Hvass Laboratories
Magnus Erik Hvass Pedersen
Magnus Erik Hvass Pedersen
My finance research has been made into simulation models that everyone can easily run. Give it a try!
How should you combine different assets into a portfolio when investing? In 1990 the Nobel prize in finance was awarded for a portfolio method invented in the 1950's that is still the standard used today. My work first shows why that method fails, and then I present a new portfolio method that is simple, extremely fast and very robust.
How does the valuation ratio of a stock affect its long-term returns? Is it possible for stocks and the entire stock-market to be over- or under-valued? In 2013 the Nobel prize in finance was partially awarded for empirical studies on this topic, although a formal explanation has never been given until now. My work formally explains how the valuation ratio matters along with growth and dividends in forecasting long-term stock-returns.
Corporations worldwide spend around US$ 1 TRILLION on share buybacks every year, without knowing whether it is good or bad for shareholder value. My work provides the formulas for calculating how long-term shareholder value is affected by share buybacks in different situations.
How to calculate the effect on shareholder value when corporations issue new shares and the intrinsic value is uncertain.
New finance research on long-term investing, portfolio optimization, etc. written as interactive Python Notebooks with video talks.
Old finance research written as traditional papers.
I also designed and implemented the SimFin Python API and Tutorials for easily obtaining and processing financial data, and sharing your research with others. I am no longer involved in that project.
My research was on so-called Meta-Optimization where two levels of optimization are being used: The first level is optimizing a given problem as usual, while the second "meta-level" is for tuning the parameters of the optimizer in the first level. This can often lead to greatly improved results, because the meta-optimizer can find counter-intuitive parameters that cause the primary optimizer to perform much better.
Implementation in Python and other languages (SwarmOps)
More recent and advanced methods in Python Notebooks (MetaOps)
In 2016 there was a revolution in Artificial Intelligence. I wanted to learn the new methods, but the documentation and tutorials were of very low quality. So I made this series of tutorials while I was learning it myself. Hundreds of thousands of people throughout the world have learned Deep Learning and TensorFlow from these tutorials, which set a new standard for how teaching materials should be made.
STOP! Please do NOT write technical questions about TensorFlow. I am NOT interested in doing freelance projects. I am NOT interested in "networking" and making new "e-mail friends". Too many people write me because they selfishly want something from me. Please only contact me if you have a good reason.
magnus (at) hvass-labs.org