LSTM NETWORK FOR HIGH-VOLATILITY STOCK PRICE PREDICTION: TESLA INC
TL;DR
The findings support adopting hybrid approaches that combine the point forecast accuracy of traditional econometric models with the directional predictive capabilities of deep learning architectures for financial forecasting in turbulent market conditions.
Abstract
This research investigates the efficacy of Long Short-Term Memory (LSTM) networks for predicting stock prices of high-volatility equities, with application to Tesla Inc. (TSLA). Addressing a gap in financial machine learning literature, we develop an advanced LSTM architecture trained on Tesla's daily closing prices from January 1, 2017, to November 20, 2024. Through meticulous preprocessing, strategic dropout regularization, and sophisticated sequence modeling, our model achieves a Root Mean Square Error (RMSE) of $12.17 and a Mean Absolute Error (MAE) of $8.51. For comparative purposes, we implement a walk‑forward ARIMA benchmark, which achieved an RMSE of $8.30 and an MAE of $5.76, indicating superior point forecast accuracy. However, the LSTM model demonstrated better directional accuracy (50.60% against 47.62%), suggesting complementary strengths across evaluation metrics. The Diebold‑Mariano test confirmed a statistically significant difference between the two models (DM = 4.58, p < 0.01). This study contributes to the understanding of deep learning applications in financial markets and establishes new benchmarks for volatile stock prediction. The findings support adopting hybrid approaches that combine the point forecast accuracy of traditional econometric models with the directional predictive capabilities of deep learning architectures for financial forecasting in turbulent market conditions.