Download FORECASTING EARNINGS

FORECASTING
EARNINGS
TIME SERIES
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Stimulus for development of the literature on
time series:
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Researchers trying to use models to value
securities
The demand for “better” earnings expectation
models
To explain managers’ choises of accounting
procedures
THE RELEVANCE OF TIME SERIES
FORECAST OF EARNINGS
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Infer the process generating the numbers by
looking only at the numbers’ sequence
Investigate the time series of past earnings of
a firm to try to determine what it tells us about
the firm’s future earnings
THE RELEVANCE OF TIME SERIES
FORECAST OF EARNINGS
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Use of forecasts of earnings in valuation
models
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Valuation models requires estimates of expected
future cash flows
One of the most popular surrogates is a forecast
of future accounting earnings
THE RELEVANCE OF TIME SERIES
FORECAST OF EARNINGS
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Use of forecasts of earnings in valuation
models
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One way to predict accounting earnings is to
estimate a process that describes the time series
behavior of past earnings and use that process to
forecast future earnings
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Example: constant process
THE RELEVANCE OF TIME SERIES
FORECAST OF EARNINGS
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Obtaining “better” earnings expectations
models
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The better the approximation of the market’s
expectation of earnings, the more accurately
earnings are separated into unexpected increases
and decreases and the more likely the
hypothesized increases or decreases in stock
price are observed
THE RELEVANCE OF TIME SERIES
FORECAST OF EARNINGS
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Explaining management choice of accounting
techniques
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Gordon (1964):
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Corporate managers maximize their utility
Corporate stock prices are a function of the level, the
rate of growth, and the variance of accounting earnings
changes
Corporate manager’s compensation (their utility)
depends on the corporation’s stock price
THE RELEVANCE OF TIME SERIES
FORECAST OF EARNINGS

Explaining management choice of accounting
techniques
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Gordon, Horwtiz & Meyers (1966)
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Test Gordon’s proposition: managers try to reduce the
variance of earnings changes – to “smooth” reported
earnings
ALTERNATIVE TIME SERIES
MODELS
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Simple types of time series models
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Deterministic models: forecast future earnings to
be deterministic and not depend on obeserved
earnings
Random walk models: generate expectations of
future earnings that depend solely on the most
recent earnings observation
THE APPLICATION OF TIME SERIES
MODELLING TO EASTMAN KODAK
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Random walk model is better than linear
model
Structural change favors random walk
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Since random walk requires least amount of data,
it is less susceptible to structural changes
IMPLICATIONS FOR STOCK PRICES AND THE
SMOOTHING HYPOTHESIS
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The relation between earnings and stock prices
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For a given level of unexpected earnings, the stock price
change is much greater if earnings follow a random walk
than if they follow a deterministic process
The smooting hypothesis
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Managers can be smoothing earnings and the time series
process of reported earnings can be a random walk
However, the presoothed series can not be a random walk
THE EVIDENCE ON THE TIME SERIES OF
ANNUAL EARNINGS AND ITS IMPLICATION
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Early studies
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Little (1962), Little & Rayner (1966), Lintner & Glauber
(1967): changes in earnings are random
Balls & Watts (1968)
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Test whether the sequential arrangements of signs of earnings
changes is random: signs of changes in earnings for the
sample as a whole are random
Earnings changes for the sample as a whole are independent
over time is not rejected
Other tests also suggest annual earnings for firms in general
can be characterized as a random walk
THE EVIDENCE ON THE TIME SERIES OF
ANNUAL EARNINGS AND ITS IMPLICATION
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Future evidence on annual earnings
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Trend
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Ball, Lev, & Watts (1976): produce some evidence that a
trend existed at least in the 1958-1967 period
Rate of return
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Beaver (1970), Lookabill (1976): provide evidence that
the rates of return on assets and equity do not follow
random process
THE EVIDENCE ON THE TIME SERIES OF
ANNUAL EARNINGS AND ITS IMPLICATION
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Future evidence on annual earnings
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Rate of return
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Watts (1970)
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Number of firms’ estimated process that differed from
random walk is larger than would be expected by chance
Random walk models predict as well as the estimated
models
Watts & Leftwich (1977), Albrecht, Lookabill & McKeown
(1977): individual firms’ earnings can be described as a
random walk
THE EVIDENCE ON THE TIME SERIES OF
ANNUAL EARNINGS AND ITS IMPLICATION
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Implications and evidence on those implications
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The evidence of random walk process contradicts the
implications of the usual joint smoothing hypothesis
But this evidence does not refute the proposition that
managers smooth earnings
Beaver, Lambert & Morse (1980):
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Annual earnings are the sum of quarterly earnings
Annual earnings will “appear” to be generated by random walk
THE EVIDENCE ON THE TIME SERIES OF
QUARTERLY EARNINGS
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Watts (1975,1978), Griffin (1977), and Foster
(1977): quareterly earnings are composed of an
adjacent quarter-to-quarter component and a
seasonal component
If one wants to predict annual earnings, the best
way to do this is to predict the next four quarterly
earnings using a quarterly forecasting model and
then sum the four quarters
THE PREDICTIVE ABILITY OF
FINANCIAL ANALYSTS
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Brown & Rozeff (1978): supports the
hypothesis that Value Line consistently
makes better prediction than time series
models
Fried & Givoly (1982): find that one-yearahead analyst forecast have a greater
association with abnormal stock return over
the next year than do one-year-ahead time
series models of earnings forecasts