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Yes, these are the fundamental building blocks for a money making strategy. To partially solve the issues you mention (small/low positive means/profits with large standard errors), you can investigate on many assets simultaneously. The idea is to take the advantage of Central Limit Theorem. Assuming the signal for each asset are i.i.d., and each signal ...


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You estimate a model $$ Y_t = \beta X_t + \epsilon_t. $$ which is just time-series regression. Concerning your question 1): One usually looks at the beta of a single security w.r.t. a stock index (see the CAPM). High beta (above 1)will indicate that the stock would rise and fall more than the market. Other approaches where one estimates a beta is in ...


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Approach 1 is parametric regression, whereas approach 2 is non-parametric regression. How are they related: non-parametric regression models the entire distribution of all possible function forms, and then do the integration to calculate a single value E[Y|X]. It is function-form free. In contrast, parametric linear regression ASSUMES that the function ...


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look at the econometrics literature on "total Least squares" (van huffel has a text out by that name)...or more generally think about what principal components does (hint: it's minimizing the distance to a regression line as in #2..it's not minimizing just the "vertical" distance)


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You can apply the Kolmogorov-Smirnov test. I simply quote from the entry: "The two-sample K–S test is one of the most useful and general nonparametric methods for comparing two samples, as it is sensitive to differences in both location and shape of the empirical cumulative distribution functions of the two samples." There is an R-implementation too.



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