FORECASTING MARKET MOVEMENTS USING MACHINE LEARNING ALGORITHMS

Abstract

Forecasting stock market movements is a complex and challenging task due to the dynamic, nonlinear, and volatile nature of financial markets. Accurate prediction of stock prices is essential for investors, traders, and financial analysts to make informed investment decisions while minimizing financial risk. This paper presents an intelligent stock market forecasting framework that employs Long Short-Term Memory (LSTM) networks to predict future stock closing prices using historical financial time-series data. The proposed system utilizes historical stock data obtained from Yahoo Finance, including opening price, closing price, highest price, lowest price, and trading volume. The collected data undergoes preprocessing, normalization, and sequence generation before being used to train the deep learning model. The LSTM-based architecture is designed to capture long-term temporal dependencies in stock price movements, enabling more accurate forecasting than conventional statistical and machine learning approaches. The framework is implemented using Python, with TensorFlow, Keras, Pandas, NumPy, and Scikit-learn for model development, data preprocessing, and performance evaluation. The predicted results are visualized through interactive graphs that compare actual and forecasted stock prices, providing users with meaningful insights into market trends. Model performance is evaluated using standard regression metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to assess prediction accuracy and reliability. Experimental results demonstrate that the proposed LSTM-based framework effectively captures stock price trends and generates reliable short-term forecasts while reducing prediction error compared with traditional forecasting techniques. Although stock market fluctuations are influenced by unpredictable external factors, the proposed system serves as an effective decision-support tool by providing data-driven insights into future market behavior. The study highlights the potential of deep learning for financial time-series forecasting and presents a scalable framework for intelligent stock market prediction and investment analysis.