AI Investment Frontier AI Investment Frontier — Turnover Is the Missing Variable in Diversity-Weighted Portfolios A new equity-market model says turnover, not drift alone, stabilizes the capital distribution—and absorbs much of the gain from rebalancing.
AI Investment Frontier AI Investment Frontier — Financial Advice Needs a Lifecycle Test A new study of AI financial advice shifts the investment-AI question from fluent answers to lifecycle outcomes, suitability, and governance.
AI Investment Frontier AI 投资前沿：风险偏好也应成为组合模型的输入 一篇新论文把机器学习的收益预期放进结构化组合模型，提示投资 AI 不应只预测资产，也要理解决策者的行为与约束。
AI Investment Frontier AI Investment Frontier — Risk Preferences Are a Portfolio-Model Input A new paper combines machine-learned return expectations with a structural portfolio model. The lesson for investment AI is to model behavior and constraints, not just forecasts.
AI Investment Frontier AI Investment Frontier — The Portfolio Solver Needs a Contract A new comparison of photonic, classical, and reinforcement-learning optimizers points to a practical rule: make the solver’s contract explicit before comparing performance.
AI Investment Frontier AI Investment Frontier — Measure Reliance, Not Just Forecasts A new study on AI-supported investment decisions points to a practical design rule: measure how systems change investor judgment, not just forecast accuracy.
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 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 — 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 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”。
