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The 10 Reasons Most Machine Learning Funds Fail

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 Marcos Lopez de Prado, Chief Executive Officer at TRUE POSITIVE TECHNOLOGIES

 Tuesday, July 3, 2018

The rate of failure in quantitative finance is high, particularly in financial machine learning applications. The few managers who succeed amass a large amount of assets and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that the author explains in this article. In the author's experience, 10 critical mistakes underlie those failures.


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2 comments on article "The 10 Reasons Most Machine Learning Funds Fail"

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 private private,

 Friday, July 6, 2018



Finance or not, I never found a successful example of machine learning working with timeseries data, may it be historical OHLC or anything else. But i’d love to be proven wrong.


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 Gerardo Lemus, Quantitative Finance Practitioner:

Applying all available data to get an edge over the market.

 Saturday, July 7, 2018



The following paper claims to predict the direction of the next transaction (and disputes the non iid behaviour of financial series) as long as you have access to terabytes of LOB HF data - has anyone else being able to replicate it ? (I do not have access to such data) https://arxiv.org/abs/1803.06917

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