AgPa #72: Machine-Reading of Private Equity Prospectuses

Limited Partners versus Unlimited Machines: Artificial Intelligence and the Performance of Private Equity Funds (2023)
Reiner Braun, Borja Fernández Tamayo, Florencio López-de-Silanes, Ludovic Phalippou, Natalia Sigrist
CEFS Research Paper, URL/SSRN

This week’s AGNOSTIC Paper is somewhat outside my major area of competence, but I think it is a good example where we are heading to in the investment industry. Over the last years, it became quite standard that investors use the latest tools of machine learning to analyze non-quantitative information like text or images at a scale that hasn’t been possible before. So far, however, the efforts were mostly focused on public markets. In their not yet published working paper, this week’s authors show that there seems to be also a lot of potential for such methods in private markets.

  • Portfolio Company, Management Team, Investment Opportunity – The most common words of PE-managers
  • The complexity of PE-fund documents is related to fundraising success and performance
  • Machine learning and text data helps to select PE-funds
  • The machines seem to pick up meaningful concepts

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AgPa #32: Agnostic Fundamental Analysis (3/3)

Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets (2022)
Matthias X.Hanauer, Marina Kononova, Marc Steffen Rapp
Finance Research Letters 48, URL/SSRN

The third and final post about agnostic fundamental analysis. This week’s AGNOSTIC Paper challenges the simple linear methodology and introduces vastly improved valuation models…

  • More sophisticated valuation models yielded better performance
  • Different models emphasize different fundamental variables

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AgPa #17: European Fund Selection

Fund Selection: Sense and Sensibility (2022)
Guido Baltussen, Stan Beckers, Jan Jaap Hazenberg, Willem Van Der Scheer, CFA
Financial Analysts Journal, 78(3), 30-48, URL

Coincidentally, this week’s AGNOSTIC Paper is a pretty good sequel to the last one. The authors study the performance of globally investing mutual fund that were available for European investors between 2008 and 2020. The results are seamlessly consistent with the literature and are anything but a sales-pitch for active fund managers…

  • In aggregate, active managers underperformed the passive alternative
  • Cheap funds with good track records were more likely to outperform

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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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