With the help of the conceptual design the different factors of influence and variables were selected to formulate hypotheses of importance in understanding the valuation of R&D. After having established the value relevance of R&D in a theoretical study, the variables that affect R&D can be examined through an empirical study. To conduct an empirical study on R&D I have focussed on a few different variables to compare to each other within the software and programming industry. When looking at the financial figures for software and programming companies accounting variables like revenue and the R&D investment ratio seems to catch the attention of stakeholders when giving value to the company. This leads to the following two hypothesizes:
H1) Research and development is more value relevant to investors for firms with a high R&D ratio than for firms with a low R&D ratio within the software and programming industry.
H2) Research and development is more value relevant to investors for firms which generate profits than for firms which generate losses within the software and programming industry.
To test whether the software and programming industry within the
Netherlands values R&D in accordance with other countries we make an international comparison using the following hypothesis:
H3) Investors in the Netherlands value capitalised and expensed R&D the same as investors in England.
In previous chapters different opinions have been brought forward about capitalising and expensing. To conduct an empirical analysis on the stakeholder’s opinion on the capitalisation of R&D an international comparison was made for the following hypothesis:
To answer the formulated hypotheses the data was gathered, separated according to the different variables that have influence on the value relevance and inserted into an empirical model. The selected valuation model will be applied to the variables in the paragraphs hereunder and the findings and results from the empirical research will be discussed in chapter 6.
5.1 Variables selected in the model
This research has focussed on the difference in value relevance of R&D between companies that capitalise and that expense. To gain more insight as to why these companies capitalise or expense and to measure the difference in the value relevance of R&D the data was analysed according to the next variables: R&D ratio, profits and losses. After analysing the variables an overall comparison was made between data from the Netherlands and the United Kingdom.
5.2 The development of the model
To test my hypothesis I modified a valuation model used by Callimaci and Landry (2004). The valuation model relates stock price (P) to book value of common equity per share (BVS) and earnings per share (EPS). The following regression model will be used as a basis to determine the value relevance of capitalised and expensed R&D:
Pjt = a1 + ß1(ABV)jt + ß2(CapR&D)jt + ß3(AEPS)jt + (1) ß4(ExpR&D)jt + (ejt)
To determine relevance of capitalised and expensed R&D costs earnings per share (BVS) decomposed into:
(ABV) = Book value per share before capitalised R&D (CapR&D) = Capitalised R&D per share
The earnings per share (EPS) are disaggregated into:
(AEPS) = Earnings per share before R&D expense (ExpR&D) = R&D expense per share
(ejt) = Error term, representing the unexplained portion of stock price
For share price data Callimaci and Landry (2004), and Aboody and Lev (1998) use the share price listed around the month of March, three months after the fiscal year- end. This is the time when most financial statements are released and when relevant information about R&D spending is reflected into share prices. Consistent with Ohlson (1995) I assume that the accounting measurements satisfy the clean surplus relation. The clean surplus relation is an accounting proposition which implies that all changes in book value are reported as either income or dividends. This means that the accounting income for the selected companies must equal to the fiscal year change in book value of equity adjusted for dividends and capital contributions, and that the companies accounting incomes summed over their lifetime are identical (Ohlson, 1988).
The coefficient of R&D capitalisation (CapR&D), ß2, is expected to be positive, reflecting a positive association between R&D capitalisation costs and stock price (P). This would imply that the amount of capitalised R&D provides value relevant information to investors, which in turn is reflected in the stock prices.
Callimaci and Landry also introduced a return model suggested by Easton and Harris (1991). Easton and Harris investigated the relevance of earnings divided by price at the beginning of the stock return period for evaluating earnings/returns associations. Their primary motivation of the empirical analysis was to evaluate the relevance of the earnings level variable (A/P-1) and after to evaluate the relevance of change in the earnings variable (?A/P-1) (Easton and Harris, 1991). When
introducing both earnings level and earnings change in the model two annual flow variables need to be introduced; the level of capitalised R&D and the change in R&D expense. After adapting the variables within the model by deflating them by the opening share price it will result in:
Rjt = a1 + ß1?(ABV)jt + ß2?(CapR&D)jt + ß3?(AEPS)jt + (2) ß4(ExpR&D)jt + (ejt)
Price three months after year end, adjusted for stock splits and consolidations
?(ABV)jt = First difference in the adjusted book value of common equity per share from time t to time (t-1) deflated by opening share price, Pj(t-1)
?(CapR&D)jt = First difference in the adjusted earnings per share from time t to time (t-1) deflated by opening share price, Pj(t-1)
?(AEPS)jt = First difference in the adjusted earnings before R&D expense per share from time t to time (t-1) deflated by opening share price, Pj(t-1)
(ExpR&D)jt = The level of adjusted R&D expense per share at time t deflated by opening share price, Pj(t-1)
(ejt) = The unexplained portion of return from time t to (t-1)
Callimaci and Landry made one more modification to the model by
incorporating the change in R&D expense and not only the level of R&D expense and the change in R&D capitalisation as mentioned before. This results in a third
equation:
Rjt = a1 + ß1?(ABV)jt + ß2?(CapR&D)jt + ß3?(AEPS)jt + (3) ß4(ExpR&D)jt + ß5?(ExpR&Djt) + (ejt)
Decompose (ExpR&Djt) into (ExpR&Dj(t-1)) and ?(ExpR&Djt):
?(ExpR&Djt) = First difference in the adjusted amount of R&D expense per share from time t to time (t-1) deflated by opening share price, Pj(t-1)
(ejt) = The unexplained portion of return from time t to (t-1)
For the last few modifications I introduced the variable other intangible assets (IALRD) and the level of the adjusted earnings before R&D expense (AEPS) based on McCarthy and Schneider (1995), who have conducted a study showing the difference in the pricing of intangibles to each other and to other assets which has been used in several relevance studies e.g. Abrahams and Sidhu (1998). The model
was also adapted for the variable ?(ABV) which was excluded as it is enclosed in the variable Rjt. Consequently we also disaggregate the other intangible assets from the R&D related intangible assets resulting in the fourth and final regression equation:
Rjt = a1 + ß1?(CapR&D)jt + ß2?(AEPS)jt + ß3(AEPS)jt (4) ß4(ExpR&D)jt + ß5?(ExpR&Djt) + ß6?(IALRD) + (ejt)
Where:
?(IALRD) = First difference on the adjusted book value of other intangible assets (total assets less capitalised R&D costs) per share from time t to time (t-1) defla ted by opening share price, Pj(t-1) (AEPS) = The level of the adjusted earnings before R&D expense per
share from time t to time (t-1) deflated by opening share price, Pj(t-1)
The next chapter assesses the data selection and reports the results using model 4.