It is common to consider financial markets as made up of stocks which can be grouped or clustered according to similar characteristics, for example some stocks offer low returns with respect to improvement in their value but pay high dividends, while others offer small dividends but increase in value relatively quickly. The understanding of grouping characteristics is important for understanding the overall dynamics of the market in which such clustering takes place. The appearance of grouping or clustering behaviour may be due to random effects (noise) since there are bound to be overlapping properties when the number of stocks is high. Hence it is necessary to investigate methods for measuring possible noise contributions to clustering. Furthermore, grouping may change over time so that it is necessary to identify the time horizons for which clusters are stable. Is it possible to identify sectors, groups of stocks which display similar behaviour with respect to returns, and states, time periods for which the market behaves similarly, in SA financial data, by purely quantitative methods under the constraint that noise and temporal stability are understood?
Thuthuka grant: TTK2004072200035 (Funded from 2004)
TEAM in 2004
- DW = Diane Wilcox (applicant);
- TG = Tim Gebbie (co-investigator);
- 1 Msc student [Project :Fourier Method for the Measurement of Univariate and Multivariate Volatility in the presence of High Frequency Data (graduated 2006)]
- 1 Internal project proposal reviewer (UCT Dept Mathematics & Applied Mathematics)
- 10 External reviewers (appointed by the NRF)
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