AGNOSTIC Papers

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 #45: Factor Investing in Private Debt

Investing with Style in Liquid Private Debt (2022)
Thomas Mählmann, Galina Sukonnik
Financial Analysts Journal 78(3), URL

This week’s AGNOSTIC Paper is yet another out-of-sample test of the Momentum and Value factor. The authors apply the factors within the relatively new asset class of private debt. More specifically, for “[…] loans to non-investment grade issuers, commonly known as leveraged loans.” This is obviously not my main area of expertise, but I learned from the paper that there is quite some trading of such loans in private secondary markets. Implementing a factor strategy for leveraged loans is obviously more complicated than for equities, but this is exactly what makes this study so interesting.

  • Private debt improves multi-asset portfolios
  • Value and Momentum are profitable within private debt

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AgPa #44: Betting against Quant – Thematic Indices

Betting against Quant: Examining the Factor Exposures of Thematic Indexes (2021)
David Blitz
The Journal of Beta Investment Strategies Winter 2021, URL/SSRN

This week’s AGNOSTIC Paper examines a recent trend in the asset management industry: thematic indices. The sales pitch is simple. With a thematic index you can easily invest in the “next big things”. Artificial intelligence, aging population, e-sports and gaming, healthcare breakthroughs – just name your buzzword and you will find an investment product for it. This week’s paper is among the first that examine such thematic investments through the lens of the major factor premiums.

  • Thematic indices are more volatile and have higher betas than the overall market
  • Thematic indices tend to hold expensive, low-quality stocks with neutral momentum
  • There are still reasons why thematic indices exist

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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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AgPa #42: Global Factors since 1800

Global factor premiums (2021)
Guido Baltussen, Laurens Swinkels, Pim van Vliet
Journal of Financial Economics 124(3), 1128-1154, URL

This week’s AGNOSTIC Paper is another out-of-sample test of the major factors and goes even further back in time than the last one. The authors examine the major factor premiums among equity indices, government bond indices, currencies, and commodities in a sample that ranges from December 31, 1799 to December 31, 2016.

  • Momentum, Value, and Low-Risk “worked” globally, in different asset classes, and out-of-sample
  • There is little evidence for factor decay

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AgPa #41: US Factors before 1926

The Cross-Section of Stock Returns before CRSP (2023)
Guido Baltussen, Bart van Vliet, Pim van Vliet
SSRN Working Paper, URL

This week’s AGNOSTIC Paper is an unprecedented out-of-sample test of the four major factors (Momentum, Value, Low-Risk, Size). The authors construct a novel dataset of US stocks that reaches from 1866 to 1926. It therefore extends the extensively studied CRSP dataset by 60 years.

  • Momentum, Value, and Low-Risk were there before 1926
  • Factors weren’t stronger before 1926
  • Machine learning models find the same factors

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AgPa #40: Size Effect – Fact and Fiction

Fact, Fiction, and the Size Effect (2018)
Ron Alquist, Ronen Israel, Tobias Moskowitz
The Journal of Portfolio Management Fall 2018, 45 (1) 34-61, URL/AQR

After examining several Facts and Fictions around factor investing in general, momentum, value, and low-risk, this week’s AGNOSTIC Paper tackles the final anomaly. The size effect received a lot of attention in both academia and the investment industry, probably because it is one of the oldest documented anomalies. In this final paper of their Fact and Fictions series, the authors examine some myths around it.

  • Fiction: Size is the strongest documented factor
  • Fact: The size effect weakened since its discovery
  • Fiction: The size effect is robust across different measures
  • Fact: The size effect is strongly related to the January effect
  • Fiction: Size also works in international equity markets
  • Fact: Size does not work within other asset classes
  • Fact: Most of the size effect are micro cap stocks
  • Fact: Size is difficult to implement in real-world portfolios
  • Fiction: The size effect is more than just a liquidity effect
  • Fiction: There are economic theories for the size effect
  • Fiction: Size works because other factors are stronger among small cap stocks
  • Fact: There are reasons to overweight small caps even without the size effect
  • Fact and Fiction: The size effect is stronger when controlling for other factors
  • Fact: Size receives a lot of attention despite weak evidence

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AgPa #39: Low-Risk Investing – Fact and Fiction

Fact and Fiction about Low-Risk Investing (2020)
Ron Alquist, Andrea Frazzini, Antti Ilmanen, Lasse Heje Pedersen
The Journal of Portfolio Management Multi-Asset Special Issue 2020, 46 (6) 72-92, URL/AQR

After examining value and momentum, this week’s AGNOSTIC Paper examines some Fact and Fictions around defensive / low-risk investing. The defensive / low-risk factor captures various well-known effects like the low-volatility and Betting Against Beta effect, but also fundamental strategies like quality (a.k.a. the Quality Minus Junk factor).

  • Fact: Low-risk securities generate risk-adjusted outperformance
  • Fiction: The low-risk premium is weaker than other factors
  • Fact: Low-risk strategies worked out-of-sample
  • Fiction: Low-risk profits come from industry bets
  • Fact: Low-risk investing worked across geographies and asset classes
  • Fiction: Low-risk investing doesn’t work because the CAPM is dead
  • Fact: There is economic theory behind the low-risk premium
  • Fiction: Low-risk investing does not survive trading costs
  • Fact: Low-risk investing can lose money in bear markets
  • Fiction: Low-risk factors became too expensive

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