外扰再起，短期延续震荡——经济分析与资产展望0706-0712
本期聚焦全球地缘扰动与AI景气对大类资产的影响，回顾海外市场与国内市场的分化表现，结合PB-ROE量化模型对仓位的指引，提出在当前环境下维持中高仓位的策略信号，并点出AI算力、旅游服务及新材料等高景气方向的投资机会 [page::1][page::2][page::6]
金融研报AI分析
化工行业周报20260712-国际油价上涨，维生素、尿素价格下跌
本周化工行业在国际油价上涨背景下呈现分化态势，价格驱动主要来自原油市场波动、下游需求变化及供应端调整；维生素与尿素价格承压，部分品种月度价格走弱但下游需求仍具韧性，染料、长丝等子行业有望在后续周期修复。报告给出7月重点关注点：中报行情、断供稀土带来涨价机会以及龙头企业的景气修复趋势。长期看好油价维持在中高位将推动优质石化与煤化工资产的盈利与估值修复，以及新材料领域的快速发展。综合投资主线包括：在地缘风险与市场供应格局下，聚焦具备定价权与成本优势的龙头，以及受益于下游需求扩张的新材料企业，关注炼化、聚酯、染料、有机硅、农药等细分领域的景气回升与投资机会 [page::0][page::1]
医药生物行业周报-手术机器人商业化加速，产业迈入兑现拐点
本期报告聚焦手术机器人在国内外市场的商业化拐点，结合全球市场规模、国内渗透率及医保立项等政策要素，判定行业有望在2026-2030年持续放量并带来结构性投资机会，建议关注具备商业化能力的平台型企业 [page::0].
【金融工程】量化CTA周报：CTA策略涨跌分化，主观策略表现相对较优
本期报告基于7月初CTA策略环境监测，显示趋势策略在中性偏强的市场环境中不具备配置优势，套利策略偏强但并非推荐配置对象；同期CTA子策略表现存在显著分化，主观策略在本周表现最佳（0.76%），股指策略与套利、截面等策略分列在后，涨跌幅各有差异。报告还指出波动率因子与持仓因子上升，动量因子与期限结构因子下跌，呈现风格因子分化的特征。以上结论强调了在当前阶段的策略选取应聚焦主观策略与板块轮动机会，并警惕股指端回转及趋势跟踪类策略的压力与风险 [page::1]
供需双轮驱动胶市重心上移 跨境交割革新天胶期现生态
本专题报告系统梳理了天胶期现价格的双轮驱动机理，强调供需格局与跨境交割改革对天胶期现生态的深刻影响。上半年经历两轮显著波动期，2月末至3月中旬因地缘与宏观因素叠加带来方向性不确定性，5-6月则受厄尔尼诺相关供需预期与季节性开割等因素推动波动率走高，市场情绪与仓单/持仓结构共同作用推动价格分歧与趋势演化[page::1]。机构与外资在RU/NR市场均发挥关键作用，机构多头在高位震荡、空头增仓随价格走高而加强；外资多头力量在NR市场呈现显著抬升态势，叠加散户参与度持续低迷，形成边际推动力[page::2]。跨境交割相关改革逐步落地，提升套利空间与现货市场对期货价格的价格发现功能，从而推动天胶期现生态升级及风险对冲能力的提升[page::3]。整体来看，胶市在全球宏观与区域性供需变化共振下，呈现出更强的价格发现与波动传导性，后续需关注跨境结算政策进展、产区气候与季节性产量变化，以及产业端库存调整对价格的敏感性[page::1][page::2][page::3]
<paper_title>专题报告 | 豆粕：7月USDA供需报告偏多，提振豆价
本报告基于7月USDA供需报告对美豆与豆粕的价格与供需平衡进行解读：全球大豆平衡表收缩，库存消费比下降，全球与美豆 ending stocks 走紧。对新作美豆平衡表，尽管产量上调，但期初库存下调及出口上调使 ending stocks 未显著增加，库存压力仍偏紧，因此美豆价格具备上涨动能，豆粕价格将受美豆带动并随成本传导走高。报告同时强调交易节奏与风险管理，建议逢低与波段操作为主。 [page::0]
外汇、宏观、策略、高频数据-中银证券总量周观点7.13
本报告聚焦7月周观点，认为人民币汇率受多重因素影响，短期利多占优并可能加速升值，同时存在外部及结构性不确定性，提示市场应强化风险中性和对冲工具，监管需维护汇率弹性与市场信心 [page::1]。上半年人民币汇率坚挺、CFETS指数创新高，外需改善与经贸缓和推动人民币表现，为资产配置提供短期机会但需警惕双向波动带来的中短期冲击 [page::1]。5月外汇市场分析显示美元走强压力仍在、但人民币中间价与即时汇率呈现分化，企业汇兑损失风险上升，汇率目标需以弹性与预期管理为核心 [page::2]。策略周报强调风格均衡尚未发生根本拐点，成长主线仍具弹性，AI相关投资逻辑持续受关注；同时高频数据提示油价对通胀影响有限，市场对美联储加息的预期仍存 [page::3][page::4]。总体而言，需以稳态下的分散配置与对冲风控来应对汇率波动与宏观不确定性 [page::4]。
A novel robust mixed integer linear programming model for index tracking problem under no rebalancing: heuristic optimization approach
本文提出一种对指数跟踪问题具有鲁棒性的混合整数线性规划模型，并在不需要再平衡的前提下实现对基准的长期高精度跟踪。通过将相关性的下行偏差整合入模型并引入GALB启发式算法，能够在多市场数据上优于传统模型的外样本表现，同时在仅10只资产的子集上也能实现长期稳定跟踪，显著降低交易成本与操作复杂度。这一方法将全局搜索能力与局部搜索相结合，提升了求解效率与鲁棒性，尤其在NP-hard的问题规模上展现出优势。核心在于用鲁棒优化减少相关性估计的不确定性，并通过卡方/边界约束等实现现实世界的约束整合；实验结果显示，在Dow Jones、CAC 40、EURO STOXX 50等市场的对比中，所提模型在外样本上的跟踪误差显著下降，且无再平衡下的长期追踪能力突出。 [page::1]
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
As agentic AI systems are increasingly applied to cyber-physical environments, their evaluation requires assessment of both task performance and trustworthiness. In decentralized energy markets, autonomous agents may improve market utility, but may also exploit invalid physical data, create artificial liquidity, and produce unstable governance decisions. Therefore, we propose SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents. It formulates market governance as a Gymnasium-compatible Markov Decision Process, where agents make hourly decisions. SolarChain-Eval evaluates each policy across multiple dimensions, including market utility, physical safety, slippage, action smoothness, spatial fairness, and auditability. To support agentic evaluation, SolarChain-Eval incorporates an LLM-based Planner/Auditor layer. The Planner defines episode-level action bounds and audit rules, while the Auditor reviews and revises high-risk actions. All interventions are recorded through structured logs, including trigger signals, proposed actions, revised actions, and audit rationales. Experiments with static, random, myopic, RL, and RL+LLM policies reveal a clear utility-safety trade-off. RL agents improve market utility but can still produce unsafe behavior. When the physics penalty is removed, reward-maximizing agents exploit invalid generation and increase artificial liquidity. The LLM Planner/Auditor improves auditability and mitigates selected risks, but it cannot fully compensate for a misspecified reward function. These results indicate that trustworthy agentic AI evaluation requires both physical constraints and transparent intervention traces. We release data and code as open access on GitHub for replicability. [page::1]
Does Regulation Bite at Gateways? Evidence from MiCA and Stablecoins∗
MiCA regulation creates a sharp asymmetry between USDT and USDC by denying issuer authorization for USDT while approving USDC, prompting EU-facing exchanges to delist USDT pairs. The paper shows that aggregate market shares and trading volumes move little around the event, but cross-sectional analysis reveals a substitution toward USDC on Regulated-facing exchanges (USDC share up by 0.82 standard deviations and relative volume up by 0.54 standard deviations). The mechanism is primarily USDT contraction (about 20%) on Regulated-facing venues rather than a significant expansion of USDC. Preliminary price evidence suggests a small deviation from parity for USDT on Regulated-facing venues post-event. Robustness checks in the Online Appendix support these findings. [page::0][page::5][page::7][page::7][page::16][page::8][page::16]
Objective and subjective entropy measures of portfolio suboptimality
本研究提出并系统推导了在连续时间市场中，凯利（增长最优）投资组合的增长率及其相关的“增长缺口”量度。研究表明，在真实测度下，子最优投资组合的对数财富短缺等同于从真实测度P到拟定最优测度P^π 的相对熵（KL散度），而在拟定测度P^π 下，子组合的表观超越等效于反向相对熵；这一对偶关系通过Girsanov变换和Itô公式给出严密证明，且在确定性市场价格风险下两方向的KL散度相等。核心结论揭示了客观与主观视角下的增长-风险权衡的双重性及相对熵在投资策略评估中的量化含义。 [page::1][page::2][page::3][page::4]
Deep Learning for Dynamic Programming with Recursive Utility Using First-order Conditions
CEFOL introduces a Certainty-Equivalent First-Order Learning algorithm that solves discrete-time dynamic programming with recursive utility by learning the state-control certainty-equivalent C(s,t). It decouples the nonlinear certainty-equivalent transformation from policy updates by using a dedicated neural network for C(s,t). The method trains value, policy, multiplier, and C networks via model-specific first-order residuals (Euler/KKT), enabling direct handling of equality and inequality constraints without penalty terms. The approach applies to risk-sensitive, Epstein–Zin, small-noise robust-control, and DSGE models, achieving small out-of-sample Bellman errors and residuals (order 10^-4 to 10^-3) and close alignment with VFI benchmarks when available. It also generalizes to expected utility as a special case and supports vector controls and occasionally binding constraints; this framework provides diagnostics (Bellman errors and residuals) for value consistency and policy optimality [page::1][page::2][page::3][page::4].
The Quarter-Hour Effect: Periodic Algorithmic Trading and Return Predictability in Cryptocurrency Futures
Using six Binance USDT-margined perpetual futures, the study unveils pronounced intrahour periodic bursts in volatility and volume that align with calendar-bound boundaries, especially at quarter-hour marks. The bursts correlate with algorithmic trading, evidenced by a sharp decline in trade-size roundness within the burst windows and a phase-specific Autocorrelation Map (ACM) that highlights dependence concentrated around quarter-hour openings. The opening 10-second returns are forecastable from their boundary-aligned lagged values, and quarter-hour order imbalance contains medium-horizon information for future returns, with predictive content strengthening as horizons lengthen from four to twelve hours. A two-stage decomposition shows that the shorter-horizon component is driven by same-phase lagged flow, while the longer-horizon content is increasingly tied to observable pre-boundary price-volume signals. These findings imply a clock-time coordination channel where boundary execution timing materially influences liquidity provision and price discovery, with practical implications for how traders schedule boundary trades. [page::4][page::23][page::50]
Ever since Ellsberg
本研究对有限样本个体的揭示偏好数据进行非参数检验，评估在不确定性下主观EUT及其非EUT替代模型的完整公理集合的可行性与稳健性，并将结果与在风险情境下的等效分析进行对照。基于Ahn等2014的三状态投资组合实验，利用 Afriat 公理及其扩展（GARP），通过四类FOSD-rationalizability与EUT-rationalizability 的嵌套测量（包含对多先验的FOSD-rationalizability），揭示不确定性条件下存在显著的非EUT模型空间，且主体在不同公理要求下呈现层次化的合规性差异。与风险情境相比，个体在不确定性中的基本理性和EUT一致性水平相近，但多先验FOSD与统一先验FOSD之间的差距构成对RDU/REU等不确定性偏好模型的主要支持证据，显示对模糊性态度的实证空间。本文亦强调不对称性与非完整性（如WARP/GARP违规的组合）在个体层面的分布特征及其对结构估计的启示。 [page::1]
When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futures
This paper investigates event-conditioned microstructure in Binance BTCUSDT and ETHUSDT futures (2023–2026), defining a supervised discrete L2 liquidity-state transition task (calm/mixed/stressed) using pre-event state and top-20 L2 features, and evaluating a staged model sequence under rolling-month out-of-sample folds and robust controls. The main finding is a state-first design: the pre-event L2 state is the primary predictor of post-event liquidity regimes; linear continuous L2 features do not outperform the coarse state baseline; a shallow nonlinear L2-shape model yields a comparable additive gain; order flow adds incremental value only when layered atop the L2 model, and this gain is ETH-dominant and stress-amplified, while BTC shows no robust cross-horizon regime. The cycle concludes with a practical evaluation protocol and a baseline for future reinforcement-learning or context-layer extensions to exceed before crediting extra context signals [page::1]
AI Adoption in S&P 500 Firms
本研究基于对2016-2025年S&P 500公司10-K文本的AI采用程度量表，量化企业层面的深入AI整合程度。到2025年，约21%的企业处于4-5分区间，其中11%实现深度整合至核心业务流程，约10%在商品与服务生产/交付环节使用AI。AI采用自2022年以来大幅提升，科技行业贡献最大，且科技行业深度采用与员工规模、Tobin’s Q正相关。回归分析显示，在控制企业与年份固定效应后，深度AI整合与利润率呈现J型关系，但对资本开支与生产率未显著提升。非科技企业的利润率提升更明显，而Capex与AI采用关系不显著。研究亦强调模型扩展、组织变革与网络风险对AI扩散的关键作用，以及存在的内生性与因果关系不确定性，需要未来研究进一步厘清因果性。 [page::0][page::1][page::21][page::22][page::24]
Experimental Evidence on the Learning Impact of Generative AI
We report causal evidence from a two-session randomized experiment assessing how access to off-the-shelf generative AI affects undergraduate learning. In Session One, AI access raises immediate knowledge-test scores by about 0.27 standard deviations, with roughly 76% of these gains persisting to Session Two a week later. Essays show modest quality gains during Session One but deliver stronger improvements in writing style and prompt relevance in Session Two when students write unaided, consistent with durable skill gains. Gains are larger for augmentation users (AI used to explain concepts) and fade for automation users (AI drafted text). We identify two mechanisms: AI shifts study time away from drafting toward reading and information searching, and it increases learning enjoyment. [page::2][page::14][page::16][page::17][page::18][page::19]
新版基药目录发布，关注用量增长空间— 医药生物行业周报
本周医药生物行业周报聚焦新版基药目录发布及对用量的潜在增量空间，指出新增品种及创新药纳入有助于提升可及性并驱动用量增长；并维持中性评级，聚焦创新药、CXO及生命科学上游产业链的投资机会，同时提示半年报催化、脑机接口、AI应用等新兴技术带来的发展潜力；面临海外地缘政治、降价压力及 R&D 风险等多重风险 [page::1]
【固收】利率窄幅偏强震荡 利率债周报
本期利率走势呈窄幅偏强震荡，6月通胀与生产端数据对市场分化，资金面明显边际放松但对中长期利率仍存在分歧，短端继续受益于操作支撑但上行空间有限。央行净回笼资金超5000亿元，DR007回落至1.4%以下，短端利率趋稳。一级市场发行节奏放缓，国债净融资规模约2.9万亿元，总体融资进度约43.5%，专项债发行放量与时点将影响曲线强弱。展望7月，政治局与货币政策会议强调精准调控与结构性分化，债市或延续震荡，超长端需保持谨慎；风险点包括经济与政策超预期变化及政府债供给的不确定性。 [page::0][page::1]
【宏观】经济难改短期格局——2026年7月宏观经济月报
本月宏观月报指出全球经济在短期内难以显著改变的格局，尽管美国通胀回落但路径不稳，货币政策分化与金融市场波动将成为常态，国内经济处于新旧动能转换阶段，基建及政策性工具有望在下半年发力，财政与货币政策的协同将成为托底与推进结构性改革的关键。总体风险点聚焦地缘政治与外部需求放缓对国内的传导。 [page::0][page::1]
