Comparative Analysis of Regression Models for Tesla Closing-Price Prediction

Ze Ni ()
Additional contact information
Ze Ni: The Ohio State University, Department of Economics

A chapter in Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025), 2026, pp 288-293 from Springer

Abstract: 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.

Keywords: Technical indicators; Stock-price prediction; Regression analysis; Cross-validation (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-2-38476-585-0_34

Ordering information: This item can be ordered from
http://www.springer.com/9782384765850

DOI: 10.2991/978-2-38476-585-0_34

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().