Taking the perspective of an equity investor seeking to maximize risk-adjusted returns through financial statement analysis, we apply a machine learning algorithm to estimate Nissim and Penman's (2001) structural decomposition framework of profitability. Our approach explicitly takes account of the nonlinearities that precluded Nissim and Penman from estimating their framework. We first forecast profitability and then estimate intrinsic values using different subsets of Nissim and Penman's framework and different fundamental analysis design choices; we find that trading on these estimates generates substantial risk-adjusted returns. Choices that improve performance include increasingly granular ratio disaggregation and long-horizon forecasts of operating performance. Perhaps surprisingly, we find only weak evidence of benefits from a fundamental analysis that incorporates historical financial statement information beyond the current-period information or focuses only on core items. While taking account of non-linearities improves model performance for all firms, the effect is strongest for small, loss-making, technology, and financially distressed firms.

This paper examines how agents’ response to macroeconomic uncertainty affects firms’ revenues, expenses, and profitability in a global sample of firms spanning 1997 to 2018. Consistent with consumers reducing purchases and managers cutting costs, I find that increases in macroeconomic uncertainty lead to both lower revenues and lower expenses. The net short-term effect on profitability is positive as the reduction in expenses exceeds the fall in revenues. This favorable profitability effect is attenuated for firms whose institutional environment restrains cost-cutting, holds for both the cash and accrual components of earnings, and is robust to instrumental variable estimation employing exogenous variation in macroeconomic uncertainty arising from natural disasters, political unrest, revolutions, and terrorist attacks.

No abstract

No abstract