This page is currently in the works. Please see here for the presentation slides from my QR Workshops at UChicago FinMath.
Linear regression is the most important tool in quantitative research—and, as a result, it is also the most frequently tested topic in interviews. Despite the growing popularity of complex machine learning models, linear regression remains a cornerstone of quantitative analysis due to its interpretability, transparency, and ease of implementation. Whether you're building a predictive model, constructing a risk factor, or analyzing the sensitivity of returns to certain exposures, a deep understanding of regression is essential.
It’s absolutely necessary to know the ins and outs of linear regression. Of course, you need to know the basics (e.g., “state the assumptions of OLS”), but interviews often go deeper. You’ll be asked to interpret outputs, assess robustness, and identify edge cases where the model might fail. Often, even when the question doesn’t explicitly mention regression, solving it well requires regression intuition under the hood.
This section is structured into several parts:
Core assumptions and mechanics of OLS
Applied reasoning with regression setups
Practical edge cases and conceptual pitfalls
Extensions such as regularization and weighted least squares
The goal is not to memorize formulas but to internalize how regression behaves—so you can reason flexibly and rigorously in unfamiliar or messy situations, just as you would in actual research work.
Coming soon...
Case study questions are one of the most important, yet often overlooked, parts of quantitative research interviews, particularly in final rounds and take-home assignments. These problems are designed to mimic real-world research scenarios: they’re open-ended, messy, and ambiguous. The goal isn’t just to test your technical skills, but to evaluate how you reason through uncertainty, structure a solution, and communicate your thought process clearly.
The topic may be finance-related or something entirely unrelated as long as it involves data analysis and modeling. For example:
How would you test the hypothesis: "Stock prices tend to decline following CEO resignation announcements"?
How would you determine the optimal number of CitiBikes to allocate to each docking station in New York City?
You might be asked to explain your process verbally, walk through a Jupyter notebook, or deliver a short research write-up. In all cases, balancing statistical rigor with practical intuition is key. Often, your ability to think critically and transparently will matter more than the specific answer you arrive at.
This section provides a structured framework for tackling these problems so you can demonstrate the kind of rigorous, hypothesis-driven thinking that top firms are looking for.