Hedge Funds

AgPa #78: Hedge Funds – Man vs. Machine

Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance (2017)
Campbell R. Harvey, Sandy Rattray, Andrew Sinclair, Otto Van Hemert
The Journal of Portfolio Management 43(4), URL/SSRN

This week’s AGNOSTIC Paper examines the ongoing Man vs. Machine question in asset management at the example of hedge funds. The paper is therefore a predecessor to AgPa #21 that examines the same question for AI-powered mutual funds. The authors mention that there are still myths around systematic investing and many investors seem to have some kind of algorithm aversion. This is in-line with my own experiences, so I believe the paper fills an important gap for better education. In addition to that, the authors provide a practical framework to evaluate the performance and risks of hedge funds which I believe goes beyond the question of Man vs. Machine.

  • Macro hedge funds: systematic beat discretionary
  • Equity hedge funds: a draw between systematic and discretionary
  • Systematic and discretionary funds are quite similar
  • Hedge fund investing is more difficult than averages suggest

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AgPa #68: Machine-Learned Manager Selection (4/4)

A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection (2021)
Wenbo Wu, Jiaqi Chen, Zhibin (Ben) Yang, Michael L. Tindall
Management Science 67(7), URL/SSRN

The fourth and at least for the moment final AGNOSTIC Paper on Machine Learned Manager Selection. After examining equity mutual funds in the last three papers, this week‘s authors provide an interesting out-of-sample test and explore machine learning models for selecting hedge funds. Importantly, this week‘s paper appeared in one of the leading business journals already back in 2021. This increases the likelihood that the results are actually robust and strengthens the evidence.

  • Machine learning helps to identify outperforming hedge funds
  • Risk measures and VIX-correlations are the most important features

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AgPa #61: Minivans versus Sports Cars

Sensation Seeking and Hedge Funds (2018)
Stephen Brown, Yan Lu, Sugata Ray, Melvyn Teo
The Journal of Finance 73(6), 2871-2914, URL/SSRN

Tell me about the car you drive and I tell you who you are. In the hope of not offending the car enthusiasts too much, this week’s AGNOSTIC Paper relates the performance and characteristics of hedge fund managers to the type of car they drive. As announced in last week’s article, this is a funny example for the important soft factors that investors should consider when selecting an asset manager.

  • Sports car drivers take more risk and deliver lower performance
  • Funds of sports car drivers come with more operational risk
  • Sports-car-driving investors want sports-car-driving fund managers

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#3: Big Data & Machine Learning in Asset Management

This week I gave a talk on “Big Data and Machine Learning in Asset Management” at Goethe-University in Frankfurt. Thanks again to my thesis-supervisor Sasan Mansouri for the invitation. In this post I will summarize a few points of the talk and share the slides. The key result is the following framework to evaluate investment strategies that claim to use big data and machine learning. I also apply this to several real world funds.

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