USA

AgPa #29: Cost-Mitigation Techniques

Comparing Cost-Mitigation Techniques (2019)
Robert Novy-Marx, Mihail Velikov
Financial Analysts Journal 75(1), 85-102, URL/SSRN

This week’s AGNOSTIC Paper examines three techniques to mitigate trading costs of systematic equity strategies and compares them by after-cost performance. The empirical evidence clearly speaks for the application of more sophisticated trading rules (Technique #3)…

  • Trading costs decreased but are still important
  • Technique #1: focus on “cheap-to-trade” securities
  • Technique #2: rebalance less frequently
  • Technique #3: create better trading rules
  • Value- versus equal-weighted portfolios

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AgPa #28: “Not Selling” of Insiders

Is “Not Trading” Informative? Evidence from Corporate Insiders’ Portfolios (2022)
Luke DeVault, Scott Cederburg, Kainan Wang
Financial Analysts Journal 78(1), 79-100, URL/SSRN

Transactions of insiders are usually a useful source of information when evaluating a stock. Insiders typically have a good understanding of the underlying business and buys are therefore often considered as positive signal. On the other hand, insider sales are not necessarily negative. There are many non-informative reasons to cash out. Maybe the insider needs some cash for personal expenditures or just wants to diversify his assets. This week’s AGNOSTIC Paper challenges this asymmetry and creatively shows that even those transactions convey important information…

  • “Not sold” stocks from insider portfolios outperformed
  • A portfolio of “not sold” stocks easily beat the US market
  • “Not sold” stocks with momentum are even better
  • Corporate insiders know more than institutional investors

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AgPa #26: Trading on Price Charts

(Re-)Imag(in)ing Price Trends (2022)
Jingwen Jiang, Bryan T. Kelly, Dacheng Xiu
The Journal of Finance, Forthcoming, URL

This week’s AGNOSTIC Paper is about technical analysis. Full disclosure: I never believed in technical analysis in the sense of drawing lines on charts or imagining somewhat arbitrary patterns.

But the approach of this week’s authors is quite different. They borrow methodology from image recognition and train a machine learning model to detect predictive patterns in price charts (Yes, the machine receives the price chart as picture, not the underlying numbers!)…

  • The model identifies very profitable short-term signals
  • The signals are also profitable over longer horizons
  • Some of the machine-learning-signals are explainable
  • The model disagrees with conventional technical analysis

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AgPa #21: AI-Powered vs. Human Funds

Do AI-Powered Mutual Funds Perform Better? (2022)
Rui Chen, Jinjuan Ren
Finance Research Letters, Volume 47, Part A, URL/SSRN

This week’s AGNOSTIC Paper compares the performance of AI-powered- and human mutual funds between 2017 and 2019 in the US. Although AI-powered funds are not the holy grail some investors may have hoped for, they still added value compared to their human peers…

  • AI-powered mutual funds did not outperform the US market
  • But AI-powered funds outperformed their human peers
  • And AI-powered funds avoided the disposition- and rank effect

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AgPa #18: ESG Confusion (1/2)

ESG Rating Disagreement and Stock Returns (2021)
Rajna Gibson Brandon, Philipp Krueger, Peter Steffen Schmidt
Financial Analysts Journal 77(4), 104-127, URL/SSRN

This week’s AGNOSTIC Paper is about a quite controversial topic: Environmental, Social, and Governance a.k.a. ESG. ESG refers to the idea that investors should consider those dimensions in their decisions and thereby contribute to a more sustainable economy. But as this week’s paper shows, there is little agreement on what ESG actually is…

  • ESG ratings disagree: the average correlation is just 0.45
  • There is less ESG disagreement for larger, more profitable firms with credit ratings
  • Investors demanded a risk premium for ESG uncertainty

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AgPa #16: Concentrated Stock Markets (7/7)

Mutual Fund Performance at Long Horizons (2022)
Hendrik Bessembinder, Michael J. Cooper, Feng Zhang
SMU Cox School of Business Research Paper No. 22-11 via SSRN, URL

The seventh and final AGNOSTIC Paper on the extreme concentration in stock markets. This one is an out-of-sample test and documents very similar concentration and positive skewness for US mutual funds between 1991 and 2020.

  • Longer investment-horizons lead to extremer return distributions – also for mutual funds
  • Most active managers underperform passive benchmarks – especially over the long-term
  • Compared to the S&P 500, mutual fund investors lost about $1.3T between 1991 and 2020

But a picture is worth a thousand words…


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AgPa #15: Concentrated Stock Markets (6/7)

Extreme Stock Market Performers, Part I: Expect Some Drawdowns (2020)
Hendrik Bessembinder
SSRN Working Paper, URL

The sixth of seven AGNOSTIC Papers on the extreme concentration in stock markets. This one shows that even for the top wealth-creators, the road to success has been anything but smooth…

  • Even the best companies during their best decades had substantial drawdowns
  • Today’s drawdowns of tomorrow’s winners are even worse

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AgPa #14: Concentrated Stock Markets (5/7)

Extreme Stock Market Performers, Part IV: Can Observable Characteristics Forecast Outcomes (2020)
Hendrik Bessembinder
SSRN Working Paper, URL

The fifth of seven AGNOSTIC Papers on the extreme concentration in stock markets. This one will finally examine how to identify the few big winners ex-ante (at least it will try). Future winners have some distinct fundamental characteristics today. That said, the picture remains noisy and it’s very difficult to find them systematically…

  • Future top-performers tend to be younger, produce higher drawdowns, and spend more on R&D
  • Future wealth-creators tend to be older, more levered, and pay higher dividends
  • Identifying big winners remains challenging

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AgPa #13: Concentrated Stock Markets (4/7)

Extreme Stock Market Performers, Part III: What are their Observable Characteristics? (2020)
Hendrik Bessembinder
SSRN Working Paper, URL

The fourth of seven AGNOSTIC Papers about the extreme concentration in stock markets. This one goes one step further and examines the fundamental characteristics of big winners ex-post. The main insight is quite intuitive: outstanding stock performance usually comes with outstanding fundamental performance of the underlying company…

  • Big winners grow faster, are more profitable, and have smaller drawdowns
  • Observable fundamentals still explain relatively little

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AgPa #12: Concentrated Stock Markets (3/7)

Extreme Stock Market Performers, Part II: Do Technology Stocks Dominate? (2020)
Hendrik Bessembinder
SSRN Working Paper, URL

The third of seven AGNOSTIC Papers about the extreme concentration within stock markets. This one examines the industry composition of the most and least successful companies between 1950 and 2019 in the US. Unfortunately, just looking at industries is not really helpful to identify the few big winners…

  • The Tech-Industry is not as dominant as it seems at first glance
  • There is (unfortunately) not “the one” industry to look at

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