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The dynamic nature of stock market makes its forecasting a difficult task. This paper presents an innovative approach to improve the stock market prediction and uses advanced Al model along with sentiment analysis. The proposed system employs Python, LSTM (Long Short-Term Memory) deep learning models, and FinBERT to analyse historical stock data, market and quarterly financial reports. Along with this candle technique a technical indicator is used to recognise growing, falling and neutral trends in stock market. The system works as a trading bot which automates the purchase and sell decisions for its end users based on predictive insights. The main motive of the system is to reduce losses and increase profits for investors and reduces the human intervention. In contrast to existing system, this approach combines technical and sentiment analysis, delivering a broader view of stock market. With potential applications in global stock markets and real-time trading updates, this project represents a n step forward in financial technology. Eventually, it empowers the end users with data driven decisions, establishing rational investment and refined financial results.
"Bridging Technical and Sentiment Analysis: Advanced AI Models for Stock Market Forecasting.", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b718-b721, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504190.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator