Given the growing number of stocks and other financial instruments available in the investment market, there is always a need for quick and efficient methods of asset selection for investment purposes. The Fama–French three-factor model has the allure of simplifying asset selection by narrowing the number of parameters required to assess risk, but the usual technique of ordinary least squares regression (OLS) used for the estimation of the coefficients or sensitivity to the three factors suffers from the problem of modeling the conditional mean of the distribution inherent to OLS. In this chapter, we use the technique of data envelopment analysis applied to the Fama–French three-factor model to choose stocks from the Dow Jones Industrial Index. We also apply the more robust technique of quantile regression to estimate the coefficients for the factor model and show that the assets selected using this regression method lead to the construction of superior portfolios with higher returns in equally weighted portfolios when contrasted with the outcomes from OLS.
|Title of host publication||Rethinking valuation and pricing models|
|Subtitle of host publication||lessons learned from the crisis and future challenges|
|Editors||Carsten Wehn, Christian Hoppe , Greg N. Gregoriou|
|Place of Publication||Oxford|
|Number of pages||13|
|Publication status||Published - 2013|
Allen, D. E., Singh, A. K., & Powell, R. J. (2013). Asset selection using a factor model and data envelopment analysis– A quantile regression approach. In C. Wehn, C. Hoppe , & G. N. Gregoriou (Eds.), Rethinking valuation and pricing models: lessons learned from the crisis and future challenges (pp. 443–455). Oxford: Elsevier. https://doi.org/10.1016/B978-0-12-415875-7.00027-0