Statistics and Analysis of Netflix Stock Price in the Post-Pandemic Era Based on Machine Learning Algorithms

Zimo Tang

2024

Abstract

This paper will focus on the analysis and prediction of Netflix's stock performance during and after the pandemic. In the era following the influenza pandemic, there has been a significant change in consumer entertainment consumption habits. As a leading player in the streaming industry, Netflix’s stock data is highly representative and serves as a critical point for analyzing trends in the streaming sector. This study will select various machine learning models, including deep learning algorithms and supervised learning algorithms, to analyze Netflix’s stock. The main objective is to observe the predictive capabilities of these models under abnormal conditions. The methodology involves several key steps: first, data preprocessing, including cleaning and visualization; second, modeling analysis and parameter tuning; and finally, comparing the predicted trend charts to assess the effectiveness of the models. The final conclusion will rank the models’ performance, with XGBoost performing the best, followed by Random Forest, and Long Short-Term Memory (LSTM) showing relatively lower performance. By examining various algorithms, it sheds new light on the application of advanced predictive models for financial forecasting, particularly during significant market disruptions.

Paper Citation

in Harvard Style

Tang Z. (2024). Statistics and Analysis of Netflix Stock Price in the Post-Pandemic Era Based on Machine Learning Algorithms. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 606-610. DOI: 10.5220/0013277500004568

in Bibtex Style

@conference{ecai24,
author={Zimo Tang},
title={Statistics and Analysis of Netflix Stock Price in the Post-Pandemic Era Based on Machine Learning Algorithms},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={606-610},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013277500004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Statistics and Analysis of Netflix Stock Price in the Post-Pandemic Era Based on Machine Learning Algorithms
SN - 978-989-758-726-9
AU - Tang Z.
PY - 2024
SP - 606
EP - 610
DO - 10.5220/0013277500004568
PB - SciTePress