Equity Research Resume Rewrite

Reviewed by Fallon Han· Updated 2026-06-01 06:05:33.037

This conversation was generated using Cook'd AI's career assistant and reviewed by Fallon Han from our editorial team for accuracy.

I'm a senior math student at a Canadian university and I'm trying to pivot into equity research. Can you review whether my project bullets actually show stock-picking judgment, model building, and market intuition? I feel like they read too academic right now, especially one bullet about a regression model. I want this resume to look credible for research roles, not just like a class project list.

Absolutely. For equity research, your bullets need to show three things clearly: what you predicted, why it mattered for an investment view, and how you used the data to form a judgment. Right now, if a bullet sounds like "built a regression model" without mentioning a market question or stock implication, it will read as academic. The key is to replace method-first language with decision-first language. For example, instead of focusing on the technique, emphasize the signal you tested, the universe you screened, and the takeaway for valuations or relative returns. If you want, paste the bullet and I can rewrite it to sound more research-oriented.

Here is the one that feels weak: "Built a multivariate regression model to analyze the relationship between macroeconomic variables and stock returns across Canadian financials using Python and R." It feels like it shows technical work, but not whether I had any actual investment judgment. How would you rewrite that?

You're right, that version shows technical execution but not research judgment. It tells me you can run a regression, but not what investment question you were trying to answer or what conclusion mattered. A stronger version should frame the project like a research memo. Try something like: Analyzed Canadian financial stocks to identify which macro factors had the strongest impact on relative returns, using Python and R to test valuation and rate sensitivity signals for an equity research thesis. If you want it to sound even more credible, add the output or implication: Analyzed Canadian financial stocks to isolate rate and macro sensitivities, using Python and R to test which signals best explained relative returns and to inform sector positioning ideas. This keeps the model work, but makes the judgment and market relevance obvious.

That helps. I also have a bullet for a project where I screened TSX stocks for value and momentum factors, but it currently just says I used Excel and Python to rank companies. I did not literally recommend buys, but I did compare the names and wrote notes on why some looked more attractive. How do I make that sound like research and not a data exercise?

Great example. For research roles, you do not need to pretend you made formal buy calls. You do need to show that you evaluated names like an analyst would, not just ranked rows in a spreadsheet. A better version could be: Screened TSX-listed companies for value and momentum signals in Excel and Python, then compared relative attractiveness across names and wrote investment-style notes on the strongest and weakest candidates. If you want it even sharper for equity research: Screened TSX-listed companies for value and momentum factors in Excel and Python, then compared relative upside and downside across names to support stock selection notes. That phrasing signals stock-picking judgment without overstating it.

I like that. Another concern: I have a bullet about forecasting earnings using a time-series model, but it sounds a bit too classroom-y. I want to show market intuition. What should I change so it sounds like I understand how earnings surprises affect price action?

You want to connect the forecast to how analysts actually use it. Earnings forecasting becomes more compelling when you show that you cared about surprises, revisions, or estimate risk rather than just model accuracy. For example: Built a time-series forecast for earnings trends and assessed how surprise risk could influence post-earnings price moves and analyst expectations. Or, if you want a more polished research tone: Modeled earnings trends to estimate surprise risk and evaluate how revisions could affect near-term stock performance. Those versions sound more like someone thinking about catalysts and market reaction, which is exactly the equity research angle.

This is what I needed. One last thing: do my bullets need numbers to sound credible? I do not have trading results, but I do have sample sizes and model outputs. I worry that without performance metrics, the resume will still look too theoretical.

Numbers help a lot, but they do not have to be trading returns. For your background, the most useful metrics are sample size, coverage, error reduction, signal strength, ranking consistency, or how many names you screened. Good examples: - screened 120 TSX names - tested 8 macro variables across 10 years of data - reduced forecast error by 15 percent versus a baseline - identified top quartile value names Those numbers make the work feel real and research-driven. If you want, I can help you turn your three strongest bullets into equity research versions that sound credible for Canadian banks and asset managers.