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It is no coincidence that every market correction in history has created its experts (who called that modification right) and those gurus have almost always found a way to discredit themselves ahead of the next one. What is a bubble? The lazy definition is that any time you see a sizable market correction, it’s the result of a bubble bursting, but that is neither a good description, nor is it true. If you ask me, a bubble displays market disconnect from basics, where prices steeply rise, with no help from the fundamentals. If cash moves increase, growth rates surge, risk free rates drop or macroeconomic risk subsides, stocks should go up, and sometimes steeply, and there is absolutely no bubble.
At the other extreme, if stock prices go up as cash flows decrease, development rates are more negative and risk free rates and equity risk increase, you have a bubble. The benefits of having the ability to identify a bubble, if you are in in its midst rather than after it bursts, is that you might be able to protect yourself from its consequences.
But any kind of mechanisms that identify bubbles? And if they exist, how well do they work? The hottest metric for detecting bubbles is the purchase price earnings (PE) ratio, with variants thereof that declare to boost its predictive power. Thus, while the conventional PE proportion is approximated by dividing the current price (or index level) by profits in the last year or twelve months, you could consider at least three modifications.
The first is to clean up earnings getting rid of what you view as amazing or non-operating what to produce a better measure of operating income. In 2002, in the aftermath of accounting scandals, S&P began computing core profits for all of us companies which can differ from reported cash flow significantly. The second is to average income over a longer period (say five to a decade) to remove the year-to-year volatility in cash flow. The 3rd is to change the wages from prior intervals for inflation to get a inflation-consistent or real PE ratio. In fact, Robert Shiller has a period series of PE ratios for US stocks extending back to 1871, that uses normalized, inflation-adjusted cash flow.
Normalized PE used average earnings over last a decade & My CAPE uses my inflation modified normalized earnings. First, today are predictive of lower stock earnings in the future the negative correlation beliefs show that higher PE ratios. Second, that correlation is weak with one-year forward returns (observe that none of the t statistics are significant), become with two-year returns and strongest with three-year returns stronger. Defenders of the PE or one its variants will undoubtedly argue you do not generate income on correlations which the use of PE is within detecting when stocks are over or under price.
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The predictive power improves for PE ratios with this test, since returns in the years following high PE ratios are regularly lower than returns following low PE ratios. Normalizing the wages does help, but more in detecting when stocks are cheap than when they are expensive. Finally, the inflation modification does nothing to improve predictive returns.
Note, though, that test is biased by the actual fact that the quartiles were created using data from the time which the test is run. One of the biggest perils of using the known level of PE ratios as an indicator of stock market pricing, as we’ve in the last section, is it ignores the amount of interest rates. If interest rates are lower, PE ratios should be higher and ignoring that relationship will lead us to summarize much too frequently (and erroneously) that stocks are over priced in low-interest rate environments. It is clear that EP ratios are high when rates of interest are high and low when rates of interest are low.
In reality, not controlling for the amount of interest rates when comparing PE ratios for a market over time is an exercise in futility. Both PE ratios and EP percentage spreads (like the Fed Model) can be faulted for looking of them costing only part of the value picture. A fuller analysis would require us to look at every one of the motorists of value, and that you can do within an intrinsic value model.