Stock market trend prediction using supervised learning

Author First name, Last name, Institution

Asad Masood Khattak, Zayed University
Habib Ullah, Gomal University
Hassan Ali Khalid, Gomal University
Ammara Habib, Gomal University
Muhammad Zubair Asghar, Gomal University
Fazal Masud Kundi, Gomal University

Document Type

Conference Proceeding

Source of Publication

ACM International Conference Proceeding Series

Publication Date

12-4-2019

Abstract

© 2019 Association for Computing Machinery. The stock trend prediction has received considerable attention of researchers in recent times. It is an important application in machine learning domain. In this work, we propose a machine learning based stock trend prediction system with a focus on minimizing data sparseness in the acquired datasets. We perform outlier detection on the acquired dataset for dimensionality reduction and employ K-nearest neighbor classifier for predicting stock trend. Results obtained show the effectiveness of the proposed system, when compared with baseline studies.

DOI Link

10.1145/3368926.3369680

ISBN

9781450372459

Publisher

Association for Computing Machinery

First Page

85

Last Page

91

Disciplines

Computer Sciences

Keywords

Machine learning, Supervised learning, Trend prediction

Scopus ID

85077816106

Recommended Citation

Khattak, Asad Masood; Ullah, Habib; Khalid, Hassan Ali; Habib, Ammara; Asghar, Muhammad Zubair; and Kundi, Fazal Masud, "Stock market trend prediction using supervised learning" (2019). All Works. 3207.
https://zuscholars.zu.ac.ae/works/3207

Indexed in Scopus

yes

Open Access

no