Quantitative Models and Construction 1. Model Name: Macro + Volume-Price Dual-Driver Large-Cap and Small-Cap Rotation Strategy - **Model Construction Idea**: This model integrates macroeconomic factors and micro-level volume-price factors to predict the rotation between large-cap and small-cap styles[6][7] - **Model Construction Process**: - The model uses multiple single-factor signals, including PMI seasonal average difference, social financing growth rate, monetary liquidity, US-China interest rate spread, macro adjustment momentum, and style crowding indicators[7] - Each factor is assigned a signal value: 1 for large-cap signals, -1 for small-cap signals, and 0 for no effective signal[7] - The comprehensive score is calculated by summing the signals of all factors. If the score > 0, the portfolio is fully allocated to the CSI 300 Index; if the score < 0, it is fully allocated to the CSI 1000 Index; if the score = 0, the portfolio is equally weighted between the two indices[7] - **Model Evaluation**: The model demonstrates a high backtest win rate of 82.22% as of Q1 2025, indicating strong predictive power[6] 2. Model Name: Macro + Volume-Price Dual-Driver Value-Growth Rotation Strategy - **Model Construction Idea**: This model integrates macroeconomic factors and micro-level volume-price factors to predict the rotation between value and growth styles[12][13] - **Model Construction Process**: - The model uses multiple single-factor signals, including PMI new orders seasonal average difference, PPI-CPI growth rate, 1-year government bond yield, 3-month US bond yield, macro adjustment momentum, style crowding indicators, and market sentiment[13] - Each factor is assigned a signal value: 1 for value signals, -1 for growth signals, and 0 for no effective signal[13] - The comprehensive score is calculated by summing the signals of all factors. If the score > 0, the portfolio is fully allocated to the CSI Value Index; if the score < 0, it is fully allocated to the CSI Growth Index; if the score = 0, the portfolio is equally weighted between the two indices[13] - **Model Evaluation**: The model demonstrates a backtest win rate of 77.78% as of Q1 2025, showcasing its effectiveness in predicting style rotations[12] 3. Model Name: Industry Rotation Model (Single-Factor Multi-Strategy and Composite Factor Strategy) - **Model Construction Idea**: This model evaluates industry rotation using factors from historical fundamentals, expected fundamentals, sentiment, volume-price technicals, and macroeconomics[18][19] - **Model Construction Process**: - Single-factor multi-strategy: Constructs portfolios based on individual factors and evaluates their performance[18] - Composite factor strategy: Combines multiple factors into a composite score to rank industries and construct portfolios[18] - Both strategies select the top 5 industries from the 30 first-level industries in the CITIC classification and construct equal-weighted long portfolios[18] - **Model Evaluation**: The single-factor multi-strategy outperformed the composite factor strategy in May 2025, with higher monthly absolute and excess returns[20] --- Backtest Results of Models 1. Macro + Volume-Price Dual-Driver Large-Cap and Small-Cap Rotation Strategy - **YTD Return**: -2.41%[11] - **Annualized Return**: -5.83%[11] - **Annualized Volatility**: 17.17%[11] - **Maximum Drawdown**: 10.49%[11] - **Sharpe Ratio**: -0.34[11] - **Calmar Ratio**: -0.56[11] 2. Macro + Volume-Price Dual-Driver Value-Growth Rotation Strategy - **YTD Return**: 1.79%[17] - **Annualized Return**: 4.48%[17] - **Annualized Volatility**: 18.06%[17] - **Maximum Drawdown**: 10.36%[17] - **Sharpe Ratio**: 0.25[17] - **Calmar Ratio**: 0.43[17] 3. Industry Rotation Model - **Composite Factor Strategy**: - **Monthly Absolute Return**: 2.43%[20] - **Monthly Excess Return**: -0.64%[20] - **YTD Absolute Return**: 4.81%[20] - **YTD Excess Return**: 3.98%[20] - **Single-Factor Multi-Strategy**: - **Monthly Absolute Return**: 3.31%[20] - **Monthly Excess Return**: 0.33%[20] - **YTD Absolute Return**: 4.56%[20] - **YTD Excess Return**: 3.83%[20]

Core Insights - The report emphasizes the construction of ETF portfolios based on industry rotation models, highlighting the potential for enhanced returns through strategic sector allocation [1][2] - The industry rotation model has demonstrated stable excess returns over the backtesting period from January 2019 to April 2025, achieving a total return of 212.87%, significantly outperforming major indices like the CSI 300, CSI 500, and CSI 1000 [9][10] Industry Rotation Model - The model incorporates six factors: momentum, main buying amount, turnover rate change, deviation rate, intra-industry return deviation, and volatility, with a monthly rebalancing frequency [5][6] - The model's performance is evaluated across different market phases, showing varying factor effectiveness, with momentum and main buying amount consistently positive across the tested periods [6][8] ETF Market Overview - As of the end of 2024, the total scale of stock ETFs reached 29,259.35 billion yuan, with industry-themed ETFs accounting for 6,161.25 billion yuan, indicating a growing trend towards sector-specific investment strategies [25][26] - The report notes the increasing feasibility of using ETFs as tools for industry rotation strategies due to the expanding variety of newly issued industry-themed ETFs [25] ETF Portfolio Construction - The report outlines the construction of ETF portfolios based on the industry rotation model, recommending specific ETFs that align closely with the identified sectors [32][34] - The recommended ETF combinations for June 2025 include sectors such as oil and petrochemicals, banking, coal, transportation, steel, and agriculture, reflecting the model's latest insights [18][37]

Investment Rating - The report indicates a positive investment rating for the automotive industry, highlighting it as one of the sectors with significant net inflows of capital [2][9]. Core Insights - The automotive sector has seen a net inflow of 27.05 billion in the past 5 days, making it the top industry for capital inflow [9]. - The report utilizes a model that assesses industries based on six factors: capital, valuation, sentiment, momentum, overbought/oversold conditions, and earnings, with the automotive sector scoring high in these evaluations [13][14]. - The consensus stock selection model identified stocks such as Baiyang Co., Zhongchong Co., and Hunan Silver as top picks based on high-frequency capital flow and price movement similarity [16][17]. Industry Capital Inflow Statistics - In the past 5 days, the automotive industry led with a net inflow of 27.05 billion, followed by home appliances with 8.44 billion and machinery equipment with 5.26 billion [9]. - Over the past 30 days, the automotive sector experienced a net outflow of 446.97 billion, indicating a contrasting trend compared to the recent 5-day performance [10][12]. A-Share Industry Rotation Model - The A-share industry rotation model ranks the automotive sector among the top performers, alongside non-bank financials and communications, based on a composite score derived from the six factors [14][15]. - The automotive sector received a high score in capital and valuation, indicating strong investor interest and favorable market conditions [15]. Consensus Stock Selection - The consensus stock selection model highlighted industries such as feed, precious metals, and animal health II as high-performing sectors, with specific stocks selected based on their capital flow and price movement [16][17].