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The essence of the article is that they underestimated how flawed their algorithms are and how hard it is to build a good lasting algorithm in a dynamic world.
by 1cvmask 5y ago
The essence of the article is that they underestimated how flawed their algorithms are and how hard it is to build a good lasting algorithm in a dynamic world.
Many seasoned wall street algorithms have suffered many times over 5 decades, and when they fail we call them black swan events.
- mjdesa 5y agoThat’s not what I read in that article at all. What I read was that their data and methodology was flawed, and they weren’t willing to pay the price to fix it.
- human 5y agoTheir methodology might have been flawed. The author is speculating. He uses Zillow to explain how datasets – especially the ones with money tied-in – can’t be trusted blindly. Building a high-quality dataset is an expensive endeavour.
- seoaeu 5y agoZillow thought they already had enough data and accurate enough models to buy and sell houses profitably. The last two quarters proved they didn’t. In the first quarter they were puzzled by making too much money and in the second they lost a whole bunch The author is arguing that they should have pivoted from “we already have models” to “we’re intentionally gambling hundreds of millions of dollars so we can build good models over the next few years”. That might be a good strategy for a startup with loads of VC money and no other products, but it makes less sense for a more established company to risk going under on that bet
- throwawayboise 5y agoAnd wall street algorithms should be easier because securities are fungible. One share of AAPL is the same as another. Houses are not like that. Real estate is local, local, local. Every house has a hundred unique attributes that each potential buyer will value differently.