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Title: Accounting and environmental determinants of stock returns
Author: Collett, Nick
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 1994
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Significant prior work exists in both the macroeconomics field and in finance and accounting into determinants of stock returns, although very few studies have tried to link both types of information together. This research seeks to bridge that gap. Seven macroeconomic variables and 15 accounting identities are hypothesised to explain stock returns, using a stepwise multiple regression procedure. Three macrovariables, GDP, real interest rates, and an exchange rate variable, together with four accounting variables are found to explain 3 month returns in an estimation sample. The results are confirmed in a holdout sample, which has similar industry and size characteristics. When the same variables are forced into equations for longer time periods, it is found that the variables are still influential for up to 12 months. An alternative specification, dealing explicitly with systematic risk through beta, shows that similar macroeconomic data, and five accounting variables, explain 3 month abnormal returns. Again results are confirmed in the same holdout sample. A second hypothesis proposes that the explanatory power of the accounting data may be enhanced by conditioning the accounting variables upon the macroeconomic variables in turn. For example, gearing might be more influential in explaining stock returns when real interest rates are historically high than when they are low. A new method, within the OLS tradition is used to model conditionality, and this is tested on randomly generated data in a series of simulations. Results show that a number of conditional relationships improve the explanation of both raw returns and abnormal returns. Nominal contracting theories are therefore supported and investors are clearly shown to interpret value relevant accounting information according to the macroeconomic circumstances.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available