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Micrtsoft_Stock_Price: An Efficient Framework For Microsoft Stock Price Prediction Using Computational Intelligence

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Last updated: 03 Jan 2025

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Abstract

Econometrics uses statistical methods to analyze relationships using data. While its name suggests a focus on economics, it's widely used in various social sciences and beyond.One of the challenges in predicting stock prices is data availability since obtaining data can often be quite challenging.Predicting stock prices is difficult because it involves analyzing data with various methods, but it's not always accurate due to many factors involved. These methods help understand trends but aren't foolproof for making investment decisions.In this paper, we have proposed an efficient framework for the prediction of Microsoft stock price using nine different machine learning algorithms (AdaBoost, kNN, Linear Regression, Gradient Boosting, Tree, Neural Network, SVM, Constant, Random Forest) on six different datasets.The best algorithm in the four datasets was adaboost, with the smallest percentage of errors, 0.004, and the best algorithm in the two datasets was linear regression.The best result algorithm in all datasets is AdaBoost.

DOI

10.21608/jocc.2024.339927

Keywords

Machine Learning, Stock Price Prediction, Econometrics, adaboost, Linear Regression

Authors

First Name

Maged

Last Name

Farouk

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

melsayed@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Nashwa

Last Name

Shaker

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

nragab@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Diaa

Last Name

AbdElminaam

MiddleName

s

Affiliation

Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt

Email

diaa.salama@miuegypt.edu.eg

City

-

Orcid

0000-0002-1544-9906

First Name

Omnia

Last Name

Elrashidy

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

oelrashidy@aiu.edu.eg

City

Alamein

Orcid

-

First Name

Belal

Last Name

Fathy

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Buisness, El Alamien International University, El Alamein, Egypt

Email

belal.khalel.2023@aiu.edu.eg

City

El Alamien

Orcid

-

First Name

Mohamed

Last Name

Khames

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Buisness, El Alamien International University, El Alamein, Egypt

Email

mohamed.saleh.2023@aiu.edu.eg

City

-

Orcid

-

First Name

Mohamed

Last Name

Mansour

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Buisness, El Alamien International University, El Alamein, Egypt

Email

mohamed.mahgoub.2023@aiu.edu.eg

City

-

Orcid

-

First Name

Mohamed

Last Name

Abdelrazeq

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Buisness, El Alamien International University, El Alamein, Egypt

Email

mohamed.fadl.2023@aiu.edu.eg

City

-

Orcid

-

First Name

Mohamed

Last Name

Ali

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Buisness, El Alamien International University, El Alamein, Egypt

Email

mohamed.gamal.2023@aiu.edu.eg

City

-

Orcid

-

First Name

Reda

Last Name

Elazab

MiddleName

-

Affiliation

Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt

Email

relazab@aiu.edu.eg

City

Alamein

Orcid

-

Volume

3

Article Issue

1

Related Issue

45956

Issue Date

2024-01-01

Receive Date

2024-01-08

Publish Date

2024-02-04

Page Start

88

Page End

103

Online ISSN

2636-3577

Article File

JOCC_Volume 3_Issue 1_Pages 88-103.pdf

PDF . 1.1MB

Link

https://jocc.journals.ekb.eg/article_339927.html

Detail API

https://jocc.journals.ekb.eg/service?article_code=339927

Order

7

Type

Original Article

Type Code

731

Publication Type

Journal

Publication Title

Journal of Computing and Communication

Publication Link

https://jocc.journals.ekb.eg/

MainTitle

Micrtsoft_Stock_Price: An Efficient Framework For Microsoft Stock Price Prediction Using Computational Intelligence

Details

Type

Article

Created At

24 Dec 2024