Market data for financial studies typically derives from either historical transactions or contemporaneous surveys of sentiment and perceptions. The research communities analyzing data from these opposing categories of source data see themselves as distinct, with advantages not shared by the other. This research investigates these latter claims in an information theoretic context, and suggests where methods and controls can be improved. The current research develops a Fisher Information metric for Likert scales, and explores the effect of particular survey design decisions or results on the information content. A Fisher Information metric outperforms earlier metrics by converging reliably to values that are intuitive in the sense that they suggest that information captured from subjects is fairly stable. The results of the analysis suggest that varying bias and response dispersion inherent in specific surveys may require increases of sample size by several orders of magnitude to compensate for information loss and in order to derive valid conclusions at a given significance and power of tests. A prioritization of quality of design, and the factors relevant to survey design are presented in the conclusions, and illustrative examples provide insight and guidance to the assessment of information content in a survey.
Financial transactions Information theory Survey Economics Mathematical models