The Business of Value Investing.pdf

(Romina) #1
56 The Business of Value Investing

a $ 100,000 investment in 1982 would have made you more than
$ 1 million by the end of 1999.
The starting point matters. While you can never expect to buy
at the exact bottom (say, in 1982) and sell at the peak (as in 1999),
you can avoid doing the exact opposite, which is what many inves-
tors did by buying in the late 1990s at infl ated prices. Excited by the
quick and unsustainable rise in stock prices fueled by the Internet
boom, investing turned into speculating motivated by greed rather
than commonsense business principles. Very few individuals would
pay $ 10 million to buy an entire business that had no customers,
much less profi ts. Yet millions of well - educated people were buying
shares of companies valued at billions of dollars without a single
dollar of profi ts.
A very good rule of thumb and decades of data suggest that the
best starting points occur when the price to earnings (P/E) ratios
are lower rather than higher. The P/E ratio is simply the share price
of stock divided by the per - share earnings of a business. It repre-
sents how much investors are willing to pay for the future earnings
of a business based on future business expectations. If a compa-
ny ’ s shares trade at $ 20 and its earnings per share for the year are
$ 2, then the P/E ratio is 10 (20/2). The inverse of the P/E ratio
is known as the earnings yield or the percentage of earnings per
share. In the example, the earnings yield is 10 percent (2/10). As
you can see, the lower the P/E ratio, the higher the earnings yield.
Interestingly, you don ’ t need decades of data to tell you that it is
more prudent (and more likely profi table) to look for quality busi-
nesses that are trading at lower P/Es. Anyone would rather pay $ 1
million for a business that earns $ 200,000 in profi ts versus one that
earns $ 100,000, all else being equal. Similarly, the odds of favorable
market returns increase when the general market has a lower P/E
ratio. In fact, decades of data confi rm this logical assumption, as
shown in Table 4.1 and Figure 4.1.

CH004.indd 56CH004.indd 56 9/2/09 7:06:50 PM9/2/09 7:06:50 PM

Free download pdf