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I would like to point out a recent paper by Lewellen, Nagel and Shaken which has changed a little bit the way factor models are tested. The standard procedure was to run time series regression of a factor model on Fama&French 25 size and BE/ME sorted portfolios to obtain factor loadings, and then cross sectional regressions using $R^2$ as a good measure ...

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For calculating the AIC for factor models, I calculate the likelihood based on the multivariate distribution of the factor model. I try to make any assumptions as explicit as possible. Bayesians typically do not use the (so-called) BIC. WAIC (Watanabe-Akaike Information Criteria) is becoming more common among Bayesians. When thinking about the AIC, you ...

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Thanks for the answers and comments above. In particular to Eric Brady, who had me reading a lot of Bayesian papers. In the end, I think the answer to the question is that on the monthly time-frame robust factor algorithms aren't really necessary. On daily and lower time frames, large spikes in returns due to events (earnings ect.) can really mess with ...

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I've often thought about the same thing. To try and figure out some concrete (in my opinion) information about a stock or mutual fund, I wrote something in Python to simulate: buying stock at some interval stock paying dividend at some interval adjusting returns for inflation subtracting out fees (if a mutual fund or something with an expense ratio) do ...

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