500+ quant interview questions for Jane Street, Citadel, Two Sigma, DE Shaw, and other top quantitative finance firms.
C++ and Python coding challenges for quant developer interviews
Statistical analysis and quantitative modeling problems
Trading MCQs, probability brainteasers, and market scenarios
Practice quant interview questions on MyntBit - the all-in-one quant learning platform. Free questions available for C++ coding, Python problems, probability brainteasers, and trading MCQs.
Difficulty: Medium
Category: Linear Algebra & Machine Learning
Practice quant interview questions from top firms including Jane Street, Citadel, Two Sigma, DE Shaw, and other leading quantitative finance companies.
Topics: logistic-regression, loss-function, machine-learning, binary-cross-entropy
You are training a logistic regression model to predict whether a stock will go up (1) or down (0) tomorrow. You have a dataset of $n$ historical trading days. For each day $i$, you have the true outcome $y_i$ (either 0 or 1) and your model's predicted probability $\hat{p}_i$ that the stock will go up. Which of the following formulas represents the binary cross-entropy loss function (also known as the log loss) that you should use to train your model?
Practice this medium trader interview question on MyntBit - the all-in-one quant learning platform with 500+ quant interview questions for Jane Street, Citadel, Two Sigma, and other top quantitative finance firms.