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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AgPa #1: Index Whales

Index Providers: Whales Behind the Scenes of ETFs (2021)
Yu An, Matteo Benetton, Yang Song
Invited for submission to the Review of Financial Studies, URL

The first research paper examines a specific area of the asset management industry: Exchange Traded Funds (ETFs) and index providers, the companies selling market indices like the S&P 500.

In a remarkable combination of empirical analysis and theoretical modeling, the authors present several interesting results:

  • Index providers are an oligopoly
  • ETF investors care about the index
  • Index providers capture 1/3 of ETF fees
  • Index providers are extremely profitable

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AGNOSTIC Papers: Introduction

The idea of AGNOSTIC Papers is to combine theory and practice by presenting one research paper per week. The papers should provide scientifically robust insights that are relevant and applicable to real-world investing. More details about it in this brief introduction.

Of course, you are more than welcome to follow the series and share your comments!

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Report Analytics USA #2

This post contains a lot of unsexy calculations and is fairly technical. But (in my opinion) there are some very interesting results. Not just for my particular strategy but for everyone who is active on Wikifolio.

First. Overall and especially after costs, my two Wikifolios weren’t a good alternative to a standard ETF on the S&P 500 index (from inception to March 11, 2022). To my defense, however, I stressed several times that the two Wikifolios are just a real-world test of my master thesis and I never marketed them as investments.

Second. I still believe that Wikifolio is a great platform to test strategies like mine, but it is not perfect. There are annoying technical issues, pretty high fees, and significant indirect trading costs. Depending on the liquidity of the stock, bid-ask-spreads and/or unfavorable FX rates amount to 40-80 basis points per transaction on average.

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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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Report Analytics USA #1

This is the start of an ongoing series intended to share updates, insights, and backgrounds on the Report Analytics USA portfolios. To start with, I present the methodology that I currently use to implement the live portfolios on Wikifolio.

Heart of the process is a stock selection based on copy-paste of the most recently published annual and quarterly reports. I further divide this selection by market capitalization to create a “Large” and “Small” version of the Report Analytics USA portfolios.

All of this is just a starting point and I conclude this post with a roadmap of ideas to improve the strategy.

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Copy-Paste Outperformance – Summary

Every year, US companies must publish three quarterly and one annual report. Preparing those reports, however, is a lot of effort, does not improve operations, and reveals information to competitors.

How to deal with this? Correct, spend the time to create one comprehensive template and reuse it as long as possible. In an excellent research paper titled “Lazy Prices” (2020), the authors show that US companies are no exception from this: many annual and quarterly reports are basically just updated copies from the previous year.

What does this mean for investors? Since most of the report is just copy-paste, they should rather focus on differences between the current and previous report (for example, new paragraphs). It turns out that such changes are indeed very important: quantitative measures for report copy-paste predict future stock returns and help to achieve outperformance vs. common US indices.

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