ForecastNet: A Predictive Framework for Stock Market Trends

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Mansi; Himanshu Sharma; Naveen Singh; Jayshree Surolia

ForecastNet: A Predictive Framework for Stock Market Trends

Authors: Mansi, Himanshu Sharma, Naveen Singh, Jayshree Surolia

Unique Paper ID: 176514
Volume: 11
Issue: 11
PageNo: 6107-6112

Keywords: Stock Prediction LSTM Machine Learning Sentiment Analysis Technical Indicators Financial Forecasting Time-Series Analysis Deep Learning.

Abstract:
This research presents a Stock Price Prediction System that utilizes the principles of machine learning and sentiment analysis in order to predict stock prices for the future. The prediction generated by the system has greater accuracy because it merges historical market data, technical indicators and real-time news sentiment perceived by the market using machine learning models such as LSTM and Random Forest. The proposed solution is cloud-based, scalable, and has a user-friendly platform for analysts and investors to operate. The system gets data, analyzes data and visualizes information, which has the potential to significantly ease decision making in volatile financial markets.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 6107-6112

ForecastNet: A Predictive Framework for Stock Market Trends

Available:https://ijirt.org/article?manuscript=176514

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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