Machine Learning Topics for Quantitative Finance Interviews

Summary

The document asks which machine learning and statistical methods candidates should prepare for quantitative researcher and developer interviews at hedge funds and proprietary trading firms. It situates the question alongside probability puzzles and coding challenges, then names ordinary least squares regression as a commonly encountered method and gradient boosted learners as another possibility.

It also asks whether candidates should study logistic regression, support vector machines, neural networks, clustering, principal component analysis, and random forests. The text does not answer that question or provide interview evidence beyond the author’s impressions and hearsay. It is therefore a useful outline of topics to investigate, but not a reliable ranking of what employers test or a guide to how deeply each method should be understood.

Key ideas

• Quant interviews may include machine learning alongside probability and coding questions.
• Ordinary least squares regression is identified as a commonly asked method, based on the author's impression.
• Gradient boosting and several classification, neural network, clustering, and dimensionality reduction methods are raised as possible interview topics.
• The document offers no confirmed guidance about interview frequency or expected depth.

Tags

# What machine learning algorithms are important for quant interviews?


# What machine learning algorithms are important for quant interviews?












I'm not sure if this question is appropriate for this SE board. If not, I can definitely remove it. FWIW, I saw a few other interview-related questions posted on here.

Anyways, I will be interviewing for quantitative researcher and developer positions at hedge funds and prop trading firms this summer.

My understanding is the interview processes will certainly solve (1) Probability (brain-teaser) questions (2) Coding questions (puzzles and/or C++-specific questions).

I'm also under the impression that interviews may also cover machine learning methods and other classical statistical concepts, and I was wondering if someone could give me an idea of what kind of ML methods I should know for interviews?

Linear regression using OLS appears to be the most commonly asked ML method, and I've heard that sometimes gradient boosted learners are tested on as well. What about methods like logistic regression, SVM, neural nets, clustering methods, PCA, random forests, etc...?

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.