Abstract
Purpose:
The purpose of this study is to analyze, in the context of the last economic crisis, the prediction capacity of the different risk measures and the relationship between risk and return.
Design/methodology/approach:
We selected three risk measures constructed using annual accounting data obtained from Spanish companies. A logistic regression was then developed to verify whether the companies’ predictions were eventually correct, considering those companies that were able to survive the crisis. A multiple linear regression was subsequently employed in order to review Bowman’s paradox, that is, in the risk-return relationship.
Findings:
The research results support the two hypotheses formulated:
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1) variability measures of risk have a greater predictive power than that of downside risk measures;
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2) the risk-return paradox is more likely to exist in the more uncertain environment of a pre-crisis period of time.
Originality/value:
Managers could employ the frameworks developed in this study as important diagnostic tools in order to attain advance warning of whether an organization may be close to failure. An analysis of this nature would then allow a firm to take appropriate action to arrest the process.