Stock Market Prediction Using Machine Learning

Abdulhamit Subasi; Faria Amir; Kholoud Bagedo; Asmaa Shams; Akila Sarirete

doi:10.1016/j.procs.2021.10.071

Stock Market Prediction Using Machine Learning

Abdulhamit Subasi
, Faria Amir
, Kholoud Bagedo
, Asmaa Shams
, Akila Sarirete

College of Emergency Preparedness, Homeland Security & Cybersecurity

University at Albany

Effat University

Research output: Contribution to journal › Conference article › peer-review

47 Scopus citations

Abstract

Due to the complex nature of stock market prediction, it has been a trending area of interest. This paper presents a comparison of the prediction by inputting different classifiers. Furthermore, the results of the comparison are done on an accuracy basis. Each machine learning algorithm is tested against the National Association of Securities Dealers Automated Quotations System (NASDAQ), New York Stock Exchange (NYSE), Nikkei, and Financial Times Stock Exchange (FTSE). Furthermore, several machine learning algorithms are compared with a normal and a leaked data set.

Original language	English
Pages (from-to)	173-179
Number of pages	7
Journal	Procedia Computer Science
Volume	194
DOIs	https://doi.org/10.1016/j.procs.2021.10.071
State	Published - 2021
Event	18th International Learning and Technology Conference, L and T 2021 - Virtual, Online, Saudi Arabia Duration: Jan 28 2021 → …

Keywords

FTSE
Machine learning
NASDAQ
NYSE
Nikkei
Stock market

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10.1016/j.procs.2021.10.071

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title = "Stock Market Prediction Using Machine Learning",

abstract = "Due to the complex nature of stock market prediction, it has been a trending area of interest. This paper presents a comparison of the prediction by inputting different classifiers. Furthermore, the results of the comparison are done on an accuracy basis. Each machine learning algorithm is tested against the National Association of Securities Dealers Automated Quotations System (NASDAQ), New York Stock Exchange (NYSE), Nikkei, and Financial Times Stock Exchange (FTSE). Furthermore, several machine learning algorithms are compared with a normal and a leaked data set.",

keywords = "FTSE, Machine learning, NASDAQ, NYSE, Nikkei, Stock market",

author = "Abdulhamit Subasi and Faria Amir and Kholoud Bagedo and Asmaa Shams and Akila Sarirete",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors. Published by Elsevier B.V.; 18th International Learning and Technology Conference, L and T 2021 ; Conference date: 28-01-2021",

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doi = "10.1016/j.procs.2021.10.071",

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T1 - Stock Market Prediction Using Machine Learning

AU - Subasi, Abdulhamit

AU - Amir, Faria

AU - Bagedo, Kholoud

AU - Shams, Asmaa

AU - Sarirete, Akila

PY - 2021

Y1 - 2021

N2 - Due to the complex nature of stock market prediction, it has been a trending area of interest. This paper presents a comparison of the prediction by inputting different classifiers. Furthermore, the results of the comparison are done on an accuracy basis. Each machine learning algorithm is tested against the National Association of Securities Dealers Automated Quotations System (NASDAQ), New York Stock Exchange (NYSE), Nikkei, and Financial Times Stock Exchange (FTSE). Furthermore, several machine learning algorithms are compared with a normal and a leaked data set.

AB - Due to the complex nature of stock market prediction, it has been a trending area of interest. This paper presents a comparison of the prediction by inputting different classifiers. Furthermore, the results of the comparison are done on an accuracy basis. Each machine learning algorithm is tested against the National Association of Securities Dealers Automated Quotations System (NASDAQ), New York Stock Exchange (NYSE), Nikkei, and Financial Times Stock Exchange (FTSE). Furthermore, several machine learning algorithms are compared with a normal and a leaked data set.

KW - FTSE

KW - Machine learning

KW - NASDAQ

KW - NYSE

KW - Nikkei

KW - Stock market

UR - https://www.scopus.com/pages/publications/85121799440

U2 - 10.1016/j.procs.2021.10.071

DO - 10.1016/j.procs.2021.10.071

M3 - Conference article

SN - 1877-0509

VL - 194

SP - 173

EP - 179

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 18th International Learning and Technology Conference, L and T 2021

Y2 - 28 January 2021

ER -

Stock Market Prediction Using Machine Learning

Abstract

Keywords

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