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.
AI Investment Frontier Deep Time-Series Models Need Deployment Diagnostics 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.
AI Investment Frontier AI 策略需要黑箱审计层 A new arXiv paper by Irene Aldridge proposes a model-free way to audit sequential AI investment policies from observable inputs and outputs, shifting the question from backtest wins to policy regret.
AI Investment Frontier AI Strategies Need a Black-Box Audit Layer A new arXiv paper by Irene Aldridge proposes a model-free way to audit sequential AI investment policies from observable inputs and outputs, shifting the question from backtest wins to policy regret.
AI Investment Frontier 预测模型需要压力测试基准 FinStressTS, a new arXiv/KDD 2026 paper, argues that financial forecasting benchmarks should expose why models fail under volatility clustering, regime shifts, heavy tails, jumps, and sparse processes.
AI Investment Frontier Forecasting Models Need Stress-Test Benchmarks FinStressTS, a new arXiv/KDD 2026 paper, argues that financial forecasting benchmarks should expose why models fail under volatility clustering, regime shifts, heavy tails, jumps, and sparse processes.
AI Investment Frontier 深度收益模型需要组合现实层 A new Journal of Empirical Finance paper on deep learning for market return predictability is a useful prompt to separate forecasting accuracy from deployable portfolio value.
AI Investment Frontier Deep Return Models Need a Portfolio Reality Layer A new Journal of Empirical Finance paper on deep learning for market return predictability is a useful prompt to separate forecasting accuracy from deployable portfolio value.
AI Investment Frontier 订单簿 RL 需要下行感知策略层 A fresh arXiv paper applies group-aware policy optimization to limit order book trading, useful less as a trading claim than as a design pattern for downside-aware RL evaluation.
AI Investment Frontier Order Book RL Needs a Downside-Aware Policy Layer A fresh arXiv paper applies group-aware policy optimization to limit order book trading, useful less as a trading claim than as a design pattern for downside-aware RL evaluation.
AI Investment Frontier 组合 LLM 需要相关性感知基准 A new arXiv benchmark tests LLM portfolio managers on cross-asset correlation, full-pipeline allocation, stress regimes, and error propagation.
AI Investment Frontier Portfolio LLMs Need Correlation-Aware Benchmarks A new arXiv benchmark tests LLM portfolio managers on cross-asset correlation, full-pipeline allocation, stress regimes, and error propagation.
AI Investment Frontier LLM 预测需要记忆防火墙 A newly posted SSRN paper quantifies look-ahead bias in GPT-4 financial forecasts, showing why investment AI evaluation needs point-in-time memory controls.
AI Investment Frontier LLM Forecasting Needs a Memory Firewall A newly posted SSRN paper quantifies look-ahead bias in GPT-4 financial forecasts, showing why investment AI evaluation needs point-in-time memory controls.
AI Investment Frontier 智能体交易：为什么 LLM 交易智能体需要证据账本 《Agentic Trading: When LLM Agents Meet Financial Markets》显示，LLM 交易智能体在进入生产信任之前，需要证据账本、真实成本、执行语义和可复现评估。
AI Investment Frontier Agentic Trading: Why LLM Trading Agents Need an Evidence Ledger The arXiv paper “Agentic Trading: When LLM Agents Meet Financial Markets” shows why LLM trading agents need reproducible evidence ledgers, realistic costs, and auditable execution semantics.
AI Investment Frontier 风险模型需要瞬态因子 A new arXiv paper from Stanford and BlackRock researchers shows a practical way to extend an existing equity risk model with short-horizon statistical factors learned from realized returns.
AI Investment Frontier Risk Models Need Transient Factors A new arXiv paper from Stanford and BlackRock researchers shows a practical way to extend an existing equity risk model with short-horizon statistical factors learned from realized returns.
AI Investment Frontier LLM 选股预测需要摩擦测试 A recent hedge-fund-oriented review of LLM stock forecasting argues that the hard problem is not only prediction, but leakage control, market frictions, liquidity, and workflow robustness.
