Statistical Benchmarking of Domain-Adapted Transformers for Financial Text Sentiment

Thomas M. Menino Convention & Exhibition Center 

Sentiment analysis in financial news presents statistical challenges due to high-dimensional lexical features and domain-specific polarity, where common terms deviate from general-usage sentiment. In this study, we present a comparative framework for three-class sentiment classification, benchmarking six model families: classical linear and non-linear learners (Logistic Regression, Random Forest, LightGBM), a recurrent neural network (GRU), and transformer architectures (ALBERT and FinBERT). Using a normalized corpus of 32,583 financial news items, we implement a standardized experimental pipeline with grid and random search for hyper-parameter optimization under a 60/20/20 data partition. Our evaluation framework moves beyond point estimates by employing nonparametric bootstrapping (n=2,000) to quantify statistical uncertainty and generate 95% confidence intervals for Weighted F1-scores and Macro Area Under the Curve (AUC). Results show that the domain-pretrained FinBERT model achieves superior performance (F1: 0.8705 [0.8621, 0.8785]; Macro AUC: 0.9612 [0.9560, 0.9658]). We apply McNemar's test to verify predictive improvements of domain-specific transformers (p<0.001).

Statistical learning

Sentiment classification

Domain adaptation

Financial text analysis

Transformer models

Model evaluation 

Section on Statistical Learning and Data Science