公司研究 [Table_ReportType] 公司点评报告 [晨Ta光bl生e_物Sto(c3k0A0n1d3Ra8n)k] 投资评级 信达证券股份有限公司 CINDA SECURITIES CO.,LTD 金隅大厦 B 座 邮编：100031 证券研究报告 [Table_Title] Q1 业绩大增，反转趋势确立 [Table_ReportDate] 2025 年 4 月 28 日 [T事ab件le：_S公um司ma公ry布] 2025 年一季报。25Q1，公司实现营业收入 17.16 亿元， 同比-0.92%；归母净利润 1.09 亿元，同比+183.69%；扣非归母净利润 9592 万元，同比+97.83%。 点评： 请阅读最后一页免责声明及信息披露 http://www.cindasc.com 1 上次评级 邮 箱：zhaolei@cindasc.com 收入表现平稳，多款产品销量创历史新高。分板块看，Q1 植提业务实 现收入 8.67 亿元，同比+13.78%；棉籽业务实现收入 7.60 亿元，同比 -9.49%。植提业务中，多款产品以量补价，辣椒红收入同比+7%，销 量快速增长，销售价格随成本相 ...

Core Insights - The brokerage industry is experiencing a "turnaround" in 2024, with five firms reporting net profits exceeding 10 billion yuan, an increase of three firms compared to 2023 [2] - CITIC Securities leads the industry with a net profit of 22.6 billion yuan, followed by Huatai Securities, Guotai Junan, China Merchants Securities, and Galaxy Securities [2] - The industry is undergoing a reshuffle, with leading firms optimizing costs and innovating services, while smaller firms seek survival through differentiated strategies [2] Brokerage Performance - In 2023, 27 out of 43 listed brokerages reported revenue growth, while 16 experienced declines [3] - The highest revenue growth was recorded by Dongxing Securities at 97.89%, while the lowest was by Xiangcai Securities at -24.36% [3] - 33 brokerages reported positive net profit growth, with 10 seeing declines; Hualin Securities achieved the highest net profit growth at 1014.54% [3][6] Financial Data - CITIC Securities reported revenue of 63.79 billion yuan, a year-on-year increase of 6.20%, and a net profit of 22.59 billion yuan, up 9.98% [4] - Guotai Junan achieved revenue of 43.40 billion yuan, a 20.08% increase, and a net profit of 13.55 billion yuan, up 37.06% [5] - Hualin Securities' net profit surged to 0.35 billion yuan from 0.032 billion yuan in 2023, driven by strong performance in proprietary trading and investment banking [6] Investment Banking and Proprietary Trading - The average revenue from investment banking for brokerages fell by 52.04% to 6.72 billion yuan, with CITIC Securities leading at 4.03 billion yuan, down 35% [8][9] - Proprietary trading revenues increased, with 8 brokerages exceeding 10 billion yuan in income, and 10 firms reporting over 100% growth [11][12] - The average proprietary trading income was 16.33 billion yuan, up 11.42%, while brokerage income from asset management and brokerage services declined [8] Notable Performers - First Capital and Shouchuang Securities reported over 90% growth in investment banking revenue, although First Capital remained in a loss position [10] - Among smaller firms, Hualin Securities and Hongta Securities saw significant profit increases, with net profit growth exceeding 100% [7] - The top-performing proprietary trading firms included CITIC Securities, Huatai Securities, and Guotai Junan, all exceeding 10 billion yuan in income [12][13]

在投资领域，"困境反转"一直是一类重要的投资机会。根本原因在于，其提供了低价买入优质资产的机 会，蕴含着基本面改善与预期差的双重价值空间。 如今的上海家化，也正在释放这种困境反转的信号。直观来看，上海家化交出的2024年报及2025年一季 报中核心财务指标表现并不亮眼，但困境反转的逻辑逐渐清晰，展现出高弹性复苏的潜力。资本市场的 态度同样较为乐观，其财报发布翌日股价不跌反涨，收涨4.2%。 更具体的，笔者进一步梳理上海家化的困境反转逻辑。 1、业绩下滑的另一面："深蹲起跳"加速完成 深入挖掘上海家化的财务数据来看，在业绩下滑背后，更多线索指向了基本面的改善。这种现象恰恰印 证了"困境反转"的预期。 根据财报，2024年及2025年一季度，上海家化实现营业收入56.79亿元、17.04亿元；净利润-8.33亿元、 2.17亿元，均呈同比双降。 但拆解后可以看到，上海家化2024年的亏损主要受到商誉减值和改革阵痛的影响，其中计提商誉减值约 6.1亿，实际经营性亏损程度更轻。在这背后，其商誉减值源于汤美星经营成果与前期业务规划的较大 差异，也与更换管理层，对战略定位、业务规划重做调整有关。改革方面，上海家化一直在解 ...

最新数据显示，随着部分贸易紧张局势出现缓和迹象，金价已较上周盘中高点回落逾5%。美国商品期 货交易委员会数据显示，对冲基金经理已将黄金期货和期权净多头头寸削减至一年多来最低水平。 本月由关税政策引发、以"美国例外主义终结"为主题的避险风潮，推动黄金表现远超国债、美股等其他 资产类别。 智通财经APP获悉，本月黄金涨势冠绝各大资产类别——甚至被部分投资者比作比特币——随着特朗普 的关税战重塑全球经济秩序，避险资金持续涌入金市。然而，期权头寸的变化正引发部分市场观察人士 保持警惕。 随着现货金价上周创下历史新高，黄金ETF-SPDR(GLD.US)期权合约交易量突破130万份，刷新历史纪 录。与此同时，该ETF的看跌对冲成本却维持在去年8月以来最低水平，而隐含波动率大幅飙升，形成 反常的市场格局。 Bloomberg Intelligence首席全球衍生品策略师Tanvir Sandhu表示："黄金与比特币近期呈现现货与波动率 同步上涨的特征，类似近年美股'七巨头'的表现。"他补充道，黄金看涨期权需求激增，而近期金价回 调使得各执行价的隐含波动率分布更趋均衡。 巴克莱银行的Stefano Pascale指出，金价 ...

内需跟踪及展望：从挖机到非挖 20250427 摘要 • 工程机械行业显现见底信号，向上修复趋势明确。设备租赁价格和混凝土 机械价格已触底，设备利用率逐步修复，供应商投资回报率见底回升，前 期高杠杆客户还款接近尾声，预示行业周期反转。 • 中国挖掘机保有量约 180 万台，临近淘汰设备占比约 15%。二手机出口 加速清理存量设备，提升老旧设备占比，增加更新换代需求。挖掘机终端 需求结构去地产化，小型和大型挖掘机销量占比超 80%，需求转向高标准 农田、水利和新农村建设。 • 2025 年 1-4 月挖掘机市场表现良好，销量增速保持增长，延续行业见底 修复态势。预计全年国内挖掘机销量有望实现至少 20%的增长，更高增速 取决于后续政策推进情况。 • 非挖掘设备市场中，混凝土机械一季度已转正，汽车起重机降幅收窄，预 计二季度末接近转正。后周期设备复苏通常比挖掘机晚半年到一年，关注 混凝土机械和起重机械转正后的变化，或将增加主机企业利润弹性。 • 国内工程机械板块业绩边际改善显著，收入和利润增速均提升，弹性修复 使其成为 2025 年表现突出的部分。出口贡献板块收入占比超 50%，利润 贡献接近 70%，新兴市场需 ...

Investment Rating - The investment rating for the company is "Buy" [7] Core Insights - The company is expected to turn profitable in 2024, with a significant increase in net profit in Q1 2025, achieving a net profit of 0.8 billion yuan, a year-on-year increase of 232% [1][5] - The operational data shows a 24% year-on-year increase in capacity in Q1 2025, with a passenger load factor of 79.8%, only a 1 percentage point decrease compared to the same period in 2019 [2] - The company has seen a recovery in utilization rates, which is expected to lead to better-than-expected performance, benefiting from the high proportion of tourism routes [4] Financial Performance - In 2024, the company achieved operating revenue of 6.7 billion yuan, a year-on-year increase of 30%, and a net profit of 0.27 billion yuan, marking a return to profitability [1] - For Q1 2025, the company reported operating revenue of 1.77 billion yuan, a 10% increase year-on-year [1] - The company’s other income in Q1 2025 was 0.36 billion yuan, a 48% increase year-on-year, primarily due to the continuous recovery of flight volumes [3] Capacity and Fleet Expansion - The company added 5 aircraft in 2024, bringing the total fleet to 75 aircraft, and added 2 more in Q1 2025, increasing the fleet to 77 aircraft [2] - The fleet consists of 39% A320 series, 47% CRJ900 series, and 14% C909 series [2] Profit Forecast - The company is projected to achieve net profits of 0.6 billion yuan, 1.0 billion yuan, and 1.2 billion yuan for the years 2025, 2026, and 2027 respectively, with a significant growth trajectory expected [5][6]

Quantitative Models and Construction Methods 1. Model Name: Timing System Signal (Wind All A Index Long-term and Short-term Moving Averages) - **Model Construction Idea**: The model uses the distance between the 120-day long-term moving average and the 20-day short-term moving average of the Wind All A Index to identify the overall market environment and potential trend shifts[2][9]. - **Model Construction Process**: - Calculate the 20-day moving average (short-term) and the 120-day moving average (long-term) of the Wind All A Index. - Compute the relative distance between the two moving averages: $$ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} $$ - A negative distance indicates the short-term average is below the long-term average, signaling a potential downtrend. Conversely, a positive distance suggests an uptrend[2][9]. - **Model Evaluation**: The model effectively identifies the market's transition to a downtrend when the distance narrows and becomes negative, providing actionable insights for timing decisions[2][9]. 2. Model Name: Industry Allocation Model - **Model Construction Idea**: This model identifies medium-term industry allocation opportunities by focusing on sectors with turnaround potential or strong growth drivers[2][10]. - **Model Construction Process**: - Analyze industry-specific metrics and trends to identify sectors with "distressed reversal" characteristics or those benefiting from structural growth themes. - Recommended sectors include: - Healthcare (e.g., Hang Seng Medical) - Low-valued sectors like new energy and consumer-related industries - Technology sectors driven by domestic substitution, such as AI chips and information innovation (Xinchuang)[2][10]. - **Model Evaluation**: The model provides a systematic approach to identifying sectors with medium-term growth potential, aligning with macroeconomic and structural trends[2][10]. 3. Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on identifying high-growth sectors, particularly in technology, by leveraging beta factors[2][10]. - **Model Construction Process**: - Analyze beta coefficients of various sectors to identify those with higher sensitivity to market movements. - Emphasize sectors like technology, including AI chips and information innovation, which are expected to outperform due to domestic substitution trends[2][10]. - **Model Evaluation**: The model effectively highlights high-growth sectors, particularly in technology, aligning with market trends and policy support[2][10]. 4. Model Name: Position Management Model - **Model Construction Idea**: This model determines optimal equity allocation levels based on valuation and trend signals[3][10]. - **Model Construction Process**: - Use valuation metrics such as PE and PB ratios of the Wind All A Index: - PE is at the 50th percentile, indicating a moderate valuation level. - PB is at the 20th percentile, indicating a relatively low valuation level. - Combine valuation insights with trend signals (e.g., moving average distances) to recommend a 50% equity allocation for absolute return products[3][10]. - **Model Evaluation**: The model provides a balanced approach to equity allocation, considering both valuation and trend factors[3][10]. --- Model Backtesting Results 1. Timing System Signal - **Distance between 20-day and 120-day moving averages**: -3.08% (indicating a narrowing gap and a potential downtrend)[2][9][13] - **Profitability Effect**: -1.7% (indicating negative short-term market sentiment)[2][10][13] 2. Position Management Model - **PE Ratio**: 50th percentile (moderate valuation level)[3][10] - **PB Ratio**: 20th percentile (relatively low valuation level)[3][10] - **Recommended Equity Allocation**: 50% for absolute return products[3][10] 3. Industry Allocation Model - **Recommended Sectors**: - Healthcare (e.g., Hang Seng Medical) - New energy and consumer-related industries - Technology sectors (e.g., AI chips, information innovation)[2][10][13] 4. TWO BETA Model - **Recommended Sectors**: - Technology sectors, including AI chips and information innovation, driven by domestic substitution trends[2][10][13]

Quantitative Models and Factors Summary Quantitative Models and Construction Methods Model Name: Liquidity Factor - **Construction Idea**: The liquidity factor is based on the market's liquidity conditions and the effectiveness of the main fund factor[12] - **Construction Process**: The model selects industries such as computers, electronics, machinery, electrical equipment and new energy, communications, and automobiles based on the liquidity factor. The recommended weight for the main fund factor is 5%[12] - **Evaluation**: The effectiveness of the main fund factor has stopped declining and is beginning to recover as market liquidity becomes abundant again[11][12] Model Name: Long-term Prosperity Expectation - **Construction Idea**: This factor serves as a proxy variable for prosperity investment[16] - **Construction Process**: The model selects industries such as non-bank finance, non-ferrous metals, electric power and utilities, steel, home appliances, and electronics. The recommended weight is 10%[16] - **Evaluation**: The effectiveness of this factor has recently declined, and the clustering effect has not formed[16] Model Name: Short-term Prosperity Expectation - **Construction Idea**: This factor focuses on short-term prosperity expectations, particularly in domestic demand[20] - **Construction Process**: The model selects industries such as home appliances, basic chemicals, agriculture, forestry, animal husbandry, and fishery, automobiles, machinery, and national defense. The recommended weight is 30%[20] - **Evaluation**: The short-term prosperity expectation factor shows significant upward movement in excess returns[20] Model Name: Momentum Reversal - **Construction Idea**: This factor focuses on long-term low and short-term strong defensive sectors[23] - **Construction Process**: The model tracks one-month momentum and selects industries such as electric power and utilities, agriculture, forestry, animal husbandry, and fishery, retail, banking, real estate, and transportation. The recommended weight is 10%[23] - **Evaluation**: The model suggests tracking defensive sectors with long-term low and short-term strong performance[23] Model Backtesting Results - **Liquidity Factor**: Excess return of the long position is shown in the chart[14] - **Long-term Prosperity Expectation**: Excess return of the long position is shown in the chart[18] - **Short-term Prosperity Expectation**: Excess return of the long position is shown in the chart[22] - **Momentum Reversal**: Excess return of the long position is shown in the chart[25] Quantitative Factors and Construction Methods Factor Name: Financial Report Quality Factor - **Construction Idea**: This factor focuses on the quality of financial reports[11] - **Construction Process**: The factor is used to select industries such as banking, non-bank finance, transportation, and construction[11] - **Evaluation**: The financial report quality factor has shown concentrated absolute returns recently[11] Factor Name: Correlation Factor - **Construction Idea**: This factor focuses on the correlation between industries[11] - **Construction Process**: The factor is used to analyze industry rotation and select industries based on their correlation[11] - **Evaluation**: The correlation factor has shown concentrated absolute returns recently[11] Factor Name: Momentum Factor - **Construction Idea**: This factor focuses on the momentum of industries[11] - **Construction Process**: The factor is used to analyze industry momentum and select industries based on their momentum[11] - **Evaluation**: The momentum factor has shown concentrated absolute returns recently[11] Factor Name: Main Fund Factor - **Construction Idea**: This factor focuses on the main fund flows in the market[11] - **Construction Process**: The factor is used to analyze the effectiveness of main fund flows and select industries based on their fund flows[11] - **Evaluation**: The effectiveness of the main fund factor has stopped declining and is beginning to recover[11] Factor Backtesting Results - **Financial Report Quality Factor**: Excess return of the long position is shown in the chart[14] - **Correlation Factor**: Excess return of the long position is shown in the chart[14] - **Momentum Factor**: Excess return of the long position is shown in the chart[14] - **Main Fund Factor**: Excess return of the long position is shown in the chart[14]

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如上图，9.24之后三次快速回撤掉入情绪冰点之后的走势和情绪反转模型都非常相似。市场语言都是：次级别横盘蓄势，准备下一笔上升。 再来看30分钟级别的走势展开： 上次情绪观察我提到，横盘结构在做第二次回落，今天上午则是第二次升起，一个经典的VCP结构已经豁然形成，上顶不破下底抬高，围而不打静待突 变。 如上次所言，这种波动收缩多见三次起落，不出意外的话，很快可以见到一个幅度更小的回落，操作意义上，愿意抢先手的可以尝试入仓了。 这是根据经验模型和指标给出的信号进行推导，不是凭空臆测。 昨天，我重点批判了北京马拉松的恶搞，这种完全不着调的比赛，既没有科技官方的主办方，也没有能代表科技水准的参赛方，一堆大学生的毕业设计作 品，纯粹娱乐，被一群自媒体小丑不知羞耻地调侃，妄图挫伤国民对科技发展的信心。作为一名交易者，必须要保持独立思考，去了解去求证，而不是跟 着小丑们扒狗粮。 今天开盘之后很给力，机器人板块的机构主力不为所动，有的甚至迅速放量发起了3C结构的第一次反击测试： 谣言、阴谋、荒诞也总是不攻自破！ 板块方面，防守行情看股息和消费，突破行情应该还是科技的份儿。 ...