Active portfolio management

What Hacker News says

1. dia80Jun 2023
   Quant trader here... I'm a big seller of this list. Making money tends to be a relatively empirical endeavor. It's all about having information about the future and using that in judiciously way and less so about any particular theory or model. I see someone else mentioning Grinold and Khan "Active Portfolio Management", I can't recommend it enough, it's basically a how to for making money quantitatively in a principled way, there are lots of "tips and tricks" that go on top of this and it really helps to have some good intuition for the space you are trying to operate in (by that I mean understanding the eigenvalues and eigenvectors of your risk matrix). T-costs are also extremely important and the main "enemy" it's trivial to make money if you don't have to pay to trade.

   Steven Boyd at Stanford and his students / colleagues are probably the richest seam of up to date portfolio optimization wisdom. If you are using python you shoult probably be using CVXPY to build your portfolio. He has lots of good papers, e.g. see [2].

   Of course you also need an "edge", that information about the future, and that's the jealously guarded part...

   [1] https://books.google.co.uk/books/about/Active_Portfolio_Mana...

   [2] https://stanford.edu/~boyd/papers/pdf/cvx_portfolio.pdf

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2. smabieSep 2020
   You need to stop using price data, it's worthless. Price is non-stationary and non-normal. Look at log returns instead.

   But in general, I would advice you to take a more quantitative factor approach and stop looking at black magic technical indicators.

   A good introductory book on equity strategies is Active Portfolio Management by Grinold and Khan. I think reading that book will change your entire approach.

   Also, I would focus on market neutral factor strategies instead of your long only price analysis.

   Not to be harsh, but what you're doing makes very little sense. It's great that you're interested in quantitative finance, but you gotta learn the fundamentals before publishing blog posts.

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3. vowellessDec 2019
   * Elements of Statistical Learning - Hastie, Tibshisrani

   * (Lot's of machine learning books to list: PRML, All of Stats, Deep Learning, etc.)

   * Active Portfolio Management - Kahn, Grinold

   * Thinking, fast and slow - Kahneman

   * Protein Power (the Eades') / Why we get fat (Taubes)

   * Why we sleep (Walker)

   * Deep Work / So Good They Can't Ignore You (Newport)

   * Flowers for Algernon (Keyes)

   * Getting to Yes (Fisher)

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4. dmmalamJan 2013
   If you want something more 'top shelf', Active Portfolio [1] Management by Grinold & Kahn is considered the bible, though you'll need an understanding of undergrad maths.

   [1] http://www.amazon.com/Active-Portfolio-Management-Quantitati...

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5. joshuOct 2011
   People think all quant finance is the same as HFT. It's not.

   A big part of algorithmic trading and stat arb is portfolio management, including deriving alpha, building risk models, etc.

   The bible is: http://www.amazon.com/Active-Portfolio-Management-Quantitati...

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6. imcoconutApr 2018
   Its possible to do so, but it is difficult. A few years of experience in a successful systematic team is extremely helpful. Among many other things, you learn that running a profitable strategy involves the coordination of a number of different types of tasks, which are similar but different enough so that its difficult for one person to be simultaneously good enough at all of them.

   To run a successful strategy requires strong signals (indicators that dictate what to buy/sell and when), execution (actual filling of orders on exchanges), risk management (which can include statistical risk modelling as well as draw-down controls), and infrastructure (the wrong type of bug can be costly enough to kill the whole operation!).

   As mentioned earlier, there is overlap in the skills and experience required for these broad categories, but people in the quant hedge fund/asset management industry typically specialize in one. This is where larger shops have an advantage. The entire strategy is only as good as its weakest link. Its common for people who haven't worked in the space to focus mostly, or even exclusively, on the signals and infrastructure aspects.

   If you were to group most of the successful quant funds out there by alpha time horizon you would see that funds within the same bucket are generally running very similar types of strategies using very similar signals (the general concepts behind successful signals/execution of varying time horizons are actually not that complicated, but just might take a while to explain). Again, that's not say its easy to do. With most of the equities and derivatives markets getting ever more efficient, tighter coordination and implementation of the entire strategy pipeline (signals, execution, risk mgmt, infrastructure) can lead to significant Sharpe/Information ratio improvements.

   If you wanted to get a feel for how some successful people in the industry think about the problem, I would recommend reading through the following books in roughly the corresponding order:

   1 - https://www.amazon.com/Efficiently-Inefficient-Invests-Marke...

   2 - https://www.amazon.com/s/ref=nb_sb_noss_1?url=search-alias%3...

   3 - https://www.amazon.com/Active-Equity-Management-Xinfeng-Zhou...

   And I didn't personally like this one so much but its quite popular among the more "academic" types

   4 - https://www.amazon.com/Active-Portfolio-Management-Quantitat...

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