Financial ratios are used for various purposes, such as credit evaluation,
security analysis, and management performance analysis. Ratios can change over
time because of industrywide conditions and attempts by managers to move the
ratios toward long-term targets. The authors present an econometric model that
describes the dynamics of ratio adjustment, including the passive changes in
ratios resulting from exogenous factors and the active changes in ratios
resulting from management intervention. They use the model to measure the
relative importance of ratio changes associated with these two factors. The
authors find that the amount of ratio smoothing from active adjustment by
management is substantial and the speed of convergence toward the optimal target
varies with industry and firm size.
Financial ratios are used to measure a firm's financial and operating performance. Uses
include evaluating credit, valuing a firm, monitoring a firm's operations, and measuring
the performance of management. Ratios change over time because of the dynamic
interaction of industrywide and other exogenous forces and because of active management
of the financial and operating ratios by the firm's management. Normally, ratio analysis
is conducted in a static environment by comparing a firm's ratios with some benchmark,
typically an industry average or standard. Because, as previously mentioned, ratios
change over time, Wu and Ho use a model that captures the dynamic adjustment process.
The model assumes that a firm's financial ratios are related to its respective industry
averages. The dynamics of adjustment of a firm's ratios are determined by a rational
distributed-lag model of industry average ratios. The model requires the firm's ratios
to converge to some target value, not necessarily the industry mean. The model separates
the changes in ratios into passive industrywide effects and active adjustment by
management, and it allows for measurement of the speed of the adjustment process. The
model also can be estimated using regression techniques, and as a result, the target
values need not be specified. In addition, the model is well suited for forecasting
future values of the ratios.
The authors test the model using Compustat data on 105 firms in nine industries for a
20-year period. They examine six ratios that measure liquidity, long-term solvency,
financial leverage, inventory management, and asset profitability.
The results of the model testing indicate that the speed of convergence toward the target
values is highest for the current ratio and the ratio of net operating income to assets.
With respect to passive industrywide effects, the authors find that the ratios of
inventory turnover and net operating income to sales exhibit the strongest effects. They
determine that a substantial amount of financial ratio smoothing is the result of active
adjustment by management, with the speed of adjustment varying across firms of different
sizes and in different industries. Small firms tend to be more susceptible to external
effects than large firms but are able to restore their ratios to desired levels faster
than large firms.
Analysts are also interested in forecasting ratios for use in credit evaluation and
bankruptcy predictions. Wu and Ho find that their model has a higher degree of
predictive ability than a simpler adjustment model developed by Lev (
Accounting Research
smaller average forecasting error and standard deviation of forecasting error than Lev's
model. The authors conclude that their model is a more suitable framework than the
simpler model for describing the behavior of financial ratios.