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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM60419
Volume:
10 |
4 |
April 2026
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
An intelligent stock market forecasting using LSTM platform is designed to assist investors, analysts, and financial enthusiasts in making informed trading decisions using advanced deep learning techniques. Unlike traditional forecasting approaches that depend on statistical models, this system utilizes Long Short-Term Memory (LSTM) networks to capture temporal patterns in historical stock price data, enabling accurate predictions. The platform analyses key financial indicators such as historical prices, trading volume, and market trends to generate future price forecasts. By learning from sequential data, the LSTM model effectively handles market volatility and long-term dependencies, providing meaningful insights into stock behaviour. In addition to numerical data, the system incorporates Natural Language Processing (NLP) techniques to enhance prediction accuracy by analyzing financial news, social media, and market reports. Text data is processed using tokenization, stop-word removal, and sentiment analysis to classify information as positive, negative, or neutral, which is integrated with time-series data. The platform features an intuitive interface that allows users to visualize stock trends, predicted values, and performance metrics. Users are provided with a personalized dashboard to monitor selected stocks and analyse historical performance. Secure authentication mechanisms protect user data through encryption and session management. To ensure reliability, the system continuously updates its model with new market data, improving prediction accuracy over time. By combining deep learning and NLP-based sentiment analysis, the platform simplifies financial data and supports smarter investment decisions.
KEYWORDS
Publication Date
4/16/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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