Practical Learning Resources for Portfolio Construction and Optimization
Summary
The document responds to a student seeking practical guidance for building portfolios with real assets and data. It recommends an applied portfolio-optimization course using R as a starting point, covering time-series work and several portfolio methods: Markowitz optimization, robust optimization, alternative risk measures, risk parity, factor models, and Black-Litterman. The answer also points readers toward practitioner and academic articles in a portfolio management journal, noting that applied papers may use limited mathematics but can still require background to follow their methods.
For asset-management applications of machine learning, it mentions a book as a possible introduction to industry procedures and pitfalls, while explicitly saying the recommender has not read it. Another respondent adds established portfolio texts and a book chapter discussing alpha horizons, multifactor risk, slippage, market impact, and Kelly sizing. These are recommendations rather than evaluations or comparative evidence; readers should treat suitability as dependent on their existing knowledge and access to the cited materials.
Key ideas
- An applied R course can connect time-series analysis with portfolio optimization methods.
- Suggested optimization topics include robust methods, risk parity, factor models, and Black-Litterman.
- Portfolio management journals can offer practical research, though some papers require background knowledge.
- The machine-learning book recommendation is tentative because the respondent had not read it.
- Portfolio construction also involves risk models, trading costs, alpha horizons, and position sizing.
Tags
Full text
# Best books on portfolio construction? # Best books on portfolio construction? 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. ## Answer by Pleb (score 4, accepted) https://quant.stackexchange.com/a/65788 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! ## Answer by Michael Isichenko (score 5) https://quant.stackexchange.com/a/67872 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.
Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.