Don’t make the mistake of thinking that you should build everything yourself.
I have made this mistake, and I continue to see it being made among investment managers.
Read more about it here: substack.com/profile/1707...
#quant #quantsky #finance #markets #python #investing #cvar #cml
How to get the most out of your market views and stress tests for fully general Monte Carlo simulations.
Find the latest version of the Sequential Entropy Pooling (SeqEP) article and a lot of support material below.
#quant #quantsky #finance #markets #python #investing #entropypooling #views
How to analyse the dynamic aspects of tail risks:
www.linkedin.com/posts/fortit...
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Some of the best feedback you can get regarding investment methods is that they are obvious.
This might sound surprising, but it usually implies that the solution is impactful and elegant:
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The April edition of the Portfolio Construction newsletter gives a summary of the past and reveals a bit about the future.
At the end, there is a popular posts recap. Make sure to check it out :-)
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Perspectives on full and fractional differencing of investment time series:
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Analyzing the investment risks of wars in a causal and predictive way for multi-asset portfolios.
This Python case study simulates the P&L of multi-asset instruments and analyzes the effect of various scenarios for current and future potential wars.
#quant #quantsky #finance #investing #python
Watch the Conditional Maximum Loss (CML) presentation from CQF Institute’s Portfolio Management conference:
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Celebrating 5000 subscribers for the Quantamental Investing publication:
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This article explains where you will be able to find my future and updated scientific work.
It is an improvement for the reader, creating the best possible environment for learning the material through reading, watching videos and exploring Python code.
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Check out the latest version of the Portfolio Construction and Risk Management book:
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Video walkthrough of the Conditional Maximum Loss (CML) Portfolio Optimization article and Python code.
CML is a new path-dependent tail risk measure that works for fully general Monte Carlo market simulations.
The video below will help you learn about it more effectively.
#quant #quantsky #data
How to effectively find the best solution to investment problems.
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Practical perspectives on stationarity in investment markets.
Investment data is probably not stationary in the strict mathematical sense, certainly not the price series.
Read why it's useful assumption anyway: substack.com/profile/1707...
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Is mean-variance really sufficient?
Read more about it here: substack.com/@antonvorobe...
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A summary of modern investment technology, including what it looks like and what it can do.
In the end, there is a popular posts recap. Make sure to check it out :-)
#quant #quantsky #finance #markets #python #investing #investment
How large path-dependent tail risk optimization problems can we solve on normal-sized servers?
This Python case study presents how fast we can solve large Conditional Maximum Loss (CML) portfolio optimization problems.
#quant #quantsky #finance #markets #python #investing #investment #data #risk
It is essential to get the foundation right for investment models.
I often see models that “take many things into account” while being fundamentally wrong.
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#quant #quantsky #finance #markets #python #investing #investment #trading #models
What's happening in quant talent? Find out in our webinar tomorrow, Tues 24 February. 4:30pm GMT (11:30AM ET / 8:30AM PT). Can’t make it live? Still register and we'll send you a link to watch on demand. us06web.zoom.us/webinar/regi... Here's a preview at one of several insights to expect #quantsky
Perspective on structural breaks in investment markets.
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Check out the first lecture of the Applied Quantitative Investment Management course.
It gives an overview of the Fully General Investment Framework (FGIF) that is carefully presented in the Portfolio Construction and Risk Management book.
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An updated version of the Conditional Maximum Loss (CML) Portfolio Optimization article.
Laura Kristensen and I have recently added some minor clarifications.
Find the latest version of the article and its Python code below.
#quant #quantsky #finance #markets #python #investment #risk #cml
Utility theory is the pinnacle of anecdotal dogma.
Let’s be perfectly clear: the empirical support for utility theory is non-existent.
Read more about it here: substack.com/profile/1707...
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Important nuances of good risk budgeting and portfolio construction.
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Understanding the benefits of resampling for high-dimensional investment simulation.
Resampling methods have the convenient feature that they can capture the cross-sectional dependencies, no matter how complex or high-dimensional they are.
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Understanding the Fully General Investment Framework (FGIF):
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Perspectives on econophysics and investment analysis including entropy.
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This newsletter tells a story that I have told many times. Still, I present new perspectives here.
At the end, there is the usual popular posts recap. Make sure to check it out :-)
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Conditional Maximum Loss (CML) is a game changer for path-dependent tail risk optimization.
This article introduces a new investment risk measure specifically designed for fully general Monte Carlo path simulations.
#quant #quantsky #finance #markets #python #investing #investment #cvar #cml