Stock Price Prediction Model

Stock Price Prediction Model

Amit Kumar Sachan1, Abhishek Kumar Tiwari2, Ayush Verma3, Brijesh Kumar4, Piyush Pandey5

*1 Assistant Professor, Computer Science and Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh, India

*2,3,4,5 Student, Computer Science and Engineering (AI&ML), Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh, India

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Abstract - This paper presents a hybrid stock price prediction model that combines Long Short-Term Memory (LSTM) networks with traditional statistical models like ARIMA, as well as real-time sentiment analysis, to improve forecasting accuracy and adaptability in volatile market conditions. Our model integrates historical stock data with external factors such as news articles and social media sentiment, providing a holistic approach to price prediction. The proposed system is evaluated using performance metrics including MAE, RMSE, and R², and shows superior results compared to standalone models. Furthermore, we provide practical implementation details using Python, TensorFlow, and real-time APIs to demonstrate the feasibility and scalability of our approach.

Key Words: ARIMA, Deep Learning, LSTM, Sentiment Analysis, Stock Price Prediction. The keywords are separated by commas and end with a period.