Predicting Stock Price Direction on Earnings Announcement Days using Multi-modal Deep Learning
Abstract
Predicting stock price movements during Earnings Announcements (EAs) is a significant challenge due to market noise and high-impact price discontinuities. In this study, we evaluate whether pre-announcement news sentiment, firm fundamentals, and recent market dynamics jointly predict the directional price movement of equities on EA days. We construct a multi-modal feature space combining 15 fundamental metrics, 3 price-based technical indicators and sentiment scores derived from financial news articles processed using FinBERT. We compare a Long Short-Term Memory (LSTM) network and a Transformer-based architecture against a logistic regression baseline, and further assess all models with and without sentiment features to quantify their incremental value. Our results indicate that while the LSTM demonstrates higher precision through a conservative safe-bet strategy, the Transformer model exhibits superior sensitivity in identifying volatile movements, achieving a higher macro F1-score, with ablation experiments showing a consistent benefit from incorporating news sentiment.
Cite
@article{arxiv.2605.25894,
title = {Predicting Stock Price Direction on Earnings Announcement Days using Multi-modal Deep Learning},
author = {Manuel Noseda and Nathan Soldati and Marco Paina},
journal= {arXiv preprint arXiv:2605.25894},
year = {2026}
}