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yuvaltaylor
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I came across an excellent short introduction to machine learning--its limitations and potential. If you're interested, it's here: https://www.aqr.com/Insights/Research/Alterna...n-Machines-Learn-Finance? Yuval Taylor Product Manager, Portfolio123 invest(igations) Any opinions or recommendations in this message are not opinions or recommendations of Portfolio123 Securities LLC. |
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InmanRoshi
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This is interesting insight from Patrick O'Shaughnessy on Ted Seides podcast about their learning curve with machine learning and it's limitations... At the ~ 23:55 mark https://capitalallocatorspodcast.com/2019/06/09/osam/ |
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Edit 2 times,
last edit by
InmanRoshi
at Jun 12, 2019 7:51:09 AM
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Jrinne
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This is interesting insight from Patrick O'Shaughnessy on Ted Seides podcast about their learning curve with machine learning and it's limitations... At the ~ 23:55 mark https://capitalallocatorspodcast.com/2019/06/09/osam/ Yeah. There is some simple AND USEFUL stuff in here so: THANK YOU CARY!!!! O’Shaughnessy notes that linear regressions are not so good for nonlinear data. Hmmm. Where have I heard that? Also, notes that non stationary data does not work. People who do time series might take note. But where have I heard that before?. We still see multiple examples of uses of time series that use non stationary data where the statistics are just wrong and completely inappropriate at P123: but the statistics look so good they have to be believed. And no doubt, if you listen to it Patrick O’Shaughnessy is doing mainstream MACHINE LEARNING and makes no apologies. And he is sold on it working. "If the dataset is large enough a quant will find it." Meaning the signal. I do not think Patrick is different from his father. There are just some new methods out there. -Jim From time to time you will encounter Luddites, who are beyond redemption. --de Prado, Marcos López on the topic of machine learning for financial applications |
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Edit 16 times,
last edit by
Jrinne
at Jun 12, 2019 4:04:20 PM
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geov
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“All [dynamic resource allocation] examples need to deal with changing inputs and environments, which are highly dynamic and difficult to estimate and predict, as the future load is not statistically dependent on the current load,” says Eiko Yoneki, a senior researcher leading the data centric systems group at the University of Cambridge’s Computer Laboratory. “One change triggers another change, and if you want to control the system with accurate decisions, one must consider the future status of the system.” The trouble is that most existing methods rely on historical data to make predictions. http://www.bbc.com/future/story/20190606-the-...at-modern-life-depends-on |
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