Forecasting stock prices using a data mining method: Evidence from emerging market

Saqib Farid, Rubeena Tashfeen, Tahseen Mohsan and Arsal Burhan

International Journal of Finance & Economics, 2023, vol. 28, issue 2, 1911-1917

Abstract: Stock price forecasting is considered a challenging task because of the various characteristics of financial time series. In this study, we attempt to predict stock prices using data mining techniques in an emerging market. The study uses decision tree model, CRISP‐DM (Cross‐Industry Standard Process for data mining) for analysis by employing WEKA software. The study sample consists of ten firms of five different sectors listed on Pakistan Stock Exchange (PSX). The findings show accuracy ratios range between 50% and 60%. The findings imply that market participants can disclose higher returns by considering information embedded in previous stock prices. In addition, investors can take more prudent buy‐sell decisions in view of our findings.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/ijfe.2516

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:ijfiec:v:28:y:2023:i:2:p:1911-1917

Ordering information: This journal article can be ordered from
http://jws-edcv.wile ... PRINT_ISSN=1076-9307

Access Statistics for this article

International Journal of Finance & Economics is currently edited by Mark P. Taylor, Keith Cuthbertson and Michael P. Dooley

More articles in International Journal of Finance & Economics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().