This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.

Advanced stock forecasting system using LSTM neural networks with real-time sentiment analysis. Predicts price movements and volatility by combining technical indicators, news sentiment from Finnhub API, and multivariate analysis. Features dual LSTM models, intelligent alerts, and comprehensive risk assessment for informed trading decisions.

📈 Forecast stock prices and volatility using LSTM neural networks and sentiment analysis for informed trading decisions and risk assessment.

Open-source Python toolkit for forecasting Nigerian Stock Exchange (NSE) prices using hybrid LSTM-sentiment analysis. Includes data pipelines, model training, and volatility-aware evaluation. Designed for researchers and investors.

Predicting stocks is complex due to volatile prices and news impacts. This project combines LSTM networks for analyzing historical trends with sentiment analysis of real-time news. By fusing these insights, the model delivers more accurate, explainable stock forecasts for smarter decisions.