USA

AgPa #82: Equity Risk Premiums and Interest Rates (2/2)

Expected Stock Returns When Interest Rates Are Low (2022)
David Blitz
The Journal of Portfolio Management 48(7), URL/SSRN

The second AGNOSTIC Paper on equity risk premiums when interest rates are high(er). This one was actually published before the last one, so David Blitz deserves credit for the original idea. He also examines a longer and more comprehensive dataset that serves as a nice out-of-sample test. So I think it makes sense to conclude the posts on equity risk premiums and interest rates with this more comprehensive paper.

  • Equity risk premiums were lower when interest rates are higher
  • Controlling for other factors doesn’t change the negative relation
  • EPS growth seems to explain the pattern

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AgPa #81: Equity Risk Premiums and Interest Rates (1/2)

Honey, the Fed Shrunk the Equity Premium: Asset Allocation in a Higher-Rate World (2024)
Thomas Maloney
The Journal of Portfolio Management 50(6), URL/AQR

Risk-free interest rates, the most fundamental anchor of asset prices, increased dramatically in 2022 and are still considerably higher than over the last 10+ years. At the same time, equity markets around the world posted strong performance in 2023 and 2024 (so far). Many investors thus wonder how this fits together. Why should we pay the same multiple for stocks when the risk-free alternative is much better than a few years ago? Or more technically, why should we accept such a low equity risk premium? This week’s AGNOSTIC Paper is the first of two that sheds some light on this (very important, but also very difficult) issue.

  • Equity returns and risk-premiums were lower in higher-rates environments
  • EPS growth, valuations, and interest rate changes explain the effect
  • Treasuries and absolute-return strategies historically benefitted from higher rates

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AgPa #80: Forget Factors and Keep it Simple?

Keeping it Simple: The Disappearance of Premia for Standard Non-Market Factors (2023)
Avanidhar Subrahmanyam
SSRN Working Paper, URL

This week’s AGNOSTIC Paper is almost a cheat as it is only 3 pages long. I found the paper in the newsletter of a German journalist and thought it is so unconventional that I have to write about it. The author, Avanidhar Subrahmanyam, is a well-known financial economist at the UCLA School of Management and articulates a very simple statistical critique on factor investing. I believe it is important to seek disconfirming evidence, so I regard it as duty to look at this paper with an open mind.

  • Only two factors are significant over the last 27+ years

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#4: Warren Buffett is not an Index Hugger

Two weeks ago, the Financial Times (FT) Unhedged Newsletter (URL) joined many others to write about Warren Buffett and Berkshire Hathaway (BRK) in the week of its famous annual general meeting in Omaha. The FT also published an outstanding series on the future of Berkshire Hathaway without the now 93 year-old legendary CEO and Chairman (URL).

I stumbled across some statements in the two Unhedged Newsletters “Warren Buffett: The world’s richest index-hugger” (URL) and “Berkshire’s next move” (URL) from May 6 and 7, respectively. I have nothing qualified to say about Buffett’s succession, but I do believe the statement that Warren Buffett is an index hugger deserves some more discussion.

  • Berkshire Hathaway’s returns over the last 21 years
  • Berkshire Hathaway’s “risk” over the last 21 years
  • What is risk?
  • Berkshire Hathaway in good and bad markets
  • Is Warren Buffett an index hugger?

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AgPa #77: Too Much Passive Investing?

The Rise of Passive Investing and Active Mutual Fund Skill (2023)
Da Huang
SSRN Working Paper, URL

This week’s AGNOSTIC Paper is a quite recent working paper that examines the impact of passive investing on the US stock market. The debate about a potential tipping point when too many assets go passive is ongoing and often quite emotional. Depending on who you ask, you hear everything from “fundamentally broken” markets to the idea that we only need very few skilled active managers who compete for all the alpha. This week’s paper provides some interesting theoretical and empirical results on that matter.

  • Passive investing in the US grew tremendously
  • Passive investing forces unskilled managers to quit
  • Surviving active managers have more skill, but take less risk
  • We are probably not yet at the point of too much passive

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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 #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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AgPa #66: Machine-Learned Manager Selection (2/4)

Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha (2023)
Victor DeMiguel, Javier Gil-Bazo, Francisco J. Nogales, Andre A. P. Santos
SSRN Working Paper, URL

The second AGNOSTIC Paper on the application of machine learning in manager selection. This week’s paper follows essentially the same idea as Kaniel et al. (2022) in AgPa #65. The authors also examine a comprehensive sample of US mutual funds and although they use slightly different methodology, arrive at generally similar conclusions. This, of course, increases the evidence that machine learning is indeed helpful for manager selection…

  • Machine learning helps to identify outperforming funds
  • Past performance and measures of activeness are the most relevant variables
  • Given their alpha, machine-selected funds remain too small

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