An LSTM-Based Approach for Stock Price Prediction in the Metaverse

Abstract

This study proposes a novel approach for predicting stock prices in the metaverse using a Long Short Term Memory (LSTM) model with an attention mechanism. The model is trained on a large dataset of historical stock price data from the company called META. The proposed method preprocesses the original data by normalizing, splitting it into training and test sets, and transforming it into tensors. The performance of the model is evaluated using various metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and optimization is carried out by increasing the number of model layers, adjusting the learning rate and batch size, adding regularization, and using different activation functions. The results demonstrated that the proposed approach provides an accurate and effective tool for predicting stock prices in the emerging industry of the metaverse. Accurate predictions of stock prices can significantly impact investment strategies and enable investors to make informed decisions and optimize returns. The study highlights the importance of considering new emerging industries for financial analysis and investment strategies. This research provides a new perspective on financial analysis and investment strategies in emerging industries and demonstrates the feasibility of using an LSTM model with an attention mechanism to predict stock prices in the metaverse. The proposed approach has the potential to revolutionize financial analysis and investment strategies in the emerging industry of the metaverse, providing a valuable tool for investors and financial analysts to make informed decisions and optimize returns.