TimeLock Research
An AI research assistant that answers 'what did the world know about Tesla on March 15, 2024?' — citing only documents published on or before that date, so analysts can trust it in backtests and investment memos without leaking the future.
Hedge fund analysts and investment researchers who backtest strategies on historical information
- Date-locked Q&A: ask anything about a public company and get answers sourced only from filings, news, and transcripts published up to that date
- Lookahead-bias detector that scans draft memos for any reference dated after the chosen as-of date and flags it with a one-click correction
- Auditable citation trail showing the exact publication date and source URL for every claim
- Bulk evaluation that scores historical research narratives for temporal cleanliness before they're filed
New arXiv research from Yale quant faculty proves standard LLMs silently embed future information into historical queries, and a just-released pipeline shows the gap to a clean point-in-time model is closable — but no commercial product exists for finance teams yet
Multiple 2024-2025 papers and articles (Profit Mirage, Look-Ahead-Bench, Ghost in the Backtest) confirm look-ahead bias in LLM financial backtests is a widely recognized and actively studied problem.The Ghost in the Backtest: How Financial LLMs Hallucinate Artificial Alpha ↗
A direct commercial competitor already exists: PiT Inference (pitinference.com) sells 'Point-in-Time LLMs for Finance' with released 'Pitinf Models,' and the NBER paper authors released their full open pipeline — the 'no commercial product' claim is false.PiT Inference — Point-in-Time LLMs for Finance ↗Scaling Point-in-Time Language Models | NBER ↗
Hedge funds pay premium for research tooling (Bloomberg, AlphaSense, RavenPack), and the leakage problem has real P&L and compliance stakes, supporting willingness to pay; however, addressable market is small (top quant funds) with long enterprise sales cycles.Introducing Pitinf Models: Point-in-Time LLMs for Finance ↗
Point-in-time data integrity is a permanent regulatory and methodological requirement in finance; as long as LLMs are trained on future data, third-party point-in-time wrappers have structural demand, though base-model improvements could partially commoditize the layer over time.LLM Financial Forecasts Need Point-in-Time Tests ↗
NBER pipeline and PiT Inference both demonstrate the technical path is viable (timestamped corpora + retrieval/training), but productionizing for finance requires massive dated document corpora, continuous ingestion, and rigorous evaluation — non-trivial but clearly buildable.Scaling Point-in-Time Language Models | NBER ↗