Efficient markets advocates often highlight the asymmetry in publication results: if you find an empirically improbable result, where improbable is defined relative to a standard rational model, you get published; if you find a probable result, you usually do not. Since the set of all possible violations of standard rational models is large, you should expect to see many "violations" of efficient markets even if there are not any. This is because improbable is a statistical concept, and if you do 1000 tests against a true null hypothesis, we should expect 50 of them to be significant violations at the 5% level. Lo and MacKinley (see Lo's pubs here) and White (see his publication links here), have tried to explicitly correct for this data snooping bias, but in my opinion the net result is still very qualitative: be skeptical of non-standard findings. This is not to say that all violations are spurious, only we need an appropriate amount of skepticism when we see any new result.
This is not restricted to finance, of course. Medical research has the same problem. CNN notes (see here) that many findings are subsequently debunked:
This is not restricted to finance, of course. Medical research has the same problem. CNN notes (see here) that many findings are subsequently debunked:
[A] review of major studies published in three influential medical journals between 1990 and 2003, including 45 highly publicized studies that initially claimed a drug or other treatment worked.
Subsequent research contradicted results of seven studies -- 16 percent -- and reported weaker results for seven others, an additional 16 percent.
...
Editors at the New England Journal of Medicine added in a statement: "A single study is not the final word, and that is an important message."
The refuted studies dealt with a wide range of drugs and treatments. Hormone pills were once thought to protect menopausal women from heart disease but later were shown to do the opposite, and Vitamin E pills have not been shown to prevent heart attacks, contrary to initial results.
HedgeFundGuy - am 2005-07-14 16:02
Strophyx meinte am 16. Jul, 07:07:
Advancement Bias
As one of my professors in graduate school noted, professional journals want to publish results that are statistically significant much more results that fail to detect any significant associations. Since academic and professional advancement is strongly tied to records of publications, there's a strong tendency for researchers to keep tweaking their models until they find something with the veneer of statistical significance, then creating a theory or hypothesis which purports to be supported by these results. In one of my first professional jobs a colleague and I wrote a program to take any dataset and run a huge number of different models against the data. (I think that 10 variables resulted in roughly 1,000,000 different models.) The results were then sorted by the standard tests of statistical significance, and the most "significant" models printed. We did it as a joke, only to be disgusted to find that many researchers at the same company wanted to get a copy to use.
Mahalanobis antwortete am 16. Jul, 11:56:
He who mines data may strike fool's gold
You have probably heard of David Leinweber who sifted through a United Nations CD-ROM and discovered that the single best predictor of the Standard & Poor's 500 stock index was butter production in Bangladesh... ;-D.A friend of mine once estimated a huge number of ARMA models of GDP growth rates for his diploma thesis... I told him that besides the fact that I wouldn't estimate models with MA terms with Eviews and besides the fact that nobody who has ever seen recursive parameter estimates of ARIMA models speaks positively of such models that he would somehow have to adjust the test size...
His answer: I do not need a good model, I need a dimploma thesis.