A 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 2016In 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.MT201
