An AI framework combining FinBERT sentiment embeddings and LSTM-based stock trend modeling for real-time stock movement prediction using financial news and historical stock data.

An LSTM model to predict stock price trends using FinBERT sentiment analysis on financial news.

FinSight leverages LSTM networks and FinBERT sentiment analysis to predict stock prices with enhanced accuracy, integrating financial news sentiment for more informed and precise market forecasts.

In this project, we combine historical stock price data with sentiment analysis of financial news using FinBERT to predict stock prices for Apple Inc. (AAPL) using a Long Short-Term Memory (LSTM) neural network.

LSTM-based stock sentiment analysis model that combines Unusual Whales sweeps with multi-source sentiment data (Alpha Vantage, Polygon IO, TWS News + FinBERT), aligned with OHLCV features, to predict 4-hour future returns and classify bullish vs bearish signals.

Predict stock market’s insight using pre-trained FinBERT and LSTM, where one can make smart decisions to predict stock’s closing price using News on the Web sentiment.

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stock predictor using NLP with finBERT and OHLC data with an LSTM

Predict stock movements from social media sentiment using FinBERT and LSTM

Predicting stock movement using technical indicators and FinBERT-based sentiment analysis with LSTM.

FinTech Stock Trend Predictor uses LSTM to forecast stock prices and FinBERT to analyze financial news sentiment. Users can view historical and predicted prices along with sentiment insights in an interactive Streamlit dashboard.

This project explores the effectiveness of combining **historical stock price data** with **news sentiment analysis** to predict short-term stock market trends. The hybrid model leverages: LSTM, FinBERT

A deep-learning project that predicts NVIDIA stock prices using an LSTM model and analyzes financial news sentiment with FinBERT. Includes fully interactive dashboards for stock prediction and sentiment insights.

Predict gold stock price trends while maintaining data privacy. Combine Federated Learning, Bi-LSTM, and FinBERT sentiment analysis. Provide an accurate and privacy-aware forecasting system.

Predicts short-term stock price movement using financial news sentiment and machine learning. Combines stock data with sentiment from news APIs (e.g., GNews, Mediastack), processed using NLP and analyzed with models like VADER, FinBERT, XGBoost, and LSTM.

Machine learning system that predicts short-term stock price movements using real-time financial news sentiment analysis (FinBERT, KeyBERT) and time-series modeling (LSTM). Includes full NLP preprocessing pipeline, feature engineering, and experimental notebooks.

A machine-learning project that predicts stock price movements by combining historical data with real-time sentiment. Uses financial APIs, FinBERT for sentiment extraction, and a Hybrid LSTM model achieving 73.2% accuracy, showing sentiment strongly improves prediction.

FinX AI is an AI-powered financial analytics platform for stock, IPO, and portfolio analysis. It predicts stock trends using LSTM/GRU, estimates IPO gains with GMP and sentiment models, analyzes news via FinBERT, and optimizes portfolios using MPT and RL, all through an interactive dashboard.

In my bachelor thesis I was trying to predict stock price fluctuations using historical stock prices and financial news. I was comparing two preprocessing models: TF-IDF and FinBERT and four prediction models: logistic regression, random forest, MLP and LSTM and how well do they perform.

Predicts stock price direction on earnings announcement days using a multi-modal setup combining firm fundamentals, technical indicators, and FinBERT-based news sentiment. Compares LSTM and Transformer models against logistic regression, showing Transformers achieve higher macro F1 and consistent gains from sentiment features.