FinBERT vs. Dictionary Sentiment Analysis - Quant Researcher Interview Question

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