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.

This is the the final project of the course: L330 Data Science: principles and practice at the University Of Cambridge. The task for this project is stock market prediction using a diverse set of variables. In particular, given a dataset representing days of trading in the NASDAQ Composite stock market, our aim is to predict the daily movement of the market up or down conditioned on the values of the features in the dataset over the previous N (trading) days.

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Predicting Nasdaq Movements using Daily News using Recurrent Neural Networks. Multiple models tested such as multi layer perceptron, char/word level RNNs. Used Pytorch to train and run the models.

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Empirical Analysis of Nasdaq Magnificent Seven Stocks Movement Prediction Integrating News Sentiment with P-sLSTM

This repository contains machine learning models for analyzing stock market movement. Using historical NASDAQ data, it applies XGBoost and Random Forest classifiers to detect patterns and classify short-term price direction. The project serves as a baseline for trend classification and experimentation in financial prediction.

A machine learning model predicting the daily movement of the NASDAQ Composite index. Using historical data and Random Forest Classifier, it classifies if the index will rise or fall the next day. Enhanced with trend and ratio features, backtested for precision, and includes interpretation of prediction accuracy for realistic insights.