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。
AI Investment Frontier AI Investment Frontier — Causal Separation Makes Portfolio AI Harder to Fake A new arXiv paper argues that portfolio AI should separate declared drivers from residual risk, turning Markowitz into a projected problem with clearer estimation and causal tests.
AI Investment Frontier AI 投资前沿：模型真正赢在少交易 本周两篇新论文说明，投资 AI 的边界正在从“更准的预测”转向“更稳的策略、更低的换手率、以及更像真实市场的状态估计”。
AI Investment Frontier AI Models Win by Trading Less, Not Just Forecasting Better Two new July papers suggest the edge in investment AI is shifting from better predictions to better policies, lower turnover, and cleaner state estimation.
AI Investment Frontier End-to-End Portfolio Policies Are Getting Real A new futures-timing paper shows when end-to-end AI policies beat simple rules, and why transaction costs still decide whether the edge survives.
AI Investment Frontier 基金数据正在变成投资 AI 的新人格层 Fund2Persona 提示我们：真正有用的投资 AI 不是会模仿语气，而是能被基金披露、持仓变化和经理评语约束住。
AI Investment Frontier Fund Data Is Becoming the New Persona Layer for Investment AI A new Fund2Persona paper shows how fund disclosures, holdings transitions, and manager commentary can ground investment personas instead of relying on generic prompts.
AI Investment Frontier ML Term-Structure Forecasts Should Feed Duration Policy A June 2026 preprint shows neural term-structure models can improve bond-curve forecasts, but the real value is in duration control and portfolio policy.
AI Investment Frontier 时间序列基础模型是先验，不是阿尔法引擎 一篇新的 arXiv 基准研究提醒投资 AI 团队：预训练时间序列基础模型能降低建模成本，但相对随机游走的优势仍然稀疏。
AI Investment Frontier Time-Series Foundation Models Are Priors, Not Alpha Engines A new arXiv benchmark finds pretrained time-series foundation models can reduce modeling work in return forecasting, but their gains over random walk remain sparse.
AI Investment Frontier 分位数任务需要组合策略，而不是一个预测 一篇 6 月修订的组合选择论文提醒投资 AI：下行保护、收益和成长任务，应当训练并评估不同的策略，而不是共用一个平均收益预测。
AI Investment Frontier Quantile Mandates Need Portfolio Policies, Not One Forecast A revised portfolio-choice paper shows why downside protection, income, and growth mandates should train different AI policies instead of sharing one return forecast.
