The Impact of Complexity on Analysts' Effective Tax Rate Forecasts

Abstract

Information complexity, which increases with the amount of attentional capacity or mental processing required to use information, is directly related to the cost of using information. I employ a framework from the judgment/decision-making research to provide a link between information complexity and analysts' forecast accuracy consistent with an information processing cost explanation for analysts' failure to use all information in forming earnings forecasts.

This paper provides empirical evidence that analysts choose to use information that is less costly to incorporate into their forecasts. Using a sample of analysts' effective tax rate forecasts from Value Line and a measure of tax law complexity, I investigate the impact on analysts' accuracy of the variation in the costs of incorporating that information. As predicted, I find that the magnitude of analysts' forecast errors is positively related to the firm complexity. I also find that analysts incorporate low complexity information (which is less costly to process) in their forecasts, but omit high complexity information (which is more costly to process) from those forecasts.

JEL Classification: G29, M41, G14