AgPa #8: Neuroscientific Insights for Alpha

Harnessing Neuroscientific Insights to Generate Alpha (2022)
Elise Payzan-LeNestour, James Doran, Lionnel Pradier, Tālis J. Putniņš
Financial Analysts Journal, 78(2), 79-95, URL

We are all prone to psychological biases that are very hard to control. This week’s AGNOSTIC Paper examines the after-effect, one particular example for this.

The idea of the after-effect is simple. If you are long enough exposed to a certain stimuli, you will have the illusion of the exact opposite stimuli after the first one disappears. Apparently, this pattern was very relevant for the US stock market…

  • The after-effect distorted the VIX Index
  • Exploiting the after-effect yielded significant alpha

Read the Full Post

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

Read the Full Post

#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.

Read the Full Post