JOURNAL ARTICLE

Predicting ETF prices using linear regression

Keqing Li

Year: 2023 Journal:   BCP Business & Management Vol: 36 Pages: 25-31

Abstract

Machine learning has allowed computers to analyze data and make future predictions based on those dates. One of the most common and easiest to implement machine learning algorithms used to do this is simple linear regression. Simple linear regression finds trends in a data set by graphing a line that shows the relationship between two variables. This paper will show how Simple linear regression can predict future ETF prices by finding linear trends in two particular exchange-traded funds: Invesco QQQ and vanguard VGT, predict their value six months later using their five-year closing price in yahoo finance and compare their respective predicted growth rate.

Keywords:
Simple linear regression Vanguard Linear regression Closing (real estate) Regression Econometrics Simple (philosophy) Data set Regression analysis Computer science Set (abstract data type) Proper linear model Polynomial regression Statistics Artificial intelligence Mathematics Economics Machine learning Finance Geography

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Topics

Stock Market Forecasting Methods
Social Sciences →  Decision Sciences →  Management Science and Operations Research
Financial Markets and Investment Strategies
Social Sciences →  Economics, Econometrics and Finance →  Finance
Energy Load and Power Forecasting
Physical Sciences →  Engineering →  Electrical and Electronic Engineering

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