As I was checking the new entries in my RSS feeds, I recently stumbled
upon this blog entry.
Go ahead, read it. I'll wait right here.
Are you back, gentle reader? What do you think? Although the
blogger referred in particular to computer science, his argument
applies to almost all fields. In fact, Mark Joshi made the very same
point in a conversation we had last summer. How much can you trust the
results of a quantitative-finance paper if they're not repeatable?
Of course, one could reproduce the results - in a way. Based on the
author's description, one could code the same calculations. Like we
have that kind of time, right?
In fact, the insight toolkit mentioned at the end of the post is a
nice idea. If authors wrote their code on top of such a library and
made it available, the advantage would be twofold. On the one hand,
they would build upon classes and functions reviewed and tested by
scores of other users. On the other hand, readers familiar with the
toolkit would find it much easier to read the author's code and could
focus on understanding the actual new algorithms rather than the
scaffolding.
Need I mention that we have a similar
toolkit for quantitative finance?
Thoughts anyone? I'll be glad to read them.
Luigi
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