Authors : Chandu Vaidya; Gulrukh Nazneen; Nidhi Singh; Kapil Katariya; Aditya Ramtekkar; Diptanshu Nasare; Diksha Lalmore

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/2jf8hhtz

Scribd : https://tinyurl.com/jh8eedk4

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY059

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This groundbreaking research introduces an innovative stock market prediction methodology that integrates financial modeling, machine learning, and real-time data analysis. Anchored in a deep understanding of stock market dynamics, including economic statistics, company performance, and market sentiment, our approach employs natural language processing (NLP) and predictive modeling to analyze live data for accurate stock price predictions. The method involves collecting and preprocessing a dynamic dataset enriched with financial indicators and historical stock prices. Utilizing Long Short-Term Memory (LSTM) algorithms, our model exhibits an impressive 96% accuracy in forecasting stock movements, showcasing adaptability to diverse market scenarios and responsiveness to economic factors and sentiment shifts. The incorporation of live data proves pivotal in providing timely insights for informed decision-making, establishing our model as a valuable tool for navigating the complexities of the modern financial landscape.

Keywords : Stock Market, Machine Learning, Natural Language Processing, Prediction.

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