Stock Market Variation Forecasting Using LSTM-RNN Model

Predicting stock prices is a complex yet vital endeavor in finance. This research introduces a machine-learning approach to stock market prediction, using various models and historical data. The impact of data quantity and indicator adoption on accuracy is investigated, with a real-world case study confirming the strategy’s effectiveness. The study examines factors affecting stock prices, including market trends and external events, emphasizing machine-learning’s potential in forecasting. It advocates for sophisticated models and diverse criteria to enhance accuracy. In today’s intricate financial landscape, precise asset value predictions remain elusive. This paper aligns with the intelligent computing trend, which addresses Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM). It aims to assess machine-learning algorithms’ precision in stock market prediction and the role of training epochs. The stock market’s influence on the economy highlights the significance of accurate price forecasts. This paper takes a model-independent approach, employing deep learning to predict stock prices for NSE-listed companies like Apple Inc., Google LLC, and Tesla, Inc., with performance evaluated using RMSE and MAPE metrics. This research contributes to stock market prediction with a comprehensive machine-learning strategy, emphasizing accuracy factors and advanced deep learning techniques. It makes us understand that it is important to stay up to date with the latest technology because it can help to maximize the profit and reduce the chances of risks as well.