来源：滚动播报 （来源：新消费日报） 文|徐赐豪 京东私有化达达后的业务整合迈出关键一步。 京东物流今日（9日）在港交所公告，于10月8日与京东集团（JD.com）订立业务转让协议，拟以2.70亿 美元收购原达达集团的本地即时配送业务，具体通过受让达疆、达盛两家全资子公司100%股权实现。 根据公告，此次收购的核心标的为京东集团旗下从事本地即时配送服务的全资子公司 —— 达疆与达 盛。其中，达疆为中国境内注册企业，注册资本达7亿美元；达盛则为2025年1月在香港新成立的公。二 者在收购前均由京东集团100%持股。 新消费日报记者从京东处获悉，此次拟收购的本地即时配送业务为原达达集团的即时配送业务。 值得注意的是，此次收购是京东私有化达达进程的关键收尾。回溯整个流程，京东于今年1月25日向达 达提交初步非约束性私有化提案，拟以每股ADS2.0美元（或每股普通股0.5美元）收购全部已发行股 份；4月，达达集团宣布达成私有化交易；6月10日，私有化协议经达达临时股东大会通过；到如今10 月，核心业务资产转让便已完成。 从达达业绩来看，其2024年营收为96.6亿元，较2023年的105亿元同比下滑8%；净亏损为20 ...

基于显著优于预期的上半年营收成长速度，第3季度环比加速提升的即时配送业务需求，交银国际调顺 丰同城2025-2027年收入14%/12%/12%，至220-304亿元人民币，年增40%/20%/15%，同时受到公司单量 密度提升、优质的骑手部管理能力，AI加持下，营运费率的持续优化，该行上调2025-2027年利润预测 26%/23%/27%，至3.4-7.6亿元人民币，Non-IFRS净利润率分别1.5%/2%/2.6%。 智通财经APP获悉，交银国际发布研报称，上调顺丰同城(09699)目标价至15.4港元,维持"买入"评级。该 行认为顺丰同城仍是外送补贴战的受益方之一，未来补贴回归理性，业务空间仍大，考虑到长期其与顺 丰的业务协同持续加深，无人车配送规模化落地持续降低成本。 ...

Group 1 - The core viewpoint of the report is that the target price for SF Express (09699) has been raised to HKD 15.4, maintaining a "Buy" rating due to its position as a beneficiary in the delivery subsidy war [1] - The report highlights that as subsidies return to rationality, there remains significant business potential for SF Express, supported by deepening business synergies with SF Holdings and the ongoing cost reduction from the scaling of autonomous vehicle deliveries [1] - Based on a significantly better-than-expected revenue growth in the first half of the year, the demand for instant delivery services is expected to accelerate in the third quarter [1] Group 2 - Revenue forecasts for SF Express have been adjusted for 2025-2027, with expected growth rates of 14%/12%/12%, leading to revenues of RMB 22-30.4 billion, representing annual increases of 40%/20%/15% [1] - Profit forecasts for the same period have been raised by 26%/23%/27%, resulting in projected profits of RMB 340 million to 760 million, with Non-IFRS net profit margins of 1.5%/2%/2.6% [1] - The improvements in operational efficiency are attributed to increased order density, effective management of quality riders, and continuous optimization of operating costs aided by AI [1]