Browsing by Author "Guney, Yilmaz"

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    Herding in frontier markets: Evidence from African stock exchanges
    (Journal of International Financial Markets, Institutions & Money, 2017) Komba, Gabriel Vitus; Guney, Yilmaz; Kallinterakis, Vasileios
    We investigate herding in eight African frontier stock markets between January 2002 and July 2015, given the limited evidence on herding in frontier markets. Herding appears significant throughout the 2002-2015 period for all markets, with smaller stocks found to enhance its magnitude. Herding entails no clear asymmetries conditional on market performance; conversely, it appears notably asymmetric when conditioned on market volatility, as it is significant (or stronger) mainly during low volatility days, without this pattern, however, surviving when accounting for the 2007-2009 crisis. The US and South African markets motivate herding on a small number of occasions only, while the return dynamics of a regional economic initiative’s member markets are found to induce herding in each other very rarely, thus demonstrating that investors’ behaviour in markets with low integration in the international financial system is not significantly affected by non-domestic factors.
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    Testing for the weak-form market efficiency of the Dar es Salaam Stock Exchange
    (Elsevier, 2016) Komba, Gabriel Vitus; Guney, Yilmaz
    This paper investigates into the weak-form efficiency of the Dar es Salaam Stock Exchange (DSE), a frontier market, in Tanzania. The study covers the period from January 2007 to December 2014. To establish the consistency and robustness of the obtained conclusions, we employ different tests (i.e., Augmented Dickey-Fuller test, Variance-ratio test, and Ranks and Sign test) to examine the hypothesis that the returns based on the price and return indices follow a random walk process. The results provide convincing evidence that returns series based on price indices indeed follow a random walk. However, when the same tests are performed for the returns based on the return indices, the findings reveal that these series are not weak-form efficient, suggesting that investors might be able to predict future returns based on the current and past data.