An empirical analysis of controlled risk and investment performance using risk measures: a study of risk controlled environment

Haidar, Haidar (2014) An empirical analysis of controlled risk and investment performance using risk measures: a study of risk controlled environment. Doctoral thesis (PhD), University of Sussex.

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In this thesis, I study the performance behaviour of hedge funds and mutual
funds. I study a basket of various risk statistics that are widely used to measure
the fluctuation of asset prices. Those risk statistics are used to rank the performance
of the assets. The linear dependence relation of these risk measures in ranking assets
is investigated and the set of risk measures is reduced by excluding risk measures
that produce linearly dependent ranking vectors to other risk measures. The ranks
within each of the selected remaining risk statistics are standardised and then linearly
transformed into a new set of linearly independent factors where principal component
analysis is carried out as a variable reduction technique to remove the noise
while preserve the main variation of the original data. The transformed factors are
sorted in descending order according to their contribution to the variation of the original
data. The factor loadings of the first two principal components PC1 and PC2 are
reviewed and interpreted as styles (PC1 as consistency and PC2 as aggression). The
universe of a set of hedge funds is classified according to these styles as BL=(low consistency, low aggression), BR=(high consistency, low aggression), TL=(low consistency,
high aggression) and TR=(high consistency, high aggression). I examine
the performance behaviour of the four different classified classes whereby this classification
method provides an indication on returns and management styles of hedge
funds. A three-factor prediction model for asset returns is introduced by regressing
12 weeks’ forward rank of return on the historical ranks of risk statistics. The first
few principal components, which explain the main variation of information captured
by risk statistics, are used in the prediction model. The robustness of the model is
tested by applying the model to the following 12-week period using the set of independent
factors. An investment strategy is constructed based on the prediction
model using the set of independent factors. I discover high evidence of predictability
and I test for out-of-sample forecasting performance. I then examine the use of subsets
of risk statistics from the basket rather than using the set of all risk statistics. I
further study the use of the so-called σ2/μ risk measure in predicting the market “turning
point” of performance of a portfolio of hedge funds. Risk measure quantity σ2/μ
replaces the traditional variance σ2 in the Black-Scholes option valuation formula
when it is evaluated for hedge funds.

Item Type: Thesis (Doctoral)
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
Subjects: H Social Sciences > HB Economic theory. Demography > HB0522 Income. Factor shares > HB0615 Entrepreneurship. Risk and uncertainty. Property
H Social Sciences > HG Finance > HG4501 Investment, capital formation, speculation
Q Science > QA Mathematics > QA0297 Numerical analysis
Q Science > QA Mathematics > QA0299 Analysis. Including analytical methods connected with physical problems
Depositing User: Library Cataloguing
Date Deposited: 04 Apr 2014 15:18
Last Modified: 18 Sep 2015 13:43

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