# Tag Info

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I think the only valid answer is you can't. The techniques you describe would work of the signal was much stronger than the noise but it seems that with your fund returns this is not the case. You could try to get more data or look at other risk measures like max drawdown to get some idea of the risks involved.

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More measurable effects to add to your list: "window dressing" - returns of the fourth quarter or 12th month (i.e. year-end) are higher on average than oher returns; the same to returns of 4th months (qtr-end) vs. others; "herding": changes in asset-classes shares of "big" funds (whatever you define "big") granger-cause changes in asset-classes shares of ...

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If you are interested in evaluating forecasts accuracy, you could compare Value-at-risk forecasts. It has the advantage to take into account the forecast density (via quantile). Then you can compare easily their forecast accuracy via the Kupiec test for instance. Because if you just use points forecasts as it seems you are doing your results won't take the ...

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It's not a zero cost as you need to pay the initial margin and should have some extra cash in case you go below maintenance margin. You need some cash to trade, pick a sensible amount for an initial figure and measure against that.

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Firstly, I suggest you to use more recognized source to study and compute quantitative finance model or indicators; in such case, for instance, you could take as example the following paper as reference. Precisely there, the authors describe some common errors that one can do in computing the Sortino ratio; although surely you did not do any of them, ...

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I don't know if there is a standard way of solving the problem, but I solve it thus: Strategy A bought for $C_a$ dollars and sold for $S_a$ dollars for a result of $R_a = S_a - C_a$ over $T_a$ days. Strategy B bought for $C_b$ dollars and sold for $S_b$ dollars for a result of $R_b = S_b - C_b$ over $T_b$ days. Where $C_a$ and $C_b$ is the total sum of ...

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