Applying the Support Vector Machine for Testing Pricing Inefficiency on the Stock Exchange of Mauritius

Aleesha Mohamudally-Boolaky, Teemulsingh Luchowa and Kesseven Padachi

Applied Economics and Finance, 2019, vol. 6, issue 5, 177-192

Abstract: A popular Machine Learning Technique called the Support Vector Machine (SVM) is adopted on the Stock Exchange of Mauritius (SEM) to determine if stock market returns are predictable based on information from past prices, allowing arbitrage opportunities for abnormal profit generation. The serial correlation test, used as benchmark, and the SVM technique show evidence that previous information on share prices as well as the indicators constructed are useful in predicting share price movements. The implications of the study are that investors have the prospect of adopting speculative strategies and profits from trading based on information and advanced techniques and models are possible.

Keywords: support vector machine; arbitrage pricing theory (search for similar items in EconPapers)
JEL-codes: C14 D53 G14 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://redfame.com/journal/index.php/aef/article/view/4495/4682 (application/pdf)
http://redfame.com/journal/index.php/aef/article/view/4495 (text/html)

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:rfa:aefjnl:v:6:y:2019:i:5:p:177-192

Access Statistics for this article

More articles in Applied Economics and Finance from Redfame publishing Contact information at EDIRC.
Bibliographic data for series maintained by Redfame publishing ().