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By Cheng-Few Lee

Advances in Quantitative research of Finance and Accounting, New sequence is an annual e-book designed to disseminate advancements within the quantitative research of finance and accounting. it's a discussion board for statistical and quantitative analyses of concerns in finance and accounting, in addition to functions of quantitative the way to difficulties in monetary administration, monetary accounting, and company administration. the target is to advertise interplay among educational study in finance and accounting, utilized study within the monetary group, and the accounting career.

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Not surprisingly, a decline in the value-relevance of information from financial statements is expected (Brown, Lo and Lys, 1999). Intangible assets, by definition, are nonphysical rights that are able to generate a future stream of benefits for the owner. By their very nature, they are difficult to value. In high-tech industries, such as computer, chemical and electronic/electrical, intangible assets are a major component of a firm’s assets with much of them generated from R&D activities. , 2001). Patent counts weighted by forward citations are a common measure of R&D output in those studies.

As Trajtenberg (1990) suggests, the number of times a patent is cited by subsequent patents as measured by forward citations can reflect technological significance for both legal and economic reasons. Most of the related studies in past literature were managed to find a positive relationship between R&D expenses and market values. The objective of this study is to examine another important component — knowledge spillovers — that may contribute to the observed inconsistency between market values and book values.

Therefore, a linear regression equation is constructed as follows. MV(i, j, t) = BV(i, j, t) 1 RND(i, j, t) PAT(i, j, t) + γ1 + γ2 BV(i, j, t) BV(i, j, t) BV(i, j, t) ERN(i, j, t) TRA(i, j, t) INT(i, j, t) + β1 + β2 + γ3 BV(i, j, t) BV(i, j, t) BV(i, j, t) TER(i, j, t) + ε(i, j, t), (1) + β3 BV(i, j, t) +φ where ε(i, j, t) is a white noise, is a vector of constant and industry dummies and PAT(i, j, t) is the total number of successful patent applications made by firm i at time t. Following the suggestion of Lo and Lys (2000), all variables are normalized by BV(i, j, t) to control for scale effect in the valuation equation.

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