Forecasting Financial Frontiers: A Comparative Analysis of LSTM-Based Stock Price Prediction for Meta and Tesla

The anticipation of stock market prices remains a central focus in research due to its financial implications and the intricate dynamics involved. In recent times, the surge in interest has been observed for employing deep learning techniques, specifically Long Short-Term Memory, renowned for it's proficiency in deciphering complex temporal patterns within financial data. This study delves into the application of LSTM networks to predict stock market prices, commencing with meticulous data collection from various sources. Employing data preprocessing techniques to address missing values, outliers, and normalize the data, the dataset is then partitioned into training and testing sets, constructing sequences of historical stock prices for LSTM input. The core of the research lies in crafting an LSTM-based neural network architecture tailored to discern temporal dependencies and patterns within stock price data. The model undergoes training on historical stock price data to glean insights from past trends and generalize to new data. Despite the acknowledged challenges in achieving complete accuracy due to diverse market influences, the study's findings underscore the potential of LSTM networks in stock market prediction. Rigorous evaluation of the model's performance, utilizing a testing dataset and diverse evaluation metrics, emphasizes its predictive capabilities. This research contributes to the evolving body of financial forecasting through deep learning techniques, indicating that LSTM-based models offer valuable insights for informed decision making by investors and traders. However, it is imperative to recognize the inherent complexity and uncertainty in stock market prediction, positioning this study as a foundational step for continued exploration and refinement of predictive models within the financial domain.