Playing with the PE ratio

The price-to-earning ratio is the most popular fundamental ratio. There are interesting ways to dissect it, using technical analysis, to develop new insights.

Fundamental analysts relate PE to earnings growth rates with the PEG ratio, and compare with peer company PEs, etc.

A fundamental analyst pegs a PE as 'high', 'low', or 'fair-value', depending on EPS projections.

Fair-value is a subjective calculation, based on earnings expectations and interest rates (the benchmark of risk-free returns). The theory is that PE will correct from the high or low end to fair-value.

PE is a time-series that changes with price fluctuations. Standard technical indicators are designed for time-series analysis. From a technician's point of view, some properties of PE are obvious.

In technical terms, PE is a momentum oscillator, that fluctuates between minimum and maximum values.  It can be normalised like other oscillators.

The PE has a long-term average and a tendency to stay close to that average. The PE average is not fair-value, according to the fundamental definition. But the average is likely to be close to historical fair-value in a reasonably efficient market.

Combining technicals with fundamentals offers a useful method of checking fair-value calculations. 

A big divergence between historical average and fair-value is in itself, interesting. It implies something has changed about the business, or the fair-value calculation has a hole.

When such divergence exists, the data suggests PE is likely to trend closer to the historical average, rather than current fair-value.

The nearly-normal PE distribution means predictability. Around 65-70 per cent of the time, the PE will stay within one standard deviation of average.

It strays beyond two standard deviations only around 5-7 per cent of the time for most stocks. Beyond two standard deviations can be defined as over-bought (high PE) and oversold (low PE) zones.

Seen this way, PE is a technical indicator, ideally used over a long time-frame. It differs in this respect from classic technical indicators that can be used in any timeframe.

Technical data confirms that if a stock, or a diversified portfolio (like the Nifty) is bought below average PE,  (regardless of current fair-value) the returns are higher than normal.

A fundamental investor can therefore, go overweight whenever PE is below average with some confidence, rather than relying on subjective fair-value

Conversely if the PE is above average, even if fair-value suggests that it is 'low', the investor should be very cautious.

Technical analysis also tells us how sensitive specific stocks are to results . It can help to identify periods in every quarter when results are announced and earnings change. If there's a sharp EPS change, or surprising projections, does the PE remain stable due to corresponding sharp price changes?

Obviously this varies with individual stocks. But it's data that can be correlated to holding patterns (promoter holdings, institutional interest, retail & operators). Understanding this behaviour can again help with buy-sell tactics and offers insights into volatility.

Another interesting application is targeting turnarounds. Typically in a highly cyclical industry, PE may go from negative (when earnings are in the red) to a large positive PE (as earnings edge into the black). Then the PE reduces even while price rises through a period when EPS growth is strong enough to outrun price.

Technical analysis can isolate such patterns and offer pointers about  long-term price targets. A little research suggests those targets are likely to be more accurate than the purely fundamental fair-value approach because PEs swing a lot in turnarounds.

Another possible insight comes from turning PE upside-down and comparing to interest rate movements. Comparing the earnings-to-price-yield to risk-free returns is of course, standard for fundamental analysts.

We know stock prices trend up when EY is higher than the interest rate and vice-versa, stock prices trend down if the EY is lower.

However, the relationship can be more efficiently mapped using technical analysis. Technicians can for example, back-test and correlate to discover which interest rate has the highest inverse correlation to price movements.

Another possibility is to study the spread or differential between interest rates and EY to judge when the stock price is likely to correct and in which direction. That spread is also an oscillator – the difference never gets too large in either direction.

These are all unconventional ways to use PE. They will shock purists in both technical and fundamental camps. A vast amount of number-crunching would be needed to refine the processes. But, they could produce interesting ways to beat the street.