135 results for “topic:finbert”
Deep Learning Transformer models in MATLAB
Financial Domain Question Answering with pre-trained BERT Language Model
Notebooks for fine-tuning a BERT model and training a LSTM model for financial QA
Deep Reinforcement Learning (DRL) stock trading system with LSTM-PPO, integrating technical indicators and FinBERT-based news sentiment analysis.
An AI-powered personal finance tracker to manage expenses, predict spending, and plan budgets using machine learning.
Single-stock analysis using Python and local machine learning/ AI tools (Ollama, LSTM).
AI-powered algorithmic trading system that combines FinBERT sentiment analysis, spaCy NER, and TimeGPT forecasting to generate BUY/SELL/HOLD signals from real-time financial news.
Real-time Smart Money sentiment tracker for X/Twitter — NLP-powered Buy/Sell signals delivered to Telegram. FinBERT + VADER hybrid scoring, tiered account watchlist, and configurable confidence threshold.
Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting
Earnings Call Sentiment Analysis. This repository includes my work on extracting the focus area of companies from their earnings calls transcripts.
Sentiment Analysis On Financial News Headlines With BERT & FinBERT
Stock Sentinel is a web app providing stock market investors with sentiment analysis and news aggregation. It uses FinBERT for sentiment analysis and offers stock information, similar stocks suggestions, summaries, and news stories. With real-time insights, it helps users make informed investment decisions. Installation is easy with cloning, depend
Hands-on guide for sentiment analysis in quarterly conference calls
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A Deep Learning Sentiment Analysis on News Headlines using FinBERT (Python)
OSE Scientific Computing for Economists
This project is aimed to create an automated method that is able to identify emerging risks faced by multiple businesses and industries, and the trends of those risks.
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
An end-to-end Python implementation of Cao et al.'s (2025) HLPPL methodology for the identification of financial (asset price) bubbles. Implements 7-parameter Log-Periodic Power Law model fitting, confidence-weighted sentiment analysis, regime-dependent 'BubbleScore' fusion, and Transformer-based forecasting with a backtesting framework.
A deep learning platform that combines FinBERT-based sentiment analysis with LSTM-driven stock forecasting, delivering real-time, API-powered financial insights with confidence scoring.
Thesis of my masters in Data Science. This project implements a deep learning framework applied to stock portfolio management. Using the top 20 stocks of FTSE (Financial Times Stock Exchange) top 100 by market share.
No description provided.
FinBERT pre-trained models as a containerized HTTP service.
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Production-grade, open-source quantitative research engine in Python - backtesting, portfolio construction, ML validation, NLP sentiment, live paper trading, and a real-time dashboard in one cohesive framework.
Full code for my Medium article exploring the use of sentiment analysis as a buy trading signal.
A romanian version of BERT, Distil-BERT-base-ro, fine-tuned for Sentiment Analysis on translations of Financial Phrasebank.
NVDA Stock AI Prediction: Full-stack forecasting platform integrating LSTM neural networks with FinBERT sentiment analysis for real-time NVIDIA stock projections. Features a high-performance FastAPI backend and a reactive Vue 3 cyberpunk dashboard with live market telemetry from Yahoo Finance and News API.
Stock market sentiment analysis platform • Near real-time news aggregation • FinBERT ML + Gemini AI • 91%+ accuracy
Utilizing the sophisticated Recurrent Neural Network (RNN) models, particularly Long Short-Term Memory (LSTM), in combination with FinBERT sentiment analysis, to improve the precision of stock portfolio analysis.