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Comment by jacques_chester | Hacker News Reader

A few years ago I spoke to a data scientist who, after a bunch of NLP work, had hit on a very simple technique for quickly identifying good news and bad news.

How to tell that it's good news: there are numbers near the top.

How to tell that it's bad news: the figures are buried as deeply as legally permissible.

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Ironically, I figured the authors of the article weren't very impressed with their results since they weren't outlined in the summary...

Moreover, when the news is good, the numbers at the top are the right ones.

That sounds accurate that most money making strategies are based off simple logic and observations. Seems people are blindly impressed by ML and convolutional neural networks, even if they do not yield any impressive results.

I built system to determine which fundamental or technical factors have the most influence over a stock price. It required a fair amount of manual tinkering and sample runs to eliminate variance. The system itself is not that complex but it works. When I have demo'd it in a interview usually the interviewers are not that impressed... they are expecting that the system must be very complicated to have any results.

That just goes to show that Pearl's approach on causal reasoning is solid. Instead of looking patterns and correlation in data, one could consider building causal graphs from the domain knowledge.

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