Scoring Methodology
To navigate the high-volume landscape of quantitative finance, thequant.space utilizes a dual-axis scoring framework. This system decomposes complex ArXiv research into two primary dimensions: Theoretical Complexity and Empirical Rigor.
The Classification Framework
1. Math Complexity
This metric quantifies the “theoretical overhead” required to parse the paper. We analyze the density of mathematical notation and the sophistication of the underlying machinery.
- High Score (7–10): Heavy reliance on Stochastic Calculus, Partial Differential Equations (PDEs), bespoke optimization proofs, or advanced topology.
- Low Score (1–3): Primarily descriptive statistics, high-level conceptual frameworks, or basic linear algebra.
2. Empirical Rigor
This metric assesses the “path to implementation.” We look for signals that the strategy has been stress-tested against the realities of the market.
- High Score (7–10): Extensive backtesting on high-fidelity data (Tick-level, WRDS, Bloomberg), clear mention of transaction costs, and rigorous out-of-sample validation.
- Low Score (1–3): Use of “toy” datasets, synthetic data, or purely logical derivations without historical verification.
Strategic Quadrants
By plotting these scores, we categorize research into four distinct strategic “vibes.” This allows you to filter papers based on your current objective—whether it’s deep R&D or immediate alpha generation.
| Quadrant | Designation | Strategic Utility |
|---|---|---|
| High Math + High Rigor | Holy Grail | Institutional-grade research. Validated theory ready for sophisticated production environments. |
| High Math + Low Rigor | Lab Rats | The “Research Frontier.” Highly innovative math that lacks empirical testing—high potential for “hidden” alpha. |
| Low Math + High Rigor | Street Traders | Applied Alpha. Robust, data-driven strategies that prioritize execution and simplicity over theoretical flair. |
| Low Math + Low Rigor | Philosophers | Market Meta-Analysis. Conceptual frameworks and “think pieces” that shape broad market perspectives. |
Current Library Distribution
| Quadrant | Density | Primary Sources |
|---|---|---|
| Holy Grail | – | J. of Finance, Quant. Finance |
| Lab Rats | – | ArXiv (Math.OC, Stat.ML) |
| Street Traders | – | Industry Whitepapers, SSRN |
| Philosophers | – | Commentary, Meta-Research |
The Strategic Map
Pro-Tip: Identifying “Alpha Clusters” Watch for clusters in the Lab Rats quadrant. These represent theoretical breakthroughs that have not yet been commoditized by the broader market. Bridging the gap from Lab Rat to Street Trader is where individual quants find their edge.
Disclaimer: Scoring is performed via automated semantic analysis. If you believe a paper has been misclassified, please open an issue.