Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)

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Research on the Stock Price Forecasting of Netflix Based on Linear Regression, Decision Tree, and Gradient Boosting Models

Authors
Xinwen Xu1, *
1New College, University of Toronto, Toronto, M5S 3J6, Canada
*Corresponding author. Email: fdvsv1067299@mail.wcccd.edu
Corresponding Author
Xinwen Xu
Available Online 29 December 2022.
DOI
10.2991/978-94-6463-102-9_127How to use a DOI?
Keywords
Stock return forecast; Linear regression; Decision tree; Gradient boost; k-fold cross-validation
Abstract

Stock return forecasting has always been a popular research topic in the stock market. This paper adopts three models, including linear regression, decision tree, and gradient boosting approaches, to predict the eighth day's stock return of Netflix stock based on its last seven days' stock return, based on the price data of Netflix stock from 2002 to 2021. Prediction results and model performances are compared with the five-fold cross-validation and Python score method. The results indicates that the linear regression model is the best model for predicting Netflix-type stocks’ return on a long-term scale and has no sharp nor abnormal fluctuations. This research result enriches the existed stock return forecasting literature and provides a certain revelation for investors towards predicting stock return growth trends and stock investment values accurately.

Copyright
© 2023 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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Volume Title
Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
29 December 2022
ISBN
978-94-6463-102-9
ISSN
2589-4900
DOI
10.2991/978-94-6463-102-9_127How to use a DOI?
Copyright
© 2023 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.

Cite this article

risenwbib
TY  - CONF
AU  - Xinwen Xu
PY  - 2022
DA  - 2022/12/29
TI  - Research on the Stock Price Forecasting of Netflix Based on Linear Regression, Decision Tree, and Gradient Boosting Models
BT  - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
PB  - Atlantis Press
SP  - 1232
EP  - 1242
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-102-9_127
DO  - 10.2991/978-94-6463-102-9_127
ID  - Xu2022
ER  -