Stock Market Analysis and Forecasting using Machine Learning
Abstract
Stock market forecasting is a challenging task owing to the highly dynamic and volatile nature of financial markets. This paper presents the design and implementation of a Machine Learning-based stock market analysis and forecasting system that leverages a Long Short-Term Memory (LSTM) neural network for time-series prediction. The system automates the end-to-end pipeline from historical data acquisition using the finance API to preprocessing, model training, multi-step forecasting, and interactive visualization via a React.js frontend. Multiple machine learning algorithms including Linear Regression, Support Vector Machines, Random Forest, and LSTM are evaluated using performance metrics such as Root
Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Experimental results on stocks including AAPL, MSFT, and TSLA demonstrate that the LSTM model achieves R² values of 0.90–0.96 and RMSE values of 10–25, confirming its effectiveness in capturing temporal dependencies in stock price sequences. The system provides a practical decision-support prototype for investors, traders, and financial analysts.
References
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