AgPa #55: Backtests in the Age of Machine Learning

A Backtesting Protocol in the Era of Machine Learning (2019)
Rob Arnott, Campbell R. Harvey, Harry Markowitz
The Journal of Financial Data Science Winter 2019, URL/SSRN/PDF

I have already written about the pitfalls of research in asset management and the importance of good research practices for the application of machine learning. This week’s AGNOSTIC Paper takes this idea even further and provides a seven-point protocol for empirical research in finance.

Exhibit 2 of Arnott et al. (2019).

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AgPa #50: Should We Trust Asset Management Research?

The Pitfalls of Asset Management Research (2022)
Campbell R. Harvey
Journal of Systematic Investing Volume II Issue 1, URL/SSRN

Can we trust the results of academic and practitioner research in asset management? For a blog focusing on summaries of research papers, this is of course a very important question. But even without such an obvious bias, this is a very interesting issue for all who use some form of research for their investment decisions. The author of this week’s AGNOSTIC Paper presents several concerning facts and strongly recommends to not take all research insights at face value…

  • Some concerning facts about finance research
  • Research incentives and multiple testing
  • Practitioner research in asset management

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AgPa #49: Machine Learning in Quant Asset Management

How Can Machine Learning Advance Quantitative Asset Management? (2023)
David Blitz, Tobias Hoogteijling, Harald Lohre, Philip Messow
The Journal of Portfolio Management Quantitative Tools 2023, URL/SSRN

This week’s AGNOSTIC Paper is a broad overview about machine learning in investment management. The authors outline the benefits and pitfalls of machine learning compared to “traditional” econometrics and present several use cases in the world of (quantitative) asset management. They also provide ideas for research governance to keep those powerful methods under control.

  • Benefits and pitfalls of machine learning in finance
  • Use cases of machine learning in asset management
  • Keeping it under control: research governance and protocol

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