In Finance 1 last year, we were introduced to the idea of multi-factor models (MFM) originally explained by Fama and French as an alternative to the traditional Capital Asset Pricing Model (CAPM) for assessing systematic risk. Additional factors include small versus big (SML) and value versus growth (HML).
In our Business Analysis and Valuation class, we discussed a merger case in which a large company acquired a smaller company. We talked about what would be the best way to approximate beta. The method I used (which was the best method I could conceive, I’d be happy to hear criticism or suggestions otherwise) was to weight the betas by market cap and take an average.
However, there was some discussion about the fact that one entity was much smaller than the other. While we were having a conversation of what that would actually mean, I would suggest a mathematical method for expressing the quantitative effect of size, using FF’s MFM.
- 1. Express both company’s re as a three factor MFM
Re1 – RFR = beta1 (Rm – RFR) + betas1 (SMB) + betav1 (HML)
Re2 – RFR = beta2 (Rm – RFR) + betas2 (SMB) + betav2 (HML) - Take the larger company’s size beta and apply it to the smaller entity
betasnew = betas2 - Recalculate re for both entities
Re1new – RFR = beta1 (Rm – RFR) + betasnew (SMB) + betav1 (HML)
Re2new – RFR = beta2 (Rm – RFR) + betasnew (SMB) + betav2 (HML) - Take a weighted average (by market cap) as the expected return of the combined entity
By taking the larger company’s size beta for both, what you are saying is that you expect the smaller company to have the size “characteristics” of the larger entity. I might even be more appropriate to add the size factors (Would that be appropriate? As the MFM is a linear regression, is it appropriate to add these factors?) and use that for new return on equity for each entity as it relates to the combined entity.
betasnew = betas1 + betas2
While there are some significant assumptions which are required for this to work, it is the best solution I can conjure based on information given. I would really appreciate any additional ideas for creating a more robust model.