financial-sentiment-analysis

A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097

Optimized Large Language Models for Financial Applications – Efficient, Scalable, and Domain-Specific AI for Finance.

This repository contains the code and data for an exploratory study on financial sentiment analysis of annual reports from the current member companies of the Philippine Stock Exchange index. The study was conducted as part of the MSFIN299 course at the University of the Philippines Diliman

binary topic and 3-class sentiment classification of full-length news articles

My Master Thesis: Developing a financial market sentiment analysis model for social media content

SenseAI is pioneering the frontier of financial AI through data.

Repo for the financial sentiment analysis project from scratch

Xueqiu financial posts extraction

Deep Learning-Based Financial Sentiment Analysis (DLBFSA) is a deep learning algorithm that analyzes financial news and social media to gauge market sentiment and make informed trading decisions.

Work materials for the Text Analytics Bootcamp at PGGM

FinBERT fine-tuning with quantum optimization (QAOA/VQE) for financial sentiment; notebooks + environment for reproducible runs.

Divergence-aware multi-agent routing that cuts LLM inference cost 10-100x. One signal routes queries, keys a multilingual cache, and detects hallucinations at AUC 0.90. CIKM 2026.

Entity-level sentiment analysis on financial news. Extracts news data, performs named entity recognition and sentiment analysis at the company level, and models relationships between news and stocks using a graph-based representation to study correlations with market price movements.

A FinBERT-LoRA financial news classifier with 0.7956 held-out macro F1, SHAP explanations and a reusable 1.82 MiB adapter.

Investigating the transmission of financial stress and systemic risk through countries by applying technical tools as PCA, DFM, FAVAR, FEVD, Wavelet, and Network analysis)

End-to-End Python implementation of Luo's (2026) benchmark construction and evaluation stack for financial NLP. Harmonizes five financial-text corpora into a 33,549-row three-class benchmark, adapts Qwen2.5-7B, LLaMA3-8B and Mistral-7B via QLoRA, then tests whether the resulting sentiment scores rank forward returns on a 2019 S&P 100 news panel.

Regime-aware, sentiment-driven decision support system for risk-controlled equity trading. Research code and experiments