0
$\begingroup$

The success of backtesting supports the prediction that an adaptive trading strategy fares better than using fixed rules. It also suggests that the positive results in the literature are not due to data mining

I come from a data science/engineering background, so the data mine I understand are unsupervised models building things like clustering.

But I don't think that is what it means in quantitative finance. I've read lots of paper and funds shunning the word "data-mining", as if it is some illusion a trader sees in their backtesting portfolio and not attributable to "real skill".

Explain what it means and how does one typically commit this problem.

$\endgroup$
0

1 Answer 1

4
$\begingroup$

"Data mining" is also used in another negative or derogatory sense: it means to perform a large number of statistical tests hoping to find one that is statistically significant at 5% level and publishing only that one. This leads to "false discoveries". Recall that 1 out of 20 statistical test can be expected to show as positive even thogh the null hypothesis is valid. In fields like Finance or Genetics, where it very easy to run an enormous number of tests on a limited amount of data, this is a major problem, and it shows up as inability to replicate the result on another sample.

There are special techniques in Statistics for dealing with this issue, google False Discovery, FWER, Holm-Bonferroni, Multiple Comparisons Problem, and similar terms. In Finance Lopez De Prado and Cam Harvey among others have written about this issue. Anyone who performs "backtests" needs to be aware of it.

$\endgroup$
1

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.