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].

Group 1: Report Industry Investment Rating - No relevant content Group 2: Core Viewpoints of the Report - The Financial Street Securities Research Institute constructs a strategy portfolio based on industry and thematic ETFs, and the model recommends allocating sectors such as marine equipment, liquor, and securities in the week of March 30, 2026 [2][12] - The strategy will newly hold products like the Ship ETF Fuguo, Securities and Insurance ETF E Fund, and Aerospace ETF Huatai-PineBridge, and continue to hold products like the Liquor ETF Penghua and Building Materials ETF Guotai in the next week [12] - As of last weekend, the trading timing signals of some ETFs and underlying indexes gave daily or weekly risk warnings [12] Group 3: Summary by Relevant Catalogs Strategy Portfolio Construction - The Financial Street Securities Research Institute constructs a strategy portfolio based on industry and thematic ETFs, referring to the strategy reports "Strategy Portfolio Report under Industry Rotation: Quantitative Analysis from the Perspective of Industry Style Continuity and Switching" (20241007) and "Research on the Overview and Allocation Methods of the Stock ETF Market: Taking the ETF Portfolio Based on the Industry Rotation Strategy as an Example" (20241013) [2] ETF Portfolio Information - The ETF portfolio includes multiple products such as the Ship ETF Fuguo, Liquor ETF Penghua, and Securities and Insurance ETF E Fund, with details on their market values, holding situations, heavy - held Shenwan industries and their weights, as well as weekly and daily timing signals [3] Performance Tracking - From March 23 to March 27, 2026, the cumulative net return of the strategy was approximately - 1.33%, and the excess return relative to the CSI 300 ETF was approximately 0.15% [3] - From October 14, 2024, to March 27, 2026, the out - of - sample cumulative return of the strategy was approximately 27.53%, and the cumulative excess return relative to the CSI 300 ETF was approximately 8.23% [3] ETF Portfolio Changes - In the week of March 23 - 29, 2026, some ETFs such as the Film and Television ETF Yin Hua, Telecommunications ETF E Fund were调出, while the Liquor ETF Penghua and Building Materials ETF Guotai were continued to be held. The average return of the ETF portfolio was - 1.33%, and the excess return relative to the CSI 300 ETF was 0.15% [11]

Investment Rating - The report maintains a positive outlook on short-term bonds, undervalued convertible bonds, and gold assets [2][10][22]. Core Insights - The report predicts an increase in the level factor, steepening of the slope factor, and convexity of the curvature factor in the bond market, recommending the holding of 1-year short-duration bonds [10]. - As of March 27, 2026, the "hundred-yuan conversion premium rate" stands at 41.71%, indicating a low relative value for convertible bonds compared to their underlying stocks [13][15]. - The expected return on gold for the next year is projected to be 33%, with a historical absolute return of 62% based on TIPS yield strategies [22][24]. Summary by Sections Multi-Asset Allocation Viewpoints - The report advocates for a bullish stance on short-duration bonds, undervalued convertible bonds, and gold assets [2]. - The bond duration timing perspective suggests holding 1-year short-duration bonds due to predicted market movements [10][12]. Stock and Bond Allocation Viewpoints - The report is bearish on equity assets, with the latest equity position at 4.21% [26][31]. - The stock-bond rotation strategy has shown a negative return of -0.44% for March, with an average equity position of 4.72% and a bond position of 95.28% [31][33]. Industry Rotation Insights - The report recommends a bullish outlook on the banking, pharmaceutical, electrical equipment, media, textile, and commercial sectors [4][41]. - The growth style is favored over value style, with a higher score for growth sectors [41]. - The ETF rotation strategy includes specific ETFs for banking, healthcare, electrical equipment, and media, with recent performance showing an excess return of 1.14% compared to the average industry return [50][46].

- The bond duration timing model uses an improved Diebold2006 model to predict the spot yield curve and map the expected returns of bonds with different durations. The model predicts the level, slope, and curvature factors, with the level factor prediction based on macro variables and policy rate following, and the slope and curvature factors prediction based on the AR(1) model[10] - The convertible bond allocation model compares the relative valuation of convertible bonds and stocks using the "100-yuan conversion premium rate" and calculates the rolling historical percentile to measure the current relative allocation value of convertible bonds and stocks. As of March 27, 2026, the "100-yuan conversion premium rate" was 41.71%, with a rolling three-year percentile of 92.8% and a rolling five-year percentile of 95.7%, indicating a relatively low cost-effectiveness compared to stocks[13][15] - The convertible bond style rotation model constructs a convertible bond style rotation portfolio by excluding high-valuation convertible bonds using the conversion premium rate deviation factor and the theoretical value deviation factor, and capturing market sentiment using the 20-day momentum and volatility deviation of convertible bonds. From February 14, 2018, to March 13, 2026, the annualized return of the convertible bond style rotation was 25.60%, with a maximum drawdown of 15.89% and an IR of 1.51. The return since 2026 was 9.34%[16] - The gold expected return model links the forward real returns of gold and US TIPS, constructing the expected return model for gold. The formula is $E[Real\_Return^{gold}]=k\times E[Real\_Return^{Tips}]$ and $E[R^{gold}]=\pi^{e}+k\times E[Real\_Return^{Tips}]$, where the parameter k is estimated using an expanding window OLS, and the long-term inflation target of the Federal Reserve (2%) is used as the proxy for $\pi^{e}$. As of March 27, 2026, the model estimated the expected return of gold for the next year to be 33.0%[22][23] - The A-share equity market timing framework is constructed from six dimensions: macro liquidity, credit expectations, cross-border capital flows, derivatives expectations, market capital flows, and technical analysis. Based on timing signals, a stock-bond rotation portfolio is constructed using a risk budget model. As of March 27, 2026, the comprehensive signal was -0.23, indicating a bearish view on equity assets[29][31] - The industry rotation model constructs sub-models from six dimensions: trading behavior, prosperity, capital flow, chip structure, macro drive, and technical analysis, and dynamically synthesizes the models to select industries on a bi-weekly basis. The latest industry configuration recommendations are banking, pharmaceuticals, electrical equipment, media, apparel, and commerce. The style judgment recommends a growth style over a value style[35][41] Model Backtest Results - Bond duration timing model: March return of 18.3bp, equal-weighted benchmark return of 6.4bp, strategy excess return of 11.9bp. The return over the past year was 1.57%, equal-weighted benchmark return of -0.12%, strategy excess return of 1.69%[12] - Convertible bond style rotation model: Annualized return of 25.60%, maximum drawdown of 15.89%, IR of 1.51. The return since 2026 was 9.34%[16] - Gold expected return model: Expected return for the next year is 33.0%. The absolute return of the timing model based on TIPS yield over the past year was 62.0%[22][24][25] - Stock-bond rotation portfolio (risk budget): Annualized return of 8.16%, maximum drawdown of 3.74%, return volatility ratio of 2.76, return drawdown ratio of 2.19. March return of -0.44%, latest equity position of 4.21%[33][36] - Industry rotation model: March long portfolio return of -6.42%, short portfolio return of -7.73%, equal-weighted benchmark return of -7.23%, long excess return of 0.81%, short excess return of 0.5%, long-short portfolio return of 1.65%[38][40] - ETF rotation portfolio: March return of -5.69%, average return of tracked industries of -6.84%, excess return of 1.14%. Latest ETF rotation portfolio holdings: Game ETF Huaxia, Battery ETF Guangfa, Medical ETF Huabao, Banking ETF Huabao[46][50][53]

Core Insights - The report analyzes the impact of the closure of the Strait of Hormuz on global oil supply and prices, predicting a potential rise in Brent crude prices above $100 per barrel due to a supply reduction of nearly 20% [3][4][8] - Historical comparisons indicate that the current oil crisis resembles past events, particularly in terms of economic and monetary cycles, suggesting a potential for prolonged high oil prices or a rapid return to pre-crisis levels depending on geopolitical developments [17][18] - The report highlights the sectors that may benefit from the current crisis, including oil, precious metals, and defense, while also noting the potential for a shift in market focus towards more stable sectors like technology and consumer goods in the event of a price drop [23][27] Section Summaries Impact of the Closure of the Strait of Hormuz - The closure of the Strait of Hormuz could lead to a significant reduction in oil supply, with predictions indicating a 20% decrease in oil and LNG supplies, and a 50% reduction in sulfur supply [3][4] - The Dallas Federal Reserve's model suggests that if the Strait remains closed for a quarter, WTI crude prices could rise to $98 per barrel, with a corresponding 2.9% decline in global GDP growth for Q2 2026 [8][11] Historical Comparisons - The report compares the current crisis to previous oil crises, noting that the economic environment prior to the current conflict is similar to that of the Kosovo War, characterized by fiscal expansion and demand recovery [17] - It discusses the potential outcomes of oil price movements post-crisis, indicating that if prices remain high for an extended period, inflation and demand could be adversely affected, while a quick price drop could lead to a return to previous economic trends [17][18] Sector Performance During Crises - Historical data shows that during past oil crises, sectors such as oil, precious metals, and defense typically outperform, while technology and consumer sectors may gain traction once the immediate crisis subsides [23][27] - The report emphasizes that no sector has consistently delivered absolute returns during bear markets following oil price spikes, indicating a complex relationship between oil prices and sector performance [23][27]

Group 1: Financial Engineering Research - The report identifies four dimensions (macro, technical, sentiment, and economic) to drive industry rotation and constructs an ETF monthly rotation portfolio based on primary industry recommendations [2][3] - The strategy has shown strong performance since its inception in 2018, with an annualized excess return of 13.85% and a compound factor strategy annualized excess return of 7.28% by the end of 2025 [2][3] - In 2025, the single-factor multi-strategy portfolio achieved an absolute return of 36%, with an excess return of 12.29% compared to an equal-weight benchmark [3] Group 2: Power Equipment and New Energy Research - The company, Megmeet (麦格米特), is expected to significantly increase its product value from 2 RMB/W to 5-6 RMB/W by forming a complete AI power solution [5][6] - The company has been innovating alongside NVIDIA, developing a comprehensive product layout including high-power PSUs, HVDC, BBU, and supercapacitors, and is positioning itself to enter the ASIC supply chain [6] - The AI power market is projected to exceed 100 billion RMB, with increasing demand for power supply as NVIDIA's chip power consumption rises [6] Group 3: Medical Devices Industry - The report maintains an "overweight" rating for the medical device sector, highlighting the acceleration of commercialization for surgical robots [10][11] - The approval of a unique integrated surgical robot platform by 精锋医疗 is expected to enhance commercialization processes [10] - The report notes that the global commercial orders for 微创机器人 have surpassed 200 units, indicating strong growth in overseas sales [11] Group 4: Hydrogen Energy in Construction Engineering - The report discusses the government's initiative to promote hydrogen energy applications, with a target to reduce hydrogen prices and increase the number of fuel cell vehicles [13][25] - 华电科工 is actively exploring integrated projects in renewable energy and hydrogen production, aiming to lead in the hydrogen market [14][15] - 中钢国际 has successfully implemented hydrogen metallurgy technology, contributing to the steel industry's transition to low-carbon processes [24][27] Group 5: Wealth Management in Financial Services - The report indicates a significant increase in non-cash fund holdings among the top 100 institutions, with a 14.7% increase to 11.7 trillion RMB [16][19] - The growth in equity funds is primarily driven by third-party channels, reflecting a shift in wealth management strategies [17][19] - The concentration of fund holdings among leading institutions is becoming more pronounced, indicating a trend towards headquarter concentration in wealth management [19]

Core Viewpoint - The article discusses the "Four Quadrant Monthly Industry Rotation Strategy," which utilizes four dimensions: economic conditions, sentiment, technical analysis, and macroeconomic factors to construct investment strategies. The strategy has shown strong performance since its inception in 2018, with annualized excess returns of 13.85% for single-factor multi-strategies and 7.28% for composite factor strategies by the end of 2025 [2]. Summary by Sections Performance Metrics - By 2025, the single-factor multi-strategy portfolio achieved an absolute return of 36%, with an excess return of 12.29% compared to an equal-weight benchmark. The composite factor strategy portfolio had an absolute return of 38.1% and an excess return of 14.38%. Both portfolios had a monthly excess return win rate of 58.3% [2]. Factor Analysis - In 2025, factor effectiveness showed significant differentiation. The macroeconomic factor performed exceptionally well with an annualized excess return of 23.8% and a monthly win rate of 67%. In contrast, the economic conditions and sentiment factors contributed modestly with excess returns of 4.1% and 7.1%, respectively. The technical factor underperformed with an excess return of -1.1%, consistent with historical trends during market uptrends [2]. Market Environment Interaction - The performance of factors is closely linked to market conditions. In rising markets, macroeconomic, economic conditions, and sentiment factors drive industry performance, while the technical factor serves a defensive role in declining markets. Future research aims to incorporate market environment predictions into the strategy to achieve more stable excess returns [3]. ETF Strategy Performance - Since 2014, a strategy portfolio based on ETFs has achieved an annualized excess return of 11.4% relative to the CSI 800 index, with an information ratio of 1.01 [3].

Investment Rating - The report does not explicitly state an investment rating for the industry, but it discusses the performance of various strategies and their relative returns against benchmarks [36]. Core Insights - The industry rotation strategy utilizes four quadrants: macroeconomic, sentiment, technical, and economic conditions to construct factors that drive industry rotation. The strategy has shown strong performance since its inception in 2018, with annualized excess returns of 13.85% for single-factor multi-strategy and 7.28% for composite factor strategies by the end of 2025 [36]. - In 2025, the absolute return for the single-factor multi-strategy was 36%, with an excess return of 12.29% compared to an equal-weighted benchmark. The composite factor strategy achieved an absolute return of 38.1% with an excess return of 14.38% [36]. - The effectiveness of factors in 2025 showed significant differentiation, with macro factors performing exceptionally well, contributing over 23.8% in excess returns, while sentiment and economic factors contributed modestly at 4.1% and 7.1%, respectively. Technical factors underperformed with a -1.1% excess return [36]. Summary by Sections 1. Strategy Overview - The industry rotation strategy framework includes four dimensions: economic conditions, sentiment, technical indicators, and macroeconomic factors, which are used to construct scoring systems for industry selection [8][9]. 2. Factor Performance Analysis - Long-term performance of factors indicates that macro, sentiment, and economic factors have shown superior returns, with macro factors leading in long positions [19]. - Yearly performance of factors has demonstrated strong complementary effects, with at least one effective factor present each year [19]. 3. Weekly Performance of Strategy Holdings - In 2025, the strategies maintained a win rate above 50% throughout the year, with the first week post-recommendation showing weaker performance, followed by three weeks of stable positive excess returns [29][39]. 4. ETF Combination Strategy - The ETF strategy, which has been in place since 2014, has achieved approximately 11% annualized excess returns relative to the CSI 800 index, with an information ratio of 1.01 [34][39]. 5. Conclusion - The report concludes that the industry rotation strategy effectively utilizes multiple factors to achieve superior returns, particularly highlighting the strong performance of macroeconomic factors in 2025 [36].

- The report identifies the strongest sectors in each bull market cycle, highlighting the importance of sectors driven by growth and prosperity trends, such as telecommunications and metals, which have shown significant gains in the current bull market[6][20][79] - The report emphasizes the concept of "连庄" (consecutive years of top performance), noting that this phenomenon occurs when the sector's Beta attributes align with a stable macro environment, with 11 instances of "连庄" recorded historically[5][23][97] - The report provides detailed analysis of the performance of various sectors across different bull market cycles, including the first bull market (2005-2007), the second bull market (2012-2015), the third bull market (2018-2021), and the current fourth bull market (2024-present), with specific focus on sectors like telecommunications, metals, and AI-driven industries[17][18][19][20][79][80]

Quantitative Models and Construction Methods Model Name: Price Anomaly Detection - **Model Construction Idea**: Identify stocks with price movements that deviate significantly from the benchmark index by calculating the correlation coefficient between the stock's intraday price series and the benchmark index's price series [11][13] - **Model Construction Process**: 1. Calculate the minute-level closing price series for individual stocks and the benchmark index (e.g., Wind All A Index) [13] 2. Compute the correlation coefficient between the stock's price series and the benchmark index's price series for the trading day [13] 3. If the correlation coefficient < 0, the stock is considered to have experienced a price anomaly on that day [13] - **Model Evaluation**: The model captures stocks with price movements deviating from the market but fails to generate effective alpha signals as the future excess return win rate is below 50% [15][19] Model Name: Upward and Downward Price Anomalies - **Model Construction Idea**: Refine the price anomaly detection by incorporating the direction of excess returns relative to the benchmark index [17][18] - **Model Construction Process**: 1. Define "Upward Price Anomaly" as stocks with a correlation coefficient < 0 and stock return > benchmark return on the same day [18] 2. Define "Downward Price Anomaly" as stocks with a correlation coefficient < 0 and stock return < benchmark return on the same day [18] 3. Test the model using the CSI 800 index constituents over the period 2016/01/01–2026/02/28 [18] - **Model Evaluation**: Both upward and downward price anomalies fail to provide significant excess returns, with win rates below 50% and average excess returns near zero [19][23] Model Name: Anomaly Radar Event Cluster - **Model Construction Idea**: Extend the anomaly detection framework by incorporating multi-dimensional capital flow indicators and systematically producing event-driven signals [28][29] - **Model Construction Process**: 1. **Correlation Coefficient Calculation**: Compute the correlation coefficient between intraday capital flow indicators (e.g., transaction volume, transaction amount) of individual stocks and the benchmark index. If the correlation coefficient < 0, the stock is considered anomalous [29][30] 2. **Excess Return Direction**: Incorporate the direction of excess returns relative to the benchmark index to classify anomalies as "upward" or "downward" [39] 3. **Signal Screening and Synthesis**: Batch-produce event signals, evaluate their effectiveness and correlation, and synthesize effective signals into a stable event-driven strategy [42][43] 4. Construct a capital channel strategy using selected signals, with a 20-day holding period and weekly rebalancing [42][43] - **Model Evaluation**: The synthesized anomaly radar signals demonstrate strong performance, with an annualized excess return of 7.51% and an IR of 2.48 during the backtest period [45][48] --- Model Backtest Results Price Anomaly Detection - Annualized excess return: Near zero [15] - Win rate: Below 50% across all time horizons [15] Upward Price Anomalies - Annualized excess return: Near zero [19] - Win rate: Below 50% across all time horizons [19] Downward Price Anomalies - Annualized excess return: Near zero [23] - Win rate: Below 50% across all time horizons [23] Anomaly Radar Event Cluster - Annualized excess return: 7.51% [45][48] - IR: 2.48 [45][48] - Maximum drawdown: 4.13% [45][48] Anomaly Radar + Negative Signal Filtering - Annualized excess return: 9.77% [51][53] - IR: 2.92 [51][53] - Maximum drawdown: 2.85% [51][53] --- Quantitative Factors and Construction Methods Factor Name: Industry Anomaly Factor - **Factor Construction Idea**: Map individual stock anomaly signals to industry-level factors for use in sector rotation strategies [66][67] - **Factor Construction Process**: 1. Calculate the number of stocks triggering anomaly signals within each industry daily [66] 2. Normalize the number of triggered stocks by the total number of stocks in the industry and compute a 20-day rolling average [66] 3. Calculate the historical percentile of the rolling average to define the industry anomaly factor [66] - **Factor Evaluation**: The factor demonstrates moderate predictive power with a monthly IC of 0.03 and low correlation (11%) with traditional industry trend factors [68] --- Factor Backtest Results Industry Anomaly Factor - Monthly IC: 0.03 [68] - Multi-group backtest: Top quintile significantly outperforms other groups [68] Sector Rotation Strategy: "Anomaly + Strong Trend + Low Crowding, Exclude Low Prosperity" - Annualized excess return: 9.50% (vs. 6.78% without anomaly factor) [70][73] - IR: 1.09 (vs. 0.74 without anomaly factor) [70][73] - Maximum drawdown: 9.62% (vs. 18.64% without anomaly factor) [70][73] Sector Rotation Strategy: "Anomaly + Strong Trend + High Prosperity, Exclude High Crowding" - Annualized excess return: 9.04% (vs. 5.97% without anomaly factor) [75][78] - IR: 0.80 (vs. 0.47 without anomaly factor) [75][78] - Maximum drawdown: 18.66% (vs. 30.72% without anomaly factor) [75][78]