Finance Welcome Guide

The Finance-specific parts of the first year: seminar culture and finance courses.

Welcome

Congratulations on getting into the PhD! This guide exists because onboarding at the start of the program can feel a bit thin, and the goal is simply to make the transition easier for incoming students. It covers the practical, day-to-day side of the first year more directly than the standard welcome letter does.

For the first-year economics course sequence – Math Camp, Microeconomics, Econometrics, and how to survive them – see the Economics Welcome Guide, which applies to all students taking those classes.

This is a living document maintained by current students. If you’d like to contribute or suggest changes, please reach out – the more perspectives, the better.

Academia FAQ

PhD peers

Incoming Rotman students are usually the most approachable. Depending on your department and year, you may have several peers in your exact field or very few – either way, it’s worth building good connections with whoever is around.
If you are taking economics classes, the economists are usually also approachable. Many come from the same UofT Masters, so they know each other beforehand. Keep in mind that this first year is extremely demanding for them, with Micro + Macro + Metrics + a TA assignment all at once. In some periods (exams and assignment deadlines), the stress becomes palpable and affects everyone.
Upper-level PhD colleagues from your department are also approachable, but keep in mind that they are very busy and usually also stressed out.

Faculty

In general, faculty have relatively little to do with brand-new PhD students, since the first two years are mostly coursework. It’s good to let yourself be known, without overdoing it.
Most faculty are amicable, but don’t expect a grand reception. Most professors are quite busy, and only a few have new-PhD status-tracking as part of their role.
While it’s not expected, it’s a good idea in the first year to get to know the faculty and maybe identify a few people you’d be interested in working with. Note that not all professors will be interested in or available for supervision.

A note on faculty and colleagues: many people in quantitative fields tend to keep to themselves or be more introverted. This is not to say they are unfriendly or dislike you. Academia is also stressful, and stress affects everybody differently, so keep in mind that how people come across is usually a reflection of themselves, not of you.

Early research

Unless otherwise noted, you are not expected to conduct research in your first year, beyond what your classes require. The course load for most paths (certainly if you are taking economics classes) is heavy and doesn’t leave much room for research anyway.
It’s still a good idea to keep track of what you like and dislike, and of developments in your preferred subfields.
It’s also worth doing something related to your long-term research interests whenever a class gives you the opportunity. This avoids spending time on unrelated projects that just end up abandoned in a folder.

Seminars

Most departments hold weekly or biweekly seminars, usually featuring visiting professors presenting their papers, Rotman professors or students presenting their research, or (rarely) professionals pitching something.
Officially, seminar attendance is mandatory for all PhD students and faculty. In reality, some professors are always there, most are there most of the time, and a few are never there; most PhD students attend most of the time, unless they’re on the job market.
A reasonable approach: try to be there, unless you have a conflict (like a class) or you’re preparing for something important (like an exam). Give priority to seminars with an accompanying PhD pre-seminar (this is specific to Finance).
You will probably struggle to follow most seminars, because you’re not yet at that level and usually not in that field. It can help to skim the paper or have a quick discussion with an AI assistant beforehand, and to keep notes during the seminar to stay engaged. It’s okay if you don’t follow all of it – no one will quiz you on the seminars afterward.
There is also a sheet circulating that lets you sign up to present a paper in one pre-seminar during the year. You can usually pick more than one, but signing up for just one to start – the closest to your research interests – is plenty. You’re not graded on this; it’s just good practice and useful background.

Finance courses

The structure and grading of finance classes are much gentler than economics classes. You usually have only one class per week (sometimes none), and the assignments tend to be short and manageable.
Where economics classes aim to challenge you with specific problems and concepts, finance classes are more about thinking creatively about the subject. The requirements are looser and the atmosphere is more casual.
Your main deliverable is usually a term paper, where you can work on your own research agenda.
Most students do well in these, typically finishing around A-/A.

Financial Theory 1

Structure: Foundations of Asset Pricing, CAPM, CAPM refinements, Biases.
Description: The lectures are engaging and value participation. The material is fundamentally hard, but the approach is intuitive and cuts through the math.
Lecture difficulty: Moderate. The lectures take your input into account, which makes the material much more approachable.
Problem sets: Moderate.
Exam difficulty: A casual setting overall, though certain portions can be quite difficult and come out of nowhere.
Tips: What matters most is your participation and commitment, not exam heroics. This is a rare case where the suggested book (Cochrane, Asset Pricing) is genuinely good. Show up to every class and participate.

Empirical Asset Pricing

Structure: Foundations of Empirical AP, Fama-French, Earnings, ML.
Description: A good overview of empirical asset pricing, along with useful research- and academia-related insider knowledge.
Lecture difficulty: The concepts are generally moderate, though the organization can be a bit chaotic.
Problem sets: Easy if you code well; otherwise challenging.
Exam difficulty: None (term paper). Can range from very easy to very difficult depending on your research and subject-matter experience. The instructors are quite accommodating about the paper topic, so don’t stress about it.
Tips: Programming is central to this course, but modern AI coding tools have made it far more approachable than it used to be – even with limited programming background, you can get a lot done. The bar is now less about writing code from scratch and more about understanding the analysis you run. Lean on current AI coding assistants; agentic coding tools and AI-integrated IDEs can scaffold, write, and debug your analysis quickly. Use everything available to you, short of plagiarism – but audit what they produce and make sure you can explain and defend the code and results you submit, since the goal is a paper you can stand behind. The instructors are typically very helpful and happy to bounce around ideas on your term paper, so take advantage of that. There’s a lot of jargon and domain-specific knowledge, but this is tempered by the relatively limited amount of mathematics in this field.