Assignment Quality Criteria

A reference guide to code and writing quality standards for empirical finance assignments — with concrete examples.

You were encouraged to use all available tools, including AI assistants, to polish your submission to the highest possible standard. This page makes the evaluation criteria explicit. They are organised into five categories: code quality, reproducibility, communication, writing mechanics, and AI-specific pitfalls.

The criteria are not a checklist to satisfy mechanically — they reflect habits of a careful empirical researcher. A well-structured document that renders cleanly, presents results honestly, and writes about them precisely will satisfy almost all of them naturally. The most common failure modes we observe are not conceptual errors but finishing touches: magic numbers left in place, captions that say “Figure 1”, prose that describes results vaguely while the exact number sits in a table two lines above, and writing voice that shifts register mid-document.

Use the interactive table below to explore each criterion. Click any row to see a side-by-side example of a problematic and an improved version.