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

Author

Listed:
  • Aleesha Mohamudally-Boolaky
  • Teemulsingh Luchowa
  • Kesseven Padachi

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.

Suggested Citation

  • Aleesha Mohamudally-Boolaky & Teemulsingh Luchowa & Kesseven Padachi, 2019. "Applying the Support Vector Machine for Testing Pricing Inefficiency on the Stock Exchange of Mauritius," Applied Economics and Finance, Redfame publishing, vol. 6(5), pages 177-192, September.
    • Handle: RePEc:rfa:aefjnl:v:6:y:2019:i:5:p:177-192
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    References listed on IDEAS

    as
    1. Kim, Jae H. & Shamsuddin, Abul, 2008. "Are Asian stock markets efficient? Evidence from new multiple variance ratio tests," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 518-532, June.
    2. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Rafael Rosillo & Javier Giner & David De la Fuente, 2014. "Stock Market Simulation Using Support Vector Machines," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 488-500, September.
    3. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    More about this item

    Keywords

    support vector machine; arbitrage pricing theory;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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