Stock Price Prediction Based on Multiple Linear Regression Model
- DOI
- 10.2991/978-94-6463-298-9_48How to use a DOI?
- Keywords
- stock price; machine learning; multiple linear regression; finance; statistics
- Abstract
In the modern society, the rise and fall of stocks price have become the most discussed topic among people. It is difficult to make precise stock buying and selling decisions based on personal experience in current stock market. Statistics and programming can effectively solve this problem. In machine learning field, there are many models that can be utilized to predict stock price like Recurrent Neural Network (RNNs), LSTMS, and regression. This article explores the utilization of the multiple linear regression model in prediction the stock price and use the Alphabet company as the example. All the data is extracted from Yahoo Finance. Initially, processing all the data by python pandas, numpy, and statsmodels library. Then, visualize the prediction result by matplotlib. In the end, the article obtained relatively accurate prediction results that much higher than the original expectation. There is a small difference between the prediction price with the actual price. Users can also use this model to predict other parameters while making discussions.
- 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
TY - CONF AU - Runqing Hu PY - 2023 DA - 2023/11/30 TI - Stock Price Prediction Based on Multiple Linear Regression Model BT - Proceedings of the 2023 International Conference on Finance, Trade and Business Management (FTBM 2023) PB - Atlantis Press SP - 439 EP - 447 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-298-9_48 DO - 10.2991/978-94-6463-298-9_48 ID - Hu2023 ER -