Empirical Facts

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 #10: Concentrated Stock Markets (1/7)

Do stocks outperform Treasury bills? (2018)
Hendrik Bessembinder
Journal of Financial Economics 129(3), 440-457, URL

I try to be careful with superlatives, but I think that this week’s AGNOSTIC Paper(s) are a must-read for everyone seriously interested in stock markets.

A few very successful companies drive the entire US market while the majority of stocks underperform even risk-free treasuries. Moreover, the most frequent lifetime return for U.S. companies is -100%. Those brutal empirical facts have strong implications for investors.


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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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AgPa #2: What Moves Stock Prices?

What Moves Stock Prices? The Role of News, Noise, and Information (2022)
Jonathan Brogaard, Thanh Huong Nguyen, Talis J. Putnins, Eliza Wu
The Review of Financial Studies, Forthcoming, URL

This week’s AGNOSTIC Paper attempts to answer a very fundamental question: What drives the day-to-day volatility of stock prices? Of course, there are many things active at the same time. News about the underlying businesses, news about the economy, market impact of large investors, and various more.

The authors develop a novel model to isolate the impact of those different types of information. They also applied it to the US stock market and derive some interesting results:

  • Stock-Specific information is most important
  • Markets became more efficient over time
  • Smaller stocks are more noisy

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