Jay Vivian, former managing director of the IBM Retirement Funds, on how mutual fund performance can be manipulated. Learn more about mutual fund performance and the facts on index investing.


There’s a lot of issues in trying to determine who has beaten benchmarks. One issue is what benchmark do you choose, and that’s not an easy question. Another question is what time period you look at. And some funds will perform well for some periods and some for other periods and some for longer periods and some for shorter periods.

But it’s really hard to tell, it’s really hard to get a consistent metric for how well they do that. And, indeed, some funds will choose the time periods that they look good for. Like, you might see an ad that says “our trailing one-year, our trailing three-year looked great.”  And you wonder, “But I just read in the paper how they underperformed so badly for the last three months.” Or that they lost, they did really badly for the five years.

There’s another, a little, unknown twist in benchmark measurements, and that is that when you look at groups of funds that have done well, there’s something called “survival bias.” And those funds which didn’t do well typically get closed or merged into other funds.

So if you go look at every fund at some big house — some big mutual fund family — they’re not going to have a really bad fund because guess what happens to a fund that does badly? They close it or merge it into another one. So their numbers are consistently going to look better than the actual performance of all the funds that were in there.

Time-period sensitivity is a very interesting aspect of determining whether funds are beating their benchmarks are not. If a fund does well for one year, that’s great. If they do well for a second year in a row, that’s great. And now their two-year period is going to look good. If they do well for a third year in a row, that’s great, too.

There are very few funds that consistently outperform benchmarks. And, in fact, the number of funds that consistently outperform benchmarks is almost identical to the number that would be predicted if performance was totally random.

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