Comparing LLMs for Sentiment Analysis in Financial Market News

Battle Count: 190

The paper is moderately relevant to quantitative trading. Sentiment analysis of financial news is a well-known alpha signal used in trading strategies. The comparative evaluation helps practitioners choose appropriate models for sentiment extraction. However, the paper does not directly address trading strategy construction, backtesting, or portfolio optimization. The connection to trading is primarily through the sentiment signal as an input feature for downstream prediction models (as referenced in the authors' prior work on stock price prediction).

Moderate complexity. Classical models (RF, SVM, MLP) with TF-IDF and SVD preprocessing are straightforward to implement using scikit-learn and Keras. LLM-based approaches (Gemma, DeBERTa, BART, XLM-RoBERTa) require HuggingFace transformers library and GPU resources. Gemini API integration adds external dependency. The main complexity lies in the preprocessing pipeline for classical models and prompt engineering for LLMs. No custom architectures or novel algorithms are proposed.

The paper describes datasets (FPB, StockEmotions, TFN), model names, hyperparameters, and preprocessing steps in detail. However, no code repository is provided, and some implementation details (exact prompts, random seeds, hardware specifications) are not fully specified. The use of publicly available datasets and standard libraries (scikit-learn, Keras, HuggingFace) aids reproducibility.