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The technique is sometimes referred to as full information maximum likelihood. It is more general than the technique you describe, but it is similar. Basically you start with the data with the longest horizon and get the covariance matrix, then for the data with the next longest horizon you regress them against the data with the longest horizon, finally you ...


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Here are some quick fixes to your problems: return should not be 0.4% or 4%, but 1.004 or 1.04, respectively. probability of failure (by the way, what is that? losing everything? Losing an amount equal to potential gain?) should be subtracted from 1, not reciprocal: $1-P$ rather than $1/P$. This is more rational and will already fulfill your wish of ...


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I think the only thing throwing your desired results for these examples is the 12-fold advantage given by the probability. You could consider using a (natural) log of the probability, which would dampen the advantage (in this case) to two-fold (and take the negative, as I presume all your probabilities are <= 1). That said, beware tailoring your ...


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I think you are pretty much mixing apples with oranges in your formula :-) A slightly more meaningful, but yet very simple approach, could be first of all to "normalize" each score in the interval [0, 1]: Value - Min Value Index01 = ---------------------- Max Value - Min Value (your quantities are all positive, so ...



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