The effects of economic variables in the UK stock market

This thesis examines the links between economic time-series innovations and statisticalrisk factors in the UK stock market using principal components analysis (PCA) and thegeneral-to-specific (Gets) approach to econometric modelling.A multi-factor risk structure for the UK stock market is assumed, and it is foundthat the use of economic 'news' (innovations), PCA, the Gets approach, and differentstock grouping criteria helps to explain the relationships between stock returns andeconomic variables.The Kalman Filter appears to be more appropriate than first-differencing orARIMA modelling as a technique for estimating innovations when applying the Getsapproach. Different combinations of economic variables appear to underpin the riskstructure of stock returns for different sub-samples. Indications of a possible influence offirm 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 specificexplanatory economic variables across different sub-samples is the general economiccontext of investment. The influence of firm size on stock returns seems in particular tobe highly sensitive to the wider economic context. There is an apparent instability in theeconomic underpinnings of the risk structure of stock returns (as measured by principalcomponents) that might also be a result of changing economic conditions.