REAL-TIME INTERNATIONAL STOCK PRICE PREDICTION SYSTEM USING LONG SHORT-TERM MEMORY
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Abstract
The integration of Long Short-Term Memory (LSTM) networks and real-time data processing is revolutionizing stock market analysis by addressing critical challenges such as price volatility, low prediction accuracy, and delayed decision-making. This study explores the impact of LSTM combined with real-time data feeds on enhancing prediction accuracy and investor confidence in stock trading. Using a mixed-methods approach, the research analyses historical stock data, real-time market trends, and simulated trading scenarios to evaluate the effectiveness of LSTM models. Results indicate a significant improvement in prediction accuracy, higher investor confidence, and a potential reduction in trading risks by up to 25% when LSTM is paired with real-time data streams. The findings underscore the importance of developing high-frequency data processing, robust model training, and seamless integration with trading platforms to fully realize the benefits of LSTM in stock market prediction. This study provides insights vital for traders, analysts, and developers aiming to improve decision-making, reduce financial risks, and innovate the future of algorithmic trading.
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