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Alternative Market Briefing

Presentation: The 7 Reasons Most Machine Learning Funds Fail

Tuesday, October 10, 2017

Matthias Knab, Opalesque:

Marcos Lopez de Prado from Guggenheim Partners says that the rate of failure in quantitative finance is high, and particularly so in financial machine learning. 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 will become apparent in his presentation.

"Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are 7 critical mistakes underlying most of those failures:

  • The Sisyphus paradigm
  • Integer differentiation
  • Inefficient sampling
  • Wrong labeling
  • Weighting of non-IID samples
  • Cross-validation leakage
  • Backtest overfitting" The full presentation (45 slides) can be downloaded at the Source link below.

    The contents of this presentation are based on de Prado's forthcoming book: Advances in Financial Machine Learning, Wiley (2017).

    Marcos López de Prado is a Senior Managing Director at Guggenheim Partners, where he manages $13 billion for institutional investors using machine learning algorithms. Over the past 19 years, his work has combined advanced mathematics with supercomputing technologies to deliver billions of dollars in net profits for investors and firms. A proponent of research by collaboration, Marcos has published with over 30 leading academics, resulting in some of the most read papers in Finance......................

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