Factor Investing

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 #36: Factor Investing – Fact and Fiction

Fact, Fiction, and Factor Investing (2023)
Michele Aghassi, Cliff Asness, Charles Fattouche, Tobias J. Moskowitz
The Journal of Portfolio Management Quantitative Special Issue 2023, URL

Whenever AQR writes about systematic investing, it’s (in my opinion) time to listen. This one is a very good overview about factor investing. Given that this is the intellectual basis of many things I do here on the website, it perfectly fits to the series.

  • Fiction: Factor investing is just data-mining
  • Fact: Factors are risky
  • Fiction: Factor diversification doesn’t work
  • Fact: Factors work in different market regimes
  • Fiction: Factors don’t work anymore
  • Fact: Factors were and are not crowded
  • Fiction: Everyone should (and can) invest in factors
  • Fact: Factor discipline beats factor timing
  • Fact: Sticking with factors is often difficult

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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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Report Analytics USA #2

This post contains a lot of unsexy calculations and is fairly technical. But (in my opinion) there are some very interesting results. Not just for my particular strategy but for everyone who is active on Wikifolio.

First. Overall and especially after costs, my two Wikifolios weren’t a good alternative to a standard ETF on the S&P 500 index (from inception to March 11, 2022). To my defense, however, I stressed several times that the two Wikifolios are just a real-world test of my master thesis and I never marketed them as investments.

Second. I still believe that Wikifolio is a great platform to test strategies like mine, but it is not perfect. There are annoying technical issues, pretty high fees, and significant indirect trading costs. Depending on the liquidity of the stock, bid-ask-spreads and/or unfavorable FX rates amount to 40-80 basis points per transaction on average.

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Report Analytics USA #1

This is the start of an ongoing series intended to share updates, insights, and backgrounds on the Report Analytics USA portfolios. To start with, I present the methodology that I currently use to implement the live portfolios on Wikifolio.

Heart of the process is a stock selection based on copy-paste of the most recently published annual and quarterly reports. I further divide this selection by market capitalization to create a “Large” and “Small” version of the Report Analytics USA portfolios.

All of this is just a starting point and I conclude this post with a roadmap of ideas to improve the strategy.

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#2: Copy-Paste Outperformance

Every year, US companies must publish three quarterly and one annual report. Preparing those reports, however, is a lot of effort, does not improve operations, and reveals information to competitors.

How to deal with this? Correct, spend the time to create one comprehensive template and reuse it as long as possible. In an excellent research paper titled “Lazy Prices” (2020), the authors show that US companies are no exception from this: many annual and quarterly reports are basically just updated copies from the previous year.

What does this mean for investors? Since most of the report is just copy-paste, they should rather focus on differences between the current and previous report (for example, new paragraphs). It turns out that such changes are indeed very important: quantitative measures for report copy-paste predict future stock returns and help to achieve outperformance vs. common US indices.


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