FORECASTING EARNINGS

Time Series Stimulus for the development of the literature on time series: –Researchers trying to use models to value securities. They wanted forecasts of earnings to use as surrogates for future cash flows –The demand for “better” earnings expectation models To study the relation between stock prices and accounting earnings –To explain managers’ choices of accounting procedures

The Relevance of Time Series Forecasts of Earnings 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

Use of Forecasts of Earnings in Valuation Models Valuation models requires estimates of expected future cash flows One of the most popular surrogates is a forecast of future accounting earnings 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

Obtaining “Better” Earnings Expectations Models The better the approximation of the market’s expectation of earnings: –The more accurately earnings are separated into unexpected increases and decreases –The more likely the hypothesized increases or decreases in stock price are observed. In an efficient market, the stock price will impound information that is available to many investors at low cost –The past time series of company’s annual and quarterly earnings is readily available –Hence, the market’s expectation of future earnings is likely to impound this information

Explaining Management Choice of Accounting Techniques Gordon (1964): –Corporate managers maximize his/her 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 (his/her utility) depends on the corporation’s stock price Gordon, Horwtiz & Meyers (1966) –Test Gordon’s proposition: managers try to reduce the variance of earnings changes  to “smooth” reported earnings

Alternative Time Series Models The lack of theory –Researchers have used various statistical approaches to explaining the time series behavior of earnings –There have been few attempts to develop a theory that explains why reported earnings follow one particular process or another (Lev, 1983)

Alternative Time Series Models (Cont’d) Simple types of time series models –Deterministic models: forecast future earnings to be deterministic and not to depend on observed earnings –Random walk models: generate expectations of future earnings that depend solely on the most recent earnings observation Deterministic and random walk models are extremes –Deterministic  the most recent observation of earnings has no effect on expected future earnings –Random walk  the most recent observation determines expected future earnings

The Application of Time Series Modeling to Eastman Kodak Random walk model is better than linear deterministic model Is the time series stationary? –The year-to-year variation in earnings has changed Accounting changes Inflation Structural change favors random walk –Since random walk requires least amount of data, it is less susceptible to structural changes

Implications for Stock Prices and the Smoothing Hypothesis Random walk models and deterministic models have very different implications for the relation between earnings and stock prices and for the smoothing hypothesis

Implications for Stock Prices and the Smoothing Hypothesis (Cont’d) The relation between earnings and stock prices –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 smoothing hypothesis –Often this hypothesis assumes earnings are generated by the linear deterministic model and that managers “smooth” Observing that earnings are generated by a random walk is consistent with the hypothesis –But, the underlying cash flows and the resulting reported time series are not following a deterministic time trend The reported earnings series can be a random walk

The Evidence on the Time Series of Annual Earnings and Its Implications Early studies –Little (1962), Little & Rayner (1966), Lintner & Glauber (1967): changes in earnings are random Balls & Watts (1968) –The results of all of Ball and Watts’s test are consistent with earnings being generated by a random walk process

The Evidence on the Time Series of Annual Earnings and Its Implications (Cont’d) Further evidence on annual earnings –Trend Ball, Lev & Watts (1976): produce some evidence that a trend existed at least in the period –Rate of return 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 Implications (Cont’d) Further evidence on annual earnings (Cont’d) –Rate of return (Cont’d) Watts (1970): –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 Implications (Cont’d) Implications and evidence on those implications –Beaver, Lambert & Morse (1980): 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 Watts (1975, 1978), Griffin (1977), and Foster (1977): quarterly 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 Brown & Rozeff (1978): supports the hypothesis that Value Line consistently makes a better prediction than time series models Fried & Givoly (1982): find that one-year-ahead analyst forecasts have a greater association with abnormal stock returns over the next year than do one-year-ahead time series models of earnings forecasts