Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is designed to monitor the critical points of trend changes in the diffusion index, which reflects the breadth of stock price movements within the micro-cap index constituents[5][40]. - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window. For example, if all stocks in the micro-cap index drop by 5% after 5 days, the diffusion index value is 0.13. The formula is as follows: $ DI = \frac{\text{Number of stocks meeting criteria}}{\text{Total number of stocks}} $ The model uses three methods to generate signals: - **Initial Threshold Method (Left-Side Trading)**: Triggered a sell signal on May 8, 2025, when the index reached 0.9850[43]. - **Delayed Threshold Method (Right-Side Trading)**: Triggered a sell signal on May 15, 2025, at 0.8975[47]. - **Dual Moving Average Method (Adaptive Trading)**: Triggered a sell signal on June 11, 2025[48]. - **Model Evaluation**: The model effectively identifies overbought conditions and provides timely sell signals, but its predictive power may be limited by the dynamic updates of index constituents[40][5]. --- Quantitative Factors and Construction Methods 1. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Measures the illiquidity of stocks, which is inversely related to trading volume and price impact[4][35]. - **Factor Construction Process**: The factor is calculated as the ratio of absolute daily returns to trading volume over a specific period. $ Illiquidity = \frac{|R_t|}{Volume_t} $ - $R_t$: Daily return - $Volume_t$: Daily trading volume - **Factor Evaluation**: This factor showed the highest rank IC this week (0.174), significantly outperforming its historical average (0.038), indicating strong predictive power in the current market environment[4][35]. 2. Factor Name: 1-Year Volatility Factor - **Factor Construction Idea**: Captures the historical price volatility of stocks over the past year[4][35]. - **Factor Construction Process**: The factor is calculated as the standard deviation of daily returns over the past 252 trading days. $ Volatility = \sqrt{\frac{\sum_{i=1}^{252}(R_i - \bar{R})^2}{252}} $ - $R_i$: Daily return - $\bar{R}$: Average daily return - **Factor Evaluation**: This factor ranked second in rank IC this week (0.155), a significant improvement from its historical average (-0.033), suggesting its relevance in identifying outperforming stocks[4][35]. 3. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market returns[4][35]. - **Factor Construction Process**: The factor is calculated using regression analysis: $ R_i = \alpha + \beta R_m + \epsilon $ - $R_i$: Stock return - $R_m$: Market return - $\beta$: Beta coefficient - **Factor Evaluation**: This factor ranked third in rank IC this week (0.146), outperforming its historical average (0.004), indicating its effectiveness in the current market[4][35]. 4. Factor Name: Free Float Ratio Factor - **Factor Construction Idea**: Represents the proportion of shares available for public trading relative to total shares outstanding[4][35]. - **Factor Construction Process**: $ Free\ Float\ Ratio = \frac{\text{Free Float Shares}}{\text{Total Shares Outstanding}} $ - **Factor Evaluation**: This factor ranked fourth in rank IC this week (0.113), significantly better than its historical average (-0.011), highlighting its predictive strength in the current market[4][35]. 5. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of a company, indicating its debt level relative to equity[4][35]. - **Factor Construction Process**: $ Leverage = \frac{\text{Total Debt}}{\text{Total Equity}} $ - **Factor Evaluation**: This factor ranked fifth in rank IC this week (0.031), slightly above its historical average (-0.005), showing moderate predictive power[4][35]. --- Backtesting Results of Factors Weekly Rank IC Performance 1. **Illiquidity Factor**: Weekly rank IC = 0.174, Historical average = 0.038[4][35] 2. **1-Year Volatility Factor**: Weekly rank IC = 0.155, Historical average = -0.033[4][35] 3. **Beta Factor**: Weekly rank IC = 0.146, Historical average = 0.004[4][35] 4. **Free Float Ratio Factor**: Weekly rank IC = 0.113, Historical average = -0.011[4][35] 5. **Leverage Factor**: Weekly rank IC = 0.031, Historical average = -0.005[4][35] Weekly Rank IC Underperformance 1. **Nonlinear Market Cap Factor**: Weekly rank IC = -0.285, Historical average = -0.033[4][35] 2. **Log Market Cap Factor**: Weekly rank IC = -0.285, Historical average = -0.033[4][35] 3. **10-Day Return Factor**: Weekly rank IC = -0.205, Historical average = -0.061[4][35] 4. **10-Day Free Float Turnover Factor**: Weekly rank IC = -0.183, Historical average = -0.06[4][35] 5. **10-Day Total Market Cap Turnover Factor**: Weekly rank IC = -0.16, Historical average = -0.059[4][35] --- Strategy Performance Small-Cap Low-Volatility 50 Strategy - **Strategy Description**: Selects 50 stocks with the smallest market capitalization and lowest volatility from the micro-cap index constituents, rebalanced biweekly[19][37]. - **Performance**: - 2024 Return: 7.07%, Excess Return: -2.93% - 2025 YTD Return: 53.93%, Weekly Excess Return: 0.38% - Benchmark: Wind Micro-Cap Index (8841431.WI) - Transaction Cost: 0.3% per side[19][37]

Core Viewpoint - The article discusses various sectors in the market, highlighting trends and potential investment opportunities, particularly in the context of recent geopolitical developments and market dynamics. Financial Market Overview - A-shares have shown strong growth, with the North China 50 index rising by 6.84% weekly, leading major broad-based indices. Market sentiment is positive, with trading volume steadily increasing, indicating a shift towards bullish signals for most indices, except for the North China 50 which remains cautious [3]. Oil and Gas Sector - Geopolitical risks have eased, with reports of a ceasefire agreement between Israel and Iran, which may lead to a restart of consolidation among overseas oil and gas giants. As of June 27, Brent and WTI crude oil prices were reported at $66.34 and $65.07 per barrel, reflecting declines of 12.5% and 12.1% respectively from the previous week [4]. Agriculture Sector - In the pig farming sector, the industry capacity cycle has reached a bottom, but high inventory levels continue to impact market dynamics. Recent policy initiatives are accelerating the process of reducing inventory, which is expected to realign supply and demand. A long-term perspective suggests that after inventory reduction, the sector may enter a prolonged period of profitability [6]. Coal Mining Sector - There are signs of a turning point in coking coal inventories, with a reported decrease in both raw and refined coal stocks for the first time since May. As of the week of June 23-29, the inventory of raw coal was 683.5 million tons, down by 17.9 million tons, and refined coal was 463.1 million tons, down by 36.1 million tons. Additionally, the average price of thermal coal at Qinhuangdao port increased by 7 yuan to 616 yuan per ton, indicating the start of a seasonal price rise [7].

Group 1: Fixed Income and Economic Outlook - The Federal Open Market Committee (FOMC) maintained the federal funds rate target range at 4.25% to 4.5%, marking the fourth consecutive meeting without a rate change, aligning with market expectations [2] - The statement indicated a slight reduction in uncertainty regarding the economic outlook, although it remains at a high level [2] Group 2: Energy Transition and High-Temperature Superconductors - Shanghai Superconductor, a leading company in high-temperature superconducting materials, reported projected revenues of CNY 0.83 billion and CNY 2.40 billion for 2023 and 2024, respectively, reflecting year-on-year growth of 133% and 187% [2] - The company's gross profit margins are expected to improve to 55.77% and 60.52% in 2023 and 2024, respectively, with a significant increase in net profit to CNY 0.73 billion in 2024, indicating a turnaround [2] - The report anticipates that the demand for controllable nuclear fusion will drive down costs and expand application scenarios for high-temperature superconductors [2] Group 3: E-commerce and Retail Growth - The 2025 "618" e-commerce promotion is expected to see stable double-digit growth, driven by a slightly extended activity cycle, government subsidies, and increased user engagement through instant retail scenarios [4] - Major e-commerce platforms are expected to continue competing on improving merchant operations and enhancing user stickiness through multi-channel marketing [4] - Recommended stocks include Alibaba (BABA US/9988 HK) and JD.com (JD US/9618 HK) due to their strong brand support and potential for cross-selling in instant retail [4] Group 4: Utilities and Environmental Sector Performance - The report anticipates a mixed performance among major thermal power companies in Q2 2025, with coal prices expected to decline month-on-month [5] - Hydropower generation is projected to decline year-on-year, while nuclear power operations remain stable [5] - Key focus areas for green power operators include electricity pricing mechanisms and cash flow management for environmental companies [5] Group 5: New Energy and Technology Trends - The report highlights optimism in the profitability of battery and structural components in the electric vehicle sector, alongside advancements in solid-state battery technology [8] - Wind power demand is expected to remain robust, with profitability recovery driven by offshore wind projects [8] - Recommended stocks include CATL, EVE Energy, and others involved in emerging technology sectors such as humanoid robots and AIDC [8] Group 6: U.S. Treasury Market Demand - The report analyzes the structural characteristics of U.S. Treasury investors, noting that international investors, broad-based mutual funds, and the Federal Reserve account for over 60% of the market [7] - Different investor types exhibit distinct motivations for purchasing Treasuries, with expectations for continued demand from commercial banks and pension funds in the second half of 2025 [7]

Market Overview - The market experienced fluctuations this week, with only the ChiNext index showing an increase. The ETF market continued to see net outflows, primarily from large-cap ETFs. The market is transitioning from wide fluctuations to narrower ones, with increased trading volume during this process, indicating potential consolidation in a weak market [4]. Copper Industry - In May, domestic waste copper production was 92,000 tons, a year-on-year decrease of 20% but a month-on-month increase of 5%. The negative impact of trade conflicts on the economy has not fully materialized, which continues to suppress copper price increases. Supply-side disturbances in copper mining have increased, while demand is weakening due to reduced export stocking effects and the domestic off-season [5]. Metal Prices - The price of London gold has reached a historical high. Sunac China’s offshore debt-to-equity swap plan received support from 82% of bondholders. In May, Sunac's total sales amounted to 4.9 billion yuan, a year-on-year increase of 128%, indicating strong performance [6]. Chemical Industry - Recent safety incidents in chemical parks have led to stricter approval and production regulations for high-risk chemical reactions. Leading companies in the chemical industry, with better safety management and advanced production technologies, are expected to benefit from stable production amid limited growth in high-risk products [7]. Construction Materials - The market performance showed a decline, with the CITIC building materials index down 2.16% and the CITIC construction index down 1.27%. The average price of PO42.5 cement was 365.70 yuan/ton, a slight increase, while glass prices decreased by 20 yuan/ton [8]. Agriculture and Livestock - In the pig farming sector, the industry capacity cycle has bottomed out, but high inventory levels continue to impact market dynamics. Recent policy-driven efforts are accelerating the reduction of inventory, which may lead to a rebalancing of supply and demand. Long-term, the end of inventory reduction could signal the start of a prolonged profit upcycle for the sector [9]. Renewable Energy - The nuclear fusion sector, while far from full commercialization, is seeing increased investment and research due to global military competition. Recent data from May indicates a downward trend in overall renewable energy prices, highlighting ongoing pressures in power supply and demand. Wind power, virtual power plants, and energy storage are identified as promising investment opportunities [10].

- The report mentions the "All-Weather Quantitative Timing Model" which issued two risk warning signals in the latter half of last week, indicating that the market may still be under pressure in the future [7] - The market is currently in a large box oscillation pattern, with the central position or average cost around 3300-3350 [7] - The current market is in a multi-head arrangement of large-scale moving average systems, and the oscillation during the multi-head arrangement process can often be seen as a process of oscillation and accumulation [7] - The current adjustment appears after three waves of upward movement at the daily level, coinciding with the upper edge of the oscillation center, and there is a daily level top divergence and daily TD9 count, indicating a potential adjustment period of about 3 weeks based on the common 0.382 time retracement ratio characteristic [7] Quantitative Models and Construction Methods 1. **Model Name**: All-Weather Quantitative Timing Model - **Model Construction Idea**: The model aims to provide risk warning signals based on market conditions and technical indicators [7] - **Model Construction Process**: The model uses various technical indicators such as the daily level top divergence and TD9 count to identify potential market adjustments. The model also considers the 0.382 time retracement ratio to estimate the adjustment period [7] - **Model Evaluation**: The model effectively issued risk warning signals, indicating its potential usefulness in predicting market pressure [7] Model Backtesting Results 1. **All-Weather Quantitative Timing Model**: The model issued two risk warning signals in the latter half of last week, suggesting that the market may still be under pressure [7] Quantitative Factors and Construction Methods - No specific quantitative factors were detailed in the provided content Factor Backtesting Results - No specific quantitative factors were detailed in the provided content

- The report discusses the impact of dividends on stock index futures, specifically for the Shanghai Stock Exchange 50 (SSE 50), CSI 300, CSI 500, and CSI 1000 index futures[1][2][3] - The latest dividend forecast model predicts the dividend points for the June contracts of SSE 50, CSI 300, CSI 500, and CSI 1000 indices to be 3.70, 4.71, 10.68, and 10.32 respectively[10] - The annualized hedging costs (excluding dividends, calculated on a 365-day basis) for the June contracts of SSE 50, CSI 300, CSI 500, and CSI 1000 indices are 14.67%, 4.14%, 0.51%, and 9.81% respectively[10] - The report provides detailed calculations of the impact of dividends on the futures contracts, including the remaining impact of dividends on the contracts and the annualized hedging costs (excluding dividends, calculated on both 365-day and 243-day bases)[10][11][12][13] - The process for predicting dividends involves estimating the net profit of component stocks, calculating the total pre-tax dividends for each stock, calculating the impact of dividends on the index, and predicting the impact of dividends on each contract[19][22][23][24][25][26][27][28][30] - The theoretical pricing model for stock index futures is discussed, including both discrete and continuous dividend distribution scenarios[31][32] Model and Factor Construction - **Model Name**: Dividend Impact Prediction Model - **Construction Idea**: The model aims to predict the impact of dividends on stock index futures by estimating the net profit of component stocks and calculating the total pre-tax dividends[19][22] - **Construction Process**: 1. Estimate the net profit of component stocks using annual reports, quick reports, warnings, and analyst profit forecasts[22][23] 2. Calculate the total pre-tax dividends for each stock based on the estimated net profit and dividend rate[22][23] 3. Calculate the impact of dividends on the index using the formula: $$ \text{w_{it} = \frac{w_{i0} \times (1+R)}{\sum_{1}^{n} w_{i0} \times (1+R)}} $$ where \( w_{i0} \) is the accurate weight of stock \( i \) at time \( t0 \), and \( R \) is the rate of change in stock price[24] 4. Predict the impact of dividends on each contract by summing up all dividends before the contract's delivery date[28][30] - **Evaluation**: The model provides a systematic approach to predict the impact of dividends on stock index futures, considering various factors such as net profit estimation and dividend rates[19][22][23][24][25][26][27][28][30] Model Backtest Results - **SSE 50 Index Futures (June Contract)**: - **Dividend Points**: 3.70 - **Actual Spread**: -11.23 - **Dividend-Adjusted Spread**: -7.53 - **Remaining Impact of Dividends**: 0.14% - **Annualized Hedging Cost (365 days)**: 14.67% - **Annualized Hedging Cost (243 days)**: 13.67%[10] - **CSI 300 Index Futures (June Contract)**: - **Dividend Points**: 4.71 - **Actual Spread**: -7.78 - **Dividend-Adjusted Spread**: -3.07 - **Remaining Impact of Dividends**: 0.12% - **Annualized Hedging Cost (365 days)**: 4.14% - **Annualized Hedging Cost (243 days)**: 3.86%[11] - **CSI 500 Index Futures (June Contract)**: - **Dividend Points**: 10.68 - **Actual Spread**: -11.24 - **Dividend-Adjusted Spread**: -0.56 - **Remaining Impact of Dividends**: 0.19% - **Annualized Hedging Cost (365 days)**: 0.51% - **Annualized Hedging Cost (243 days)**: 0.48%[12] - **CSI 1000 Index Futures (June Contract)**: - **Dividend Points**: 10.32 - **Actual Spread**: -21.81 - **Dividend-Adjusted Spread**: -11.49 - **Remaining Impact of Dividends**: 0.17% - **Annualized Hedging Cost (365 days)**: 9.81% - **Annualized Hedging Cost (243 days)**: 9.15%[13]

Market Overview - The A-share market saw most major indices rise last week, with the ChiNext Index experiencing the largest increase of 1.73%. The Shanghai Composite Index rose by 0.68%, while the Shenzhen Component Index increased by 1.04% [2] - As of June 10, the margin trading balance in the Shanghai and Shenzhen markets was 1,811.46 billion yuan, an increase of 12.36 billion yuan from the previous week. The financing balance was 1,799.24 billion yuan, up by 11.95 billion yuan, and the securities lending balance was 12.22 billion yuan, which increased by 0.42 billion yuan [2] Industry Insights - The electronic, computer, and machinery equipment sectors had significant net buying in margin trading, while the food and beverage, banking, and coal sectors saw less net buying [3] - The average working hours for major construction machinery products in May were 84.5 hours, a year-on-year decrease of 3.86% [5] - Excavator sales in May reached 18,200 units, a year-on-year increase of 2.12%, while loader sales were 10,500 units, up 7.24% [5] Company Announcements - Zhejiang Lino plans to acquire 100% of Xuzhou Chemical Machinery Co., Ltd. [6] - Laisai Laser has adjusted the expected operational date for its fundraising project to August 1, 2026 [6] Performance Review - From June 4 to June 10, the CSI 300 Index rose by 0.35%, while the machinery equipment sector increased by 0.73%, outperforming the CSI 300 by 0.38 percentage points [6] - The price-to-earnings ratio (TTM) for the machinery equipment sector as of June 10 was 26.18 times, with a valuation premium of 117.57% compared to the CSI 300 [8] Future Outlook - Cumulative excavator sales from January to May reached 101,700 units, a year-on-year increase of 17.40%, with domestic sales at 57,500 units, up 25.70% [8] - The report maintains a "positive" rating for the machinery equipment sector, emphasizing the potential for urban renewal initiatives to drive steady demand for construction machinery [8]

Group 1: Communication Industry - Broadcom's CPO (Co-Packaged Optics) has made significant progress, launching a single-channel 200G CPO product series in May and delivering the Tomahawk 6 (TH6) switch chip in June, which supports both conventional and CPO versions [2] - The report anticipates that technology giants like Broadcom and NVIDIA will accelerate the advancement of CPO technology, fostering a mature ecosystem within the industry [2] - The outlook for the CPO industry is positive, with opportunities expected for related passive optical devices, optical chips, and optical engines, recommending companies such as Tai Chen Guang and Tianfu Communication, while suggesting to pay attention to Zhongji Xuchuang and New Yi Sheng [2] Group 2: Multi-Financial Industry - In May, the ETF market saw a total asset scale increase of 1.6%, with stock ETFs rising by 0.9%, indicating a stable growth trend despite market fluctuations [3] - Bond funds reached a record high with a net asset value of 284.1 billion, growing by 15% month-on-month, and their market share increased by 0.8 percentage points to 6.9% [3] - The report highlights the implementation of the "Action Plan for Promoting High-Quality Development of Public Funds," which aims to enhance the scale and proportion of equity investments in public funds, suggesting that stock ETFs may experience rapid growth opportunities [3] Group 3: Electronics and Computing Industry - The outdoor sports trend and the rapid growth of social media content are driving the transition of action cameras and panoramic cameras from niche products to mainstream creative tools for outdoor enthusiasts and short video users [4] - Key players in this emerging market include Ying Shi Innovation, GoPro, and DJI, with the industry expected to evolve towards "all-in-one" personal imaging devices [4] - Competition is shifting from hardware specifications to multi-dimensional competition involving AI, software ecosystems, and differentiated innovation capabilities [4] Group 4: Financial Engineering - The LLM-FADT strategy, based on the open-source model Qwen3-8b, has shown significant improvement over the previous BERT-FADT strategy, with annualized excess returns of 12.16% for the LLM-FADT Top25 CSI 300 index combination and 18.53% for the LLM-FADT healthcare sector combination [6] - The report emphasizes the effectiveness of the enhanced strategy in stock selection, particularly in the context of the healthcare sector [6] Group 5: Transportation Industry - The aviation sector is expected to perform well due to strong demand during the summer travel season and favorable oil exchange rates, with a long-term supply growth slowdown improving supply-demand dynamics [11] - The report recommends high-dividend Hong Kong road stocks, highlighting the stability of the road sector's performance and suggesting a focus on companies like China National Aviation and China Eastern Airlines [11] - The easing of tariffs has significantly boosted shipping rates, although market expectations may have already priced this in, leading to increased volatility in the sector [11]

Quantitative Models and Construction Methods 1. Model Name: Annualized Discount Model for CSI 500 Futures - **Model Construction Idea**: The model identifies optimal entry points for building positions based on historical performance when the annualized discount of CSI 500 futures exceeds a certain threshold, indicating market pessimism. [1][11] - **Model Construction Process**: - The model uses the annualized discount rate of the next-month contract of CSI 500 index futures as the key metric. - Historical data from 2017 onwards is analyzed to determine the relationship between the discount rate and subsequent returns. - Key findings: - When the annualized discount exceeds 15%, holding the index for more than 12 trading days results in average cumulative returns trending upward. - Holding for over 33 trading days yields a probability of positive cumulative returns exceeding 50%. - Holding for over 50 trading days increases the probability of positive returns to approximately 60%. - Formula: $ \text{Annualized Discount} = \frac{\text{Spot Price} - \text{Futures Price}}{\text{Futures Price}} \times \frac{365}{\text{Days to Maturity}} $ - Spot Price: Current index level - Futures Price: Price of the futures contract - Days to Maturity: Remaining days until the futures contract expires [11] - **Model Evaluation**: The model effectively captures market pessimism and identifies potential rebound opportunities, making it a useful tool for timing market entry. [11] --- Model Backtesting Results 1. Annualized Discount Model for CSI 500 Futures - **Key Metrics**: - Holding for 12 trading days: Average cumulative returns trend upward. - Holding for 33 trading days: Positive return probability > 50%. - Holding for 50 trading days: Positive return probability ~60%. [1][11] --- Quantitative Factors and Construction Methods 1. Factor Name: Proprietary Active Trader Activity Indicator - **Factor Construction Idea**: This factor measures the activity level of speculative funds (e.g., proprietary traders) to gauge market sentiment and risk appetite. [3][13] - **Factor Construction Process**: - Data Source: Derived from "Dragon and Tiger List" (龙虎榜) data. - The indicator tracks the marginal changes in active trader participation over time. - Observations: - From late April, the indicator showed a consistent decline, reflecting reduced risk appetite and cautious market sentiment. - Recently, the indicator has shown marginal improvement, suggesting a potential rebound in risk appetite. [3][13] - **Factor Evaluation**: The factor provides timely insights into the behavior of speculative funds, which can serve as a leading indicator for shifts in market sentiment. [3][13] 2. Factor Name: BARRA Style Factors - **Factor Construction Idea**: These factors assess the performance of various style attributes (e.g., momentum, volatility, size) to understand market preferences. [23][24] - **Factor Construction Process**: - Data Source: BARRA factor model. - Key Observations for the Week: - Fundamental factors (e.g., profitability) showed significant positive excess returns. - Stocks with high short-term momentum and high volatility outperformed. - Size-related factors (e.g., market capitalization) continued to underperform, indicating a preference for mid- to small-cap stocks. - Formula: Factor returns are calculated as the weighted average of stock returns within each style category. [23][24] - **Factor Evaluation**: The factors effectively capture shifts in market preferences, providing actionable insights for portfolio adjustments. [23][24] --- Factor Backtesting Results 1. Proprietary Active Trader Activity Indicator - **Key Metrics**: - Indicator showed consistent decline from late April, reflecting reduced risk appetite. - Recent marginal improvement suggests a potential rebound in speculative activity. [3][13] 2. BARRA Style Factors - **Key Metrics**: - Momentum: +0.2% weekly return. - Volatility: +0.2% weekly return. - Profitability: +0.3% weekly return. - Size: -0.5% weekly return. - Nonlinear Size: -0.3% weekly return. [23][24]

Group 1 - The report introduces a quantitative model solution for addressing the issue of value and growth style switching, based on the combination of odds and win rates [1][8] - Last week's market performance showed a growth style portfolio return of 3.01%, while the value style portfolio return was 1.51% [1][8] Group 2 - The estimated odds for the current growth style is 1.10, while the value style is estimated at 1.08, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rates indicate that 58.26% favor the growth style, while 41.74% favor the value style, based on seven win rate indicators [3][16] Group 3 - The latest investment expectation for the growth style is calculated at 0.22, while the value style's investment expectation is -0.13, leading to a recommendation for the growth style [4][18] - Since 2013, the annualized return for the style rotation model based on investment expectations has been 27.12%, with a Sharpe ratio of 0.99 [4][19]