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I am a master of finance student and although I understand the basics and the theory of portfolio construction I am still struggling when it comes to the practical side of things, i.e. building a real-world portfolio, with real-world assets, real-world data, etc. and the challenges that come with it. I was wondering if someone on here would be able to point me to some good books that would help me get started, and that ideally also include programming applications and examples. Thanks.

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    $\begingroup$ Hi: maybe you're under the impression that there's some secret techniques involved but they're really aren't. I would check out maybe Rudd and Clasing's text on portfolio theory. It's not an exact book for your needs but based on what you described, I don't know if there is such a book like that. For quants, 3 steps might be: 1) build some objective function, 2) add constraints 3) use an optimization routine to retrieve the weights of the resulting portfolio. $\endgroup$
    – mark leeds
    Jun 30 at 0:13
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    $\begingroup$ I did some searching and it appears that DataCamp also offers a very basic yet informative, concise, and hands-on portfolio analytics course. I'll drop the link here in case someone is interested: learn.datacamp.com/courses/intermediate-portfolio-analysis-in-r. $\endgroup$
    – John Paris
    Jun 30 at 12:13
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I can list a couple of things that are very reasonable to start off with. As written in the above comment, you will not be able to find any "secret sauce" in books and journals. You will, however, be able to find some good ideas that are commonly shared in the industry.


"Free" applied course: Portfolio optimization with R

There exists a course called portfolio optimization with R taught by Prof. Daniel P. Palomar at the Hong Kong University of Science and Technology (HKUST). The course is very applied and teaches you the basics of R (if you're new to it), common time-series analysis, and of course, portfolio optimization. He has made all of his slides and R sessions available on his webpage (see above link), and much of his code are also imbedded into his slides. This makes it easy to reproduce many of the portfolio setups and problems that he walks through in his course. In general, the course teaches you about:

  • Markowitz portfolio framework.
  • Robust portfolio optimization.
  • Portfolio optimization under alternative risk-measures (VaR, Downside risk, CVaR).
  • Risk parity portfolio.
  • Factor models.
  • Black-Litterman model.

I believe this would be a good starting point if you're looking for very applied teaching materials.


Journal of portfolio management

The Journal of portfolio management is a great source to scavenge for new ideas revolving about portfolio management, cost-schemes, alternative investment strategies etc. The papers are often co-authored by people in the industry together with professors in academia. Again, many of the papers are very applied and contain minimal mathematics. Therefore, you sometimes need some background knowledge before completely understanding the paper and the authors methodology. Nevertheless, if you have access to the journal through your university, you're in luck!


Alternative read

Marcos M. López de Prado, who are also the editor of the Journal of financial data science, have recently published a book called "Machine learning for asset managers" detailing some alternative procedures and pit-falls typically seen in the asset-management industry (this book follows the same structure as his previous book "Advances in Financial Machine Learning"). If you want a perspective on how to incorporate machine-learning techniques into asset-management, then this might be a decent book to read. I haven't read it yet, but got it recommended by a colleague. You can always see if your local library has the book.


I hope this provide some insight!

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In addition to classical texts by Grinold and Kahn, and the sources cited in the previous answer, I can't help mentioning my book. It has a chapter on Portfolio Construction which includes alphas at different horizons, multi-factor risk, slippage and impact costs, and Kelly bet sizing.

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