Persistent Earnings, Cyclical Returns (III)
1. Higher earnings reliability is correlated with higher returns (r-squared = 0.24): Companies generating more reliable earnings provide higher returns over a long period of time;
2. Earnings reliability shows some persistence (slope = 0.09, r-squared = 0.08): Companies with consistent earnings tend to continue to provide consistent earnings;
3. Returns, however, do not show persistence; in fact, returns tend to be a reverse indicator! (slope = -0.24, r-squared = 0.18): Companies providing the highest investment returns in 1996-1999 showed among the lowest investment returns during 2000-2003;
4. Consequently, earnings reliability, by itself, is not a good indicator of future returns (r-squared = 0.04): If an investor had bought the companies with the highest 1996-1999 earnings reliability at the beginning of 2000, he would actually have been worse off during 2000-2003 than an investor buying companies with lower earnings reliability.
In brief: While earnings may persist, returns do not. Why not? Because there are cycles of investor optimism, when the market (Benjamin Graham's "Mr. Market" is a good analogy here) drives prices and valuation ratios (P/S, P/E, P/CF, etc.) up in anticiption of growth acceleration--only to be followed by investor pessimism, when prices plummet and companies "get shot behind the barn," as it were, for missing earnings by a few pennies or projecting slower earnings growth. This is what happened during 2001-2002, when the stock prices of fundamentally sound companies (like Home Depot and GE) with high earnings reliability fell sharply.
The message here is that investors overreact, both on the way up and on the way down, even though earnings over the longer run may be fairly steady and predictable. Stated another way, we can say that investors tend to make buy-sell decisions based on short time horizons, providing buying and selling opportunities for investors with a longer-term mindset.
Prescription: Buy companies with consistently rising earnings that have become undervalued by the market. Sell these when they become overvalued (which can typically take years). Repeat this exercise with a portfolio of stocks (or real estate properties).
Sounds easy enough, right?