The Economic Value of the R&D Intangible Asset

This study utilizes firm-specific time-series data to estimate the economic value of the Research and Development (Ramp;D) expenditures that investors consider an asset to the firm. The study uses a modification of the Ohlson (1995) model to estimate the persistence of abnormal earnings, the proportion of current Ramp;D expenditures that represents a source of future benefits to the firm, and the amortization rate of that asset. The parameters are estimated from time-series data of market and book values of equity, earnings, and Ramp;D expenditures. The study further compares the firm-specific estimates with those resulting from an application of a cross-sectional estimation procedure based on all available companies in the sample and industry-specific subsamples. Results indicate the existence of significant differences in some 2-digit SIC code industries between the time-series and the cross-sectional estimates of the parameters and the economic value of the Ramp;D asset. Differences in the capitalization parameter are associated with the growth in Ramp;D, the profitability of the firm, Ramp;D intensity and the concentration of the industry. Differences in the persistence of earnings are related to the concentration ratio. Finally, differences in the estimated economic value of the Ramp;D asset are associated with the profitability of the company as measured by its return on assets. We further compare the associations between the three different estimates of the Ramp;D asset and subsequent stock returns, as well as the contemporaneous difference between the market and book value of companies. Results indicate that the time-series estimates of the Ramp;D asset show stronger associations with both variables, followed by the intra-industry and the cross-industry cross-sectional estimates. Overall, our results provide evidence that market participants behave as if Ramp;D expenditures have significant future economic benefits to the firm, and show that the cross-sectional and time-series approaches followed when assessing its economic value provide significantly different estimates