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 #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 #58: International Diversification – Doing the Right Thing is Hard Sometimes

International Diversification—Still Not Crazy after All These Years (2023)
Cliff Asness, Antti Ilmanen, Dan Villalon
The Journal of Portfolio Management 49(6), 9-18, URL/AQR

In the last post (AgPa #57), we have already seen that international diversification is a powerful protection against the higher-than-expected risk of losing real wealth with stocks over the long term. By coincide, three of the OGs from AQR Capital Management also just released an article about the Fors and Againsts of international diversification. Unsurprisingly, I picked that one for this week’s AGNOSTIC Paper…

  • For: Not everyone can invest in the best-performing market
  • Against: Everything crashes together
  • For: Historic returns don’t show changes in valuation
  • For: Valuation levels should eventually matter
  • For: International diversification provides opportunities for active investors

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AgPa #57: Stocks for the Long-Run – Riskier Than Thought

Stocks for the long run? Evidence from a broad sample of developed markets (2022)
Aizhan Anarkulova, Scott Cederburg, Michael S. O’Doherty
Journal of Financial Economics 143(1), URL/SSRN

Stocks for the Long-Run – this is not only the title of Jeremy Siegel’s popular book but also a well-established idea among investors. If you can wait long enough and don’t need your money on the way, just put it in a diversified index fund and wait. This week’s AGNOSTIC Paper challenges this simple advice and shows that even over very long periods, the chance of losing money with stocks can be higher than previously thought…

  • History offers some scary events of wealth-destruction
  • The US equity market is not necessarily representative
  • Global diversification helps tremendously

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AgPa #52: Happier Employees, Better Returns?

Employee Satisfaction and Long-Run Stock Returns, 1984–2020 (2022)
Hamid Boustanifar, Young Dae Kang
Financial Analysts Journal 78(3), URL/SSRN

A common sales-pitch of ESG strategies is the idea that those strategies not only do good for the planet and other stakeholders, but also generate higher returns. I am generally skeptic about this, but there are studies showing that certain ESG variables historically indeed predicted higher returns. A prominent example for this is the paper on employee satisfaction by Alex Edmans (2011). This week’s AGNOSTIC Paper is an out-of-sample test of this study with somewhat more thorough testing.

  • “Best Companies” outperformed several benchmarks
  • “Best Companies” outperformed during crises and out-of-sample
  • Quality and Low-Risk factors explain some of the premium on “Best Companies”

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AgPa #51: Short Sellers vs. Firms

Go Down Fighting: Short Sellers vs. Firms (2012)
Owen A. Lamont
The Review of Asset Pricing Studies 2(1), URL

I like controversial and (in my opinion) misunderstood topics and this week’s AGNOSTIC Paper examines the next big one: short selling. The paper is unfortunately already more than 10 years old, but it is still a go-to reference for short selling. Apart from that, the fights between firms and short sellers are also quite entertaining – at least from an outsider’s perspective…

  • Short-seller-fighting firms tend to massively underperform
  • The results are robust after controlling for the major factors

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AgPa #48: Investable Machine Learning for Equities

Investable and Interpretable Machine Learning for Equities (2022)
Yimou Li, Zachary Simon, David Turkington
The Journal of Financial Data Science Winter 2022, 4(1), URL

Regular readers of this blog know that machine learning in asset management is one of my favorite topics and I recently found new interesting material. This week’s AGNOSTIC Paper is the first of two studies and examines an important issue with machine learning models in great detail: interpretability…

  • Machine learning models outperform simpler methods
  • Different models learn different investment approaches

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AgPa #47: Equity Factors without Shorting

When Equity Factors Drop Their Shorts (2020)
David Blitz, Guido Baltussen, Pim van Vliet
Financial Analysts Journal, 76(4), URL

This week’s AGNOSTIC Paper examines the important issue of performance contributions from the long and short legs of the major factor premiums. In English: can we profitably invest in factors without shorting a large number of stocks?

  • The long-legs of factors are more important than the short-legs
  • The same pattern holds in international markets

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AgPa #46: Transaction Costs and Capacities of Factor Strategies

Transaction Costs of Factor-Investing Strategies (2019)
Feifei Li, Tzee-Man Chow, Alex Pickard, Yadwinder Garg
Financial Analysts Journal 75(2), 47-61, URL

In this week’s AGNOSTIC paper, the authors develop a transaction cost model and use it to estimate the capacity of the major factors. There are many ways to define capacity in more detail, but the general idea is quite simple. It is the amount of money you can invest in a profitable strategy before you move prices too much and lose your advantage. Unfortunately, what theoretically sounds simple and intuitive is actually quite difficult to estimate in practice…

  • Implementation costs depend on tilt, turnover, and execution speed
  • Capacities of factors for a maximum cost of 0.5% per year
  • There is not yet a consensus on factor capacities

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AgPa #43: Buffett’s Alpha

Buffett’s Alpha (2018)
Andrea Frazzini, David Kabiller, Lasse Heje Pedersen
Financial Analysts Journal 74(4), URL

In this week’s AGNOSTIC Paper, the authors use the major factor premiums to examine one of the best long-term investment track records in the world – Warren Buffett and Berkshire Hathaway. The latest annual report just came out a few days ago and (as usual) summarizes Berkshire’s performance on the first page. From 1965 to 2022, Berkshire returned 19.8% per year versus 9.9% for the S&P 500. That’s a 24,708% cumulative return for the S&P 500, and an unbelievable 3,787,464% return for Berkshire. There are some investors who achieved even better results over shorter time periods. But to the best of my knowledge, there is no 58-year track record that is even remotely comparable to Buffett.

  • How good is Berkshire? Damn good…
  • The Buffett Style: cheap stocks with high-quality and low-risk
  • Don’t practice what you preach – Buffett’s Leverage…
  • Systematizing Buffett and Berkshire

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