Group 1 - The core viewpoint of the article emphasizes that a reversal from a difficult situation requires three conditions: extreme adversity, significant positive news, and a high-profile industry or company that attracts attention [1] Group 2 - NIO's current situation is widely recognized as a significant challenge, which is a consensus in the market [2] - The launch of the L90, a six-seater vehicle priced at 190,000 for BAAS and 270,000 for a buyout, is seen as a major positive development, showcasing strong product capabilities compared to competitors like the 2023 Xiaopeng G6 [2] - The electric vehicle sector inherently garners attention, and the L90 is being compared to the Li Auto i8 and the six-seater Model Y, which enhances its visibility and creates a buzz around the product [2]

反内卷，周期的价值轮回 20250613 摘要 2025 年中报业绩预喜率接近 1/3，科技制造及供需偏紧的周期板块预喜 率较高，如电子、化工、机械、汽车、电芯、有色、医药等行业公司数 量靠前。关注中报能否建立或稳定长期预期。 二季度经济复苏动能仍待增强，呈现量增价减格局，工业企业利润承压， 实际现金盈利弱于账面盈利。投资策略应把握贴现率下降主线，聚焦业 绩支撑和产业催化密集赛道，以及反内卷赛道。 7-8 月反内卷困境反转策略有望走强，关注产能出清时间久、库存水平 低、竞争格局优化的板块。大盘股优于小盘股，重视边际上具备业绩增 长或困境反转可能的大众盘股票。 推荐科技板块（军工、电子、创新药及游戏）、供给扰动资源品（有色 化工）及受益资本市场改革的保险券商。困境反转方面关注钢铁、建材、 光伏及养殖业等板块。 稀土价格上涨受中美谈判影响，内盘价格提前启动，标志着进入第三阶 段主升期。关注广晟有色、盛和资源、北方稀土、中国稀土等标的，以 及金力永磁、宁波韵升和正海磁材等核心磁材企业。 Q&A 目前中报季的披露情况如何？各行业的业绩表现有何特点？ 截至 2025 年 7 月 13 日，全 A 股共有 2,486 家 ...

Core Insights - The report emphasizes the potential for market breakthroughs amid current volatility, driven by "policy expectations + industry prosperity" as dual certainties, suggesting an optimized holding structure to prepare for the third quarter's performance and policy resonance [1][10][20]. Market Overview - The market continues to show strength supported by capital and policy expectations, with the upcoming disclosure of second-quarter economic data expected to influence market sentiment [10][20]. - The overall A-share index, excluding financial and micro-cap stocks, has seen a cumulative increase of 32.8% from August 30, 2024, to July 11, 2025, with a 7.0% increase year-to-date [22][25]. Industry Performance - Midstream industries, such as steel, electric new energy, real estate, and building materials, have significantly contributed to the upward movement of the index, indicating a recovery in valuations driven by "anti-involution" policy expectations [22][26]. - The report highlights the ongoing "anti-involution" trading, with sectors like electric new energy and steel showing continued recovery, while the banking sector experienced notable adjustments [20][21]. Domestic Computing Power Industry - The domestic computing power industry is entering a high-growth cycle, with significant developments in the GPU sector, including the IPO acceptance of domestic GPU manufacturers, which fills a gap in the A-share market for full-function GPUs [26][28]. - Industrial Fulian's mid-year earnings forecast indicates a substantial increase in net profit, driven by AI-related business growth, suggesting a positive outlook for the computing power industry [29][30]. Capital Flow and ETF Trends - The A-share market saw a net capital inflow of 61.57 billion yuan, with non-bank financials, computing, and real estate being the most favored sectors [35][36]. - The report notes a shift in ETF trends, with a significant net subscription of 4.89 billion yuan, marking the largest inflow in three weeks [35][36].

Quantitative Models and Construction Methods Models Model Name: Industry Allocation Model - **Model Construction Idea**: This model aims to recommend industry sectors based on medium-term trends and specific market conditions[2][3][10] - **Model Construction Process**: - The model identifies sectors that are likely to benefit from current market trends and conditions. - It recommends sectors such as Hong Kong innovative drugs, Hong Kong securities, and photovoltaic sectors due to their potential for reversal and growth. - The model also suggests focusing on technology sectors, including military and communication, as well as A-share banks and gold stocks[2][3][10] - **Model Evaluation**: The model is effective in identifying sectors with potential growth and aligning with current market trends[2][3][10] Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on recommending technology sectors based on their beta values and market conditions[2][3][10] - **Model Construction Process**: - The model evaluates the beta values of different sectors to identify those with higher potential for growth. - It recommends technology sectors, particularly military and communication, based on their beta values and current market trends[2][3][10] - **Model Evaluation**: The model is useful for identifying high-potential technology sectors based on their beta values[2][3][10] Model Name: Position Management Model - **Model Construction Idea**: This model aims to manage stock positions based on valuation indicators and short-term trends[3][10] - **Model Construction Process**: - The model uses valuation indicators such as PE and PB ratios to determine the stock positions. - It suggests an 80% stock position for absolute return products based on the current valuation levels of the wind All A index[3][10] - **Model Evaluation**: The model provides a balanced approach to managing stock positions based on valuation and market trends[3][10] Model Backtesting Results 1. **Industry Allocation Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] 2. **TWO BETA Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] 3. **Position Management Model**: - **PE Ratio**: 70th percentile[3][10] - **PB Ratio**: 30th percentile[3][10] - **Position Suggestion**: 80%[3][10] Quantitative Factors and Construction Methods Factor Name: Moving Average Distance - **Factor Construction Idea**: This factor measures the distance between short-term and long-term moving averages to identify market trends[2][9][14] - **Factor Construction Process**: - Calculate the 20-day moving average and the 120-day moving average of the wind All A index. - Compute the distance between the two moving averages. - The formula is: $$ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} $$ - If the distance exceeds 3%, the market is considered to be in an upward trend[2][9][14] - **Factor Evaluation**: The factor is effective in identifying market trend shifts from a volatile to an upward trend[2][9][14] Factor Name: Profitability Effect - **Factor Construction Idea**: This factor measures the market's profitability effect to predict the inflow of incremental funds[2][10][14] - **Factor Construction Process**: - Calculate the profitability effect value based on market data. - The current profitability effect value is 3.50%, indicating a positive market trend[2][10][14] - **Factor Evaluation**: The factor is useful for predicting the inflow of incremental funds based on market profitability[2][10][14] Factor Backtesting Results 1. **Moving Average Distance**: - **Distance**: 3.04%[2][9][14] - **Profitability Effect**: 3.50%[2][10][14] 2. **Profitability Effect**: - **Distance**: 3.04%[2][9][14] - **Profitability Effect**: 3.50%[2][10][14]

Group 1 - The core viewpoint of the article highlights the persistent risks in global financial markets despite the U.S. pausing the "reciprocal tariff" policy, with geopolitical tensions, trade fragmentation, and sovereign debt pressures being significant concerns [1][2] - The report from the UK Office for Budget Responsibility warns that public finances remain fragile post-COVID-19, indicating ineffective government spending control [2] - The A-share market has shown an independent trend amidst global financial turmoil, suggesting that market performance is driven by expectations rather than reality, encapsulated in the concept of "dilemma reversal" [4] Group 2 - The essence of expectation difference is rooted in information asymmetry, where understanding the true nature of transactions is crucial to overcoming this challenge [5] - An example of a stock, Zitian Technology, illustrates that despite an initial surge of over 20% in eight trading days, it subsequently faced a significant decline due to lack of institutional participation [7] - In contrast, Ruifeng High Materials demonstrated a strong correlation between institutional inventory data and market performance, with its stock price more than doubling [9] Group 3 - The importance of quantitative data has increased in the context of global financial instability, with the Bank of England planning to release more market position data to aid financial institutions in risk management [11] - Retail investors face challenges primarily due to information asymmetry, and quantitative tools can help mitigate psychological biases that lead to poor investment decisions [11][12] - The article emphasizes the need to identify genuine opportunities within the A-share market despite global uncertainties, with quantitative data serving as a tool to penetrate superficial market appearances [12] Group 4 - The article concludes that while risks persist, they often coexist with opportunities, and utilizing quantitative tools can provide clearer insights into market realities, enabling more rational investment decisions [14]

Market Overview - A-shares experienced a collective rise on July 10, with the Shanghai Composite Index surpassing the 3500-point mark, closing at 3509.68 points, up 0.48% [1] - The Shenzhen Component Index rose 0.47% to close at 10631.13 points, while the ChiNext Index increased by 0.22% to 2189.58 points [1] - The total trading volume in the Shanghai and Shenzhen markets was 149.42 billion yuan, a slight decrease of 11 billion yuan compared to the previous day [1] Sector Performance - The majority of industry sectors saw gains, with real estate development, engineering consulting services, real estate services, cement and building materials, coal, small metals, diversified finance, and steel industries leading the increases [1] - Conversely, the jewelry, shipbuilding, and manufacturing sectors experienced declines [1] Stock Movement - A total of 2947 stocks rose, with 69 hitting the daily limit up, while 2279 stocks fell, with 14 hitting the daily limit down [1] - The market showed signs of volatility, with significant fluctuations observed during the trading day, particularly in blue-chip stocks such as banks, insurance, and real estate [1] Investment Trends - Market focus on sectors experiencing a turnaround, particularly in photovoltaic, lithium battery, and real estate concepts, with significant price increases in silicon wafer prices ranging from 8% to 11.7% due to upstream silicon material price hikes [2] - Despite positive technical indicators, including a bullish engulfing pattern in the Shanghai Composite Index, the overall buying strength remains insufficient, indicating caution in stock selection is necessary [2] Company Spotlight - Xiangguo's stock performance was notable, with 86 out of 147 stocks rising, including Qidi Pharmaceutical, which hit the daily limit up after a previous gain of over 9% [3] - Qidi Pharmaceutical's main business includes "Guhang Yangshengjing" series products and traditional Chinese medicine, reporting a net profit of -16.26 million yuan for Q1 2025, a year-on-year decline of 96.23% [3] - The company is preparing for a potential change in control due to the auction of 24.47% of shares held by its controlling shareholder, and it has signed a strategic cooperation agreement to develop a traditional Chinese medicine health and wellness tourism base [3]

- The industry mainline model uses the Relative Strength Index (RSI) indicator to identify leading industries. The construction process involves calculating the past 20, 40, and 60 trading days' returns for 29 primary industry indices, normalizing the rankings, and averaging them to derive the final RSI value. Industries with RSI > 90% by April are likely to lead the market for the year[11][13][14] - The industry rotation model is based on the "Prosperity-Trend-Crowdedness" framework. It includes two sub-models: the industry prosperity model (high prosperity + strong trend, avoiding high crowdedness) and the industry trend model (strong trend + low crowdedness, avoiding low prosperity). Historical backtesting shows annualized excess returns of 14.4%, IR of 1.56, and a maximum drawdown of -7.4%[16][18][22] - The left-side inventory reversal model focuses on industries with low inventory pressure and potential for restocking. It identifies sectors undergoing a rebound from current or past difficulties. Historical backtesting shows absolute returns of 25.9% in 2024 and excess returns of 14.8% relative to equal-weighted industry benchmarks[28][30][29] - The industry ETF allocation model applies the prosperity-trend-crowdedness framework to ETFs. It achieves annualized excess returns of 15.5% against the CSI 800 benchmark, with an IR of 1.81. The model's excess returns were 6.0% in 2023, 5.3% in 2024, and 7.7% in 2025[22][27][16] - The industry prosperity stock selection model combines industry weights from the prosperity-trend-crowdedness framework with PB-ROE scoring to select high-value stocks within industries. Historical backtesting shows annualized excess returns of 20.0%, IR of 1.72, and a maximum drawdown of -15.4%[23][26][16] - The industry prosperity-trend model achieved excess returns of 3.9% in 2025, while the inventory reversal model showed absolute returns of 1.3% and excess returns of -2.1% relative to equal-weighted industry benchmarks[16][28][30]

Group 1 - The Shanghai Composite Index is approaching the critical resistance level of 3500 points, and a breakthrough is necessary for a new market trend to emerge [1][5] - A bullish sentiment is observed in both A-shares and H-shares, with potential for further gains in sectors like liquor [1][5] - The market is characterized by differing perspectives among investors, with those holding light positions hoping for a decline, while heavily invested individuals anticipate a rise [1][3] Group 2 - The index needs to achieve a significant upward movement beyond 4000 points to stabilize above 3500, indicating a structural issue of oscillating upward trends [3][5] - A slow bull market is expected, with fluctuations and sector rotations rather than a one-sided increase, as the index has reached 3500 despite widespread bearish sentiment [3][5] - The upcoming market shift could lead to a substantial rise in the index if sectors like liquor, securities, and real estate rebound simultaneously [5][6] Group 3 - The current market dynamics suggest that low-cost entry and long-term holding (around 1000 days) are essential strategies for investors [5][6] - Various sectors, including pharmaceuticals, photovoltaics, liquor, real estate, and securities, are at a cyclical low, with only the securities sector showing signs of recovery [5][6] - The market operates on the principle of speculation on expectations, where less favored stocks are more likely to experience upward movement [5][6]

- The report defines a timing system signal based on the distance between the long-term moving average (120 days) and the short-term moving average (20 days) of the Wind All A Index, which is currently at 1.76%, indicating the market is still in a consolidation pattern[1][3][9] - The industry allocation model recommends mid-term allocation to sectors experiencing a turnaround, such as Hong Kong innovative drugs, new consumption, and Hong Kong finance, with the trend still intact[2][3][10] - The TWO BETA model continues to recommend the technology sector, with a focus on military and communication sectors[2][3][10] - The Wind All A Index's PE ratio is at the 65th percentile, indicating a medium level, while the PB ratio is at the 20th percentile, indicating a relatively low level[2][10] - The position management model suggests a 50% allocation to absolute return products based on the Wind All A Index[2][10] Model Backtest Results - Timing system signal: Moving average distance 1.76%[1][3][9] - Industry allocation model: Mid-term recommendation for turnaround sectors, Hong Kong innovative drugs, new consumption, and Hong Kong finance[2][3][10] - TWO BETA model: Recommendation for technology sector, focus on military and communication[2][3][10] - Wind All A Index PE ratio: 65th percentile[2][10] - Wind All A Index PB ratio: 20th percentile[2][10] - Position management model: 50% allocation to absolute return products[2][10]

重点监测 18 城合计成交二手房总套数为 2.5 万套，环比上周增长 1%； 2025 年累计成交总套数为 60.9 万套，累计同比增长 14.6%。 土地供应（6.9-6.15） 2025 年 06 月 22 日 房地产 5 月房价环比走弱 周观点：5 月统计局全国地产数据环比走弱 2025 年 5 月，全国房地产市场底部。单月开发投资同比降幅扩大至- 12.0%（前值-11.3%），1-5 月累计投资 3.62 万亿元，同比下降 10.7%。 新房销售端动能减弱，单月商品房销售面积同比下降 3.3%，降幅较 4 月扩大 1.2 个百分点；单月销售额同比下降 6.0%。价格方面，70 城 新房价格环比下跌 0.2%，跌幅扩大 0.1 个百分点；二手房环比下跌 0.5%，跌幅扩大 0.1 个百分点，仅三城二手房环比上涨（无锡、洛阳、 南充），一线城市全面转跌。 我们认为，5 月房地产数据呈现出销售面积降幅收窄，但房价下行压 力加大的市场特征，在房价，建议关注困境反转类房企：金地集团、 新城控股等；保持拿地强度的龙头招商蛇口、绿城中国、保利发展、 滨江集团等；多元经营稳健发展的地方国企浦东金桥、外高桥等。 销售 ...