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Analyzing seasonal time series "by hand" is not a good idea because there is a lot of time series machinery developed just for that. A simple example in R can be found here. You can apply clustering if it feels more natural but the main question is whether your model works out of sample.


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Such an approach is done by the systemic investor blogger in his blog Time Series Matching with Dynamic Time Warping.


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The approach you describe of looking at the valuation metrics in one period versus the returns in the next is similar to cross-sectional factor models, like Barra, or the Fama-Macbeth procedure. In these methods, instead of looking at the correlation, you do a cross-sectional regression of the returns (or excess returns or alpha) against whatever factors, ...


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There are two things: First: You have one stock of $B$ (worth \$30) and the calculation tells you to short 1.14 stocks of $A$. Of course you can only short whole stocks. So you would have to decide wether to short 0,1 or 2 stocks. This is a question of contract size, or in this case just size. Second: Usually we speak about hedging in portfolio context. In ...


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An implied correlation $\rho_i(k_1,k_2)$ is a correlation that matches the $(k_1,k_2)$ tranche price $P_{k_1}^{k_2}$ (usually computed under a gaussian or student t copula) $$ C(k_1,k_2,\rho_i(k_1,k_2)) = P_{k_1}^{k_2} $$ For mezzanine tranches, there can sometimes be two different implied correlations matching the tranche price. A base correlation ...



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