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Â Okay, so in this last video,

Â we're going to have a look at two additional tools which you may use to find

Â the best performing managers adjusted to some kind of a risk measure.

Â And actually, this is one of my preferred measures, and

Â I'll explain you why I quite like this measurement.

Â This risk adjusted performance ratio, which is not as common as Sharpe or

Â Trainor, but in my view, has something very right to it.

Â Okay, so what we'll do, we'll talk about the MAR ratio.

Â And MAR comes from a newsletter, which has been around for many, many years.

Â And it actually says Manager Report Newsletter, and

Â it has an emphasis on hedge fund analysis, or

Â indeed, exactly what we're discussing here, peer group analysis.

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And the drawdown to me, and this is why I like this ratio,

Â it's actually a notion of risk, which makes probably

Â more sense than is intuitively appealing to a customer.

Â Who hasn't gone maybe to university and

Â does not really know what a standard deviation is.

Â And may have difficulty in grasping it, especially so if that standard

Â deviation captures a return which exceeds the average.

Â This may be somewhat difficult for somebody to associate that to risk.

Â But certainly, a notion that is more straightforward to be

Â identified with risk is the notion of maximum loss or

Â maximum drawdown, or also pick to through.

Â Basically, you say the worst you could lose by buying this security or

Â this investment fund is so much, and this, to me, has a very clear meaning.

Â Okay, so a MAR ratio in excess of 2 will be

Â a manager who delivers maybe 92% total

Â return since inception, as we see here,

Â and has had a maximum loss of 46%, and

Â so 96 divided by 46 is 2.

Â And just to highlight, again, you have seen this chart already,

Â but what the drawdown means, it's the worst possible

Â scenario of entering the market at the peak here in 2000 and

Â exiting the market when the fear is at its maximum in 2002.

Â And there, you lose 46%.

Â And this will be your drawdown, okay.

Â So, using the MAR ratio to identify,

Â especially when you have no benchmark in terms of markets,

Â market indices, may prove a very useful measure.

Â And indeed, also something which is simple to grasp,

Â you compare the total returns since inception, and

Â you divide it by the maximum loss, simple and intuitively appealing.

Â But now there's a shortcoming to this MAR ratio.

Â And I want to illustrate that with the following examples.

Â Assume we have two managers, Manager A as a CAGR,

Â that's compounded annual growth rate.

Â Total of 40%, so since inception, and

Â a drawdown of maximum loss of 20, so a MAR ratio of 2.

Â Manager B has slightly above total return of 50%,

Â but also, our shopper pick the draw of loss and

Â drawdown of 30%, so 50 divided by 30 is 1.66666.

Â Okay, so the first manager, Manager A, has a higher MAR ratio,

Â so you would be tempted, you would be inclined to allocate

Â all your investment to him and none to Manager B.

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But what if I now tell you that Manager B has been around for

Â 20 years and Manager A only for 5 years?

Â So, clearly, you'd say well, Manager B has more experience and

Â maybe he has a greater drawdown because that

Â drawdown happened when Manager A did not exist.

Â If Manager A had existed, then maybe he would have experienced a greater loss.

Â So the way to solve the shortcoming of the MAR ratio is the CALMAR ratio.

Â And the CALMAR ratio is quite simply it's the same notion of the MAR ratio,

Â but here, instead of looking at the worst

Â possible loss over since inception, basically here,

Â you're looking at the worst loss over the last 36 months, so the last three years.

Â So CALMAR and MAR ratio need to be used together because

Â maybe the idea of a maximum loss of 5% within the last

Â three years does not give you an accurate picture.

Â Because maybe the fund is actually far more riskier than that,

Â and it has experience of 46% loss 15 years ago.

Â So you need to use both.

Â But at least you can be sure that using the CALMAR ratio,

Â you're comparing apples with apples.

Â And you're comparing managers who have been in existence and

Â over the same time periods.

Â And clearly, this is something that is, obviously, very important to mention.

Â When you're doing this filtering, when you're extracting from the database,

Â managers, based on return statistics, risk measurements,

Â percentage of positive months, and etc, etc.

Â You need to make sure that you're comparing apples with apples and

Â that all the managers have a similar mandate when investing their funds.

Â So in conclusion of this set of two videos,

Â we've seen here that to perform the peer group analysis,

Â what you need is a good database that will have managers,

Â which are ranked by categories.

Â And we need to make sure that this is done professionally and

Â each category you do find managers that have a specific investment philosophy.

Â And then basically, you can filter that database using criterias,

Â which best suit your investment philosophy.

Â I give you one example.

Â If you want to assemble a fund to funds, which will be low risk,

Â then you will put a lot of emphasis on extracting from your database

Â managers who have the highest proportion of positive months,

Â who have the minimum drawdown, who have the lowest volatility.

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then you may be just shooting for the maximum beta, the people who use leverage,

Â and maximum return strategy, you will put more emphasis on return.

Â So this all depends on your philosophy, and

Â then in the second video, we saw a measurement, which in my view,

Â is very useful when we are talking about peer group analysis.

Â And we don't have a benchmark to compare ourselves to.

Â So basically, here, we're just looking at a total return,

Â dividing that total return by the maximum loss.

Â And this is, to me, a very intuitively appealing notion of risk.

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