투자기회집합을 어떻게 측정할 것인가?

Abstract

본 연구의 목적은 우리나라 자료를 이용하여 기존 문헌에 등장하는 주요 IOS 후보변수 및 변수군들의 적합성을 분석하여 연구자들이 투자기회집합(IOS) 대리변수를 선택하는데 도움을 주고자 하는 것이다. 기업의 중요한 회계재무정책과 계약비용, 투자기회는 서로 밀접히 연관되어 있다고 알려져 있어, IOS의 측정은 실증연구에서 매우 중요한 요소이다. 그러나, 기업이 직면한 투자기회는 성격상 직접적인 관찰이 불가능하고, 이를 어떻게 측정할 것인가에 대해서도 기존 문헌에서 통일된 의견을 발견할 수 없다. 이로 인해 연구자들을 임시방편적인 선택에 의존하게 하고 있다. 본 연구에서는 각 개별 IOS 후보변수들과 후보변수군들을 대상으로 한 요인분석을 통해 추출한 공통요인들이 IOS 대리변수로서 어느 정도 적합한지를 분석하여 바람직한 대리변수를 제시하고 한다. 연구결과, 제조기업 표본과 비제조기업 표본을 포함한 전체표본을 사용할 경우, 자산 시장-장부가치 비율, R&D지출-총자산 비율, 주가-주당이익 비율 변수군이 가장 적합한 것으로 나타났으며, 이 변수군을 사용하는 것이 단일 후보변수를 사용하는 것보다 더 바람직한 것으로 나타났다. 추가적으로 기업규모별로 4등분하여 실시한 분석에서는 가장 작은 규모의 기업그룹을 제외하고는 대체로 만족스러운 결과를 보이지 않았다. 본 연구는 IOS 측정에 관한 문제를 최초로 심도 있게 다룸으로써, IOS에 대한 이해의 폭을 넓혀주고 대리변수의 선택에 관해 많은 시사점을 제시해 주고 있어, 향후 관련 실증연구에 도움을 줄 것으로 기대된다.

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.