PREDICTING EARNINGS SURPRISES THROUGH NATURAL LANGUAGE PROCESSING OF MANAGEMENT COMMUNICATIONS

Earnings surprises, defined as deviations between actual reported earnings and analyst consensus forecasts,
represent critical information events that significantly impact stock prices and investment decisions. This research
investigates the application of Natural Language Processing (NLP) techniques to extract predictive signals from
earnings call transcripts and corporate disclosures. We employ FinBERT, a domain-specific transformer-based
language model undergoing specialized three-stage training, alongside traditional sentiment analysis approaches.
Through comprehensive empirical analysis of over 5,000 earnings events, we demonstrate that NLP-derived
features provide significant incremental predictive power beyond traditional financial variables. Results indicate
that FinBERT achieves 88% classification accuracy, substantially outperforming traditional machine learning
approaches including Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Convolutional
Neural Network (CNN), and Long Short-Term Memory (LSTM) networks. Furthermore, analysis of news volume
patterns around earnings announcements reveals systematic relationships with surprise magnitude, with
pronounced effects observed during the announcement period. This research contributes to the growing literature
on textual analysis in finance and provides practical insights for investors seeking to incorporate qualitative
information into earnings prediction models.