Sven

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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#1: Why the Name AGNOSTIC INVESTING?

Why is this website called AGNOSTIC INVESTING?

There are several reasons:
– I am not very creative
– It is what I am planning to do
– It starts with A

I believe that agnostically looking at facts and data should, at some point, lead to decent results. Like (almost) everyone else, I don’t have the ultimate investing strategy. Being agnostic is therefore my only chance.

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