting that the correlation between the CSI 300 and the total wealth index of government bonds was -32.28%, indicating a potential for diversification [13][15]. Group 2 - The macroeconomic outlook as of January 2026 shows a manufacturing PMI of 49.3%, indicating a contraction, while the non-manufacturing PMI also fell to 49.5%, suggesting a weak economic recovery [43]. - Inflation indicators show that the CPI rose by 0.8% year-on-year in December 2025, with expectations for a further increase to around 0.47% in January 2026 due to seasonal effects [44]. - The report discusses liquidity conditions, stating that the banking system remains "reasonably ample and slightly loose," which is expected to support economic stabilization in the first quarter [46]. Group 3 - The domestic asset BL strategy 1 has a maximum drawdown of 0.23% and an annualized volatility of 2.54%, while strategy 2 has a maximum drawdown of 0.35% and an annualized volatility of 2.64% [20][30]. - The risk parity strategy has a return of 0.94% with a maximum drawdown of 0.24% and an annualized volatility of 1.43%, indicating its stability compared to other strategies [39]. - The macro factor-based asset allocation strategy achieved a return of 1.4% with a maximum drawdown of 0.5% and an annualized volatility of 2.73%, reflecting its effectiveness in the current market environment [47].

Group 1 - The core viewpoint of the news is that the Xingyin CSI 500 Index Enhanced A fund has shown strong performance, with a recent net value of 1.3146 yuan and a six-month return of 27.88%, ranking 127th out of 757 in its category [1] - As of December 31, 2025, the fund achieved a one-year return of 34.82%, exceeding the benchmark return by 6.01%, and a three-year return of 32.63%, surpassing the benchmark return rate by 6.38% [1][2] Group 2 - The Xingyin Enhanced A fund is positioned as an index-enhanced equity fund, closely tracking the CSI 500 Index while optimizing component stock weights through quantitative models [2] - The fund's asset allocation shows a stock position of 92.18%, with only 0.71% in bonds and 6.53% in cash [2] - The manufacturing sector dominates the fund's industry allocation, accounting for 60.39% of net value, followed by information technology and finance at 4.53% and 4.34%, respectively [2] Group 3 - The top ten holdings of the fund are all CSI 500 Index component stocks, collectively representing approximately 7.24% of the fund's net asset value, indicating a diversified overall holding [2] - The fund actively invests in growth sectors, including information technology (e.g., Giant Network, Crystal Optoelectronics), high-end manufacturing (e.g., Lead Intelligent, Goldwind Technology), aerospace (e.g., China Satellite, Aerospace Electronics), and electronics (e.g., Jingwang Electronics, Xingsen Technology) [2] Group 4 - Fund manager Weng Zichen noted that the CSI 500 Index performed strongly in the fourth quarter, and despite the pressure on enhanced index products, the fund achieved stable excess returns through strict style exposure control and optimization using the Barra multi-factor model [5] - The strategy emphasizes controlling the volatility of excess returns and improving the Sharpe ratio to ensure consistent and stable performance across different market cycles [5] - Looking ahead to 2026, the CSI 500 Index will remain a key tool for investing in quality mid-cap growth stocks, with the fund continuing to leverage its quantitative model for risk control and alpha generation [5]

- The report introduces a structured static factor model for predicting 10Y government bond yield movements, utilizing four macroeconomic factors: economic growth, inflation, debt leverage, and short-term interest rates[44][50][53] - The model has achieved a historical prediction accuracy of approximately 70% since 2006, with a sample-out accuracy of 68% since 2023[47][50] - For February 2026, the model forecasts a 6BP increase in the 10Y government bond yield to 1.88%, driven by upward trends in all four macroeconomic factors[50][53] - A structured static factor model is also applied to gold price movements, incorporating four key factors: US economy, US employment, US fiscal policy, and US external debt[54][57] - The gold model has demonstrated a historical prediction accuracy of 65% since 2008, with a sample-out accuracy of 78% since 2023[54][55] - The report highlights that fiscal and employment factors are currently supporting gold price increases, while economic and external debt factors show mixed signals[57][60] - A quantitative strategy for managing gold positions based on trading congestion is proposed, using two metrics: 40-day price deviation rate and SHFE gold implied volatility (IV)[19][21] - The strategy suggests reducing portfolio exposure to 40% when the 40-day price deviation rate exceeds 9% and SHFE gold IV surpasses 30%, achieving an excess return of 53.4% and improving the Sharpe ratio from 1.26 to 1.62 during backtesting from 2020 to February 2026[21][19] - The report recommends a multi-dimensional industry allocation strategy combining "win-rate and odds" and "clearance reversal" approaches, with industries such as non-ferrous metals, basic chemicals, and steel being highlighted[99][102][115]

Group 1: Report Industry Investment Rating - No information about the report industry investment rating is provided in the content [N/A] Group 2: Core Viewpoints - On February 5, 2026, the 30y Treasury bond model's single - day output probability was 10.57%, MA5 was 39.29%, and the model's view changed from oscillating to bullish, the first MA5 bullish signal since the model output an adjustment signal on October 30, 2025. The high yield of the new 30y Treasury bond might affect the model [4][7] - The 3 - year AAA medium - short note model remained bullish, with the bullish signal lasting for 43 trading days since December 8, 2025 [4][7] - On February 4, 2026, the 10 - year Treasury bond model's MA5 changed from oscillating to bullish, ending the oscillating adjustment period since December 2025. A factor of large banks' buying and selling of 7 - 10 - year Treasury bonds was added [4][7] - The 2 - year Treasury bond model fluctuated greatly recently. On February 4, 2026, it showed a single - day output probability turning bullish, and MA5 entered the oscillating range [4][7] - The gold model has been giving bullish signals since October 29, 2025. It entered the oscillating adjustment range on January 28, 2026, and is currently on the edge of the oscillating adjustment range [4][8] - The crude oil model has been generally bullish recently. The retracement on February 2, 2026, led to a marginal decline in the model's output probability, but it remains in the bullish range [4][8] Group 3: Summary by Related Catalog 1 Model Recent New Results Display - 30y Treasury bond model: On February 5, 2026, single - day output probability 10.57%, MA5 39.29%, changed from oscillating to bullish [7] - 3 - year AAA medium - short note model: Remained bullish, bullish signal for 43 trading days since December 8, 2025 [7] - 10 - year Treasury bond model: MA5 changed from oscillating to bullish on February 4, 2026, ending the oscillating adjustment since December 2025 [7] - 2 - year Treasury bond model: Fluctuated greatly, single - day output probability turned bullish on February 4, 2026, MA5 entered the oscillating range [7] - Gold model: Bullish since October 29, 2025, entered oscillating adjustment on January 28, 2026, currently on the edge of oscillating adjustment [8] - Crude oil model: Generally bullish, retracement on February 2, 2026, led to a marginal decline in output probability, still in bullish range [8]

Core Insights - The report emphasizes that risk appetite is driving asset performance, with a focus on style rotation in equities [2][3] - The overall market sentiment is optimistic, particularly in the context of A-shares, with a notable emphasis on mid-cap blue chips [7][10] - The report suggests a cautious short-term outlook for gold, while maintaining a positive medium-term perspective [7][10] Asset Allocation Strategy - The strategy recommends increasing allocations to A-shares, Chinese bonds, and US stocks, with specific adjustments based on volatility strategies [25][52] - For low-volatility strategies, a slight increase in A-shares and US stocks is advised, while medium-volatility strategies suggest increasing A-shares and Chinese bonds, and reducing gold exposure [37][54] - The report highlights the performance of dynamic all-weather strategies, which have shown annualized returns of 6.0% since 2025 [58] Industry Rotation Strategy - The report recommends focusing on sectors such as non-ferrous metals, chemicals, new energy, military, communications, and electronics for February [39][44] - The industry rotation strategy has outperformed benchmarks, achieving an annualized return of 45.3% since 2025 [41][42] - Two approaches for industry selection are discussed: maintaining previous top sectors for stability and capturing the best-performing sectors for responsiveness [44][50] ETF Strategy - The ETF strategy aligns with the industry rotation and asset allocation strategies, recommending ETFs in sectors like non-ferrous metals, chemicals, new energy, military, communications, and information technology [46][51] - The performance of the ETF industry strategy has also outperformed benchmarks, with an annualized return of 44.8% since 2025 [48][49] - The report outlines two methods for ETF selection, balancing stability and responsiveness to market signals [50][51]