Funds & ETFs

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

Read the Full Post

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

Read the Full Post

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

Read the Full Post

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

Read the Full Post

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

Read the Full Post

SA #18: RPV – ‘Pure Value’ Is Indeed More Value Than ‘Value’

RPV: 'Pure Value' Is Indeed More Value Than 'Value'
April 08, 2023

Summary

  • Systematic value investors bet that a diversified portfolio of fundamentally “cheap” stocks should outperform a portfolio of “expensive” stocks over the long term.
  • The Invesco S&P 500 Pure Value ETF tracks the S&P 500 Pure Value Index and was incepted in March 2006.
  • Compared to other “smart-beta” value ETFs, RPV is a more aggressive value-strategy and only invests in the top 20% value stocks of the S&P 500 universe (currently 82 positions).
  • With this methodology and three fundamental valuation ratios as value signals, the investment process underlying RPV incorporates several best-practices from the academic literature on the value-factor.
  • RPV is well positioned in a value-peer group and (in my opinion) a very good instrument for investors seeking concentrated exposure to the value-factor.


Read the Full Article on Seeking Alpha

Read the Full Post

SA #17: IUSV – Transparent Value With Modest Active Risk

IUSV: Transparent Value With Modest Active Risk
March 29, 2023

Summary

  • The general idea behind the value factor is that a diversified portfolio of fundamentally cheap stocks should outperform over the long term.
  • Since January 2017, the iShares Core S&P U.S. Value ETF has tracked the S&P 900 Value Index and provides transparent exposure to the well-researched value premium.
  • S&P uses three well-known fundamental valuation ratios to identify and overweight “cheap” value stocks with respect to the overall market index.
  • Relying on multiple value signals is in line with the research consensus of the literature on the value factor and differentiates IUSV from some competitors.
  • Despite the recent value drawdown, IUSV kept up with a peer group and should be a reasonable instrument for investors who want to have U.S. large-cap value exposure at modest active risk.


Read the Full Article on Seeking Alpha

Read the Full Post

SA #16: IWD – Low Growth Is Not Necessarily Value – Also For Large Caps

IWD: Low Growth Is Not Necessarily Value – Also For Large Caps
March 27, 2023

Summary

  • There are countless methods and nuances of (systematic) value investing, but the general idea remains “cheap beats expensive”. Not always, but on average over the long run.
  • The iShares Russell 1000 Value ETF tracks the Russell 1000 Value Index and offers a simple, transparent, and cheap implementation of the value premium for US large caps.
  • The Russell value process unfortunately equates “low sales growth” with “value” which contradicts with the best practices discussed in the literature on the value factor.
  • Despite decent performance when compared to an investable value peer-group, IWD is therefore not my preferred value implementation.


Read the Full Article on Seeking Alpha

Read the Full Post

SA #15: VLUE – Transparent Value With Little Industry Bets

VLUE: Transparent Value With Little Industry Bets
February 28, 2023

Summary

  • The core idea of value investing remains unchanged since its introduction by Graham and Dodd in the 1930s: fundamentally cheap stocks tend to beat expensive stocks on average.
  • The iShares Edge MSCI USA Value Factor ETF tracks the MSCI USA Enhanced Value Index and provides cheap, efficient, and transparent systematic value exposure among US large caps.
  • Importantly, the underlying value index incorporates several insights of the literature on the value factor (multiple value signals, value-rankings within sectors, and no unintended industry bets).
  • For investors who want US value exposure without running into unintended sector bets and without taking too much active risk, VLUE is an interesting instrument.


Read the Full Article on Seeking Alpha

Read the Full Post

SA #14: IWN – Low Growth Is Not Necessarily Value

IWN: Low Growth Is Not Necessarily Value
February 28, 2023

Summary

  • Systematic value investing is the idea that fundamentally cheap stocks tend to outperform expensive stocks over the long term on average.
  • The iShares Russell 2000 Value ETF tracks the Russell 2000 Value Index and offers a simple, transparent, and cheap implementation of the value premium for US small caps.
  • Unfortunately, the index equates “value” with “low sales growth” and therefore contradicts with well-known results of the academic and practitioner literature on the value factor.
  • Despite decent performance since inception in 2000 and over the last years, IWN is therefore not my preferred value instrument.


Read the Full Article on Seeking Alpha

Read the Full Post