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Black Friday Last Chance
This post is a reminder that it is the last chance to get the Black Friday discount on yearly subscriptions to the Quantamental Investing publication.
Dec 5
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Anton Vorobets
3
1
Academic Anti-Science
December edition of the Portfolio Construction newsletter, explaining how anti-science manifests itself in academic finance and economics.
Dec 2
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Anton Vorobets
8
1
Gold in Multi-Asset Portfolios
This article analyzes the characteristics of gold in multi-asset portfolios, including an analysis of inflation and VIX shocks.
Nov 27
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Anton Vorobets
8
3
1
High-Dimensional CVaR Optimization
This article presents a Python case study that optimizes the CVaR for a portfolio of 2000 stocks, illustrating that such problems are practically…
Nov 20
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Anton Vorobets
6
1
Open-Source Investment Software Issues
This article presents the issues with open-source packages for investment analysis and risk management.
Nov 13
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Anton Vorobets
7
1
Anton Vorobets @ Quant Enthusiasts
This video post contains the Quant Enthusiasts interview with Anton Vorobets.
Oct 29
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Anton Vorobets
8
1
23:30
Resampling Persistent Time Series
This article explains how we can resample persistent time series that are expected to mean revert to long-term averages.
Oct 23
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Anton Vorobets
6
1
Quantamental Investing Updates
This article summarizes important updates for the Quantamental Investing Substack publication.
Oct 15
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Anton Vorobets
6
1
How Challenging is CVaR Optimization?
This article presents the challenges of solving fully general CVaR optimization problems in Python.
Oct 8
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Anton Vorobets
9
2
12. The Normal Distribution Myth
This video goes through example 12 from the fortitudo.tech Python package and the accompanying SSRN article.
Aug 19
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Anton Vorobets
6
1
14:55
Course Feedback
This post gives a summary of the Applied Quantitative Investment Management course and asks for your feedback.
Aug 13
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Anton Vorobets
9
2
Course Q&A
This is a practical Q&A thread for the upcoming Applied Quantitative Investment Management course.
Jun 24
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Anton Vorobets
8
3
1
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