Global

AgPa #20: Performance of ESG Exclusions

The Expected Returns of ESG Excluded Stocks. The Case of Exclusions from Norway’s Oil Fund (2022)
Erika Berle, Wanwei (Angela) He, Bernt Arne Ødegaard
SSRN Working Paper, URL

This week’s AGNOSTIC Paper examines the ESG exclusions of a popular investor: the Norwegian sovereign wealth fund, also known as the “oil fund”. Between 2005 and 2021, the fund excluded 189 companies that engage in different types of “bad” practices or products. These exclusions are interesting because they reveal insights about the impact of ESG for a large real-world institutional investor…

  • ESG-excluded stocks generated up to 6.85% alpha per year
  • There seems to be a return premium on “bad” stocks

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AgPa #19: ESG Confusion (2/2)

Aggregate Confusion: The Divergence of ESG Ratings (2022)
Florian Berg, Julian F. Kölbel, Roberto Rigobon
Review of Finance, Corrected Proof, 1-30, URL

The second AGNOSTIC Paper on the confusion around ESG. This one examines the disagreement of ESG ratings in much more detail and provide some explanations why they are so different…

  • ESG ratings disagree: the average correlation is just 0.54
  • 709 indicators and 64 categories: no wonder that there is disagreement
  • Most disagreement comes from “measurement” and “scope”
  • There is a “rater effect” for ESG ratings

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AgPa #11: Concentrated Stock Markets (2/7)

Long-Term Shareholder Returns: Evidence from 64,000 Global Stocks (2021)
Hendrik Bessembinder, Ta-Feng Chen, Goeun Choi, K.C. John Wei
SSRN Working Paper, URL

The second of seven AGNOSTIC Papers about the extreme concentration within stock markets. This one goes beyond the US and examines global stock markets between 1990 and 2020. The pattern of extreme concentration is very similar for 41 countries beside the US and in some cases even stronger.

  • Longer investment-horizons lead to extremer return distributions – also outside the US
  • Just 2.4% of all companies created the entire net wealth in global stock markets
  • All stock markets are concentrated but there are regional differences

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AgPa #9: The Age of Intangible Assets

Equity Investing in the Age of Intangibles (2021)
Amitabh Dugar & Jacob Pozharny
Financial Analysts Journal, 77(2), 21-42, URL

Given the exceptional decade for technology companies, I am late to the party with this one. This week’s AGNOSTIC Paper examines the role of intangible assets for equity investors.

The issue is at the heart of fundamental analysis and also relevant for systematic investors. The authors present several interesting results for a global sample of thousands of companies between 1994 and 2018.

  • A measure for intangible-intensity of industries
  • Book values became less relevant for intangible-intense industries but still remain important

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AgPa #7: Spotify Streaming and Stock Returns

Music sentiment and stock returns around the world (2021)
Alex Edmans, Adrian Fernandez-Perez, Alexandre Garel, Ivan Indriawan
Journal of Financial Economics, In Press, Corrected Proof, URL

This week’s AGNOSTIC Paper examines the role of music sentiment in the stock market. What sounds like statistical hocus-pocus is part of an important question. Do other factors than rational information drive stock markets?

I like the paper for its creative use of alternative data and its clean methodology. But to be honest, I was somewhat skeptical when I first heard about it. However, the authors present an intuitive economic rationale and rigorously test their hypotheses in various robustness checks. The results are quite interesting…

  • Music sentiment is related to stock market returns
  • Music sentiment is more important in less efficient markets
  • Music sentiment is also related to fund flows and bond market returns

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AgPa #5: The Return on Everything

The Rate of Return on Everything, 1870–2015 (2019)
Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, Alan M. Taylor
The Quarterly Journal of Economics 134(3), 1225-1298, URL

This week’s AGNOSTIC Paper is a deep dive into financial history. The authors estimate the total return of equity, housing, bonds, and bills in 16 advanced economies for the period from 1870 to 2015.

The paper is very comprehensive and I focused on the issues that are (in my opinion) most interesting for investors:

  • Real returns on risky assets were 7-8% from 1870 to 2015
  • Returns on risky assets were substantial but volatile
  • Realized risk premiums fluctuate widely across time and countries

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AgPa #4: Measuring the World’s Assets (2/2)

Historical Returns of the Market Portfolio (2019)
Ronald Doeswijk, Trevin Lam, Laurens Swinkels
The Review of Asset Pricing Studies 10(3), 521-567, URL

This is the second post on the global market portfolio and again examines two papers. It is designed to be a sequel, so I recommend to read the first part before.

The global market portfolio is a tough empirical challenge. Different methodology leads to different results and the papers disagree on several points. But there are some common insights that enhance our understanding of the market portfolio.

  • Most assets are not publicly traded
  • Stock markets are relatively small
  • Recent historical returns were 4.4% to 6.3% per year
  • The market portfolio is a good starting point

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AgPa #3: Measuring the World’s Assets (1/2)

The Global Market Portfolio (2021)
Gregory Gadzinski, Markus Schuller, Andrea Vacchino
The Journal of Portfolio Management 47(8), 151-163, URL

This week’s AGNOSTIC Paper attempts to translate an important theoretical concept into practice – the global market portfolio.

The global market portfolio captures all available assets and each is weighted by its market value. The authors develop two proxies for this portfolio and present some interesting insights:

  • Global assets were worth about $667T in 2019
  • The investable market portfolio returned 4.7% p.a. from 2005-2020/Q1
  • The non-investable market portfolio returned 5.9% p.a. from 2005-2020/Q1

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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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