Research and Development and Firm Risk

Abstract

J. H. (Henk) von Eije - University of Groningen, Faculty of Economics and Business. E-mail: j.h.von.eije@rug.nl Matthijs Suurmeijer - University of Groningen, Faculty of Economics and Business. Email: matthijs.suurmeijer@gmail.com Peter Smid - Dr, University of Groningen, Faculty of Economics and Business. E-mail: p.p.m.smid@eco.rug.nl Spending on R&D has grown faster than other investments. This may result in higher return and higher risk. We focus on the latter and examine how research and development (R&D) affects the risks of US firms. We analyze the impact on the firm’s beta, its systematic and idiosyncratic risk, and the combination of the latter two (total risk). Because investors prefer upside to downside risk, we also analyze whether downside risk is also influenced by R&D. We use panel and quantile regressions and control for dividend payouts, growth, leverage, asset liquidity, firm size, earnings variability, firm age and industry competition. We then show that the impact is positive and highly significant for beta, systematic risk and total risk. The impact on systematic risk contrasts to the finding by McAlister et al. (2007) who find that R&D insulates firms from market downturns and thereby lowers systematic risk. The increases in risks are, moreover, stronger at higher relative levels of R&D spending. Unfortunately for investors, downside risk is also increasing with relative R&D spending. The results may make it also more difficult for managers to defend R&D investments. R&D may indeed generate future returns, but also adds to the next year’s risk. The impact on systematic risk contrasts to the finding by McAlister et al. [2007] that R&D insulates firms from market downturns and thereby lowers systematic risk. While the magnitudes of the effects are small, the impact is relevant when compared with other accounting variables included in the model, especially for beta and systematic risk. Apart from this, there are strong indications that the hypothesized relationis non-linear.