AI Investment Frontier AI 投资前沿：市场信号可能来自谁在交易，而不只是交易什么 一项新的市场微观结构研究说明，参与者历史可以成为投资 AI 的时变证据层，但必须同时面对可迁移性、隐私和信号衰减问题。
AI Investment Frontier AI Investment Frontier — The Signal May Be Who Trades, Not Just What They Trade A new study of public trader identities shows why investment AI should model participant histories as a time-varying evidence layer—while treating portability, privacy, and decay as first-class risks.
AI Investment Frontier AI Investment Frontier — The Missing Layer Is Decision Knowledge A new investment-process paper argues that data and models matter only when their contribution to the decision can be traced and assessed.
AI Investment Frontier AI Investment Frontier — The Real Test for Financial Foundation Models Is Temporal Integrity Financial foundation models can improve research workflows, but only if their priors, labels, and evaluation windows respect the information available at the time.
AI Investment Frontier AI 投资前沿 — 三个矩阵，少一点预测幻觉 一篇最新论文用价格、成交量和市值构造三个市场状态矩阵，提醒投资 AI：状态表示和样本外纪律可能比更大的预测器更重要。
AI Investment Frontier AI Investment Frontier — Three Matrices, No Forecasting Theater A new price-only portfolio paper suggests a useful design principle: robust state representations and explicit validation may matter more than a larger return forecaster.
AI Investment Frontier AI 投资前沿 — 给 AI 资本开支背后的隐性依赖定价 AI 资本开支竞赛正在变成组合风险问题。本文把基础设施热情拆解为可验证的现金流、情景和模型风险控制。
AI Investment Frontier AI Investment Frontier — Pricing Hidden Dependencies Behind AI Capex The AI investment race is becoming a portfolio-risk problem. Here is a practical framework for separating compute enthusiasm from durable cash-flow and model evidence.
AI Investment Frontier AI 投资前沿 — 风险 AI 需要控制平面，而不只是一个模型 金融犯罪预防与可解释预测研究共同指向一个工程结论：投资风险 AI 必须把证据、升级、权限和审计放在模型周围。
AI Investment Frontier AI Investment Frontier — Risk AI Needs a Control Plane, Not Just a Model Recent finance research points to a practical frontier: AI systems in investment risk and financial crime need provenance, escalation, and audit controls around the model.
AI Investment Frontier AI Investment Frontier — The Evidence Layer Matters More Than the Stock Pick Recent evidence that LLM stock picks can follow media attention more than fundamentals makes provenance, point-in-time inputs, and abstention core investment-system features.
AI Investment Frontier AI 投资前沿 — 当市场状态代理遇上 60/40 基准 关于摩根大通市场状态代理的报道，真正值得借鉴的不是跑赢60/40的标题，而是状态识别、约束配置与模型风险之间的系统边界。
AI Investment Frontier AI Investment Frontier — Regime Agents Meet the 60/40 Test JPMorgan's reported regime-based AI portfolio simulation is a useful design signal—but only if builders separate regime classification from robust, cost-aware allocation.
AI Investment Frontier AI 投资前沿 — SciPhy RL 把组合 AI 拉回成本压力之下 一篇新的 arXiv 论文把组合优化写成成本感知的强化学习问题，提醒我们：投资 AI 的关键不只是预测，更是能否在执行成本和仓位约束下把决策真正落地。
AI Investment Frontier AI Investment Frontier — SciPhy RL Puts Portfolio AI Back Under Cost Pressure A new arXiv paper on science-informed reinforcement learning for portfolio optimization is a useful reminder that the real problem in portfolio AI is not prediction alone, but reaching the target position under execution cost and volatility constraints.
AI Investment Frontier AI Investment Frontier — Portfolio Optimization Is a Search Problem Again Cardinality-constrained portfolio optimization is pushing investment AI toward search, repair, and implementation-aware optimization instead of pure prediction.
AI Investment Frontier AI Investment Frontier — State-First Microstructure Models Beat Fancy Friction A new crypto futures paper argues that liquidity state should come before order flow, and that latency is a separate frontier from prediction accuracy.
AI Investment Frontier AI 投资前沿 — 交易频率才是模型能否落地的分水岭 一篇新的跨资产期货研究再次提醒我们：学出来的策略不怕聪明，怕的是太爱交易。真正能落地的模型必须经得住换手和成本。
AI Investment Frontier AI Investment Frontier — Learned Policies Still Live or Die on Turnover A new cross-asset futures paper shows end-to-end AI policies can beat simple rules, but the edge depends on turnover, costs, and whether the model really trades less.
AI Investment Frontier AI 投资前沿 — 因果分离让组合 AI 更难“看起来有效” 一篇新的 arXiv 论文把投资组合 AI 从“先预测再优化”推进到“先声明驱动变量，再在其诱导几何里优化”，并给出可检验的因果区分。
