FinBERT vs. Dictionary Sentiment Analysis - Quant Researcher Interview Question
Difficulty: Hard
Category: machine_learning
Practice quant interview questions from top firms including Jane Street, Citadel, Two Sigma, DE Shaw, and other leading quantitative finance companies.
Topics: nlp, sentiment_analysis, statistics, numpy
Problem Description
Natural Language Processing (NLP) is essential in quantitative finance for extracting sentiment signals from unstructured data like news headlines to predict market movements. While Transformer models such as FinBERT offer state-of-the-art accuracy, dictionary-based methods remain valuable for their computational efficiency and interpretability. Comparing these approaches allows researchers to balance performance trade-offs when building automated trading signals.
Task
Implement a comparison fr
Practice this hard researcher interview question on MyntBit - the all-in-one quant learning platform with 200+ quant interview questions for Jane Street, Citadel, Two Sigma, and other top quantitative finance firms.