Information Content of Key Performance Indicators and the Properties of Their Analyst Forecasts

Abstract

The documented decline in the information content of earnings numbers has paralleled the emergence of disclosures, mostly voluntary, of industry-specific key performance indicators (KPIs). We find that the incremental information content conveyed by KPI news is significant for many KPIs, yet it is diminished when details about the computation of the KPI are absent or when the computation of the KPI changes over time. Consistent with analysts responding to investor information demand, we find that analysts are more likely to produce forecasts for a KPI when that KPI has more information content and when earnings are less informative. We also analyze the properties of analysts’ KPI forecasts, and we find that KPI forecasts are more accurate than mechanical forecasts, and their accuracy exceeds that of earnings forecasts. Our study contributes to the literature on the information content of KPIs and increases our understanding of the factors that affect this content. We provide evidence pertinent to the debate on whether and how to regulate KPI disclosures. This study further contributes to research on the properties of analysts’ forecasts.