Definition of the problem. Quant finance literature is split across venues with different cultures: arXiv q-fin (fast, math/ML-heavy, uneven quality), SSRN (empirical finance and asset pricing, often years before journals), and journals (slow, peer-reviewed, paywalled — and the version of record that sometimes differs from the preprint you read). Efficient search means using each for what it’s good at and letting citation structure, not keywords, do the heavy lifting.
The source map
| Need | Best source | Tactic |
|---|---|---|
| What’s new this week | arXiv q-fin new/recent listings | Or let our daily pipeline triage it — that’s literally what it’s for |
| The empirical asset-pricing canon | SSRN + Google Scholar | Sort SSRN by downloads within a topic; downloads ≈ practitioner attention |
| Who settled this question | Google Scholar cited-by chains | See citation-chaining below |
| Implementable methods | arXiv cs.LG/stat.ML crossovers | Search the method name + “trading”/“portfolio” |
| The published version of record | Journal sites / author pages | Diff the tables against the preprint before citing numbers |
Citation-chaining beats keyword search
Keywords find papers; citations find literatures. The method: locate one strong anchor paper (a survey, or a high-rigor empirical piece), then walk backward through its references for foundations and forward through Google Scholar’s “cited by” for everything that built on it — filtering the forward pass by recency and citation count. Two hops from a good anchor covers a subfield more completely than any keyword session, and it surfaces the critiques (papers citing the anchor to disagree), which keyword search structurally misses and which are the most informative documents in any literature.
Finding the anchor is what our research maps and topic hubs are built for: each hub ranks its papers by rigor-weighted score, so the top of a hub is a pre-vetted anchor list.
Search tactics that pay for themselves
- Search the method, not the goal: “temporal convolutional network limit order book” beats “predict stock prices with AI” by an order of magnitude in signal-to-noise.
- Search the dataset: papers using the same data (“FI-2010”, “TAQ”, “CRSP delisting”) form natural comparison sets — and shared datasets mean comparable results, a rarity worth exploiting.
- Search the critique: append “replication”, “revisited”, “does not”, “fails” to any famous result’s name. The follow-up literature is where the truth lives.
- Author-follow beats topic-follow: a handful of consistently rigorous authors per subfield is a better feed than any keyword alert. Build the list from hub-top papers.
- Version-check arXiv papers: v1 vs v4 table diffs reveal what reviewers forced — informative in both directions.
The two-hour sweep protocol
- (10 min) Find the anchor via maps, hubs, or a survey.
- (30 min) Backward pass: skim the anchor’s references; keep ~10 foundations.
- (40 min) Forward pass: cited-by, sorted by relevance then recency; keep ~10 descendants, deliberately including 2–3 critiques.
- (30 min) Ten-minute triage the keepers; most fail.
- (10 min) Log survivors with one-line claims in your experiment log. Done — resist the tab explosion.
Questions that keep a search honest
- Am I searching to learn the answer or to find support? The second is HARKing’s research-consumption twin.
- Have I found the paper that disagrees yet? A literature review without a critique is incomplete by construction.
- Is the newest paper actually better, or just newer? Recency bias in method selection is how last year’s architecture becomes this year’s unexamined baseline.
How this site fits the workflow
The archive is a pre-triaged layer for exactly this process: search 5,000+ scored papers with rigor/quadrant filters, use hub rankings as anchor lists, follow the weekly digest instead of raw arXiv listings, and check the maps for field structure before diving. The scores don’t replace your judgment — they replace the first hour of it.
Next steps in the chain: read the survivors → · find their data → · choose what to replicate →