Course Complete
This is the October edition of the Portfolio Construction newsletter, giving an overview of the Applied Quantitative Investment Management lectures.
The Applied Quantitative Investment Management course is now complete.
The course carefully goes through the Portfolio Construction and Risk Management book and its accompanying Python code.
You can watch the first lecture for free here.
As a paid subscriber, you have full access to the lectures and other paid content as well as asking me questions through the Substack chat.
Make sure to check out the popular LinkedIn posts recap at the bottom of this newsletter. There is a link to a recent interview with me at the bottom.
The future of the Quantamental Investing publication
Going forward, I plan to share a post once every week. Every month, one of these posts will be an advanced application of the next-generation investment framework for paid subscribers.
You can get an overview of the existing paid content here. To ensure that you get access at the best price, it is recommended to subscribe as early as possible.
Applied Quantitative Investment Management lectures
Below is an overview of the 12 lectures. If you have any questions after studying them, please ask me through the Substack chat or by posting comments for the specific lecture post.
Posts recap
Below is a recap of the most important LinkedIn posts since last newsletter.
Lecture 9 on advanced portfolio optimization:
Exposing the “investment risk measure does not matter” claim:
Update on the physical version of the Portfolio Construction and Risk Management book:
Lecture 10 about resampled portfolio optimization:
How to combine a Markov Regime Switching model with resampling for high-dimensional investment simulation:
Lecture on derivatives portfolio optimization with fully general parameter uncertainty:
An issue with drawdown portfolio optimization:
Lecture 12 about tail risk hedging and analysis:
Feedback on Chapter 5 of the Portfolio Construction and Risk Management book:
Quant Conversations with Quant Enthusiasts:


