Chapter 6 Analysing Companies to Predict Future Earnings.doc

# Chapter 6 Analysing Companies to Predict Future...

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Chapter 6: Financial Statement Analysis and Investment Appraisal: Theory and Applications 6.1 Chapter 6 Analysing Companies to Predict Future Earnings The objective of this chapter is to examine the prediction of future earnings for companies. Time series properties of accounting earnings, analysts’ prediction of earnings and market prediction of earnings are considered. The ROA earnings prediction method provides a different perspective on forecasting earnings. Finally the chapter suggests some empirical rules for the practical estimation of future earnings. Introduction In examining equity securities, analysts have two choices to focus their attention. They can concentrate on the value of the security or concentrate upon the change in value of the security. From the late sixties, the popular belief, supported by research evidence, is that changes in share prices follow a random walk. It is possible, and we shall discuss the alternatives latter, that the random walk findings are the result of a failure to correctly specify the relationships. Similarly, research on earnings also suggests these follow a random walk. On this point we have reasonably strong evidence that such findings result from a failure to present an appropriate model for earnings generation. We will show that earnings are a function of a firms operating capabilities and the reigning economic conditions. First, we shall review the random walk theory. The random walk problem was one that puzzled mathematicians around the turn of the century. The problem posed was where is the best place to look for a drunkard left in a field the previous night. Because the path taken by the drunk is random, the solution was that the best place to look was where he/she was left. Applied to both shares and earnings, the theory would pose that our best estimate of tomorrow’s values are today’s values. Expressed mathematically the theory applied to earnings at time t (X t ) is: X X t t t 1

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Chapter 6: Financial Statement Analysis and Investment Appraisal: Theory and Applications 6.2 That is, this period’s earnings is expected to be last period’s earnings plus a random variation. If we substitute each prior period’s earnings (i.e. for X t 1 then X t 2 and so on) we get the following: X X X X X t t t t t t t t t t t t t n   2 1 3 1 2 1 1 This model suggests that this period’s earnings are the sum of all prior random changes in earnings. The literature suggests that earnings patterns are a little more systematic in that they follow a random walk with a drift factor. That is, the earnings change randomly but they drift upwards. The conclusion is that we cannot predict changes in earnings and that earnings are the sum of a series of random changes. Future earnings cannot be predicted from past changes. However, in a purely random walk case, the sum of the random changes should equal zero. Thus, the long-run value of earnings should approach zero.
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