The Informativeness of Balance Sheet Disaggregations: Evidence from Forecasting Operating Assets

We investigate the usefulness of balance sheet disaggregations in the context of forecasting operating asset growth and its implications for revenue growth. We show that models using disaggregated balance sheet information have greater accuracy than models that use only aggregate information, with the most accurate disaggregation scheme separately considering property, plant, and equipment, intangible assets, other noncurrent operating assets, and current operating assets. When investigating market participants’ use of disaggregated balance sheet information, we find that differences in growth predictions from a disaggregated model and an aggregate model are associated with year-ahead abnormal returns, suggesting that investors do not fully utilize disaggregated balance sheet information. We corroborate this result by showing that returns are concentrated around the earnings announcement, are more pronounced for stocks with low transient institutional ownership, and are consistent with subsequent improvements in actual financial performance. Finally, we find that these returns are driven by both the asset growth predictions of a disaggregated model and the ability of such a model to incorporate the unique revenue-generating ability of individual asset components. Importantly, we find that our results are incremental to the information embedded in income statement disaggregations, even when forecasting income statement items such as profitability. Overall, our study provides evidence on the informativeness of balance sheet disaggregations and proposes the use of disaggregated models when forecasting growth in operating assets and its implications for future revenues. This paper was accepted by Eric So, accounting. Supplemental Material: The online appendix and data files are available at https://doi.org10.1287/mnsc.2023.01413 .