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. We find greater forecast accuracy for models using disaggregate relative to aggregate balance sheet information, with the most accurate disaggregation scheme considering property, plant, and equipment (PPE), intangible assets, other non-current operating assets, and current operating assets. Models using higher-order disaggregations of intangible assets and current operating assets yield no additional accuracy improvements. Cross-sectionally, we find that forecast improvements are greatest for firms characterized by a diverse asset base (i.e., using a wide variety of asset categories), intangible asset-intensive operations, medium to low performance, medium to low growth, and a larger size. When investigating market participants’ use of disaggregated balance sheet information, we find that analysts fail to incorporate the growth information embedded in balance sheet disaggregations when forecasting book value per share (BPS). Similarly, for investors, we find that growth predictions based on disaggregated balance sheet information are associated with year-ahead abnormal stock returns, suggesting an inefficient utilization of disaggregated balance sheet information. Overall, this study provides evidence on the informativeness of balance sheet disaggregations and specifically proposes the use of disaggregated models when forecasting growth in operating assets