Stock Price Prediction Based on Multiple Linear Regression Model

Runqing Hu ()
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Runqing Hu: University of California, College of Engineering

A chapter in Proceedings of the 2023 International Conference on Finance, Trade and Business Management (FTBM 2023), 2023, pp 439-447 from Springer

Abstract: 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.

Keywords: stock price; machine learning; multiple linear regression; finance; statistics (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-298-9_48

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DOI: 10.2991/978-94-6463-298-9_48

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