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I am studying asset-pricing of "aircarft & defence" firms' stocks internationally covering 30 countries over 1980-2016. I can derive SMB, HML, and WML factors from Kenneth French's websites for GLOBAL MARKETS. How to conduct this four-factor analysis? Should I do panel analysis and account country-fixed effects, or just time-series analysis with global factors. Please advice.

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  • $\begingroup$ What's the research question? What question are you trying to answer? $\endgroup$ – Matthew Gunn Dec 14 '17 at 19:40
  • $\begingroup$ it is a empirical research of 350 defense firms from 30 countries. I have monthly returns of them for period 1980-2016. I want to run Fama-French four factor regression to check if these stocks generate significant abnormal returns. I can retrieve RF (risk free rate), RM (benchmark market return), SMB, HML, and WML for global markets from Kenneth's website and run time-series regression. My confusing point is the data is panel data, it has stocks from several countries. So, should not I fix the country effect? If yes, how to do it in Fama-French 4 factor analysis? $\endgroup$ – esqeudero Dec 14 '17 at 20:50
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I think a useful paper would be Rapach et al. (2013), International Stock Return Predictability what is the role of the united states? They detail cross country differences in a lot of detail and tell you what you need to consider. In terms of time differences etc. It is also an easy to paper to digest and there is a summary on CFA digest.

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What you try to do is not clear.

  1. If you want to reimplement FF factors on a custom universe, you can do it (but paying attention to timezones to not include future information in your data). But be careful: your result will only show you if there is a cross-sectional Factorial effect inside your firms.

  2. May be you want in reality see if your firms have a specific loading according to FF factors of their countries. I.e. do your aircarft & defence firms have a bias in terms of Size, Value or Momentum? In such a case just take their loadings and average them.

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