Applying the Support Vector Machine for Testing Pricing Inefficiency on the Stock Exchange of Mauritius
Teemulsingh Luchowa and
Applied Economics and Finance, 2019, vol. 6, issue 5, 177-192
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)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
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 ().