Alternative Data

AgPa #52: Happier Employees, Better Returns?

Employee Satisfaction and Long-Run Stock Returns, 1984–2020 (2022)
Hamid Boustanifar, Young Dae Kang
Financial Analysts Journal 78(3), URL/SSRN

A common sales-pitch of ESG strategies is the idea that those strategies not only do good for the planet and other stakeholders, but also generate higher returns. I am generally skeptic about this, but there are studies showing that certain ESG variables historically indeed predicted higher returns. A prominent example for this is the paper on employee satisfaction by Alex Edmans (2011). This week’s AGNOSTIC Paper is an out-of-sample test of this study with somewhat more thorough testing.

  • “Best Companies” outperformed several benchmarks
  • “Best Companies” outperformed during crises and out-of-sample
  • Quality and Low-Risk factors explain some of the premium on “Best Companies”

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AgPa #49: Machine Learning in Quant Asset Management

How Can Machine Learning Advance Quantitative Asset Management? (2023)
David Blitz, Tobias Hoogteijling, Harald Lohre, Philip Messow
The Journal of Portfolio Management Quantitative Tools 2023, URL/SSRN

This week’s AGNOSTIC Paper is a broad overview about machine learning in investment management. The authors outline the benefits and pitfalls of machine learning compared to “traditional” econometrics and present several use cases in the world of (quantitative) asset management. They also provide ideas for research governance to keep those powerful methods under control.

  • Benefits and pitfalls of machine learning in finance
  • Use cases of machine learning in asset management
  • Keeping it under control: research governance and protocol

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AgPa #33: World Cups and Stock Markets

Sports Sentiment and Stock Returns (2007)
Alex Edmans, Diego García and Øyvind Norli
The Journal of Finance 62(4), 1967-1998, URL

Given that this week’s AGNOSTIC Paper coincides with the final of the World Cup, I couldn’t resist the temptation. Below you can see a chart of the knockout stage of this year’s tournament. But since you are visiting a nerdy finance website, the focus is not on the results, but on the post-match stock market returns of the playing countries…


You may (understandably) say that this is some nice storytelling but not much more. However, I didn’t made this up to create a story but the idea of this analysis actually comes from this week’s AGNOSTIC paper…

  • Stock markets of losing countries tend to underperform after important matches
  • The effect most likely comes from bad mood after sport losses

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AgPa #23: Trading on the Weather

Global weather-based trading strategies (2022)
Ming Dong, Andréanne Tremblay
Journal of Banking & Finance, Volume 143, 106558, URL/SSRN

People tend to be in a better mood when the sun is shining. That’s nothing dramatically new but this week’s AGNOSTIC Paper shows that this apparently also applies to investors. An investment strategy that went long (short) the stock market index from the country with the best (worst) weather on a particular day generated meaningful (hypothetical) outperformance…

  • The global long-short weather strategy returned 15.2% p.a. between 1993 and 2012
  • The long-only version of the strategy returned 13.4% p.a.

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AgPa #7: Spotify Streaming and Stock Returns

Music sentiment and stock returns around the world (2021)
Alex Edmans, Adrian Fernandez-Perez, Alexandre Garel, Ivan Indriawan
Journal of Financial Economics, In Press, Corrected Proof, URL

This week’s AGNOSTIC Paper examines the role of music sentiment in the stock market. What sounds like statistical hocus-pocus is part of an important question. Do other factors than rational information drive stock markets?

I like the paper for its creative use of alternative data and its clean methodology. But to be honest, I was somewhat skeptical when I first heard about it. However, the authors present an intuitive economic rationale and rigorously test their hypotheses in various robustness checks. The results are quite interesting…

  • Music sentiment is related to stock market returns
  • Music sentiment is more important in less efficient markets
  • Music sentiment is also related to fund flows and bond market returns

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AgPa #6: Predicting Returns with (Alternative) Consumer Data

Predicting Performance Using Consumer Big Data (2022)
Kenneth Froot, Namho Kang, Gideon Ozik, Ronnie Sadka
The Journal of Portfolio Management 48(3), 47-61, URL

This week’s AGNOSTIC Paper is again more related to my other content. The authors use proxies for in-store activity, brand awareness, and web traffic to predict fundamentals and returns of consumer-oriented companies.

I like the paper because it examines alternative data and is published in a peer-reviewed journal. Other studies on the topic are often just white papers of data providers. So it is nice to have a more scientific analysis.

  • Alternative consumer-data predicts firm fundamentals
  • Trading on alternative consumer-data generated monthly alphas of up to 1.9%

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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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#3: Big Data & Machine Learning in Asset Management

This week I gave a talk on “Big Data and Machine Learning in Asset Management” at Goethe-University in Frankfurt. Thanks again to my thesis-supervisor Sasan Mansouri for the invitation. In this post I will summarize a few points of the talk and share the slides. The key result is the following framework to evaluate investment strategies that claim to use big data and machine learning. I also apply this to several real world funds.

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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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