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The Persistence of Stock Market Returns during the Presidential elections in Nigeria

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  • Yaya, OlaOluwa S
  • Adekoya, Oluwasegun B.
  • Adesiyan, Femi

Abstract

Following empirical evidences that political activities impact stock market performance, this present paper examines efficiency and volatility of Nigerian stock market during presidential elections. We use a 5-month event window approach to obtain the data for each election period. This implies that for each election period, we obtain the daily stock price index for the election month (4 weeks) and two months (8 weeks) before and after it. Our fractional integration technique reveals that the stock price index was persistent during most of the election years, with the exemptions of 2011 and 2019 election year, while 2015 election period recorded the highest volatility. However, accounting for structural breaks following the approach of Enders and Lee (2012a,b) that inculcates nonlinear smooth breaks in the Fourier function, the stock market seemed to be efficient only during the 1999, 2011 and 2019 presidential election periods. The 2011 and 2019 are periods when the elections produced candidates that ran for a two-term each. On the other hand, the highest stock market volatility is still maintained at the 2015 election which was also interestingly the year that the recent 2015/2016 recession in the country kick-started. Our findings have important policy implications for potential investors.

Suggested Citation

  • Yaya, OlaOluwa S & Adekoya, Oluwasegun B. & Adesiyan, Femi, 2020. "The Persistence of Stock Market Returns during the Presidential elections in Nigeria," MPRA Paper 99390, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:99390
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    References listed on IDEAS

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    More about this item

    Keywords

    Nigerian stock market; Market efficiency; Volatility; Fractional integration; Presidential election;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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