This column was originally published on RealMoney
on Feb. 13 at 10 a.m. EST. It's being republished as a bonus for TheStreet.com readers. For more information about subscribing to RealMoney,
Why do Wall Street analysts spend so much time forecasting earnings growth? At first blush, this may seem like a lock for the stupidest question of 2007. After all, the fundamental value of any stock is, in theory, the discounted stream of future dividends.
If we take "dividends" in their complete sense, which includes all forms of returning earnings to shareholders, it would seem too obvious that if anyone could forecast earnings correctly, they should be able to at least forecast the relative if not the absolute performance of stocks.
This widely held belief is not demonstrable on a macro scale. If we compare the total return on the
S&P 500 and the total corporate profits measured by the Commerce Department (motto: Please don't tell any of my childhood friends I work here), we discover two things.
The first is, stock prices do not lead profits; in statistical terms, their contemporaneous relationship is greater than their relationship at a one-quarter lag, which is greater than the relationship at a two-quarter lag, and so on.
Second, the huge stock-price bubble of the late 1990s was not supported at all by profit growth, and conversely, the ongoing and historic burst of profit growth since the fourth quarter of 2001 has not triggered a concomitant Son of Bubble.
Let's now take a step back from the earnings forecast game -- whose greatest enduring success may have been convincing investors how much they love index funds and ETFs this Valentine's season -- and focus instead on how well the 1,500 members of the S&P 1500 Supercomposite index cover their cost of capital.
In all cases,
estimates on each firm's cost of equity, cost of debt and the resulting weighted average cost of capital will be used. These estimates are just that; the cost of equity, for example, involves an estimate of the return implied in an equity index's price given estimates of growth rates, earnings, dividends and payout ratios and a beta, or relative volatility estimate, between each firm and the index. The cost-of-debt estimate includes estimates for the default probabilities for each credit rating and spreads to reference debt indices.
A return on invested capital is calculated by comparing the net operating profit after taxes to total capital invested. The difference between this return and the weighted average cost of capital is the economic value-added spread (EVA). It simply measures how well a firm converts its employed capital into profits for its shareholders.
That's the windup; here's the pitch. The notion that firms with high EVA either have high forward-looking P/E ratios or have just completed a period of high total returns, while intellectually appealing, is not demonstrable. In fact, the firms P/E estimates of 50 or higher for 2007 (rectangle) have negative EVA with two exceptions:
Blue Coat Systems
If forward-looking P/E's are not linked to EVA, what about one-year total return, a backward-looking measure? If this is true, we should see a strong positive correlation in the chart below. Instead, we see a random mess.
What if we aggregate firm data into the 10 economic Global Industry Classification Standard sectors defined by Standard & Poor's and weight the EVA by their representation in the S&P Supercomposite? Three of the 10 sectors -- Utilities, Financials and Telecommunications Services -- have negative index-weighted EVA. Energy and Consumer Staples have the largest EVA.
Now let's address the two questions raised earlier: Does EVA, this time on a sector basis, correspond to either estimated P/Es or to recent returns? The answer for P/Es is no. The Energy sector has the lowest forward-looking P/Es combined with the highest EVA. The overall relationship, highlighted with the trend line, is near-random.
In the case of total returns, the relationship is actually negative, as highlighted by the trend line. The highest trailing total return belongs to Telecommunications Services, the sector with the lowest EVA.
The overall effect may be shocking to most, but expected to those who have studied the relationship between risk acceptance, return and profitability in the stock market. It is one of those things that appear so logical we assume them to be true. Assumptions do not count; analysis does.
And what it says holds true for both technical and fundamental analysis: You cannot take information already in the market and add value to it by further transformation. The fundamental data are known. The technical data at least have the advantage of a more humble approach -- What is the market telling me? -- but past patterns are not predictive.
So, why do Wall Street analysts spend so much time forecasting earnings? Because they get paid well to do so.
Please note that due to factors including low market capitalization and/or insufficient public float, we consider Standard Register and Blue Coat Systems to be small-cap stocks. You should be aware that such stocks are subject to more risk than stocks of larger companies, including greater volatility, lower liquidity and less publicly available information, and that postings such as this one can have an effect on their stock prices.