|
Is there a way to glean useful information by examining
mutual fund holdings? Maybe, but only if you look at what
both the best and the worst mutual fund managers are doing.
Russ Wermers, associate professor of finance, developed a
statistical model that predicts the future performance of
individual stocks based on how heavily they are held or
purchased by both successful and unsuccessful fund managers.
Rather than simply looking at the results of winning
funds, Wermers and his co-authors examined good, average and
bad funds to see what highly skilled—and thus very
successful—fund managers were buying in common, and what
underperforming fund managers were not buying. The model
uses a weighted average alpha to determine the outlook for a
stock at the beginning of a given year, consisting of the
portfolio weight on a stock multiplied by a manager’s past
alpha, summed across all managers who held that stock at the
beginning of that month.
 “You can’t just consider how many winning funds bought
the stock, you have to weight how much of it they purchased,
and you also have to put some weight on the skill of the
manager,” says Wermers. “We considered the performance of
every fund manager and use it as the main factor in the
weighting of the outlook for a stock.”
Hedge funds in particular have shown interest in this
research because they need an independent source of stock
returns beyond what is already known by the masses.
Unfortunately, this information isn’t easy for the
average investor to find. Because mutual funds only disclose
portfolio holdings information on a quarterly basis, and
because the funds have a 60-day grace period to file their
holdings with the SEC, investors interested in using this
model to predict stock returns are limited by the time lag
in receiving information about what mutual fund managers are
holding. Mutual fund disclosures are also staggered, so it
isn’t easy to obtain the information—for instance, some
funds report their holdings in December, while others report
in October. Wermers used a multitude of datasets to create
the holdings data and returns data used in the study.
For more information about this research, contact
rwermers@rhsmith.umd.edu.
|
