AGNOSTIC Papers

AgPa #76: ESG Myth Debunking

Applying Economics – Not Gut Feel – to ESG (2023)
Alex Edmans
Financial Analysts Journal 79(4), URL/SSRN

I like myth debunking and this week’s AGNOSTIC Paper is one from this category. Alex Edmans, one of the leading scholars in the field, takes on a few widespread ESG beliefs. He shows that economics already offers a lot of tools to deal with the issues. To me, this paper was therefore kind of a relief as it shows that ESG doesn’t have to be as complicated as many people make it.

  • #1: Shareholder value is a very long-term concept
  • #2: Shareholders care about more than just money
  • #3: Sustainability risks affect cash flows more than discount rates
  • #4: Sustainable stocks not necessarily outperform
  • #5: Climate risk is an unpriced externality, not investment risk
  • #6: Companies‘ ESG metrics not necessarily capture their impact on society
  • #7: More ESG is not necessarily better than less
  • #8: More investor engagement is not necessarily better than less
  • #9: Paying for ESG performance does not necessarily improve ESG performance
  • #10: Not all market failures justify regulation

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AgPa #75: Optimal Investment Committees

Optimal Design of Investment Committees (2023)
Bernd Scherer
The Journal of Asset Management, URL/SSRN

After a long break of almost exactly 3 months – I had several other tasks that required my intellectual capacity – it is time for a new AGNOSTIC Paper. This one examines the design and challenges of investment committees (ICs). Even more important, the author suggests a simple and powerful solution for some of their most common challenges. As someone who regularly enjoys the process of committee-based decision-making, I believe this week’s paper is quite powerful and offers a lot of valuable lessons for both investment managers and their clients.

  • Good theory: ICs ensure the same quality for all clients
  • Bad practice: ICs suffer from psychological biases
  • Solution: Anonymous member-portfolios

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AgPa #74: Peer-Reviewed Research is Not Helpful to Predict Returns – Really?

Does peer-reviewed theory help predict the cross-section of stock returns? (2023)
Andrew Y. Chen, Alejandro Lopez-Lira, Tom Zimmermann
Working Paper, URL

This week’s AGNOSTIC Paper examines the holy grail of empirical research and systematic investing. Is all the research from those smart academics and practitioners really helpful to predict stock returns? Or are we all victims of data mining? The paper if of course not the first one examining this issue, but the approach is in my opinion quite interesting and the authors derive some thought-provoking implications. Pure data mining matches the results from decades of peer-reviewed research surprisingly well. The practical implications, however, are in my opinion not as clear as the statistical ones.

Putting all of this together, the authors may be right that peer-reviewed research and theory are (statistically) not helpful to predict stock returns. I do believe, however, that theory and rigor research in the sense of understanding what you are attempting to do is helpful for real-world investing.

  • Return predictors decay out-of-sample – with and without theory
  • Data mining generates similar patterns like peer-reviewed research
  • Out-of-sample decays are similar for data mining and peer-reviewed research

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AgPa #73: Country and Industry Momentum

Can exchange traded funds be used to exploit industry and country momentum? (2013)
Laura Andreu, Laurens Swinkels, Liam Tjong-A-Tjoe
Financial Markets and Portfolio Management, URL/SSRN

Even if you believe in factor investing, it is very difficult for most investors to actually implement it. Trading portfolios with hundreds of stocks requires considerable infrastructure, enough money, and efficient transaction cost management. This is already a challenge for many institutional investors, so it is logically even more difficult for people like you and me. This week’s AGNOSTIC Paper addresses this issue and presents an idea to still benefit from momentum via equity indices and the corresponding ETFs.

  • Country and industry momentum worked historically
  • The strategies seem to be implementable via ETFs
  • The strategies remained profitable after trading costs

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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 #71: Go Where the Earnings (per Share) Are

What Matters More for Emerging Markets Investors: Economic Growth or EPS Growth? (2022)
Jason Hsu, Jay Ritter, Phillip Wool, Harry Zhao
The Journal of Portfolio Management Emerging Markets 2022, 48 (8), URL/PDF

This week’s AGNOSTIC Paper is one from the myth-busting category and examines the relation between countries’ GDP growth and stock market returns. The idea and analyses are admittedly not new and the paper is basically an update of one of the authors previous work. Nonetheless, I think the question is very interesting and still very relevant for regional asset allocation.

  • GDP growth and stock returns are not correlated over the long-term
  • Theoretically, the missing relation is not surprising
  • Go Where the Earnings (per Share) Are

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AgPa #70: Equal vs. Market Cap Weights

Why Do Equally Weighted Portfolios Beat Value-Weighted Ones? (2022)
Alexander Swade, Sandra Nolte, Mark Shackleton, Harald Lohre
The Journal of Portfolio Management 49 (5), URL/SSRN

This week’s AGNOSTIC Paper examines one of the most common ideas of portfolio construction. Equal weighting. At least on paper, equal weighted strategies often outperform market cap weights and sometimes even more sophisticated optimizations. In a very simple, yet somehow brilliant analysis, this week’s authors examine where this historical outperformance comes from…

  • EW portfolios outperformed VW ones in the US market
  • EW bets on Size, Value, and against Momentum, Quality, and Low-Risk
  • The EW-VW spread is an imperfect, but cheap and simple proxy for the size effect

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AgPa #69: Rebalancing Luck

Fundamental Indexation: Rebalancing Assumptions and Performance (2010)
David Blitz, Bart van der Grient, Pim van Vliet
The Journal of Index Investing Fall 2010, 1(2), URL/SSRN

This week’s AGNOSTIC Paper is already more than 10 years old, but still carries a very important message. The core idea is very simple. If you design an investment strategy, you must make decisions about rebalancing. There are two aspects to consider. How much and when. This week’s authors examine the when at the example of fundamental indices. They show that choosing arbitrary rebalancing date(s) introduces substantial luck or bad luck to a strategy. Even more important, this luck or bad luck doesn’t seem to cancel out over time and thus permanently affects real-world returns. Fortunately, however, there are ways to make yourself less dependent from rebalancing luck…

  • Different rebalancing dates lead to different outcomes
  • Rebalancing luck (or bad luck) is relevant and persistent
  • There is a solution: stretch rebalancing over the year

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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 #67: Machine-Learned Manager Selection (3/4)

Selecting Mutual Funds from the Stocks They Hold: A Machine Learning Approach (2020)
Bin Li, Alberto G. Rossi
SSRN Working Paper, URL

The third AGNOSTIC Paper on the application of machine learning in manager selection. This week’s paper is very similar to AgPa #65 and AgPa #66, and again examines the data on US mutual funds. Despite somewhat different methodology, the results point in a similar direction. This, of course, increases the evidence that machine learning is actually useful for manager selection…

  • Machine learning helps to identify outperforming funds
  • The best and worst funds share common characteristics
  • Trading Frictions and Momentum are the most relevant variables

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