are still many opportunities in other years, at least enough to run a fully invested
10-stock portfolio (to be studied in the next section). The fact that before 1997 not
much seems to be happening should not be cause for alarm: the annual distribution of all
news stories inNtapers off in a similar way, primarily on account of the lower volume
of news flow back in the 1980s and 1990s.
9.4 Monte Carlo–style simulations
It behooves us now to consider the worst possible investment experience from sentiment
reversals. Imagine a scenario in which we see the marvelous forward 1-year excess return
number of 16% over the entire universeU 17 eagerly buy a portfolio of stocks matching
the appropriate parameters, then underperform the market by 10% each year over the
next four years. This is possible. Unfortunate, but in such a case we would not have
considered carefully enough the real-time quality of excess returns. It could be that the
documented excess returns all came from a bull market in the late 1990s or, for that
matter, a bear market in the early 2000s. In a sense these outcomes are unavoidable; the
stock market is not like the physical world, and the principle of scientific induction (that
the future will be like the past) should be applied with the utmost reticence. There is
more comfort, however, in the belief that something neverwillhappen because it never
hashappened, as opposed to turning a blind eye towards history.
Generally speaking, Monte Carlo simulations attempt to estimate the range of a
function by taking randomly generated arguments, performing the function on those
arguments, then aggregating the function values into a final result. There is an obvious
application of Monte Carlo methods to estimating portfolio performance in an event
study. Starting the portfolio formation process at different events, if there is a sufficient
Sentiment reversals as buy signals 239Figure 9.3.Annual distribution of sentiment reversals inU 17.