Comparative Analysis of Regression Models for Tesla Closing-Price Prediction

DOI

10.2991/978-2-38476-585-0_34How to use a DOI?

Keywords

Technical indicators; Stock-price prediction; Regression analysis; Cross-validation

Abstract

This study addresses the challenge of short-term stock-price forecasting by comparing six regression techniques for predicting the same-day closing price of Tesla, Inc. (TSLA). A ten-year dataset (September 2014–September 2024) of daily open, high, low, close, and volume data was enriched with technical indicators—simple and exponential moving averages, relative strength index, and on-balance volume—and split chronologically into 80% training and 20% testing sets. Models evaluated include ordinary least squares, ridge regression (L2 regularization), lasso regression (L1 regularization), k-nearest neighbors, random forest, and gradient boosting. Hyperparameters were selected via nested five-fold, time-series cross-validation, and out-of-sample performance was measured by root mean squared error, mean absolute error, mean absolute percentage error, and coefficient of determination. Results indicate that ridge regression with a tuned penalty coefficient (α = 0.1) achieved the lowest test RMSE of $2.60, closely followed by ordinary least squares with RMSE of $2.53, MAE near $2.00, MAPE under 1%, and R² above 0.99. In contrast, k-nearest neighbors and ensemble methods exhibited significant overfitting. These findings demonstrate that carefully engineered technical features combined with regularized linear models yield robust forecasts for highly volatile equities.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ze Ni
PY  - 2026
DA  - 2026/06/18
TI  - Comparative Analysis of Regression Models for Tesla Closing-Price Prediction
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 288
EP  - 293
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_34
DO  - 10.2991/978-2-38476-585-0_34
ID  - Ni2026
ER  -