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A lot has been written on the Fama-French and Carhart factors -- including that SMB may help proxy for the market index being too narrowly-defined. Here you may also be facing that problem. Why else might your coefficient estimates have changed so much? Perhaps HML, SMB, or MOM is multicollinear with your benchmark index. However, your benchmark index is ...


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Congratulations on trying to manage your portfolio better. That is wise. If you lose money, you may at least understand why you lost money. Ideally, you could also protect your portfolio against risk factors you do not want to take. However, this gets a little tricky since some factors are not clearly risk factors. (This is especially true for the Fama-...


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There are multiple ways to model portfolio factor exposure, for instance: 1- Return based approach: regressing returns of a portfolio vs several factors (similar to what your doing). 2- Holdings based approach (more accurate): essentially you would want to get the factor exposure of all individual holdings, then, derive the portfolio factor exposure. In ...


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Fractionally differentiated features (often time series other than the underlying's price) are generally used as inputs into a model to then generate a trading signal/return prediction. If you have a predicted return and want to convert it to the price level you could just do the actual return calculation from the previous price but I'm not sure what your ...


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You don't necessarily need to use tick data to accomplish what you want. If you have OHLC data you can just calculate RSI values using the extremes of the H and L values to get the boundary conditions of a density distribution and then use this distribution to do your testing.


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The CAPM claims that only systematic risk matters (i.e. covariation with the market) to determine an asset's expected return. So the fact that low volatility stocks have returns that are not explainable by market beta is an empirical contradiction of the CAPM to start with. The CAPM is too rigid and performs poorly in explaining the cross section of equity ...


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The way to setup the regression depends on what do you want to predict. Once you formulate exactly what do you want to predict, you should set up your regression in exactly same way. Daily regression of returns of JPYStock ~ SPX can be done in several ways, and you should consider these differences: holidays as you mention End of trading day time (i.e. one ...


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It is not clear you want to regress changes or indexes themselves? If you regress the indexes, then the 12 hour time difference will not matter. If you regress changes, you better take as the "x" variable the series that leads and as "y" series that lags (by 12 hours). It means that if you want to take d(Nikkei) = a*d(S&P)+b then you ...


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When you carry out correlation coefficient between target variable (denoted as x) and feature variable (denoted as y), the correlation coefficient is a function of sample size: $ r = \frac{n \Sigma xy - (\Sigma x \Sigma y)}{\sqrt{(n\Sigma x^2 - \bar{x}^2 )(n\Sigma y^2 - \bar{y}^2 )}}$ So daily data will impact on correlation.


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Assuming you're going to be fitting a linear regression, creating a correlation (Pearson) matrix of all assets is a common first step to filter endogenous (or multicollinear) variables from your test set.


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