Portfolio and risk management books

Hello all,

I am a complete noob when it comes to portfolio and risk management. I am currently a global macro analyst at a boutique research shop. But, saying in my report that clients should go long country X, and short country Y on a 12-month time horizon is probably really different than actually implementing that position in the context of other existing positions.

So, do you guys have any book recommendation regarding portfolio construction, allocation and risk management? For example, I am currently implementing the equal risk contribution technique, from an academic paper title (On the properties of equally-weighted risk contributions portfolios - Maillard, Roncalli et al) in Python (for fun), and I am wondering if there is a book or books where there are more strategies laid down like this.

My goal is to move away from being a strategist to actually taking/managing risks at a fund.


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Comments (6)

Jul 8, 2017 - 5:00pm


Googled who that is and he works for AQR. I love AQR, not because it's a hedge fund, but because they created one of the most useful Python package - Pandas. But, I think the guy who made it left AQR to work at two sigma.

Jul 8, 2017 - 5:01pm

Statistics Textbooks for Risk Management/Portfolio Management (Originally Posted: 02/07/2015)

Hi Everyone,

I was wondering what textbooks/materials you may have used to get a solid understanding of statistics during undergrad (or outside of school if you come from a non-business background).

The reason I'm asking is because I have a few quantitative courses approaching my last few years of uni (semi-target, finance & econ, courses with econometrics/risk management) and my experience with statistics so far (one intro course) has been terrible thanks to a lackluster prof and materials that were as insightful as fox news.

Just curious if there's anyone here with a stats/quant heavy background, or a general business/finance background who found certain materials, websites, courses useful and wouldn't mind sharing.


Jul 8, 2017 - 5:02pm

Statistics has many sub-fields such as statistical modeling, probability, time series, machine learning, and so on.

Try this one:

Stat Labs: Mathematical Statistics Through Applications, by Prof. Deborah Nolan and Prof. Terry Speed

This book is about modeling, so it's about hypothesis testing, analysis of variance, and linear modeling.

Jul 8, 2017 - 5:03pm

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