Prediction of Tesla Stock Prices Using Recurrent Neural Networks (RNN) within Financial Technology (FinTech) Framework
TL;DR
This research presents a predictive model for Tesla stock prices using a Recurrent Neural Network (RNN) as part of an AI-based FinTech framework and demonstrates that the RNN effectively captures temporal dependencies in stock price time-series data.
Abstract
The integration of artificial intelligence (AI) into Financial Technology (FinTech) has transformed the landscape of financial data analysis and prediction. This research presents a predictive model for Tesla stock prices using a Recurrent Neural Network (RNN) as part of an AI-based FinTech framework. Historical Tesla stock data obtained from the Nasdaq market were normalized and structured using a 60-day time window to train the RNN model. The results show that the proposed model achieved a prediction accuracy of 93.45 percent, with a Mean Absolute Error (MAE) of 43.14, Mean Squared Error (MSE) of 3148.46, and Root Mean Squared Error (RMSE) of 56.11. The convergence of training and validation losses confirmed the model’s stability, while residual analysis indicated unbiased predictions centered around zero. These findings demonstrate that the RNN effectively captures temporal dependencies in stock price time-series data. Although the Long Short-Term Memory (LSTM) model achieved a slightly higher accuracy of 94.82 percent, the RNN remains advantageous due to its lower computational cost and faster training process. Overall, the RNN model offers a reliable and efficient approach for real-time FinTech applications such as automated trading systems, portfolio optimization, and investment risk analysis.