The dynamics of market efficiency: testing the adaptive market hypothesis in South Africa

dc.contributor.authorSeetharam, Yudhvir
dc.date.accessioned2017-02-09T13:06:21Z
dc.date.available2017-02-09T13:06:21Z
dc.date.issued2016
dc.descriptionA thesis submitted to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of the Witwatersrand in fulfilment of the requirements for the degree of Doctor of Philosophy (Ph/D). Johannesburg, South Africa June 2016en_ZA
dc.description.abstractIn recent years, the debate on market efficiency has shifted to providing alternate forms of the hypothesis, some of which are testable and can be proven false. This thesis examines one such alternative, the Adaptive Market Hypothesis (AMH), with a focus on providing a framework for testing the dynamic (cyclical) notion of market efficiency using South African equity data (44 shares and six indices) over the period 1997 to 2014. By application of this framework, stylised facts emerged. First, the examination of market efficiency is dependent on the frequency of data. If one were to only use a single frequency of data, one might obtain conflicting conclusions. Second, by binning data into smaller sub-samples, one can obtain a pattern of whether the equity market is efficient or not. In other words, one might get a conclusion of, say, randomess, over the entire sample period of daily data, but there may be pockets of non-randomness with the daily data. Third, by running a variety of tests, one provides robustness to the results. This is a somewhat debateable issue as one could either run a variety of tests (each being an improvement over the other) or argue the theoretical merits of each test befoe selecting the more appropriate one. Fourth, analysis according to industries also adds to the result of efficiency, if markets have high concentration sectors (such as the JSE), one might be tempted to conclude that the entire JSE exhibits, say, randomness, where it could be driven by the resources sector as opposed to any other sector. Last, the use of neural networks as approximators is of benefit when examining data with less than ideal sample sizes. Examining five frequencies of data, 86% of the shares and indices exhibited a random walk under daily data, 78% under weekly data, 56% under monthly data, 22% under quarterly data and 24% under semi-annual data. The results over the entire sample period and non-overlapping sub-samples showed that this model's accuracy varied over time. Coupled with the results of the trading strategies, one can conclude that the nature of market efficiency in South Africa can be seen as time dependent, in line with the implication of the AMH.en_ZA
dc.description.librarianMT2017en_ZA
dc.format.extentOnline resource (xviii, 357 leaves)
dc.identifier.citationSeetharam, Yudhvir (2016) The dynamics of market efficiency: testing the adaptive market hypothesis in South Africa, University of the Witwatersrand, Johannesburg, <http://wiredspace.wits.ac.za/handle/10539/21982>
dc.identifier.urihttp://hdl.handle.net/10539/21982
dc.language.isoenen_ZA
dc.subject.lcshStocks--Prices--Mathematical models
dc.subject.lcshEfficient market theory--Mathematical models
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshJohannesburg Stock Exchange
dc.titleThe dynamics of market efficiency: testing the adaptive market hypothesis in South Africaen_ZA
dc.typeThesisen_ZA

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