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Portfolio Construction and Risk Management Book

Portfolio Construction and Risk Management Book

Portfolio Construction and Risk Management Book

Portfolio Construction and Risk Management Book

Portfolio Construction and Risk Management Book

A pedagogical walkthrough of the next generation investment management framework with Python code.

A pedagogical walkthrough of the next generation investment management framework with Python code.

A pedagogical walkthrough of the next generation investment management framework with Python code.

A pedagogical walkthrough of the next generation investment management framework with Python code.

Anton Vorobets
Anton Vorobets
Anton Vorobets
Anton Vorobets
2 Campaigns |
Odense, Denmark
$4,100 USD $4,100 USD by 53 backers
$2,605 USD by 26 backers on Nov 2, 2024
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Highlights
Mountain Filled 2 Projects Mountain Filled 2 Projects

Note that this is the second campaign. The top 10 is decided based on the total contributions from the first and second campaign. You can see the results from the first campaign here: https://igg.me/at/pcrm-book-init

The crowdfunding target for the second campaign is set artificially low, so it can continue after the campaign deadline. The initial target of €10,000 is more representative of the actual goal.

Portfolio Construction and Risk Management Book

This book is written based on the need to bridge quantitative analysis and investment management in practice. It contains many new and exclusive results.

The book addresses many of the complex nuances of real-world investment markets, based on my experience managing complex multi-asset portfolios for institutional investors and my ongoing dialogues with institutional asset managers.

The book is cohesive, giving you a complete investment risk and analysis framework. It focuses on investment tail risks and in particular on the conditional Value-at-Risk (CVaR) risk measure.

What You Get

Significant contributors will receive a thank you in the book's preface if they wish. The top 10 contributors by the time the book is finished will additionally get three months access to an institutional-grade implementation of the investment framework to test out their newly acquired knowledge in practice.

As of January 18, 2025, your total contribution must be above €100 to enter the current top 10. See the updated top 10 here: https://antonvorobets.substack.com/p/pcrm-book

As an extra bonus, people who contribute €100 or more will get a one-year complimentary Substack subscription to the Quantamental Investing publication. Once the book is finished, this Substack subscription will give access to exclusive case studies using the investment framework from the book. You will also have access to continuing to ask me questions through the Substack chat.

Other Ways You Can Help

If you cannot contribute to the campaign directly, you can help by simply spreading the word about it. For example, react to and share Substack or LinkedIn posts.

GitHub stars for the accompanying code repository https://github.com/fortitudo-tech/pcrm-book and https://github.com/fortitudo-tech/fortitudo.tech are also appreciated.

Finally, if you like the book and code, you are encouraged to share your positive experience with others. 

How to Stay Updated

If you want to make sure that you receive all updates, you can subscribe to the Quantamental Investing Substack at: https://antonvorobets.substack.com

I will also regularly post about this book and other quantitative investment management related content in the Applied Quantitative Investment Management LinkedIn group: https://www.linkedin.com/groups/12877102/

Previous Campaign

This campaign has been updated with some technicalities compared to the initial one, for example, the funding target is set artificially low at €1,000, so the campaign can continue after the deadline. All contributors from the first campaign have access to the repository and have been recorded for the purpose of determining the top 10. You can find information about the initial campaign here: https://igg.me/at/pcrm-book-init

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