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다국어 초록 (Multilingual Abstract)

This study addresses the choice of an investment opportunity set(IOS) proxy variable from among IOS candidate variables and/or sets of variables, based on the Korean non-financial firms listed in KOSPI and KOSDAQ as of the end of 2009. It has been known that IOS is intricately related with various accounting and financial policies including choice of accounting procedure, capital structure, ownership structure and dividend policies, which are in turn largely involved with contracting costs. The proper measurement of IOS thus is essential for many empirical studies in the areas of accounting and finance. However, IOS faced by firms is unobservable in nature and it seems that no consensus has still emerged in the related areas concerning the choice of an IOS proxy variable or measurement of IOS. Some researchers choose to use a single variable as an IOS proxy variable, while others, recognizing that IOS is imperfectly measured by any single proxy variable, conduct sensitivity tests using various proxy variables or try to derive a factor from a set of observable IOS candidate variables using common factor analysis. However, the existing studies utilizing factor analysis often do not explicitly state why they have decided to choose the set of specific candidate variables for factor analysis, do not conduct a reliability test in terms of whether one meaningful factor can reliably be derived from their factor analysis, or do not provide a validity test in terms of the degree to which the derived factor predicts the latent investment opportunities. Furthermore, none of the existing studies conducts split sample tests, ignoring the fact that the performance of an IOS proxy variable may vary according to sub-sample groups. For these reasons, researchers often rely on the ad hoc choice of an IOS proxy variable, and many of the existing studies may involve measurement problems and some unknown biases in their results. To find an appropriate IOS proxy variable, this study works with seven IOS candidate variables used in Baber et al.(1996): investment intensity, geometric mean annual growth rate of market value of assets, market-to-book value of assets, R&D expenditure to total assets, market-to-book value of equity, earnings-to-price ratio, variance of return on market value. Factor analysis has been conducted for the sets which include more than two IOS　candidate variables. A correlation analysis is also carried out between the factors derived from factor analysis/individual candidate variables and three IOS revelation variables to analyse the extent to which they predict firm`s investment opportunities. IOS revelation variables used are investment intensity, growth of book value of assets, revenue growth for the future five years, following the approach by Baber et al. Results of factor analysis are examined to judge whether it is possible to derive one meaningful factor from the chosen sets of variables. Analyses have first been carried out for the full sample, and then for split samples to ensure that the candidate proxy variable works not only for the full sample, but also for the split samples. For the full sample I find that the factors derived from the sets of variables of investment intensity, market-to-book value of assets, R&D expenditure to total assets, and earnings-to-price ratio are more appropriate than the factors derived from the sets of the variables used in existing literature in the light of correlation with IOS revelation variables. I also find that market-to-book value of assets and R&D expenditure to total assets are preferable if the use of single candidate variables is desired. Further, it is found that the use of the factor is preferable to any single candidate variables. For the split samples of manufacturing and non-manufacturing firms, I find that the use of a single variable suggests R&D expenditure to total assets for manufacturing firms and market-to-book value of assets and R&D expenditure to total assets for non-manufacturing firms. The factor from market-to-book value of assets, R&D expenditure to total assets, and earnings-to-price ratio and the factors from the most sets of variables of interest are desired for manufacturing firms and non- manufacturing firms, respectively. Combining these results, I find that for the full sample, R&D expenditure to total assets in case of a single variable, and the factor derived from market-to-book value of assets, R&D expenditure to total assets, and earnings-to-price ratio are desirable. The use of factor is also found to be preferable to that of a single candidate variable. Additionally, analyses based on the split samples according to firm size show that many of the IOS candidate variables and the factors derived from them are highly correlated with IOS revelation variables for firms in the fourth(smallest) quartile of size. However, disappointingly any meaningful relationship does not appear in other quartiles. Further research on the effect of size upon the choice of an IOS proxy variable seems to be required. By providing a comprehensive and in-depth analysis of the measurement of an IOS proxy variable, this study contributes to the enhanced understanding of IOS and helps researchers in the related areas.

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참고문헌 (Reference)

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