Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / The evidence put forward by Zhang (2010) indicates that algorithmic trading can
potentially generate the momentum effect evident in empirical market research.
In addition, upon analysis of the literature, it is apparent that algorithmic traders
possess a comparative informational advantage relative to regular traders.
Finally, the theoretical model proposed by Wang (1993), indicates that the
informational differences between traders fundamentally influences the nature
of asset prices, even generating serial return correlations. Thus, applied to the
study, the theory holds that algorithmic trading would have a significant effect
on security return dynamics, possibly even engendering the momentum effect.
This paper tests such implications by proposing a theory to explain the
momentum effect based on the hypothesis that algorithmic traders possess
Innovative Information about a firm’s future performance. From this perspective,
Innovative Information can be defined as the information derived from the ability
to accumulate, differentiate, estimate, analyze and utilize colossal quantities of
data by means of adept techniques, sophisticated platforms, capabilities and
processing power. Accordingly, an algorithmic trader’s access to various
complex computational techniques, infrastructure and processing power,
together with the constraints to human information processing, allow them to
make judgments that are superior to the judgments of other traders.
This particular aspect of algorithmic trading remains, to the best of my knowledge,
unexplored as an avenue or mechanism, through which algorithmic trading could
possibly affect the momentum effect and thus market efficiency. Interestingly, by
incorporating this information variable into a simplified representative agent
model, we are able to produce return patterns consistent with the momentum
effect in its entirety.
The general thrust of our results, therefore, is that algorithmic trading can
hypothetically generate the return anomaly known as the momentum effect. Our
results give credence to the assumption that algorithmic trading is having a
detrimental effect on stock market efficiency.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/13929 |
| Date | 24 February 2014 |
| Creators | Gamzo, Rafael Alon |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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