Stock Price Predictions Using Machine Learning Models

DOI

10.2991/978-94-6463-010-7_30How to use a DOI?

Keywords

Machine Learning; Stock Price; Predict; Time-Series Data; Neural Network

Abstract

Stock predicting is one of the most common topics nowadays. For decades, the stock has been one of the most significant problems in people's life. Many people wish to obtain a higher opportunity to profit by pouring their fortune into the economic market. Consequently, the stock price prediction grows up to be a debated topic. To further investigate the effectiveness of different stock models, this research implements linear regression, decision tree, neural network and LSTM (Long short-term memory) on Netflix stock data. As MSE (mean-squared error) performs well in the regression problem, it is used in this research as a tool for evaluating these models’ performances and optimizations. For the specific pattern of the Netflix’s stock data (from 2002–2021) the author uses to train the models, the neural network performs the best and gets almost ten times better than the other three.

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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TY  - CONF
AU  - Zebin Guo
PY  - 2022
DA  - 2022/12/02
TI  - Stock Price Predictions Using Machine Learning Models
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 290
EP  - 300
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_30
DO  - 10.2991/978-94-6463-010-7_30
ID  - Guo2022
ER  -