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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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AgPa #65: Machine-Learned Manager Selection (1/4)

Machine-Learning the Skill of Mutual Fund Managers (2022)
Ron Kaniel, Zihan Lin, Markus Pelger, Stijn Van Nieuwerburgh
NBER Working Paper 29723, URL

To conclude the posts on manager selection, at least for the moment, I will dive into one of the most recent research frontiers in this area. Since the application of machine learning in investment management has been intensively studied among equities for more than three years now, it is not surprising that researchers also start to apply such algorithms to other asset classes. A natural candidate for this are equity mutual funds and this is exactly where this and the next four week’s AGNOSTIC Papers come in.

  • Machine learning helps to identify outperforming funds
  • Less is more – not all information is necessary
  • Alpha is easier to predict than total returns

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AgPa #64: Fund Manager Multitasking

Managerial Multitasking in the Mutual Fund Industry (2023)
Vikas Agarwal, Linlin Ma, Kevin Mullally
Financial Analysts Journal 79(2), URL/SSRN

Some days ago, I came across yet another interesting study on manager selection. The idea of this week’s AGNOSTIC Paper is very straight forward. When you hire a fund manager, you want this person to focus on your money and not do much else. Probably no one would agree to a surgery where the surgeon operates on five patients at the same time. So why hire a fund manager who manages more than one fund?

  • Manager multitasking strongly increased from 1990 to 2018
  • Managers who start multitasking tend to have better track records
  • Fund performance decreases significantly after managers start multitasking
  • The number of managed funds amplifies the effect of multitasking
  • Investors put less money into existing funds of multitasking managers

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AgPa #63: Fire the Winners and Hire the Losers

The Folly of Hiring Winners and Firing Losers (2018)
Rob Arnott, Vitali Kalesnik, Lillian Wu
The Journal of Portfolio Management Fall 2018, 45 (1), URL/research affiliates

I am still in my research on manager selection, so apologies to everyone who doesn’t find that too interesting. We already touched the question on what to do with underperforming managers in AgPa #59 and #60. This week’s AGNOSTIC Paper, however, examines this problem somewhat more generally and delivers some really simple (but psychologically hard-to-execute) common-sense conclusions.

  • Current winners tend to be future losers
  • High fees are the most reliable way to underperform
  • Investors should use factor exposures and valuations to evaluate fund managers

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AgPa #62: 10 Questions to Ask Your Fund Manager

The 10 Most Important Questions to Ask in Selecting a Money Manager (1990)
Jack L. Treynor
Financial Analysts Journal 46(3), URL

Continuing with the challenge of asset manager selection, this week’s AGNOSTIC Paper again focuses on the important soft factors of money managers. Jack Treynor, the author of this week’s paper and one of the giants in finance research, presents a short and entertaining checklist “to ensure that the right questions are asked […]”.

  • Does the money manager present his ideas smoothly and without hesitation?
  • Is the money manager clear and confident about his ideas?
  • Do his ideas have common-sense appeal?
  • Are you comfortable with the money manager’s answers?
  • Does the money manager exhibit detailed knowledge of a broad range of companies and industries?
  • Does the money manager react decisively to new developments?
  • Does the money manager have a large asset base?
  • Does the money manager live baronially—with expensive clubs; houses and cars; and travel by the QE II, the Concorde, or the Orient Express?
  • Are his offices impressive?
  • Is the money manager impressively capitalized?

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AgPa #61: Minivans versus Sports Cars

Sensation Seeking and Hedge Funds (2018)
Stephen Brown, Yan Lu, Sugata Ray, Melvyn Teo
The Journal of Finance 73(6), 2871-2914, URL/SSRN

Tell me about the car you drive and I tell you who you are. In the hope of not offending the car enthusiasts too much, this week’s AGNOSTIC Paper relates the performance and characteristics of hedge fund managers to the type of car they drive. As announced in last week’s article, this is a funny example for the important soft factors that investors should consider when selecting an asset manager.

  • Sports car drivers take more risk and deliver lower performance
  • Funds of sports car drivers come with more operational risk
  • Sports-car-driving investors want sports-car-driving fund managers

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AgPa #60: Some Advice for Asset Manager Selection

Manager Selection, Deselection, and Termination (2020)
Mark Anson
The Journal of Portfolio Management Fund Manager Selection 2020, 46(5), 6-16, URL

After examining the end of the asset manager selection process (i.e. firing decisions) in the last post, this week’s AGNOSTIC Paper provides a broader overview. The author touches a lot of issues and provides some general advice how to cope with them in practice…

  • Asset owners seem to be bad in hiring and firing managers
  • How to decide about firing a manager
  • Style Drift, Style Clustering, and Fees
  • The very important difference between Alpha and Beta
  • Momentum and Crowding

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