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The original FLAM predicts the information ratio by

$$ IR = IC \times \sqrt{N} $$ where $IR$ is the Information Ratio, $IC$ is the information Coefficient and $N$ is the number of independent forecasts. Later this law is improved several times, but the term $ IC \times \sqrt{N}$ is always present. I don't seem to understand (and find a good practical example) of what is exactly $N$ and how to calculate it.

I would be grateful if someone explains the exact definition of $N$. What is an independent forecast? It may seem a stupid question to some, but for me it is fundamental.

In Zhou and Jain, Active Equity Management, it is written:

$N$ is the number of independent bets in a year, it has two aspects: the number of cross-sectional bets on different assets at any point in time and the number of independent bets on the same asset across time.

In this context what exactly is cross-sectional bet and independent bet on the same asset? It would be best if someone gives a small portfolio example.

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There is no such thing as number of independent bets when one is betting on a common random factor as we quants usually do. Grinold & Kahn’s formula is only relevant when the factor payoff is a constant over time. This is not interesting in practice. When the factor payoff is random, then Ding and Martin The Fundamental Law of Active Management: Redux (2017) showed that the portfolio IR is basically IC divided by IC standard deviations.

To put it more bluntly , the G&K formula is useless and misleading. It is a joke that CFAers have to learn this stuff.

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See the paper by Ding and Martin (2017), "The fundamental law of active management, Redux", published in the Journal of Empirical Finance.

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    $\begingroup$ Could you add some detail to make this answer self-contained? $\endgroup$
    – Bob Jansen ♦
    Commented Sep 29, 2017 at 22:53
  • $\begingroup$ please see here for more discussion on the same question: quant.stackexchange.com/questions/73655/… $\endgroup$
    – zack young
    Commented Nov 23, 2022 at 2:27
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You can think of it as the number of trading positions on Uncorrelated instruments, in a year.

So backtest your strategy in some uncorrelated instruments for a year, and sum their positions count.

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