Journal of Accounting Research
Опубликовано на портале: 21-06-2006
Madhav V. Rajan
Journal of Accounting Research.
2000.
Vol. 38.
P. 247-254.
A distinctive feature of the paper is its use of both theoretical and empirical approaches
to understanding EVA. The authors first analyze a principal-agent model using certaine
parametric assumptions. They model EVA and accounting earnings as two distinct, noisy
perfomance measures of the same uderlying construct, with both measures providing
information to the principal about the agent's action choices. They subsequently
derive a theoretical expression for the percentage value-added contributed by using
EVA as a measure for evaluating managerial perfomance. A key finding is that this
expression can be restated as a function of the observed correlations of each of
the metrics with stock price. In the second part of the paper, the authors estimate
this value for their sample of firms using time-series data, and then correlate the
estimates cross-sectionally with the decision to adopt EVA by these firms. In support
of the theoretical results, the authors find a positive association, after controlling
for other factors such as size, leverage, and growth oppportunities.


Опубликовано на портале: 21-06-2006
Gerald T. Garvey, Todd T. Milbourn
Journal of Accounting Research.
2000.
Vol. 38.
P. 209-245.
Dissatisfaction with traditional accounting-based performance measures has spawned
a number of alternatives, of which Economic Value Added (EVA) is currently the most
prominent. How can we tell which performance measures best capture managerial contributions
to value? There is currently a heated debate among practitioners about whether the
new performance measures have a higher correlation with stock values and their returns
than do traditional accounting earnings. Academic researchers have relied instead
on the variance of performance measures to gauge their relative accuracy. To formally
address the above debate, we use a relatively standard principal-agent model in which
contracts can be based on any two accounting-based performance measures plus the
stock price. Rather than model detailed differences between EVA and traditional measures
such as earnings, we focus on the problem that while the variability of each measure
is observable, its exact information (signal) content is not. The model provides
a formal method for researchers to ascertain the relative value of alternative accounting-based
measures based on two distinct uses of the stock price. First, as is well known,
prices provide a noisy measure of managerial value-added. In our model, stock prices
also can reveal the signal content of alternative accounting-based performance measures.
We then show how to combine stock prices, earnings, and EVA to produce an optimally
weighted compensation scheme. We find that the simple correlation between EVA or
earnings and stock returns is a reasonably reliable guide to its value as an incentive
contracting tool. That is, a firm could reasonably gauge the merits of adding a measure
like EVA by examining its correlation with the firm's stock price. This is not because
stock returns are themselves an ideal performance measure, rather it is because correlation
places appropriate weights on both the signal and noise components of alternative
measures. We then calibrate the theoretical improvement in incentive contracts from
optimally using EVA in addition to accounting earnings. Specifically, we empirically
estimate the "value-added" of EVA by firm and industry. These estimates are positive
and significant in predicting which firms have actually adopted EVA as an internal
performance measure.


Опубликовано на портале: 21-11-2003
Mark R. Manfredo, Raymond M. Leuthold, Scott H. Irwin
Journal of Accounting Research.
2001.
Vol. 33.
No. 3.
Economists and others need estimates of future cash price volatility to use in risk
management evaluation and education programs. This paper evaluates the performance
of alternative volatility forecasts for fed cattle, feeder cattle, and corn cash
price returns. Forecasts include time series (e.g. GARCH), implied volatility from
options on future contracts, and composite specifications. The overriding finding
from this research, consistent with the existing volatility forecasting literature
is that no single method of volatility forecasting provides superior accuracy across
alternative data sets and horizons. However, evidence is provided suggesting that
risk managers and extension educators use composite methods when both time series
and implied volatilities are available.

