Incorporation of Foreign Exchange Risk to Fama-French Factor Model: A Study on Borsa İstanbul

Höçük, Furkan
This empirical study compares the relative performances of the Fama-French five-factor model without foreign exchange risk and the five-factor model incorporating foreign exchange risk on capturing portfolio returns in Borsa İstanbul. The main contribution of our study to the asset pricing literature is the incorporation of FX risk to the Fama-French five-factor model. We propose an additional factor as a proxy for FX risk. Another contribution of this study is implementing a machine learning technique, support vector regression (SVR), to estimate portfolio returns through the FF5F model without FX risk and FF5F model incorporating FX risk for Borsa İstanbul stocks. Although there are numerous researches investigated on Borsa İstanbul, any other study did not implement SVR via CAPM or Fama French multi-factor models to the best of our knowledge. There are empirical studies that confirm the efficiency of SVR. Some studies also compare the performance of the linear factor regression method with alternative statistical tools, including machine learning methods. Our study stands out in combining predictions of simple linear regression and SVR methods. Optimal weights obtained from linear combinations imply more precise estimations through SVR. In 28 out of 36 combinations, we observed that optimal weights assigned to SVR estimations were greater than those assigned to SLR estimations. Linear regression methods may be too restrictive to reflect the non-linearity of factor exposures under the Fama-French multi-factor model scheme. Asset pricing models, which take nonlinear aspects of the stock markets into consideration, might generate more precise estimations.


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Citation Formats
F. Höçük, “Incorporation of Foreign Exchange Risk to Fama-French Factor Model: A Study on Borsa İstanbul,” M.S. - Master of Science, Middle East Technical University, 2022.