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.
AI Investment Frontier LLM Factor Forecasts Need Decision-Time Inputs A new leakage-aware benchmark shows why investment LLMs must separate forecast skill from information timing before entering factor allocation workflows.
AI Investment Frontier 金融 AI 需要确定性的生产内核 一篇关于 Mojo 与资本市场 AI 的论文提醒投资团队：速度只是生产化的一半，真正进入投研、风控和审计流程的 AI，还需要可复现的计算内核。
AI Investment Frontier Financial AI Needs Deterministic Production Kernels A new arXiv paper on Mojo for capital markets argues that speed is only half the production problem. Investment AI also needs reproducible kernels that survive audit, backtesting, and model handoff.
AI Investment Frontier AI Exposure Needs a Cross-Asset Risk Map Apollo’s latest 60/40 warning turns AI from an equity theme into a cross-asset risk measurement problem for portfolio builders.
AI Investment Frontier 投资智能体需要信念状态验证 一篇新的 arXiv 论文把智能体式投资 AI 放进 POMDP 框架，提醒团队不要只看预测准确率，而要分别验证信念、预测、动作和效用。
AI Investment Frontier Agentic Investment AI Needs Belief-State Validation A new arXiv paper frames agentic AI as a partially observable decision process, giving investment teams a better validation target than forecast accuracy alone.
AI Investment Frontier Objective-Switching AI Needs a Conservative Default A recent DOSS paper reframes investment AI as a bounded objective-selection problem: adapt when the evidence is strong, fall back when confidence is weak.
AI Investment Frontier 组合强化学习需要启发式先验层 A new arXiv paper on heuristic portfolio optimization reframes equal weight, risk parity, HRP, and RA-HRP as stable policy priors for reinforcement-learning portfolio systems.
AI Investment Frontier RL Portfolios Need a Heuristic Prior Layer A new arXiv paper on heuristic portfolio optimization reframes equal weight, risk parity, HRP, and RA-HRP as stable policy priors for reinforcement-learning portfolio systems.
AI Investment Frontier 宏观 LLM 智能体需要先验控制 A June 2026 arXiv paper tests constrained LLM macro agents for commodity-related ETF allocation, showing why agentic investing systems need prior controls, vintage data, and cost-aware evaluation.
AI Investment Frontier Macro LLM Agents Need Prior Controls A June 2026 arXiv paper tests constrained LLM macro agents for commodity-related ETF allocation, showing why agentic investing systems need prior controls, vintage data, and cost-aware evaluation.
AI Investment Frontier 深度时间序列模型需要部署诊断 A new arXiv benchmark of deep time-series models for equity portfolios shows why investment AI builders should evaluate models through costs, constraints, and regret, not just raw forecasts.
