Comparative Analysis of Traditional and LSTM Models for Netflix Stock Movement Classification

DOI

10.2991/978-2-38476-585-0_20How to use a DOI?

Keywords

Stock Movement Classification; Machine Learning; Logistic Regression; LSTM

Abstract

With the continuous development of machine learning methods, various types of models are increasingly used in stock prediction. However, the systematic comparison between traditional machine learning methods and deep learning methods is still relatively insufficient for classification prediction tasks such as upward and downward direction. In this study, a series of technical indicators were constructed as input features with the upward and downward direction of Netflix stock as the classification prediction target, and Logistic Regression, Decision Tree, Random Forest, eXtreme Gradient Boosting, Voting Classifier, and Long Short-Term Memory Network models were trained and evaluated, respectively. The results show that the logistic regression model and Extreme Gradient Boosting (XGBoost) perform well overall in all evaluation metrics, indicating their strong generalization ability in this classification task. And although Long Short Term Memory (LSTM) is good at dealing with time series, its performance on this dataset did not exceed that of traditional methods. After the study, it was found that traditional classification models have strong stability and practicality in certain situations. This paper provides empirical support to explore the applicability of traditional models in financial time series classification.

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  - He Bai
PY  - 2026
DA  - 2026/06/18
TI  - Comparative Analysis of Traditional and LSTM Models for Netflix Stock Movement Classification
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 167
EP  - 173
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_20
DO  - 10.2991/978-2-38476-585-0_20
ID  - Bai2026
ER  -