QT/QR/S&T - what statistics topics to self study in preparation for internship applications?
I'm a first year maths student interested in the Trading/Quant side of finance. I heard that probability and stats questions are all very popular for internship interviews. Unfortunately statistics (and stochastic processes) is not done at my university until the second year, so I won't be formally doing it until after recruiting season has ended (Probability will be done in first year, however, so I'm good there).
I have a copy of The Elements of Statistical Learning that I'm planning to work through, since it seems to be a very popular recommendation. I'm wondering if someone could give me a list of topics that I should make sure I know for QT/QR/S&T intern roles?
Based on the most helpful WSO content, here are some key statistics topics you should consider studying in preparation for internship applications in Trading/Quant roles:
Probability and Statistics: This is a fundamental area you need to be comfortable with. It includes understanding concepts like random variables, probability distributions, expectation, variance, covariance, correlation, and central limit theorem.
Time-Series Analysis: This is crucial for financial data analysis. You should understand concepts like stationarity, autocorrelation, partial autocorrelation, ARIMA models, and GARCH models.
Machine Learning: This is becoming increasingly important in Quant roles. You should understand supervised learning (regression and classification), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning.
Pattern Recognition: This involves identifying patterns and making predictions based on these patterns. It's a key part of algorithmic trading.
Linear Algebra: This is the basis for many machine learning algorithms. You should understand vectors, matrices, determinants, eigenvalues and eigenvectors, and be comfortable with matrix operations.
Stochastic Processes: This is a key part of quantitative finance. You should understand concepts like Markov chains, Brownian motion, and stochastic differential equations.
Algorithm Analysis: Understanding how to analyze the time and space complexity of algorithms is crucial.
Remember, it's not just about knowing these topics, but being able to apply them to real-world financial problems. Good luck with your studies!
Sources: Best internships for Quant Trading?, RE: Prop/Quant Trading - Why is it not as big a target as Investment Banking?, Ask Me Anything - Buy Side Systematic Quant
I think what is important for interviews and important for internships tends to be somewhat different as most but not all interviews do not involve any actual data while almost everything in an internship or working full time is with actual data instead of purely mathematical/theoretical. I'm not sure what country the OP is from but from my experience in the US I would expect relatively few internship applicants to have taken a course in stochastic processes and many will not have taken any formal stats classes. Some firms have interview questions similar to AMC style questions involving discrete math or probability.
Understanding linear regressions well is probably the most important and standard thing from stats. Time series models, machine learning, and stochastic processes all have their use cases too but their applications tend to be more specific and require more domain expertise than students/interns are likely to have. Real world data is not going to match the theoretical assumptions for models and evaluating model quality when those assumptions don't hold is more of an art than a science that you can learn in school.
I'm based in the UK. Thanks for your answer, I was under the assumption that regression was the main thing I needed to learn, too.
You're really not going to get trading/quant roles until your penultimate year, but I think if you get the green book and know everything there you are golden
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