Stock Price Prediction using Neural Networks
Team
- E/20/093, Edirisooriya D.M.B, email
- E/20/049, Chanuka B.D.K, email
- E/20/244, Malshan P.G.P, email
- E/20/271, Nirmani K.G.H, email
Table of Contents
Introduction
In this project, we apply the knowledge acquired from the CO 542 course to predict stock market prices. By leveraging advanced computational techniques and data-driven methodologies, we aim to develop a predictive model that can analyze historical stock data and forecast future price trends.
Problem
Predicting stock market prices manually is a challenging task, it requires analyzing large amounts of data, also it depends on News. Manual forecasting methods are often time-consuming and lead to inaccuracies, which can result in financial losses. By automating the forecasting process using advanced techniques, such as Artificial Neural Networks (ANNs), it is possible to make more accurate and timely predictions. This would enable investors to enter the market at lower prices and maximize profits by making data-driven decisions in real time.
Proposed Solution
Our project aims to enhance stock price prediction by integrating sentiment analysis of financial news with historical stock data using Artificial Neural Networks (ANNs). The model will analyze past stock prices and financial news sentiment to improve prediction accuracy.
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