Core Insights - The article discusses the "crowding degree" as an important indicator reflecting trading sentiment in popular sectors, constructed by combining four dimensions: volume, price, funds, and analyst forecasts, which quantitatively track market sentiment changes and have strong indicative significance for short-term stock price trends [1]. TMT Sector - Optical modules: Crowding degree is moderately high [6] - Servers: Crowding degree is moderately high [9] - Base stations: Crowding degree is moderately high [11] - Optical fiber and cables: Crowding degree is moderately high [12] - IDC: Crowding degree is moderate [12] - Computer equipment: Crowding degree is moderately low [15] - Optical components: Crowding degree is moderately high [15] - RF components: Crowding degree is moderately high [19] - PCB: Crowding degree is moderate [20] - State-owned cloud: Crowding degree is moderately low [23] - IT services: Crowding degree is low [23] - Financial IT: Crowding degree is low [26] - Internet of Things: Crowding degree is moderate [26] - Semiconductor equipment: Crowding degree is high [30] - Semiconductor materials: Crowding degree is moderately high [31] - Semiconductor packaging and testing: Crowding degree is high [33] - Semiconductor design: Crowding degree is moderately high [33] - Semiconductor manufacturing: Crowding degree is moderately high [37] - Semiconductor discrete devices: Crowding degree is moderately high [37] - Memory: Crowding degree is high [42] - Consumer electronics: Crowding degree is moderately high [44] - Smart driving: Crowding degree is moderate [44] - Gaming: Crowding degree is moderately low [48] - Digital media: Crowding degree is moderately low [50] - Operators: Crowding degree is low [52] Manufacturing Sector - Auto parts: Crowding degree is moderately high [55] - Passenger cars: Crowding degree is moderately high [57] - Lithium batteries: Crowding degree is moderately high [59] - Hydrogen energy: Crowding degree is moderately high [59] - Energy storage: Crowding degree is moderately high [62] - Wind power: Crowding degree is moderately high [62] - Smart grid: Crowding degree is moderately high [65] - Photovoltaic inverters: Crowding degree is moderately high [65] - Photovoltaic cells: Crowding degree is moderately high [69] - Photovoltaic modules: Crowding degree is moderate [69] - Silicon materials and wafers: Crowding degree is moderately high [73] - Industrial robots: Crowding degree is moderately high [73] - Shipbuilding: Crowding degree is low [77] - Drones: Crowding degree is moderately low [77] - Aircraft engines: Crowding degree is low [82] Consumer and Pharmaceutical Sector - White goods: Crowding degree is moderately low [83] - Baijiu: Crowding degree is low [86] - Textiles and apparel: Crowding degree is moderate [88] - Hotel and catering: Crowding degree is moderate [90] - Tourism and scenic spots: Crowding degree is moderately high [90] - Air transport: Crowding degree is moderately high [94] - Pig industry: Crowding degree is moderate [94] - Innovative drugs: Crowding degree is low [97] - Traditional Chinese medicine: Crowding degree is low [99] - Medical services: Crowding degree is low [101] Financial and Real Estate Sector - Real estate: Crowding degree is moderately high [51] - Banking: Crowding degree is low [105] - Insurance: Crowding degree is low [105] - Securities: Crowding degree is moderately low [105] Cyclical Sector - Coal: Crowding degree is high [55] - Steel: Crowding degree is low [111] - Oil and petrochemicals: Crowding degree is moderately low [112] - Thermal power: Crowding degree is moderately low [113] - Industrial metals: Crowding degree is moderate [113] - Chemical raw materials: Crowding degree is moderately low [118]

Core Insights - The report highlights that a high level of crowding in sectors may indicate a peak in short-term sentiment, leading to potential downward adjustments in those sectors [2][6] - It emphasizes that sectors supported by industrial trends or strong policy backing are likely to recover and achieve excess returns after a period of emotional digestion [2][6] Market Review - The report notes that during the week of July 21-25, the overall A-share market rose by 2.65%, with significant performance from sectors like hydropower and rare earths [2][78] - The average daily trading volume reached 1.8398 trillion yuan, indicating increased market activity [2][78] - The report also mentions a notable increase in the number of stocks rising, with 2,941 stocks up compared to the previous week [2][78] Key Themes - **Childcare Subsidies**: The introduction of a national childcare subsidy policy is expected to stabilize birth rates and positively impact sectors such as maternal and infant products, early education, and assisted reproduction [3][95] - **Anti-Competition Policies**: The report discusses the government's efforts to eliminate excessive competition, which is anticipated to lead to an orderly exit of outdated production capacity and promote high-quality industry development [3][98] - **Innovative Pharmaceuticals**: The report highlights that business development (BD) transactions are opening up growth opportunities for innovative pharmaceutical companies, supported by favorable policies [3][101] Policy Dynamics - The report outlines several recent policy initiatives aimed at optimizing state-owned asset allocation and promoting high-quality urban development [3][104] - It mentions the emphasis on improving the quality of competition in various industries, particularly in sectors facing issues with low-price competition [3][98] Industry Trends - **Artificial Intelligence**: The report notes advancements in AI technology, including the launch of new AI products and participation in global AI governance discussions [3][104] - **Robotics**: The introduction of new robotic products is highlighted, indicating growth in the robotics sector [3][104] - **Biopharmaceuticals**: The report states that the approval of innovative drugs has significantly increased, with 43 new drugs approved in the first half of the year, marking a 59% year-on-year increase [3][104]

Group 1: Report Investment Rating - The operation rating for CITIC Five-Style - Growth is ★☆☆ [3][4] Group 2: Core Viewpoints - In the public fund market, the enhanced index strategy led the gains in the past week, while the ordinary stock strategy index in the equity strategy was relatively weak. The net value of non-ferrous metal ETFs rebounded, and the performance of precious metal ETFs was divergent. The style timing signal currently favors the growth style, and the style timing strategy had an excess return compared to the benchmark [4] Group 3: Summary by Related Catalogs Fund Market Review - As of the week ending on June 27, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond Index, and Nanhua Commodity Index were 3.35%, -0.10%, and -2.00% respectively [4] - In the public fund market, the enhanced index strategy had a weekly return of 3.18%. Among equity strategies, the ordinary stock strategy index was relatively weak, and neutral strategy products had more losses than gains. In the bond market, medium - and long - term pure bonds had a small pullback, and convertible bonds outperformed pure bonds. In the commodity market, the returns of energy - chemical and soybean meal ETFs pulled back, the net value of non - ferrous metal ETFs rebounded, and the performance of precious metal ETFs was divergent, with silver ETFs rising slightly and gold ETFs continuing to weaken [4] Equity Market Style - In the CITIC Five - Style, all style indices closed up last Friday, with the growth and financial styles leading. In terms of relative strength, consumption and stability were at a relatively low level, and in terms of indicator momentum, all five styles strengthened compared to the previous week, with consumption and stability having a large increase [4] - In the public fund pool, the average returns of cycle and consumption style funds outperformed the index in the past week, with excess returns of 0.60% and 0.06% respectively. Some growth - style funds shifted towards cycle and consumption styles [4] - In terms of crowding, consumption fell from a high - crowding range to a neutral range, the cycle style increased significantly, and the growth style was in a historically low - crowding range [4] Barra Factors - In the past week, the growth, liquidity, and momentum factors had better returns, the excess return of the profitability factor was compressed, the return of the volatility factor continued to decline, the dividend factor continued to weaken in terms of winning rate, and the momentum and residual volatility factors rebounded [4] - The cross - sectional rotation speed of factors decreased compared to the previous week and was currently in a historically low - quantile range [4] Style Timing - According to the latest scoring results of the style timing model, the financial style weakened slightly this week, while consumption and growth recovered, and the current signal favored the growth style [4] - The return of the style timing strategy last week was 3.41%, with an excess return of 0.63% compared to the benchmark balanced allocation [4]

- The "Four-Wheel Drive Model" issued multiple bullish signals for the TMT sector, indicating potential opportunities based on low-frequency thermometer metrics and crowding levels. The TMT sector remains at a relatively low position compared to the market, with slight upward movement after hitting a two-year low at the end of May[6][13] - Specific industries highlighted by the "Four-Wheel Drive Model" include electronics, computers, media, non-bank financials, food and beverages, textiles and apparel, communications, machinery, and military industries, all showing potential opportunities based on recent signals and market dynamics[6][13] - The "Four-Wheel Drive Model" uses metrics such as Sharpe ratio rankings and signal dates to identify potential opportunities in various industries, with recent signals indicating upward trends or crowding effects in sectors like electronics and media[13]

- The report introduces the **Industry Basic Tracking Model**, which monitors industry fundamentals and identifies the top-performing industries based on their sentiment and activity levels. The model highlights transportation, food & beverage, and coal as the industries with the highest sentiment, while media, communication, and banking show lower sentiment levels[3][8][9] - The **Crowding Factor** is introduced to measure the disparity between leading and lagging stocks within an industry across three dimensions: volatility, liquidity, and systemic risk. Higher crowding factors indicate elevated risks such as high volatility, active trading turnover, or increased beta exposure. For example, the food & beverage industry shows historically high crowding factors, while industries like agriculture, pharmaceuticals, machinery, consumer services, and coal exhibit historically low crowding factors[3][17][18] - The **Crowding Factor** is calculated using metrics such as stock volatility, liquidity, and beta exposure. It reflects the degree of market concentration and trading activity within an industry. Higher values suggest speculative trading and heightened systemic risk, while lower values indicate reduced market activity and risk exposure[17][18][28] - The pharmaceutical industry demonstrates a divergence between sentiment and crowding factors, with sentiment decreasing by 0.06 and crowding factors increasing by 0.28. This is attributed to short-term policy benefits, event-driven catalysts, and market sentiment, despite the lack of comprehensive recovery in industry fundamentals[12][15][17] - The report emphasizes that industries with high crowding factors, such as food & beverage, may face risks of speculative trading and systemic volatility. Conversely, industries with low crowding factors, such as agriculture, pharmaceuticals, machinery, consumer services, and coal, may present opportunities for stable investment due to reduced speculative activity[17][18][28]

Core Viewpoint - The report emphasizes the importance of considering multiple trading factors in industry comparisons, as relying solely on a single factor may not yield long-term success [2][3]. Trading Factors Worth Noting - Stock prices do not always reflect fundamentals, making trading factors crucial to avoid the risk of "correct logic but poor timing" [3]. - Momentum is highlighted as a key factor, indicating potential industry benefits; industries with positive momentum are likely to perform better in the future [3]. - Turnover rate serves as a measure of how well stock prices reflect positive news; industries with low turnover rates tend to perform better than those with high turnover rates [3]. - Trading congestion is identified as a risk aversion indicator; higher congestion levels often correlate with poorer industry performance [4]. Industry Comparison Scoring Logic - A scoring system based on trading factors is proposed, focusing on industries with potential benefits that are not fully reflected in stock prices and are not overcrowded in trading [5]. - Historical data from February 2014 to January 2025 shows that industries with higher scores yield better performance, with annualized returns of 11.5% for the highest scoring group compared to 0.3% for the lowest [5]. Long/Short Strategy Performance - A long/short strategy, holding the highest scoring industries while shorting the lowest, achieved an annualized return of 10.1% and a Sharpe ratio of 0.75 from February 2014 to January 2025, indicating the effectiveness of the trading factor scoring system [7].

Group 1 - The report emphasizes the importance of "crowding" as a key indicator reflecting market sentiment in popular sectors, constructed from four dimensions: volume, price, capital, and analyst forecasts [1][11][12] - The current trading crowding in the TMT sector has rebounded from the bottom to a high level, with many segments of the AI industry chain also showing high crowding, although some remain at moderate levels [2][12] - The report suggests that when crowding is low, it indicates a bottoming phase for stock prices, while high crowding suggests potential for significant price corrections [1][11] Group 2 - The transaction ratio has reached a historical high of 46%, raising concerns about whether the AI trading sentiment has peaked [3][17][20] - The report indicates that while a high transaction ratio may lead to increased volatility, it does not typically signal a systemic end to the market trend, as internal rotation and high-low switching can help digest short-term overheating [3][20] - Historical examples are provided, showing that significant changes in industry trends or fundamentals can lead to new trend formations despite high transaction ratios [3][20] Group 3 - The report introduces a "rotation intensity" indicator to measure the speed of internal rotation within the AI sector, noting that a convergence in rotation intensity often leads to a mainline market trend [4][28] - Following the Spring Festival, the main directions within AI have become clearer, with the computer and media sectors leading the gains, resulting in a decrease in rotation intensity [4][28][29] - The relationship between the AI index and rotation intensity suggests a pattern of "linked rises and rotating adjustments," indicating resilience in the sector rather than systemic declines [4][34] Group 4 - U.S. Treasury yields are highlighted as a significant factor affecting the pricing of high-valuation growth assets, with rising yields typically suppressing market risk appetite [5][37] - The report notes a strong correlation between TMT performance and U.S. Treasury yields, suggesting that changes in yields should be closely monitored from a trading perspective [5][37][39] Group 5 - The report discusses the importance of earnings performance in the AI sector, noting that the correlation between stock price movements and earnings growth is strongest during earnings disclosure periods [7][43] - It is observed that when the market focuses on fundamentals, the TMT sector may face adjustment pressures, while periods of trading on expectations can lead to better performance [7][43][45] - The example of optical modules is provided, illustrating how sustained earnings performance can lead to a strong positive correlation with stock price movements [7][51][52]