I feel this is such a common thing due to the availability and "easy" to process data. Any time you have an area where you can get faux results without a lot of work there's sure to be tons of people writing to be read, not teach.
It's not just blog posts, by the way, it's also a certain subgenre of academic papers. They inevitably turn out to be not reproducible or not deployable. This academic subgenre is a microcosm of broader issues in academic literature (p-hacking, overfitting, post-selection...)https://twitter.com/fchollet/status/1177633367472259072 …
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You see it also in startup pitches. AI is the new sauce to add to everything. Leads to funny quiproquos when one asks candid questions :)
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Funny, your tweet is right above a
@kaggle competition on the very same topic@benhamnerpic.twitter.com/xeOSsUky7Y
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@jxmorris12 seems like the same type of thing you were talking aboutThanks. Twitter will use this to make your timeline better. UndoUndo
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Reassure me that these authors are not tenure-track academics! I get this from students taking their first data mining course. Even for them, such ignorance of finance theory is surprising.
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When you say "academic papers", do you also include research papers from the industry? Because oh god, they too have so many problems.
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