A STOCK PRICE PREDICTION MODEL USING SWARM INTELLIGENCE
Alt Text: A Stock Price Prediction Model Using Swarm Intelligence
Title: A Stock Price Prediction Model Using Swarm Intelligence
Caption: Enhancing stock price prediction accuracy using swarm intelligence and deep learning techniques.
Description: This paper presents the MS-SSA-LSTM model, which integrates sentiment analysis, swarm intelligence, and deep learning for stock price prediction. The study demonstrates that optimizing LSTM hyperparameters using the Sparrow Search Algorithm improves prediction accuracy by 10.74% compared to standard models.
Keywords: Stock Price Prediction, Swarm Intelligence, Machine Learning, LSTM, Sentiment Analysis
International Journal of Engineering and Techniques – Volume 10 Issue 3, June 2024
Dr. N. Sai Sindhuri1, N. Siva Nagamani2
1Associate Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
2Associate Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
Abstract
Accurate prediction of stock prices can reduce investment risks and enhance returns. This paper combines multi-source data affecting stock prices and applies sentiment analysis, swarm intelligence, and deep learning to build the MS-SSA-LSTM model. The study uses the Sparrow Search Algorithm (SSA) to optimize LSTM hyperparameters, integrating sentiment index and fundamental trading data for forecasting. Experimental results demonstrate that the MS-SSA-LSTM model outperforms traditional approaches, increasing prediction accuracy by 10.74% on average.
Keywords
Stock Price Prediction, Swarm Intelligence, Machine Learning, LSTM, Sentiment Analysis
How to Cite
Sindhuri, N.S., Nagamani, N.S., “A Stock Price Prediction Model Using Swarm Intelligence,” International Journal of Engineering and Techniques, Volume 10, Issue 3, June 2024. ISSN 2395-1303
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