STOCK MARKET PREDICTION USING MACHINE LEARNING

Abstract

Stock market prediction has always been a challeng- ing task due to its dynamic and non-linear nature. Traditional statistical models often struggle to capture the complex patterns inherent in financial time series data. In recent years, deep learning techniques, such as Long Short-Term Memory (LSTM) networks, have shown promising results in various time series forecasting tasks. This paper aims to explore the effectiveness of LSTM networks in predicting stock market trends. e design an LSTM-based model that takes historical stock market data as input and predicts future price movements. The model is trained using a large dataset of historical stock prices, along with relevant market indicators and technical factors. To evaluate the proposed model, we conduct experiments on real-world stock market datasets spanning multiple years. We compare the performance of our LSTM-based model with traditional statistical models and benchmark machine learning algorithms commonly used in stock market prediction. The LSTM network effectively captures long-term dependencies in stock market data, enabling it to learn intricate patterns and adapt to changing market conditions. Furthermore, we conduct sensitivity analyses to investigate the impact of various hyperparameters and input features on the model’s performance. The findings of this study have significant implications for investors, financial institutions, and market ana- lysts who rely on accurate stock market predictions. The LSTM- based approach presented in this paper offers a promising avenue for improving the accuracy of stock market forecasting, thereby aiding decision-making and enhancing investment strategies.