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­­­Behavior of intangible-intensive companies under uncertainty

Priority areas of development: economics

Goal of research: The objective of this study is to elaborate, and empirically validate, a comprehensive methodology for the analysis of company investment strategies on intangibles.

This methodology is supposed to provide an insight into intangible-driven performance of companies by discovering the transformation of intangibles input into company value. The key requirement of this methodology is to reflect specific traits of intangibles and enable at the same time test it empirically on vast corporate data.

Methodology: This study employs different tools of theoretical modelling and empirical analysis. In line with Hennessy (2004) and Strebulaev and Whited (2012) a dynamic optimization within Bellman equation framework is applied for the theoretical modelling of company investment decisions.  The evaluation methodology is based on the economic profit concept elaborated by Copeland et al. (2000) and Stern (2001). The empirical part of the study uses PCA (principal component analysis), K-mean clustering, ANOVA (analysis of variance) and Student's t-test, as well as OLS (ordinary least square) and ML (maximum likelihood) estimators. Taking advantage from the panel structure of data involved in the statistical analysis fixed-effect estimation is applied to deal the endogeneity problem. For a confirmatory analysis of investment decisions the simultaneous impact of intangibles to different performance indicators was examined with 3LS (three-stages least square) and SEM (structural equation modeling).

Empirical base of researchFor the purpose of this research two databases of European companies were designed and collected. One of the databases was employed in the comparative analysis of European companies that have significantly different level of development of knowledge economy. For some of the research questions set in this study these discrepancies play a crucial role. Another dataset consists of companies from only developed European markets but is representative according to the country and sectorial criteria. The two different datasets enabled robustness check of some of the empirical results and made the findings more rigorously validated.

The first database covers the period of 2005-2009 years. The final sample contains 340 listed companies from emerging and developed European countries including Serbia, Great Britain, Ukraine, Turkey, Finland, Denmark, and Spain, based on a country position in Knowledge Economy Index 2008 [http://data.worldbank.org/data-catalog/KEI].  Notably, only industries with the predominance of intangibles components and configuration were chosen: financial services, wholesale and retail trade, machinery and equipment manufacture, chemical, and transport and communications. This dataset is derived from the detailed longitudinal databases Bureau Van Dijk (Amadeus and Ruslana) based on corporate annual statistical and financial reports. The criterion of employee number was applied to include companies into the final sample (very small and very large companies were excluded).

The second database contains information about more then 1600 companies located in five European countries observed from 2004 till 2011. It includes companies from: United Kingdom (44%), Germany (24%), France (25%), Spain (5%) and Italy (2%). The entire GDP of these countries covers more than 70% of the European GDP. The composition of this database represents the European market according to the country criterion. It also accurately represents these countries in relation with the industry structure of the European economy. The Statistical Classification of Economic Activities in European Community (NACE) has been applied and the following sectors are included in the database: “Management of Companies and Enterprises” (25%), Manufacturing (20%), “Professional, Scientific and Technical Services” (12%), “Finance and Insurance” (10%) and “other industries” (33%). The representative rate of SME and large enterprises in the database is 36% and 64% respectively.

Results of research:

Theoretical contribution:

  • dynamic model for investment decision making with regard to intangibles,
  • specific theoretical issues on corporate government in intangible-intensive companies.

Methodological contribution:

  • an approach for measurement of intangible input and output on the basis of publicly available information was suggested and validated.
  • algorithm of the analysis of transformation of intangibles in company performance under uncertainty and other conditions of external interference.

Empirical contribution:

  • intangible-intensive strategies (profiles) of companies were identified. With a benchmark of low profile intangible-intensive ones allow better outperforming and value creation. The intangible-intensive strategy moreover protected companies during economic recession and enables faster recovery after.
  • an increasing return to scale for intangibles was established. This phenomenon is enhanced by economic crisis conditions.
  • a monetary contribution of intangibles into company market value is found out.
  • key intangible factors of company outperformance during economic crisis of 2008-2009 are revealed.
  • an interrelation between CEO’s overconfidence and investment strategic behavior for intangibles is investigated.
  • a particular institutional issues of corporate governance in intangible-intensive companies was revealed. 


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