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 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 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 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 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 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 Wealth AI Needs a Portfolio Rationale Engine BCG's same-day wealth-management report shows AI moving from advisor productivity into portfolio rationales, monitoring, and compliance workflows.
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 Agentic Trading Needs an Evidence Ledger A new arXiv survey of LLM trading agents finds fast architectural experimentation but weak reproducibility, sparse transaction-cost reporting, and inconsistent execution semantics.
AI Investment Frontier Climate Alpha Needs Heterogeneous Weather Models A new SSRN paper uses machine learning to estimate firm-level weather impacts and finds investor underreaction around earnings. The useful lesson is not a climate trade, but a better event-risk architecture.
AI Investment Frontier AI Alpha Still Has to Pass the Governance Test Mercer's new asset-management survey shows AI adoption is real, but return attribution is still scarce. The builder lesson is to instrument governance before claiming alpha.
AI Investment Frontier GenAI Risk Is Becoming a Cross-Sectional Test A new SSRN paper turns GenAI risk into an asset-pricing question: firms may be punished differently by adverse AI news depending on whether they have real implementation exposure.
AI Investment Frontier Data Quality Is the Agentic Investing Moat A fresh Clarity AI workflow note shows why agentic investment systems will be judged less by chat fluency and more by access, provenance, freshness, and methodology.
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 Stock Forecasting Needs a Friction Test 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.
