Predicting Stock Prices in the Electric Vehicle and Technology Sectors Using Long Short-Term Memory Models

Abstract

Forecasting stock prices is a complex task because of the volatility and intricate temporal dependencies present in financial markets. Long Short-Term Memory (LSTM) networks, known for their proficiency in handling extended time-series data, offer an effective solution to these challenges. This paper delves into the applications in LSTM networks to forecast stock prices for various companies in the electric vehicle and related sectors, specifically examining data from Tesla, BYD, Panasonic, and NVIDIA. The study assesses the predictive accuracy and performance of these models using RMSE, MSE, MAE, and R² metrics. By comparing these metrics across different companies, the paper provides an in-depth analysis of LSTM networks' effectiveness in financial forecasting. The findings indicate that when LSTM networks are optimized through hyperparameter tuning and architectural adjustments, they can accurately identify patterns in stock price data, offering valuable insights for investors and analysts in the electric vehicle, technology, and energy industries.