Machine Learning-Based Stock Price Prediction System
Abstract
A system using LSTM neural networks and sentiment analysis of news articles to predict short-term stock price movements with confidence scores and automated trading signals.
Price
₹11,999
Problem
Retail investors lack access to sophisticated prediction tools used by institutional investors. Technical analysis alone is insufficient as stock prices are heavily influenced by news sentiment and market psychology.
Solution
The system collects historical stock price data (OHLCV) and real-time news articles from financial news portals. An LSTM neural network processes the time-series price data while an NLP pipeline (BERT-based) performs sentiment analysis on news headlines. The combined model outputs price movement predictions with confidence scores. Automated trading signals are generated based on predefined risk thresholds and sent to users via mobile app and email.
Patent Claims
A system for stock price prediction comprising: a data collection module, an LSTM neural network, an NLP sentiment analysis module, and a signal generation module.
The system of claim 1, wherein the LSTM network processes OHLCV (Open, High, Low, Close, Volume) time-series data.
The system of claim 1, wherein the NLP module uses BERT-based sentiment analysis on financial news headlines.
The system of claim 1, wherein the system generates automated trading signals based on configurable risk thresholds.
The system of claim 1, wherein prediction confidence scores are displayed alongside each prediction for user decision-making.