Use when positioning a Mathematical Finance (Wiley) manuscript against the financial-mathematics frontier — stake the methodological contribution against prior stochastic-analysis, pricing, and control results, citing the precise theorem you sharpen, generalize, or supersede.
Scanned 6/5/2026
Install to Claude Code
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill mathfin-literature-positioning --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Mathfin Literature Positioning?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/brycewang-stanford-mathfin-literature-positioning)More formats (shields.io, HTML) on the badges page.
---
name: mathfin-literature-positioning
description: Use when positioning a Mathematical Finance (Wiley) manuscript against the financial-mathematics frontier — stake the methodological contribution against prior stochastic-analysis, pricing, and control results, citing the precise theorem you sharpen, generalize, or supersede.
---
# Literature Positioning (mathfin-literature-positioning)
## When to trigger
- The introduction reads as a survey rather than a precise contribution claim
- Unsure which prior theorem your result generalizes, sharpens, or contradicts
- A referee might say "this is already known under weaker/stronger assumptions"
## The Mathematical Finance positioning bar
Because the journal prizes **methodological novelty and contribution to financial modelling**,
positioning must be **theorem-level**, not topic-level. The reader (often a Bachelier Finance
Society member steeped in stochastic analysis) wants to know exactly which assumptions you
relax, which generality you add, or which open problem you close — and why earlier machinery
could not. A vague "the literature has studied X" invites a desk concern about novelty.
## How to position
1. **Name the closest prior result** and its assumptions precisely (model class, regularity,
filtration, market completeness). State what it *cannot* deliver.
2. **Locate your delta on one axis**: weaker assumptions, broader model class, sharper rate,
constructive vs. existence-only, time-consistent vs. not, or a genuinely new object.
3. **Cite landmark machinery, not laundry lists** — the foundational tools you build on
(e.g., semimartingale theory, FTAP/NFLVR, BSDE theory, convex duality, stochastic control)
should be cited where they do work, not as decoration.
4. **Pre-empt the "special case" objection**: show your result is not a corollary of an
existing theorem under a change of variables.
5. **Flag what you do NOT claim** — keeping scope honest is part of the rigor culture.
## Anti-patterns
- A standalone literature-review section detached from the contribution claim.
- Citing a result without its hypotheses, so the reader cannot judge your delta.
- Over-claiming generality the proof does not actually deliver.
- Ignoring a known counterexample or a sharper existing bound.
- Treating "no one has done exactly this" as novelty when the technique is routine.
## Output format
```
【Closest prior result】author/year + its assumptions + its limit
【Your delta】weaker-assumptions / broader-class / sharper / constructive / new-object
【Machinery you build on】[foundational tools, cited where they work]
【Special-case defense】why your result is not a corollary of prior work
【Scope honesty】what you explicitly do NOT claim
【Next step】mathfin-identification-strategy
```
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!