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follow an upward sloping path with no future variation. You might say that the forecast doesn't even acknowledge the existence of a cycle.2

One explanation could be that equity analysts have incentives not to predict the cycle, particularly the down part. Academic research has shown that earnings forecasts have a general positive bias that is sometimes attributed to the incentives facing equity analysts at investment banks.3 For example, pessimistic earnings forecasts may damage relations between an analyst's employer—an investment bank—and a particular company. In addition, companies that are the target of negative commentary might cut off an analyst's lines of communication. From this evidence, we could conclude that analysts as a group are unable or unwilling to predict the cycle for these

2 Similar results were found for companies with three- and five-year cycles.

3The following articles discuss this hypothesis: M.R. Clayman, and R.A. Schwartz, ''Falling in Love Again—Analysts' Estimates and Reality," Financial Analysts Journal (September/October 1994), pp. 66-68; J. Francis, and D, Philbrick, "Analysts' Decisions as Products of a Multi-Task Environment," Journal of Accounting Research, vol. 31, no. 2 (autumn 1993), pp. 216-230; K. Schipper, "Commentary on Analysts' Forecasts," Accounting Horizons (December 1991), pp. 105-121; B. Trueman, "On the Incentives for Security Analysts to Revise Their Earnings Forecasts," Contemporary Accounting Research, vol. 7, no. 1, pp. 203-222.

companies. If the market followed analyst forecasts, that could account for the high volatility of cyclical companies' share prices. Market Appears Smarter Than Consensus Forecast

We know that cycles are hard to predict, particularly their inflection points. So it is not surprising that the market doesn't get it exactly right. However, we would be disappointed if the market entirely missed the cycle as the consensus earnings analysis seems to suggest. We went back to the question, how should the market behave? Should it be able to predict the cycle and therefore exhibit little share price volatility? That would probably be asking too much. At any point, the company or industry could break out of its cycle and move to one that is higher or lower, as illustrated on Exhibit 16.4.

Suppose you are valuing a company that is apparently at a peak in its earnings cycle. Based on past cycles, you would expect the industry to turn down soon. However, there are signs that the industry is about to break out of the old cycle. A reasonable valuation approach might be to build two scenarios and weight their values. You could assume with a 50 percent probability that the cycle will follow the past, and that the industry will turn down in the next year or so. The second scenario, also with 50 percent probability, would be that the industry breaks out of the cycle and follows a new long-term trend based on current improved performance. The value of the company would

Exhibit 16.4 When the Cycle Changes

Exhibit 16.5 The Market Is Smarter

then be the weighted average of these two values. We found evidence that this is in fact the way the market behaves. We valued the four-year cyclical companies three ways:

1. With perfect foresight about the upcoming cycle.

2. With zero foresight, assuming that current performance represents a point on a new long-term trend (essentially the consensus earnings forecast).

3. Fifty percent of perfect foresight and 50 percent of zero foresight.

Exhibit 16.5 summarizes the results. As shown, the market doesn't follow either the perfect foresight or the zero foresight path; it follows a middle path, much closer to the 50/50 path. So the market has neither perfect foresight, not zero foresight. One could argue that this 50/50 valuation is the right place for the market to be.

Approach to Valuing Cyclical Companies

No one can precisely predict the cycle for an industry, and any single forecast of performance must be wrong. Managers and investors can benefit from following explicitly the probabilistic approach to valuing cyclical companies as outlined earlier, just as we saw in the last chapter the benefit of the probabilistic approach for valuing Internet companies. The probabilistic approach avoids the traps of the single forecast and allows the exploration of a wider range of outcomes and their implications.

Here is a two-scenario approach for valuing cyclical companies (of course, you could always have more than two scenarios):

1. Construct and value the ''normal cycle" scenario using information about past cycles. Pay particular attention to the long-term trend line of operating profits, cash flow, and ROIC because it will have a major impact on the valuation. Make sure the continuing value is based on a "normalized" level of profits (i.e., a point on the company's long-term cash flow trend line).

2. Construct and value a new trend-line scenario based on the recent performance of the company. Once again, focus most on the long-term trend line, because it will have the largest impact on value. Don't worry too much about modeling future cyclicality (although future cyclicality will be important for financial solvency).

3. Develop the economic rationale for each of the two scenarios, considering factors such as demand growth, companies entering or exiting the industry, and technology changes that will affect the supply and demand balance.

4. Assign probabilities to the scenarios and calculate a weighted value of the scenarios. Use the economic rationale and its likelihood to estimate the weights assigned to each scenario.

This approach provides an estimate of the value as well as scenarios that put boundaries on the valuation. Managers can use these boundaries to think about ways to modify their strategy and how to respond to signals about which scenario is likely to come true.

Is there anything managers can do to reduce or take advantage of the cyclicality of their industry? In our experience, managers often miss opportunities and even cause greater cyclicality. For example, cyclical companies often commit to major capital spending projects just when prices are high and the cycle is hitting its peak, presumably on the assumption that prices will remain high. Conversely, cyclical companies retrench when prices are low. Sometimes companies develop forecasts similar to the equity analysts, upward sloping regardless of where the company is in the cycle. Managers, who have detailed information about their markets, should be able to do a better job than the financial market in figuring out the cycle and take appropriate actions.

One further consequence of this behavior is that cyclical companies often send the wrong signals to the market. Expanding when prices are high tells the financial market that the future looks great (often just before

Exhibit 16.6 Relative Returns from Capital Expenditure Timing

Exhibit 16.6 Relative Returns from Capital Expenditure Timing

the cycle turns down). Signalling pessimism just before an upturn also confuses the market. Perhaps it should be no surprise that the stock market has difficult valuing cyclical companies.

How could managers exploit their superior knowledge? The most obvious action would be better timing of capital spending. Companies could also pursue financial strategies, such as issuing shares at the peak of the cycle or repurchasing shares at the cycle's trough. The most aggressive managers could take this one step further by adopting a trading approach, making acquisitions at the bottom of the cycle and selling assets at the top. Exhibit 16.6 shows the results of a simulation of optimal cycle timing. The typical company's returns could more than double.

Can companies really behave this way? It's actually very difficult for a company to take the contrarian view. The chief executive officer has to convince the board and the company's bankers to expand when the industry outlook is gloomy and competitors are retrenching. Or the CEO has to hold back while competitors build at the top of the cycle. Often, the companies in the industry exacerbate the cycle. Breaking out of the cycle may be possible, but it is a rare CEO who can do it.

Summary

After first glance, the share prices of cyclical companies appear too volatile to be consistent with the DCF valuation approach. We showed in this chapter, however, that the share price volatility can be explained by the uncertainty over the direction of the industry cycle. A systematic DCF approach, using scenarios and probabilities, can be used by managers and investors to value and analyze cyclical companies. Unfortunately, managers of cyclical companies are rarely willing to use the insights from this approach to break the cycle and create value for their shareholders.

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