4 ms·

This post is an exemplar of the crucial relationship between domain-specific knowledge and ML competency in the ML space. The bulk of the post is detailing the

This post is an exemplar of the crucial relationship between domain-specific knowledge and ML competency in the ML space. The bulk of the post is detailing the tricky ins and outs of trading, and overall the author gives the impression that they're broadly knowledgeable about stock markets. Contrast this post with those you see with ML hobbyists who delve into medicine or fake-news and produce useless results testament to their lack of domain-specific competency.

jacquesm 7y ago
Domain knowledge is essential to almost any project that aims for eventual commercial success, it is quite rare than an outsider will come into a field, apply some ML and make a killing.
- deepnotderp 7y ago
  RenTec (Yes, I know they do much more than ML, but still)
  - throwawaymath 7y ago
    That is not what Simons did to make Renaissance Technologies successful. Simons cultivated domain knowledge long before he started a hedge fund, because he had an interest in trading and gambling even as a professor. He also hired people with financial experience. The historical record overlooks the people he hired who knew a thing or two about trading, while fixating on the team of NLP scientists he hired from IBM. Likewise Simons wasn't initially successful in the very, very early years. It wasn't until the late 80s that the Medallion firm really came into its own.
    - deepnotderp 7y ago
      Yes, I understand that the "financial naiveness" so to speak of RenTec is overplayed in the media, but my point is that superior domain knowledge wasn't what enables Medallion to win. And to be fair, most of the early years were before systematized quant trading.
      - deleted 7y ago
        [deleted]
nsainsbury 7y ago
Bingo. I've made this comment several times on Hacker News in the past as well and in my opinion it's the number one reason I've seen ML projects fail to have impact at companies it's deployed: the operators (typically higher level Math/CS types) simply don't understand the domains well enough and so frequently end up making absurd recommendations/suggestions (often to the detriment of other business areas). The successful application of ML requires a deep understanding of the domain it's being applied in.
alexcnwy 7y ago
Totally agree. Funnily enough I think the ML hobbyist problem is most pervasive in the "predict the stock market" domain. There was a post on HN a few days ago [1] that was overfitting the validation set and hand-waving away fees and spreads. The author concluded that "there was no subtle underlying pattern" because they failed to find one. [1] https://news.ycombinator.com/item?id=21624907 https://news.ycombinator.com/item?id=21624907
- thundergolfer 7y ago
  I agree. It mixes the hype of ML with the evergreen appeal of the 'Get Rich Quick' scheme.

jacquesm 7y ago

Domain knowledge is essential to almost any project that aims for eventual commercial success, it is quite rare than an outsider will come into a field, apply some ML and make a killing.

deepnotderp 7y ago
RenTec (Yes, I know they do much more than ML, but still)
- throwawaymath 7y ago
  That is not what Simons did to make Renaissance Technologies successful. Simons cultivated domain knowledge long before he started a hedge fund, because he had an interest in trading and gambling even as a professor. He also hired people with financial experience. The historical record overlooks the people he hired who knew a thing or two about trading, while fixating on the team of NLP scientists he hired from IBM. Likewise Simons wasn't initially successful in the very, very early years. It wasn't until the late 80s that the Medallion firm really came into its own.
  - deepnotderp 7y ago
    Yes, I understand that the "financial naiveness" so to speak of RenTec is overplayed in the media, but my point is that superior domain knowledge wasn't what enables Medallion to win. And to be fair, most of the early years were before systematized quant trading.
    - deleted 7y ago
      [deleted]

nsainsbury 7y ago

Bingo. I've made this comment several times on Hacker News in the past as well and in my opinion it's the number one reason I've seen ML projects fail to have impact at companies it's deployed: the operators (typically higher level Math/CS types) simply don't understand the domains well enough and so frequently end up making absurd recommendations/suggestions (often to the detriment of other business areas). The successful application of ML requires a deep understanding of the domain it's being applied in.

alexcnwy 7y ago

Totally agree. Funnily enough I think the ML hobbyist problem is most pervasive in the "predict the stock market" domain. There was a post on HN a few days ago [1] that was overfitting the validation set and hand-waving away fees and spreads. The author concluded that "there was no subtle underlying pattern" because they failed to find one. [1] https://news.ycombinator.com/item?id=21624907 https://news.ycombinator.com/item?id=21624907

thundergolfer 7y ago
I agree. It mixes the hype of ML with the evergreen appeal of the 'Get Rich Quick' scheme.