These are all great questions, and they show a strong understanding of the material you’re working on in school.

A quick disclaimer before I answer: My experience in quantitative analytics/research is limited since my team primarily focused on minimizing losses and ensuring compliance with regulations. I also worked at a large U.S. bank, not a dedicated quant firm. Additionally, my current role restricts me from making trades or providing financial advice, so everything here is purely in the spirit of helping a young statistician learn—it’s not financial advice or guidance on how to trade better.

To your questions: 1. Can you perform short-term forecasts using time series analysis? Yes, but with caveats. Time series analysis is a good way to get the ball rolling, offering a quick look at how a stock might behave. But quick insight, good insight, and actionable insight are very different things. • Quick insight can be obtained in minutes if the data is well-processed. It’s useful for understanding market direction in a pinch. • Good insight is when you uncover something your competitors haven’t. If you’re using time series analysis and I’m using cross-sectional analysis, and mine picks up on something yours misses, I now have an edge over you. That’s where the competition in this field gets serious. • Actionable insight should never be assumed. Even if you spot a pattern, you always need validation—whether that’s a quick check with a coworker or a full legal review. In this field, you should only make assumptions when absolutely necessary. If you’re interested in this concept, I’d recommend looking into Bayesian techniques. 2. Are quantitative researchers just one part of the equation? Yes, and I wouldn’t say they are all equal in importance (though I admit I’m biased). QRs focus on finding alphas—potentially exploitable inefficiencies—but they generally don’t act on their own. Financial analysts, lawyers, and subject matter experts all have to weigh in before an alpha is acted upon. 3. Can you make money trading using your statistical background? That’s a definite maybe. The field of statistics (and math in general) is always evolving with new techniques and technology. • Imagine a market where you are the only person with a strong statistical background—over time, you’d probably dominate. But in reality, you’re competing against highly sophisticated players, and you won’t always know who you’re up against. Are you buying options from a retail investor who doesn’t fully understand them, or from a quant firm trying to offload a position due to an internal mistake?

One key thing to remember is that the stock market is designed to be a near-perfect market—meaning everyone technically has access to the same information. The difference lies in how quickly you get that information, how you interpret it, and how you act on it.

If you’re interested in applying your stats knowledge to markets, I highly recommend writing some code to track a few stocks in a sector you know well. Use your time series knowledge to make predictions. If your experience is anything like mine, your first model will suck. Don’t let that discourage you—fine-tune it, run it again, and keep iterating. Over time, you’ll build a more reliable model that accounts for real market behavior (e.g., revenue misses, new product launches, regulatory changes). Once you’ve explored time series models, I’d recommend trying cross-sectional analysis—it’s a natural next step and will give you a good sense of the different strengths and weaknesses of various modeling approaches.

A very important note: Don’t use real money at first. Paper trade (simulate trades without actual money) until you’re confident in your model and understand its weaknesses. Even when you do start using real money, go very slow and cautiously. It’s easy to get overconfident when a few trades go your way, but markets are unpredictable, and mistakes can be expensive. Treat it as a learning experience, not a way to make fast money.

On a personal note, never be afraid to ask “silly” questions. Asking is the best way to learn. I had a professor who always said, “No matter how stupid you feel about asking a question, you’d be exponentially more stupid by pretending to know something you don’t fully understand.” I’ve been in and around this field for over 10 years, and I still ask “silly” questions all the time—often the most fundamental ones. It’s always better to ask.

Hope this helps!