213 papers in our archive carry a October 2026 date, against 94 in September 2026 and an average of 143 per month over the trailing year. (The archive has thin or no coverage for June 2026–August 2026, so those months are excluded from the baseline and the comparison month is the last well-covered one.) This report is generated from the scored archive with no editorial intervention: every number below is a count or an average over our math-complexity and empirical-rigor scores, so it measures what the field submitted, filtered by our quant-relevance gate, not what was eventually published or cited.
Headline numbers
| October 2026 | Trailing year (9 covered months) | |
|---|---|---|
| Papers | 213 | 143 / month |
| Mean empirical rigor | 6.2 / 10 | 5.4 |
| Mean math complexity | 7.3 / 10 | 6.6 |
| Papers with rigor ≥ 8 | 83 (39%) | 19% |
| Papers linking code | 18.8% | 11.4% |
| LLM / NLP share | 8.9% | 11.2% |
Where the papers went
Blue: this month · grey: previous month
Papers can sit in up to three hubs, so shares sum to more than 100%. Measured against the trailing year:
- Gaining share: Market Microstructure 16% vs 11% (+5.3 pp); Reinforcement Learning 10% vs 6% (+4.1 pp); HFT & Execution 9% vs 6% (+2.9 pp)
- Losing share: Risk Management 12% vs 17% (−5.4 pp); Machine Learning 15% vs 19% (−4.1 pp); Insurance & Actuarial 5% vs 7% (−2.5 pp)
Theory or evidence?
| Quadrant | October 2026 | Trailing year |
|---|---|---|
| Holy Grail | 145 (68%) | 46% |
| Street Traders | 16 (8%) | 17% |
| Lab Rats | 49 (23%) | 32% |
| Philosophers | 3 (1%) | 5% |
Holy Grail = high math and high rigor; Lab Rats = theory without data; Street Traders = evidence with light theory; Philosophers = neither (definitions). The three most rigorous papers of the month: Latent Continuum of Regimes in Limit Order Book Dynamics (rigor 9.0); Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks (rigor 9.0); Arithmetic Models for Commodity Futures, Vanilla, and Calendar Spread Options (rigor 9.0).
Top-rated papers of the month
Ranked by rigor-weighted score (60% empirical rigor, 40% math complexity).
- Latent Continuum of Regimes in Limit Order Book Dynamics — Holy Grail · Math 8.5 · Rigor 9.0 · Market Microstructure, HFT & Execution · code ✓
- Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks — Holy Grail · Math 8.5 · Rigor 9.0 · Machine Learning, Volatility
- Arithmetic Models for Commodity Futures, Vanilla, and Calendar Spread Options — Holy Grail · Math 8.0 · Rigor 9.0 · Volatility, Commodities & Energy
- Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams — Holy Grail · Math 9.0 · Rigor 8.0 · Market Microstructure · code ✓
- Packets, Transactions and Queues: Design Principles for HFT Systems from a Measurement Study of CME Market Data — Holy Grail · Math 7.5 · Rigor 9.0 · HFT & Execution · code ✓
- A game-theoretic analysis of the market microstructure in decentralized exchanges with concentrated liquidity — Holy Grail · Math 9.0 · Rigor 8.0 · Crypto & DeFi, Market Microstructure
- GROMM: Generative Risk-neutral Option Market Models — Holy Grail · Math 9.0 · Rigor 8.0 · Options & Derivatives, Volatility
- Data-Driven Dynamic Portfolio Selection with Vine Copula Simulation and Reinforcement Learning — Holy Grail · Math 8.5 · Rigor 8.0 · Risk Management, Portfolio Optimization
Most active authors
Authors with two or more papers dated this month (author pages exist for researchers with three or more papers in the archive):
- Doohwi Cha — 4 papers
- Ke-Wei Huang — 3 papers
- Masaaki Fukasawa — 3 papers
- Junkee Jeon — 3 papers
- Stefan Zohren — 3 papers
- Mihai Cucuringu — 3 papers
- Donghwa Seo — 3 papers
- Seunghan Son — 3 papers
Method
Counts cover every paper in the archive whose own date falls in October 2026; late arXiv listings and re-scores can shift a month’s numbers by a few papers after publication. Topic hubs come from a keyword classifier, scores from an LLM-assisted rubric — heuristics, not peer review. The underlying rows are in the downloadable dataset; previous reports are in the reports archive.