This thesis examines the links between economic time-series innovations and statistical
risk factors in the UK stock market using principal components analysis (PCA) and the
general-to-specific (Gets) approach to econometric modelling.
A multi-factor risk structure for the UK stock market is assumed, and it is found
that the use of economic 'news' (innovations), PCA, the Gets approach, and different
stock grouping criteria helps to explain the relationships between stock returns and
The Kalman Filter appears to be more appropriate than first-differencing or
ARIMA modelling as a technique for estimating innovations when applying the Gets
approach. Different combinations of economic variables appear to underpin the risk
structure of stock returns for different sub-samples. Indications of a possible influence of
firm size are found in principal components when different stock sorting criteria are used,
but more definite conclusions require simultaneous sorting by market value and beta.
Overall it appears that the major factor affecting the identification of specific
explanatory economic variables across different sub-samples is the general economic
context of investment. The influence of firm size on stock returns seems in particular to
be highly sensitive to the wider economic context. There is an apparent instability in the
economic underpinnings of the risk structure of stock returns (as measured by principal
components) that might also be a result of changing economic conditions.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.